GNSS Time Synchronisation in Co-operative Vehicular Networks › 120849 › 1 › Khondokar...

GNSS Time Synchronisation in

Co-operative Vehicular Networks

A THESIS SUBMITTED TO

THE SCIENCE AND ENGINEERING FACULTY

OF QUEENSLAND UNIVERSITY OF TECHNOLOGY

IN FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

Khondokar Fida Hasan

M.Sc. ; B.Sc. (Hons)

School of Electrical Engineering and Computer Science

Science and Engineering Faculty

Queensland University of Technology

2018

GNSS Time Synchronisation in Co-operative Vehicular

Networks

A THESIS SUBMITTED TO

THE SCIENCE AND ENGINEERING FACULTY

OF QUEENSLAND UNIVERSITY OF TECHNOLOGY

IN FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

Khondokar Fida Hasan

M.Sc. ; B.Sc. (Hons)

Supervisor: Professor Yanming Feng and Professor Yu-Chu Tian

School of Electrical Engineering and Computer Science

Science and Engineering Faculty

Queensland University of Technology

2018

ii

Copyright in Relation to This Thesis

c© Copyright 2018 by Khondokar Fida Hasan

M.Sc. ; B.Sc. (Hons). All rights reserved.

iii

QUT Verified Signature

Abstract

Time synchronisation is a prerequisite for the successful operation of every distributed

network. It provides a common time frame among all nodes, thus supporting var-

ious network functions such as message transmission, channel scheduling and re-

source sharing in real-time and correct order. Time synchronisation also enables

secure connectivity, data consistency and process coordination among the nodes of

the distributed network system. Accurate time is especially important in network

applications with high mobility such as in vehicular ad-hoc networks (VANETs). In

practice, VANETs differ from other mobile networks by their ad-hoc architecture,

high mobility, and time-sensitive applications. In vehicle-to-vehicle and vehicle-to-

infrastructure communications, the network nodes are moving rapidly and establish-

ing communications by forming networks in an ad-hoc manner. Such ad-hoc links are

usually short lived, as the relative speed of the nodes can be as high as 200 km/h (56

m/s). Therefore, maintaining network-wide accurate time becomes critical. Since the

success of vehicular communication networks impacts on peoples lives and resources

on the road, VANET safety requirements (e.g., delay, reliability, scalability, fairness,

and timeliness of vehicle-to-everything (V2X) communications) are stringent. For

instance, the update rate of vehicle location information can be as high as 10-100Hz.

The requirements for absolute and relative vehicle positioning accuracy can be as high

as 10 centimetres. However, the requirements for time synchronisation in V2X com-

munications is not well understood. While many synchronisation techniques have

been developed for general networks, they are not particularly suited for VANETs.

v

That is, it is critical to understand the efficacy of existing time synchronisation tech-

niques in VANET applications. GNSS has been used for providing precise timing

information in many distributed systems. Since VANET is mostly outdoor-based, the

integration of GNSS-based time synchronisation in-vehicle networks offers promising

technological solutions for better coordination of emerging automated and connected

vehicles.

This thesis identifies some important application scenarios for precise timing and

relative time synchronisation for V2X communications. It compares various proposed

synchronisation methods, and discusses the performance benefits of GNSS timing

techniques for synchronising vehicular networks. Extensive experiments show that

GNSS time synchronisation methods can replace existing time synchronisation func-

tion (TSF) based synchronisation in VANETs. The limitations due to GNSS time

solution outages in urban streets and tunnels are also analysed using vehicle GNSS

tracking data recorded in the Brisbane CBD. The described analyses indicate that

VANET time drift solution outages can be successfully handled and strategies can

be untaken for mitigation. The results of experiments are discussed, that evaluate

the timing accuracy possible using multi-layer GNSS time synchronisation, which

demonstrate its compatibility and feasibility in a number of real Vehicle to Vehicle

(V2V) application scenarios.

vi

Keywords

V2X, Intelligent Transportation System, C-ITS, VANET, GNSS, Time Synchronisation,

Timing Advertisement, 1PPS, Security, MAC

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Acknowledgments

It is my utmost pleasure to acknowledge the roles and contributions of several indi-

viduals who were instrumental for completion of my PhD research.

In the very first place, I would like to sincerely thank my principal supervisor Profes-

sor Yanming Feng for his support and invaluable guidance throughout the journey of

my PhD degree. I also would like to extend my special appreciation to Professor Glen

Tian, my associate supervisor, for his invaluable input and significant support during

this research.

I thank my fellow lab-mates for their enthusiastic support, for their caring and also

for all the fun we have had together to make a mindful place. Dr Keyvan Ansari, Dr

Charles Wang and Dr Lei Wang are few names need to be mentioned for their time

and contribution with my research work in various ways.

I also would like to express my thank to all the academic and professional staff in the

School of Electrical Engineering and Computer Science of the Queensland University

of Technology for supporting me in various ways during this project and also for

providing me with a stimulating, supportive research environment.

Last but not the least my most profound appreciation, acknowledgment and my

thankfulness to all of my family members for their generous support throughout the

journey of my PhD degree.

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x

Table of Contents

Abstract v

Keywords vii

Acknowledgments ix

List of Figures xiii

List of Tables xv

1 Introduction 1

1.1 Research Background and Motivation . . . . . . . . . . . . . . . . . . . 1

1.1.1 Intelligent transportation Systems and Vehicular Networks . . 2

1.1.2 Co-operative Intelligent Transportation System . . . . . . . . . 5

1.1.3 Importance of Time and Time Synchronisation in Distributed

Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.2 GNSS Time Synchronisation and Challenges . . . . . . . . . . . . . . . 7

1.2.1 GNSS Time Synchronisation Prospects in Vehicular Networks . 7

1.2.2 Synchronisation Challenges and Research Questions . . . . . . 8

1.3 The Objective and Scope of the Research . . . . . . . . . . . . . . . . . . 10

1.4 Research Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

xi

1.5 Dissertation Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

1.6 List of Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2 Fundamental Theories and Related Works 17

2.1 Cooperative Intelligent Transportation System (C-ITS) Fundamentals . 18

2.2 Enabling Architectures, Technologies and Standardisation . . . . . . . 19

2.2.1 Vehicular Ad-Hoc Network (VANET) . . . . . . . . . . . . . . . 19

2.2.2 Dedicated Short Range Communication (DSRC) . . . . . . . . . 21

2.2.3 Wireless Access in Vehicular Network (WAVE) . . . . . . . . . . 21

2.2.4 Overview of IEEE 802.11P . . . . . . . . . . . . . . . . . . . . . . 22

2.3 Basic Models and Techniques of Time Keeping and Time Synchronization 25

2.3.1 Hardware Clocks . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.3.2 Software Clocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.3.3 Clock Accuracy and Precision . . . . . . . . . . . . . . . . . . . . 27

2.3.4 Clock Offset, Skew and Drift . . . . . . . . . . . . . . . . . . . . 27

2.3.5 Main Limiting Factors in Time Synchronization . . . . . . . . . 28

2.3.6 Basic Techniques for Time Synchronization in a Decentralized

System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.3.7 Types of Clock Synchronization . . . . . . . . . . . . . . . . . . . 31

2.4 Approaches to the Time Synchronization in Wireless Media . . . . . . . 32

2.4.1 Revisiting Time Synchronization in Wireless Sensor Networks . 33

2.4.2 Why is Time Synchronization an Issue in VANETs . . . . . . . . 35

2.5 Existing Recommendation for VANET Time Synchronization . . . . . . 37

2.6 GNSS Approaches for VANET Time Synchronization . . . . . . . . . . 41

2.6.1 Motivation of GNSS-driven Time Synchronization in VANETs . 42

xii

2.6.2 GNSS Time Synchronization Models for VANET . . . . . . . . . 43

2.6.3 Challenges and Solutions in Absence of GNSS Signals . . . . . 47

2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3 Significance and Requirement Analysis for Time Synchronisation in VANET 51

3.1 The Need for Time Synchronisation in VANETs . . . . . . . . . . . . . . 52

3.2 Time Synchronisation Requirements in VANET . . . . . . . . . . . . . . 54

3.2.1 Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

3.2.2 Precision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

3.2.3 Availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

3.2.4 Requirements for Different VANET Applications . . . . . . . . 57

3.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

4 GNSS Time Synchronisation In VANET 67

4.1 Motivation of GNSS-driven Time Synchronisation . . . . . . . . . . . . 68

4.2 Feasibility of GNSS Synchronisation in VANET . . . . . . . . . . . . . . 69

4.2.1 Justification of the Feasibility . . . . . . . . . . . . . . . . . . . . 69

4.2.2 GNSS Timing Information . . . . . . . . . . . . . . . . . . . . . . 72

4.2.3 Errors of the Receiver Timing . . . . . . . . . . . . . . . . . . . . 74

4.3 Availability of GNSS Time Solutions . . . . . . . . . . . . . . . . . . . . 77

4.4 Synchronisation Accuracy of 1 PPS Signals . . . . . . . . . . . . . . . . 78

4.4.1 Characteristics of 1PPS . . . . . . . . . . . . . . . . . . . . . . . . 78

4.4.2 Clock Accuracy of Low Cost GNSS Receivers . . . . . . . . . . . 79

4.4.3 Experimental Design . . . . . . . . . . . . . . . . . . . . . . . . . 81

4.4.4 Data Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

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4.4.5 Result Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

5 GNSS Synchronisation with On-board Devices 93

5.1 Problem Definition and Solution Approach . . . . . . . . . . . . . . . . 94

5.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

5.3 Testbed Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

5.4 Result Analysis and Performance Evaluation . . . . . . . . . . . . . . . 103

5.4.1 Node Clock Synchronisation With GNSS Receiver . . . . . . . . 106

5.4.2 Field test of GNSS Time Synchronisation . . . . . . . . . . . . . 109

5.4.3 Network Synchronisation with GNSS . . . . . . . . . . . . . . . 110

5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

6 Synchronisation in Occasional loss of GNSS Signals 117

6.1 Understanding Service Availability . . . . . . . . . . . . . . . . . . . . . 118

6.2 Fall Back Solutions During Signal Outage . . . . . . . . . . . . . . . . . 123

6.2.1 Number of Satellites 1 to 3 (0<NSAT< 4) . . . . . . . . . . . . . 123

6.2.2 Number of Satellite is Zero (No visible satellites . . . . . . . . . 124

6.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

7 Conclusions and Recommendations 129

7.1 Summary of the Research . . . . . . . . . . . . . . . . . . . . . . . . . . 129

7.2 Summary of the Contribution . . . . . . . . . . . . . . . . . . . . . . . . 131

7.3 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

Literature Cited 149

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List of Figures

1.1 The Concept of Intelligent Transportation System. . . . . . . . . . . . . 3

1.2 Vehicle to Everything (V2X) Communication. . . . . . . . . . . . . . . . 4

1.3 Challenges with Existing Time Synchronisation Solution Recommended

in VANET. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.1 Basic Interactions of Cooperative ITS. . . . . . . . . . . . . . . . . . . . 18

2.2 VANET Communication Architecture. . . . . . . . . . . . . . . . . . . . 20

2.3 DSRC Channel Arrangement. . . . . . . . . . . . . . . . . . . . . . . . . 21

2.4 WAVE Protocol Stack. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.5 Unsynchronsed Clocks in a network. . . . . . . . . . . . . . . . . . . . . 23

2.6 Synchronsed Clocks in a network. . . . . . . . . . . . . . . . . . . . . . 24

2.7 Message Exchanges between Two Nodes. . . . . . . . . . . . . . . . . . 30

2.8 Reference Broadcasting Synchronisation (RBS) . . . . . . . . . . . . . . 30

2.9 Two Modes of communications in 802.11 Standard Family. . . . . . . . 37

2.10 Beacon Generation Window. . . . . . . . . . . . . . . . . . . . . . . . . . 39

2.11 GPS Time Transfer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

3.1 WAVE Spectrum: (a) Frequency and Channel Allocation; and (b) Chan-

nel Synchronisation and Guard Interval . . . . . . . . . . . . . . . . . . 59

3.2 Guard Interval Requirements . . . . . . . . . . . . . . . . . . . . . . . . 60

xv

3.3 Examples of Security Issue. . . . . . . . . . . . . . . . . . . . . . . . . . 63

3.4 Concept Tier fo the Requirements of Time Synchronisation Accuracy . 65

4.1 Broad View of Time Synchronisation (a) In-band Time Synchronisation (b)

Out-of-Band External Time Synchronisation. . . . . . . . . . . . . . . . . . . 70

4.2 Time Offsets Among Different Atomic Scale Standards. . . . . . . . . . . . . 72

4.3 Time Transfer Through GNSS . . . . . . . . . . . . . . . . . . . . . . . . 73

4.4 (a) (b) Nodes N1 and N2 are Individually Synchronised with GNSS and Up-

dated with UTC. (c) Effectively, Two Nodes are Synchronised with Each Other

via GNSS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

4.5 Ideal (GPS signal) and Practical (Produced Signal by GPS receiver) 1PPS Sig-

nal Pulses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

4.6 Standard Deviation Rating. . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

4.7 Schematic Diagram of Experimental Setup for Synchronisation Assessment of

GPS Receivers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

4.8 Pulses Showing Time Difference Between Two Waveforms of Identical Re-

ceivers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

4.10 Gaussian Distribution of Dataset 5 Minutes that Represents STD of 12.67 ns

for 1σ.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

4.9 Relative Time Offsets Between Two Receivers of Identical Models. . . 86

4.11 Time offsets between receivers of same model. . . . . . . . . . . . . . . 89

4.12 Time Offset Between Receivers of Different Models over a Long Period. . . . 90

4.13 Time Offset Between Receivers of Different Models Over a Long Period. . . . 90

4.14 Time Offset Between Receivers of Different Models Over a Long Period. . . . 91

5.1 VANET Communication: Vehicle to Everything Scenario. . . . . . . . . . . . 94

xvi

5.2 VANET Communication: Vehicle to Infrastructure (V2I), and Vehicle to Vehi-

cle (v2v) Communication. . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

5.3 Existing Problem with Time-Advertisement-based Time Synchronisation. 96

5.4 Undefined Situation Using TA Mechanism in Pure Ad-hoc Communication. . 97

5.5 Wireless Access for Vehicular Environment (WAVE) Layers. . . . . . . . . . 99

5.6 Proposed Solution. GNSS Time Synchronisation to TSF Register Through

Application Layer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

5.7 Communicating Node Synchronisation using a GNSS Receiver . . . . 102

5.8 Schematic Diagram of the Experimental Setup on Moving Node to Collect

Data on Real-time Vehicular Environment Including Urban, Suburb, High-

way, etc. Route. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

5.9 System Clock Synchronised with GPS Data Signal. . . . . . . . . . . . . . . 104

5.10 System Clock Synchronised with PPS Ticks. . . . . . . . . . . . . . . . . . . 107

5.11 System Clock Synchronised with Both GPS PPS Signal. . . . . . . . . . . . . 108

5.12 Statistical Distribution of Measured Time Difference. . . . . . . . . . . . . . 109

5.13 Illustration of Clock Stability and Noises using Allan Deviation (Log-Log scale).110

5.14 GPS-PPS Enabled Clock in Different Road Scenarios. . . . . . . . . . . . . . 111

5.15 Schematic Diagram of the Experimental Setup to Measure the Time

Offsets between Two GNSS Synchronised Computing nodes. . . . . . . 112

5.16 Box-plot of the Time Offsets Between Two GNSS Synchronised Node

Developed on Rpi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

5.17 Offsets Between Individual Packets and their Moving Average from the Dataset

of 300 Packets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

6.1 GPS Challenged Environment in Urban Concept -1. . . . . . . . . . . . 120

6.2 GPS Challenged Environment in Urban Concept-2. . . . . . . . . . . . 121

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6.3 Vehicle Tracks of of GPS, BDS and GPS+BDS on High Rising Roads. . . 122

6.4 The Number of Satellites Under the Signal Coverage of BDS and GPS. 122

6.5 Node Clock Integrated with GNSS. . . . . . . . . . . . . . . . . . . . . . 124

6.6 Schematic Diagram of the Experimental Set-up Between Three Nodes. 125

6.7 Plot of the Clock Drift Recorded over 4 hours. . . . . . . . . . . . . . . . . . 125

6.8 Plot of the Clock Drift Recorded over 4 hours. . . . . . . . . . . . . . . . . . 126

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List of Tables

1.1 Overview of Multi GNSS Environment (Current Status 2018) . . . . . . 8

2.1 Slot Time with Beacon Generation Window. . . . . . . . . . . . . . . . . 38

3.1 List of Timing Accuracy Requirements for Different Applications in the Basis

of Essentialness of VANET. . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

4.1 The Number of Satellites Available with Different GNSS Services Constellation. 78

4.2 Relative Time offsets of Different Datasets in the Time range of Nanosecond

Between Receivers of Same Model (Ublox-Ublox). . . . . . . . . . . . . . . . 87

4.3 Relative Time Offsets of Different Datasets in the Time Range of Nanosecond

Between Receivers of Same Model (Furuno-Furuno). . . . . . . . . . . . . . 88

4.4 Relative Time Offsets From Different Datasets in the Time Range of Nanosec-

ond Between Receivers of Different Model (Ublox-Furuno) . . . . . . . . . . 88

4.5 Relative Offset in ns Between Receivers of Different Models. . . . . . . . . . 90

4.6 Relative Offset in ns Between Receivers of Different Models. . . . . . . . . . 91

6.1 Number of Satellites Available with Different GNSS Service Constella-

tion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

6.2 GDOP with Different GNSS Services . . . . . . . . . . . . . . . . . . . . 122

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xx

Chapter 1

Introduction

1.1 Research Background and Motivation

Transportation is an indispensable part of modern civilisation. It is inseparable from

society and exerts a powerful influence on the lives of individuals and the develop-

ment of nations. According to the US Department of Transportation, their travelling

time is about 500 million hours in vehicles per week, which signifies the importance

of transportation in everyday life in modern societies. Among different forms of

transportation, including air, water and land, the most common and popular is road-

based land transportation. In reference to the Australian Bureau of Statistics [2012],

around 78% of Australians travel to their workplace by car BITRE2 [2012]. Since road

transportation is so popular, it has a record of significantly large rate of causalities.

According to the UN Tackles Road Safety Report (2015), road transport accidents

kill about 1.3 million people every year, which equates to nearly 3400 road fatalities

per day. Road accidents injure 50 million people annually. The report also predicts

that the road crashes will be the fifth leading cause of death by 2030 [WHO, 2015]. It is

understandable that some countries possessing minimal technological facilities have

higher per-capita accident rates. However, according to the Australian Department

of Infrastructure, there was a total of 129 road deaths during December 2017 [BITRE,

2017]. Since the population of Australia is less compared to many other countries, this

1

2 CHAPTER 1. INTRODUCTION

number is alarming. BITRE [2017] also reported that the metropolitan congestion cost

is more than $16.5 billion in the year 2015 and would rise to $30 billion by 2030, which

undoubtedly would have a significant impact on the economy, society and nation as

a whole. To reduce deaths, injuries, and economic losses resulting from motor vehicle

crashes, road transportation networks demand evolution and advancement. As a part

of this endeavour, Intelligent Transportation System (ITS) technologies are emerging

as a tool to alleviate the situation.

Time is considered as one of the important and recognised parameters for success-

ful operation in existing technology-based communication systems, such as computer

networks, cellular network and sensor network. However, today’s transportation sys-

tems do not depend on precise and accurate time in their operation. In order to alle-

viate the road fatalities including death, injuries and economic losses, transportation

system researchers are evolving the concept of an ’Intelligent Transportation System

(ITS)’, where vehicles can collect data within their surrounding area to support inde-

pendent decision making. An ITS would also enable vehicles to communicate with

each other and surrounding road elements through wireless networks. Such tech-

nologies would employ existing or amended sensor, computer and communication-

based technologies together with their attendant protocols. Therefore, it can be as-

sumed that the ITS would support time-sensitive operations and applications, similar

to other existing technology-based sensor operations and communication networks.

Thus, it is essential to understand the timing accuracy and precision required by

emerging ITS-based technologies for road networks, and what would be the best way

to achieve them.

1.1.1 Intelligent transportation Systems and Vehicular Networks

In a broad view of ITS, various sensors and assisting systems are integrated into

vehicles to monitor their local environment, so that they can gain accurate knowledge

1.1. RESEARCH BACKGROUND AND MOTIVATION 3

of their surrounds Fagnant and Kockelman [2015]. Along with this, wireless commu-

nications also support exchange of information between vehicles and the other road

elements. Thus, as a whole, ITS support ranges from Autonomous Vehicle (AV) to

Connected Vehicles (CV) as shown in the Figure 1.1.

GPS

Lidar

Radar

Camera

Ultrasonic

OdometryCentral Computer

Inertial Sensors

V2V

I2I

V2I

V2P

(a) (b)

Figure 1.1: The Concept of Intelligent Transportation System. (a) AutonomousVehicle (b) Connected Vehicles Conde et al. [2015], Luettel et al. [2012].

In autonomous vehicles, a good range of sensors is integrated into the vehicle to

collect the data about the surrounding environment to enable them to operate inde-

pendently. In the case of Connected Vehicles (CV), drivers have access to wireless con-

nections to other neighbouring vehicles, infrastructure, pedestrians and other devices

within its proximity. The principal advantage of having wireless communications is

the ability to access information that may otherwise be beyond the driver’s immediate

awareness. Such wireless communications would help to prevent possible collisions

by exchanging status information (such as the location, speed and direction of travel

of nearby vehicles etc.), and event-driven safety messages (such as lane changing and

collision warnings etc.). Along with these safety warning messages, such communi-

cation would also support sharing traffic information, weather updates and Internet-

based infotainments.


This multifarious communication on the road is popularly termed as Vehicle to

Everything or V2X communication. A V2X communication concept is shown in the

Figure 1.2.

V2V

V2P

V2D

V2

I

V2I (V2H, V2G )

Figure 1.2: Vehicle to Everything (V2X) Communication.

Dedicated short-range communication (DSRC) and Long Term Evolution (LTE)

are two widely-used schemes for Vehicle to Everything (V2X) communications, which

enable Connected Vehicle (CV) applications. The implementation of V2X communi-

cations on LTE networks is known as LTE-Vehicle (LTE-V). This supports V2V com-

munications (also known as side-link) using LTE’s direct interface, called PC5. LTE-

V comprises two radio interfaces, PC5 for V2V and the other interface, called Uu,

supports V2I communications [Molina-Masegosa and Gozalvez, 2017]. In compari-

son, IEEE 802.11p-based DSRC communications use the 5.9 GHz band link to connect

both vehicles and infrastructure. Since IEEE802.11p is considered to be the de facto

standard by the vehicular transportation community and authority for V2X commu-

nication [Araniti et al., 2013]; the scope of study herein is limited to DSRC-based

vehicular communications. However, it is acknowledged that GNSS timing systems

also are equally applicable in LTE-V-enabled environments.

1.1. RESEARCH BACKGROUND AND MOTIVATION 5

1.1.2 Co-operative Intelligent Transportation System

Developing wireless communications between vehicles and other road components

leads to the concept of the cooperative environments. DSRC-enabled road commu-

nication technology is often termed a Cooperative Intelligent Transportation System

(C-ITS), which is increasingly considered to be an important tool for managing road

safety and road efficiency. Enabling wireless connectivity on the road is the primary

aim of CITS. It creates an environment of information sharing and active cooperation

for increased road safety and system operation efficiency. A key component of the C-

ITS technology is a Vehicular Ad-hoc NETwork (VANET). Generally, a typical VANET

consists of a large number of network nodes that pass each other at high speed. There-

fore, it is considered to be a highly dynamic and decentralised network. VANETs have

unique characteristics in compared with other types of wireless networks. A C-ITS

crucially relies on VANETs for real-time, wireless communication of data generated

from in-vehicle and roadside sensors. Therefore, the functionality and performance

of VANETs determine how well a C-ITS can support various applications on the road

[Ghosh et al., 2011, Golestan et al., 2012a, Sundararaman et al., 2005].

Due to the decentralized nature of VANET, efficient timing and coordination ca-

pabilities are considered to be important prerequisites for ensuring vehicle network

management and hence safety on roads. Therefore, the possibility of the integration

of GNSS time synchronisation over VANET is considered to be important for realising

C-ITS.

1.1.3 Importance of Time and Time Synchronisation in Distributed Systems

Successfully supporting communications for different services in vehicular environ-

ments is highly reliant on the accuracy of timing information at the network nodes.

The timely delivery of various messages in a correct and precise order is crucial for

effective and efficient VANET services. Some VANET applications have a time-offset


tolerance requirement below 100 ms. This may be achievable when all network nodes

operate on the same and commonly agreed clock time.

Time synchronisation is a technique for maintaining clock times at all network

nodes.1 In general, every physical clock drifts away from the actual daytime by 1 µs

to 100 µs per second. This implies a range of deviation about 5 to 15 seconds per day

[Lombardi, 2000]. As VANETs are distributed and decentralised networks in which

the nodes are physically detached from each other. This means that differences in

VANET node times become unavoidable. Maintenance of an exact, network-wide

clock time is, therefore, impossible for VANETs. Therefore, time synchronisation

services and applications need to be developed for all network nodes. The time

synchronisation is required to align the (drifting) clocks of all network nodes with a

global standard time or with each other. In this way, every node in the network could

operate with the same notion of time. This would support reliable and precise time

synchronisation in various VANET services such as coordination, communication,

security, and time-sensitive applications.

In wireless communications, time synchronisation is essential for coordination

and consistent operation of various network elements. It is also necessary for accurate

message sequencing and real-time control tasks.

While VANET communications are considered to be asynchronous in nature, time

synchronisation among vehicles is essential for many applications [Skog and Han-

del, 2011]. This is similar to the Internet, which is embedded with various time-

synchronisation mechanisms. Some VANET applications are highly time-sensitive. In

these applications, maintaining a real-time and also precise timing between commu-

nicating nodes is critical. Therefore, as in other synchronous wireless distributed sys-

tems, many VANET applications depend on synchronous communications to provide

1In contemporary terminology, adjusting merely clock frequency of the clocks in a network refers tosynchronisation of frequency or syntonization. In contrast, synchronising time means setting the clockto agree upon a particular epoch, with respect to a standard time format such as Coordinated UniversalTime (UTC). Synchronising a clock also refers to the synchronisation of both frequency and time. Inthis thesis, the terms time synchronisation and clock synchronisation are used synonymously to refer tomaintaining clocks to the same time and run at the same frequency.

1.2. GNSS TIME SYNCHRONISATION AND CHALLENGES 7

common C-ITS services. Examples includes, the coordination of activities [Cozzetti

and Scopigno, 2011, Sjoberg et al., 2011, Sjoberg Bilstrup, 2009], relative vehicle po-

sitioning, data communications, and security services [Shizhun Wang et al., 2010].

Moreover, in order to record event information over a network, a VANET needs

to maintain accurate physical time. This also demands maintenance of an accurate

standard time through time synchronisation in an asynchronous manner.

1.2 GNSS Time Synchronisation Challenges

1.2.1 GNSS Time Synchronisation Prospects in Vehicular Networks

The Global Navigation Satellite System (GNSS) is a well-known satellite-based com-

munication system that provides users with positioning, navigation and timing (PNT)

services. Initially, the USA launched the Global Positioning System (GPS) satellites,

which now provide complete coverage of the earth. Subsequently, Russia’s GLONASS,

Chinese’s BeiDou and the European Union’s Galileo joined in providing indepen-

dent satellite-based positioning, navigation, timing and communication services. The

number of satellites in current [2018] GNSS systems along with plans for future con-

stellations are given in Table 1.1. As a single satellite system, GPS currently has the

most number of satellites in orbit, namely 31. However, in the multi-GNSS concept,

this number increases to 99 and is expected to reach 134 by the year 2020. This obvi-

ously increases the probability of detecting satellite signals always from a number of

satellites that would help to support accurate PNT services.

The timing service provided by GNSS is considered to be the reference source

worldwide. It is also considered as the most accurate UTC sources because GNSS-

based satellite time is maintained with highly-precise atomic clocks, and satellite time

transfer is more reliable than other ground-based time-transfer techniques.

There are some fundamental reasons for that, first of all, the accuracy of any time

transfer system suffers from the uncertainty due to the variability of propagation path


Table 1.1: Overview of Multi GNSS Environment (Current Status 2018)

GNSS SystemNo. of Currently

OperationalSatellite #

Planned# of Satellites

Remarks

GPS 31 3236 orbit planes

Ref: [Uscg.gov, 2018]

GLONASS 24 243 orbitplanes

Ref: [Glonass iac.ru, 2018]

BEIDOU 2235

(by 2020)

6 GEO, 8 IGSO,8 MEO (in 2 planesRef: [I-GNSS, 2018])

GALILEO 2230

(by 2020)

2 under testing and4 under commissioning

Ref: [E-GNSS, 2018]Overall 99 134

delays and its . In comparison with ground-based radio communication systems,

satellite-based GNSS systems have path delays which are straightforward to measure

and calibrate. This is because the variation of path delays are small, as the paths

between satellite and receiver are mostly unobstructed. In addition, signal interfer-

ences caused by the influence of weather is usually less of a problem since they can

be mathematically modelled and compensated. Therefore, the time steering by the

GNSS systems are accurate and ubiquitous under an unobstructed sky.

Meanwhile, modern vehicles are already integrated with a GPS (Global Position-

ing System) or multi-GNSS (Global Navigation Satellite System) receiver for naviga-

tion. Accurate time support from GNSS is, therefore, plausible without any additional

costs. While GPS-based time synchronisation is used in many networks, limited

publications have been found about GPS or GNSS time-synchronisation performance.

1.2. GNSS TIME SYNCHRONISATION AND CHALLENGES 9

1.2.2 Synchronisation Challenges and Research Questions

Despite the recent modernisation of GPS receivers, it is still not well-understood

whether GNSS time synchronisation is beneficial and feasible for vehicular networks.

GNSS receivers are available in different manufacturing grades according to dif-

ferent type of applications and cost. A consumer-grade receiver commonly employs

a quartz oscillator and basic circuitry to make the system inexpensive for use in

everyday navigation applications. Most of the navigational GNSS devices built into

cars are in the consumer grade category. Since they use inexpensive circuitry, it

is crucial to understand what time synchronisation accuracy they may offer. The

IEEE 802.11p based WAVE (Wireless Access for Vehicular Environment) structure has

a synchronisation mechanism incorporated within an IEEE 1609.04 protocol stack,

known as Timing Advertisement (TA), which relies on message transfers between

nodes. A TSF (Time Synchronisation Function) register located in the MAC is respon-

sible for coordinating the synchronisation process. Figure 1.3 shows a diagrammatic

representation of the TA scheme, where the TSF of the Access Point (AP) is able to

maintain time which also sends out time through the communication channel and

moving station receives and updates according to the received time. Due to the

highly dynamic nature message-transfer-based time synchronisation is challenging

the vehicular environments, since with 200 km/h relative speeds, the nodes may be

in their communication range for only a fraction of a second. Although TA-based

schemes have challenges, it is important to investigate the prospects of GNSS time

synchronisation over such systems.

In addition, GNSS signals are prone to being obstructed by buildings, trees and

other barriers, which can lead to service disruptions. Vehicular networks are mostly

outdoors based, where the communications occur under the open sky, thereafter, in

urban areas, tall buildings, trees and tunnels pose challenges for delivering GNSS

services.


Application

TSF Register

Application

TSF Register

RSU/AP OBU/STATA

Frame

Ref clock

Data 12:00 am Hardware Medium

TSF

Data 12:00 am Hardware

TSF

Applications

Figure 1.3: Challenges with Existing Time Synchronisation Solution Recommendedin VANET Mahmood et al. [2017].

Based on the above-mentioned challenges, the primary research questions of in-

terest are as follows:

Q 1. Can consumer-grade GNSS receivers provide the time synchronisation accuracy

and precision required for VANETs?

Q 2. In vehicular environments, is GNSS time synchronisation better than existing

decentralised time synchronisation methods that were originally developed for

other wireless networks?

Q 3. To what extent are GNSS services available to provide time synchronisation

support to vehicular networks, and what possible measures can be employed

during signal outages?

1.3 The objective and scope of the research

The primary aims of this research are twofold. Firstly, investigating the feasibility and

prospects for GNSS time synchronisation in vehicular networks. This includes study-

ing the timing accuracy and precision requirements for different vehicular network

applications. The investigations involve examining the achievable accuracy offered

1.4. RESEARCH CONTRIBUTION 11

by the low-cost consumer-grade GNSS receivers. The objectives also include deter-

mining the achievable accuracy of GNSS receivers integrated with on-board devices

in laboratory-based and field experiments. Secondly, in respect of GNSS availability,

understanding the extent and impact of signal outages, and measures for maintaining

timing synchronisation within vehicular networks. Based on the objectives, goals and

challenges discussed above, the main objectives of the research are concisely restated

below.

1. To contribute an understanding of time synchronisation within different vehicle

network applications and also to identify their requirements.

2. To explore the potential of GNSS time synchronisation in vehicular networks

by carrying out comparative analyses of new and existing synchronisation sys-

tems, and conducting experimental validations using integrated on-board GNSS

receiver systems.

3. To identify GNSS timing service availability in vehicular environments and to

suggest potential timing solutions to manage GNSS signal and service outages.

1.4 Research Contribution

The pursuit of time synchronisation methods for distributed networks is a well-studied

problem which has a rich history of developments. GNSS-supported solutions have

also been proposed for various applications in different outdoor based networks, and

for the root server in many indoor-based networks. However, the outdoor nature of

vehicular networks, their unique architectures, along with recent advances of GNSS

systems, has prompted the development of a complete GNSS time synchronisation

solution for vehicular networks. The specific research contributions of this thesis are

described below.

1. One significant contribution to knowledge, is the identification of time-sensitive


applications in vehicular networks, through an extensive analysis and literature

survey. The findings of the analysis pertain to the timing accuracy, precision

and availability in vehicular environments. After identifying the time-sensitive

applications and their requirements, they are prioritised with respect to impor-

tance and categorised as essential and desirable, and discussed in the context of

the available technology. This is an important step towards the understanding

of vehicle network timing synchronisation problems.

2. Time synchronisation solutions using GNSS timing services are proposed for

attaining accurate and precise time in distributed vehicular networks. This

involved conducting a thorough feasibility analysis of GNSS time synchroni-

sation. A major research contribution includes the experimental verification of a

UTC-based-1PPS-timing-accuracy method that can be implemented on consumer-

grade GNSS receivers. It also provides an insight into time-synchronisation

performance due to the variation of receivers from different vendors.

3. The proposed GNSS time synchronisation is validated by integrating a GNSS

receiver with on-board communicating devices and conducting experiments in

urban environments. The contributions include a demonstration of GNSS time

synchronisation with a working on-board node and a comparative study with

an existing TA-based synchronisation scheme.

4. GNSS availability has improved with recent advances of multi-GNSS constel-

lations and improvement of receiver technologies. Although the presence of

multiple satellite constellations increases the probability of having visible satel-

lites available, outages do occur. Time-synchronisation performance limitations

during GNSS time solution outages were analysed using data collected from ve-

hicles travelling along high-rise streets in the Brisbane CBD. This contributes to

knowledge of GNSS-based timing availability in signal-impaired environments

such as high-rise urban areas and tunnels.

1.5. DISSERTATION OUTLINE 13

1.5 Dissertation Outline

This dissertation consists of seven chapters. Among them, four chapters describe the

core contributions of this research; three chapters are set aside for the introduction,

background, and conclusion. The content of each chapter is summarised in the fol-

lowing.

Chapter 1. Introduction

In this chapter, the research domain, motivation for the research, and the research

objectives are discussed. The research contributions are briefly summarised, and the

organisation of this thesis is described.

Chapter 2. Fundamental Theories and Related Works

This chapter reviews the applicable literature and explains the fundamental princi-

ples associated with relevant GNSS services. In particular, it surveys the underlying

communication and time synchronisation techniques that exist in different wireless

communication technologies. Subsequently, the basic model and principles of GNSS

time synchronisation for vehicular networks are explained.

Chapter 3. Significance and Requirement Analysis of Time Synchronisation in

VANET

It is important to understand the needs for accurate time, and thus the accuracy

and precision of synchronisation requirements in vehicular networks. Therefore, this

chapter is dedicated to analysing the needs of time synchronisation in V2X environ-

ments and identifying the requirements specific to different vehicular communication

applications.

Chapter 4. GNSS Time Synchronisation in VANET

This chapter analyses the feasibility of GNSS time synchronisation within vehicular

networks. It also analyses the compatibility of GNSS signal in the vehicular envi-

ronment. Some experiments are described which determine the achievable timing

accuracy and signal availability using consumer-grade low-cost GNSS receivers in


road environments.

Chapter 5. GNSS Synchronisation with On-board Devices

A GNSS receiver integrated with an on-board device is tested in a series of labora-

tory and field experiments. The ensuing results are presented and analysed in this

chapter. This involved a comparison of an existing synchronisation scheme with the

developed GNSS-based solution.

Chapter 6. Synchronisation in Occasional loss of GNSS Signals

Satellite signals are weak in the earth surfaces and challenges to access GNSS signal

in certain areas that are under covers is one of the major issues of having GNSS

services to be used. In this chapter, some GNSS outage scenarios such as the possible

GNSS signal blocked road scenario where signals are impaired, or only available

intermittently are analysed with the aid of laboratory and field tests, for which some

timing solutions are proposed.

Chapter 7. Conclusion and Future Work

This chapter concludes by summarising the contributions to time synchronisation

in vehicular networks. Finally, some future-work directions along with potential

opportunities for improvement are suggested.

1.6. LIST OF PUBLICATIONS 15

1.6 List of Publications

(Journals)

1. K. F. Hasan, Y. Feng, and Y.-C. Tian ”GNSS Time Synchronisation in Vehicular

Ad-hoc Networks: Benefits and Feasibility,” in IEEE Transaction on Intelligent

Transportation System, 2018 DOI: 10.1109/TITS.2017.2789291.

2. K. F. Hasan, C. Wang, Y. Feng, and Y.-C. Tian ”Time Synchronisation in Vehicu-

lar Ad-hoc Networks: A Survey in Theory and Practice,” Manuscript Submitted

in the Journal of Vehicular Communication, Elsevier Publication, February 2018.

3. K. F. Hasan, Y. Feng, and Y.-C. Tian GNSS-driven Accurate Time Synchroniza-

tion for VANET, In Preparation to be submitted in IEEE Transaction on Vehicu-

lar Technology, April 2018.

(Conferences)

4. K. F. Hasan and Y. Feng, ”A Study on Consumer Grade GNSS Receiver for the

Time Synchronisation in VANET,” presented at the 23rd ITS World Congress,

Melbourne, Australia, 1014 October 2016.

5. K. F. Hasan, Y. Feng, and Y.-C. Tian ”Exploring the Potential and Feasibility of

Time Synchronisation using GNSS Receivers in Vehicle-to-Vehicle Communica-

tions,” In Proceedings of ITM 2018, Reston, VA, Jan 29-2 Feb, 2018.

6. K. F. Hasan and Y. Feng and Y.-C, Feasibility Studies of Time Synchronization

Using GNSS Receivers in Vehicle-to-Vehicle Communications, International Global

Navigation Satellite System (IGNSS)-2018, Sydney, Australia, 7-9 Feb 2018.


Chapter 2

Fundamental Theories and Related Works

Time synchronisation in networked systems aims to equalise local times of all net-

work nodes. It provides a common time frame among all nodes, thus supporting

various network functions such as message transmission, channel scheduling and

resource sharing in real-time and in the correct order. Time synchronisation is impor-

tant in network applications with high mobility such as in vehicular ad-hoc networks

(VANETs), in which there are unique requirements for delivery of critical warning

and service messages between nodes. VANETs differ from other mobile networks by

their ad-hoc architecture, high mobility, and time-sensitive applications. While many

synchronisation techniques have been developed for general networks, it is necessary

to understand the applicability of existing time synchronisation techniques in VANET

applications.

This chapter surveys the theory and practice of time synchronisation in VANETs.

Through a systematic approach, insights are developed into existing and emerging

protocols for time synchronisation in VANETs. This addresses the problem and prospects

of time synchronisation in vehicular networks.

17

18 CHAPTER 2. FUNDAMENTAL THEORIES AND RELATED WORKS

Some parts of the content from this chapter has contributed to the following pub-

lications:




2.1 Cooperative Intelligent Transportation System (C-ITS) Fundamentals

Cooperative Intelligent Transportation System (C-ITS) is an advanced wireless com-

munication technology and is a branch of Intelligent Transportation System (ITS).

In vehicular environments, the basic interactions of cooperative ITS are known as

Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Infrastructure-to-Vehicle (I2V),

Infrastructure-to-Infrastructure (I2I) and Vehicle-to-Pedestrian (V2P) communication

as shown in Figure 2.1.

Vehicular communications and networks have significant potential to support

various applications associated with C-ITS goals [Karagiannis et al., 2011]. A wireless

V2V

V2I

V2P

I2I

Figure 2.1: Basic Interactions of Cooperative ITS.

2.2. ENABLING ARCHITECTURES, TECHNOLOGIES AND STANDARDISATION19

version of such network, named Vehicular Ad-Hoc Network (VANET), is a compo-

nent of C-ITS, which provides communications between vehicles and nearby road-

side equipment [Misra et al., 2009]. The IEEE has developed system architectures

in order to provide wireless access on roads, which are known as Wireless Access

in Vehicular Environments (WAVE). Collectively, the IEEE 802.11p and IEEE 1609.x

standards are called WAVE [Uzcategui and Acosta Marum, 2009]. The Dedicated

Short Range Communications (DSRC) for WAVE is sometimes referred as Cooper-

ative Intelligent Transportation System [Alexander et al., 2011]. Along with DSRC,

other candidate wireless technologies for VANET are cellular, satellite and WiMAX.

The Technical Committee of International Organisation for Standardisation (ISO) (204

Working Group) leads the development of the frameworks of C-ITS and is also known

as Communications Access for Land Mobiles (CALM) [Ansari, 2014, Emmelmann

et al., 2010].

2.2 Enabling Architectures, Technologies and Standardisation

2.2.1 Vehicular Ad-Hoc Network (VANET)

Vehicular Ad Hoc Networks (VANET) are a special kind of Mobile Ad-Hoc Network

(MANET). Communications in VANETs are governed by the protocol stack WAVE,

which conform to a series of IEEE standards. The WAVE controls the wireless medium

for VANET through a bundle of IEEE protocols, such as IEEE 1609 (e.g., 1609.2, 1609.3,

1609.4) and IEEE 802.11p. IEEE 802.11p is an amendment to IEEE 802.11 for regulation

of data link and physical layers.

The basic communication architecture in VANETs consists of two blocks: On Board

Units (OBU) and Road Side Units (RSU). An OBU is a vehicle, while an RSU refers to

the road side infrastructure for communications [Hartenstein and Laberteaux, 2010,

Joe and Ramakrishnan, 2016, Sharef et al., 2014]. This is illustrated in Figure 2.2.


RSU

RSU

Network Layer

Transport Layer

Application Layer

Data Link Layer

Physical Layer

RSU

OBU

Figure 2.2: VANET Communication Architecture.

Vehicle-to-Vehicle (V2V) communications are typical ad-hoc network communica-

tions. In V2V, mobile nodes communicate directly with each other. In such com-

munications, all types of packet deliveries, such as unicast, multicast and broadcast,

take place between vehicles without the intervention or support of any other network

components.

Vehicle-to-Infrastructure (V2I) communications are implemented through wireless

interactions between OBUs and RSUs. They enable real-time services, such as traffic

information and weather updates. V2I also provides support for secure sparse and

long-distance communications [Sharma and Tomar, 2016].

In addition, Internet infrastructure, private infrastructure and in-vehicle communica-

tions also support some VANET services and applications. This can assist with remote


identification of vehicle’s performance and monitoring drivers’ conditions such as

fatigue and drowsiness. In-vehicle communications are considered as an architectural

part of the latest definition of VANET communications. It is a significant component

of safety and other applications in VANETs Alexander et al. [2011], Emmelmann et al.

[2010].

2.2.2 Dedicated Short Range Communication (DSRC)

The DSRC spectrum is specifically reserved for the radio operation of VANET. It

operates in the band of 5.9 GHz to support VANET. The Federal Communications

Commission (FCC) allocates the spectrum from 5.850 to 5.925 GHz for this operation.

This spectrum is divided into seven 10 MHz DSRC spectrum channels [Jiang et al.,

2006, Miao et al., 2012] as shown in following Figure 2.3. On-going research is con-

cerned with improving its efficiency and complying with safety requirements [Alam

et al., 2009, Bai and Krishnan, 2006, Bilstrup et al., 2009, Kenney, 2011, Miao et al.,

2012, Tang and Yip, 2010, Yi Qian et al., 2008, Yu and Biswas, 2007].

Figure 2.3: DSRC Channel Arrangement, Based on [Ansari, 2014]

2.2.3 Wireless Access in Vehicular Network (WAVE)

Wireless Access in Vehicular Networks (WAVE) is a system architecture that was de-

veloped by the IEEE by combining IEEE 802.11p and IEEE 1609 standards [Uzcategui

and Acosta Marum, 2009]. IEEE 802.11p is an amendment of IEEE 802.11 [Standards

Association et al., 2001] standard which is basically a set of media access control

(MAC) and physical layer (PHY) specifications for implementing wireless local area


network (WLAN) computer communications. The major targeted technological de-

velopment of this research work takes place in this architecture. The WAVE protocol

architecture with its components is shown in Figure 2.4.

WAVE SecurityServices

WAVEManagement

Entity

TCP/IP

IPv6

WSMF

MLME Extension

MLME

PLME

LLC

WAVE MAC Channel Coordinaiton

MAC

PHY

Management Plane Data Plane

Scope of IEEE 1609.2 WAVE

Scope of IEEE 16.09.4 WAVE



Figure 2.4: WAVE Protocol Stack.

2.2.4 Overview of IEEE 802.11P

IEEE 802.11p employs the contention-based channel access also known as EDCA

(Enhanced Distributed Channel Access) as the MAC method, which is the basically

extended model of the basic Distributed Coordination Function (DCF) from 802.11

protocol stack. EDCA uses Carrier Sense Multiple Access (CSMA) with Collision

Avoidance (CSMA/CA). In this method, a node that is expected to transmit checks

the medium at the beginning of the communication, and it will able to proceed if it

received a free AIFS (Arbitration Inter-frame Space), otherwise, the node will defer

the transmission by a random back-off time[Cozzetti et al., 2009, Jiang and Delgrossi,

2008, Miao et al., 2012]. This is one of the specific points of interest of this research


and a significantly alternative idea, called time slotted protocol will be proposed.

sectionFundamentals of Time Synchronization in Communication Networks

Clocks used in communication networks may be grouped into hardware and

software clocks. A physical clock made up of an oscillator to generate a pulse train

and a counter to count and store them is called a hardware clock. Hardware clocks

can be constructed from different materials ranging from most precise and expensive

caesium (i.e. atomic clocks) to inexpensive quartz-powered clocks. A software or log-

ical clock is a software-enabled programmable device that uses counting algorithms

to track a local time value and maintain the time base of the system. Essentially

in a standalone system, logical clocks follow a systems hardware clock, thus clock

accuracy depends on the performance of the hardware clock.

The quality of hardware clock, however, mainly depends on the stability of the os-

cillator as well as of the counting device. The stability is subject to changes of various

parameters such as the nominal frequency of the oscillator, temperature, and other

environmental factors. Such influences create a deviation in the device clock from the

actual time, which may be known as clock drift. Figure 2.5 shows the frequency of

an ideal clock that is theoretically considered as a constant over time. However, in

practice, the frequency changes due to both internal and external influences and drift

from its theoretical value. As a consequence, each local clock system deviates from

a more precise clock time and also from each other. The difference is known as time

offset. Therefore, in a communication network as shown in Figure 2.5 (b), all the node

clocks may report different times.

Operating communication networks requires alignment of node clocks to a ref-

erence clock, or synchronization of network time to a reference time. Fundamen-

tal operations may include successful communication, channel scheduling, real-time

control messages. Alignment refers to reducing the effects of clock offset and drift

between nodes to an acceptable level. A straightforward solution is to use an accurate


Ideal Clock Practical Clock

8:15:25

8:15:26 8:15:24 8:15:27

8:15:28Communication Network

Drift

Offset

(a) (b)

Figure 2.5: Clock in a communication network. (a) Ideal and Practical physical clockand their frequency. (b) State of clocks in an unsynchronised communication network.

source of time such as an atomic clock in every device of the network, which is

expensive and not realistic in most real network scenarios. Communication network

nodes are usually equipped with inexpensive quartz clocks. A method called clock

synchronization is used to equip all node clocks with the same time. The basic idea

is to minimize clock drifts and offsets resulting from various errors and inaccuracies.

This is achieved by communicating messages that help to transfer time from one node

to another. Figure 2.6 shows the fundamental concept of message transmission that

synchronize clocks node by node. Ideally, such messages can be transmitted from

the sender node to receiver node or back and forth between sender and receiver to

attain a common agreed time Freris et al. [2011], Kadowaki and Ishii [2015]. The

accuracy and precision of clock synchronization, therefore, depends on the accurate

transmission and reception of the messages. A number of synchronization protocols

Ideal Clock Practical Clock

8:15:25

8:15:25 8:15:25 8:15:25

8:15:25Synchronized Network

(a) (b)

One Way

Two Way

Time Sync

Figure 2.6: Clock in a communication network. (a) Communication between ideal(reference) clock and practical node clock. (b) State of clocks in an Synchronisedcommunication network.

2.3. BASIC MODELS AND TECHNIQUES OF TIME KEEPING AND TIMESYNCHRONIZATION 25

have been evolved targeting both wired and wireless networks over the time. In

all cases, they deal with the fundamental problems of measuring the variation in

sending and receiving time of messages, including access and propagation time over

the medium by comparing the timing information received from the nodes Lenzen

et al. [2015], Mills [2016]. The efficiency of synchronization protocols hence lies on

the ability to accurately predict and eliminate message transmission-related delays

by comparing their clocks.

Based on the above fundamental concepts, the next section canvasses technical details

and principles of time keeping and clock synchronization.

2.3 Basic Models and Techniques of Time Keeping and Time Synchroniza-

tion

This section presents general clock models and error sources that limit accurate time

keeping towards achieving a common notion of time in a communication network.

The levels of clock accuracy are also discussed together with basic techniques for

time synchronization in decentralized communication network systems.

2.3.1 Hardware Clocks

A hardware clock consists of a counter to count time ticks, which are ideally of a fixed

length. A hardware oscillator updates the counter at a constant rate, i.e., frequency.

The quality of the clock thus depends on the stability of the oscillator. Let the reading

of a clock counter t be denoted by C(t), the rate f (t) at time t is C(t), we have

f (t) = dC(t)/dt (2.1)

For an ideal clock, the rate is 1. However, a real clock fluctuates over time due to

the fact that the rate changes because of various limiting factors. In a typical node p


with a quartz crystal, whose nominal frequency is defined as f 0p , the relative frequency

deviation is

ρp(t) = fp(t)/ f 0p − 1 (2.2)

According to [Gaderer et al., 2006], a model for a real clock is expressed as

fp(t) = f 0p · [1 + ρi

p(t) + ρap(t) + ρn

p(t) + ρep(t)] (2.3)

where, ρip(t) is the initial frequency deviation at start-time, ρa

p(t) considers the aging

effect, ρnp(t) denotes the jitter due to short-term noise, and ρe

p(t) represents the jitter

due to environment changes.

The environmental jitter ρep(t) is a major factor influencing the quartz clock drift, in

which variation in temperature typically contribute the most. According to [Packard,

1997], a jitter in order of 10−6 to 10−5 could be introduced by temperature changes.

Other environmental influences in supply voltage and mechanical effects such as

shock and vibration, can cause fluctuations. The short-term noise is typically in the

order of 10−8 to 10−12. The aging effect ρap(t) is in an order of 10−7 per month. Overall,

the systematic deviation for the initial frequency while restarting an oscillator can

grow at an order of 10−5 [Armengaud et al., 2007].

2.3.2 Software Clocks

Software clocks or logical clocks are algorithms residing in programmable devices.

They take local clock value C(t) as input and convert it to time S(C(t), which all

programs use for time-dependent applications. This time S(C(t))) is the consequence

of time synchronization. The mathematical model of such a typical software clock is

S(C(t)) = t0 + C(t)− C(t0) (2.4)


where t0 is the (correct) real time. Such a software clock runs with the speed of the

hardware clock.

2.3.3 Clock Accuracy and Precision

Clock accuracy and Clock Precision are two related yet different concepts. Clock ac-

curacy, denoted by α, refers to the degree of correctness of the clock time. In com-

parison, clock precision refers to the consistence of the clock time with some other

and/or standard clock. In synchronization nomenclature, the accuracy is the largest

or maximum acceptable clock offset between the node clock and the reference clock.

It is determined by measuring the mean of the error between the node and external

reference clock and usually represents as synchronization bias Mahmood et al. [2017].

The clock of a Node p can run with the accuracy α if the clock value Cp(t) is in an open

α-neighbourhood around the standard absolute time t in an observable period of T

Fan et al. [2004], Gaderer et al. [2006], Horauer and Holler [2002]. Thus,

|Cp(t)− t| ≤ α, ∀taT (2.5)

The clock precision β, however, is the measure of the standard deviation of the mean

clock error and quantifies the synchronization jitter. It is often also called instantaneous

precision, which is represents the boundary of the difference between two clocks p and

q, i.e.,

|Cp(t)− Cq(t)| ≤ β, ∀taT (2.6)

In internal synchronization environments, the clock precision is the maximum dif-

ference between two clocks. For external synchronization with a standard time, this

difference is the accuracy as expressed in Equation (2.5).


2.3.4 Clock Offset, Skew and Drift

A free-running clock is influenced by a number of factors as described in Equation 2.3.

It fluctuates and deviates from the actual time due to the clock drift. Theoretically,

software clocks are similar to hardware clocks, as software clock algorithms follow

the system clocks, which depend on hardware clocks. Thus, the accuracy of software

clocks relies on the accuracy of hardware clocks.

The accuracy of a clock as defined above pertains to the overall degree of clock

uncertainties relative to a reference standard time. Clock uncertainties can be further

described through Offset, Skew and Drift [Sundararaman et al., 2005]. Clock Offset is

defined as the time differences between a clock time and the standard true time. It is

|Cp(t) − t| for node Np. It is seen from Equation (2.5) that the clock accuracy is the

absolute value of the clock offset. The relative clock offset between two nodes Np and

Nq at time t is expressed as

Clock Offset = Cp(t)− Cq(t) (2.7)

Clock Skew is defined as the difference of the clock frequencies between a system clock

and a perfect clock. It is the first derivative of the clock offset with respect to the real

time t. The clock skew of a clock Cp relative to Cq at time t can be expressed as

Clock Skew = C′p(t)− C

′q(t) (2.8)

Clock Drift of a clock Cp is defined as the second derivative of the clock value with

respect to the real time t, i.e., C′′(t). Therefore, the relative clock drift between two

nodes Np and Nq is represented by

Clock Drift = C′′p(t)− C

′′q (t) (2.9)

Overall, the above three terms are frequently use to characterize the performances


of a typical clock in a communication system.

2.3.5 Main Limiting Factors in Time Synchronization

The performance of time synchronization methods are affected by two main factors:

the inherent performance of the clock oscillators and how effectively a chosen syn-

chronization technique works between them. The systematic and random errors of

clock oscillators accumulate over time [Exel and Ring, 2014], which impact on the

synchronization accuracy.

Several issues in synchronization techniques can affect the clock synchronization.

The first issue is the capability of the technique to deal with the uncertainty of mes-

sage delay during radio communication. Other issues include Clock Adjustment Prin-

ciple and Timestamping Accuracy. The estimation of various latencies during Sending

time, Accessing time, Propagation time and Receiving time is crucial for adjusting clocks

precisely over a network. The clock adjustment performance is highly dependent on

the method and quality of the synchronization algorithm. Timestamping is a method

of adding time into the packet during the transmission and reception of a message.

In packet-based synchronization techniques, precise time-stamping is crucial. By

calculating the egress and ingress timestamps, the propagation delay is measured. It

is known that the accuracy of clock synchronization varies from one protocol layer to

another [Chen et al., 2015, Cooklev et al., 2007, Weibel and Bechaz, 2004]. This is due

to the uncertainty of inter-layer delays. Physical layer time-stamping is considered to

be the most accurate way so far. After receiving the timestamps, a node needs to run

an operation to adjust the clock. The performance of this adjustment operation also

determines the accuracy and quality of the synchronisation technique.


2.3.6 Basic Techniques for Time Synchronization in a Decentralized System

VANETs are decentralized systems. To explain the time synchronization mechanism

in a distributed or decentralized network, we consider a model involving two nodes

Np and Nq, as shown in Figure 2.7. When Node Np sends a message with its local time

stamp tpx to Node Nq (Figure 2.7(b)), Node Nq receives the signal at tq

y and updates its

time accordingly. This is known as Unidirectional Synchronization. In unidirectional

synchronization, the transmission delay is not considered. It suffers from a large

synchronization error. Therefore, a more complicated Round-Trip Synchronization tech-

nique is more acceptable. In this technique, Node Np sends message at tpx to node Nq

to ask for the timestamp tqy. After getting the response from node Nq, node Np per-

forms calculation to determine the round-trip time d = tpz − tp

x . This round-trip time

is basically the time interval of two-way message transmissions as shown in Figure

2.7(c). Then, it is used to improve the precision of the time synchronization between

the two nodes. The drawback of this synchronization method is the introduction of

message exchange overheads.

Np

qN

Np

qN

xpt z

d d‘ ‘’

pt d

t yq

pt x

pt y

Np

qNqt y

qt x

d

(a) (b) (c)

Figure 2.7: Message Exchanges between Two Nodes.

Another effective method, namely, packet-based clock synchronization is Reference

Broadcasting Synchronization (RBS). Its operation is shown in Figure 2.8. In RBS, a

beacon sends a synchronising message to all nodes. For example, in Figure 2.8, node

Nb is the beacon node. It sends beacon a message to nodes Np and Nq. The delay d′

for Np and delay d′′

for Nq are almost the same. After receiving the beacon signal,

Nq sends its time stamp tqx to node Np. Then, node Np calculates the time interval


d = tpy − tp

x . The result is a measure of the time difference between nodes Np and Nq.

Np

qN

Nb

qN

pt y

(a) (b)

Nb

d

pNd‘

d ‘’ xpt

qt x

Figure 2.8: Reference Broadcasting Synchronisation (RBS).

2.3.7 Types of Clock Synchronization

Several parameters such as the source of the reference clock, the required accuracy of

the synchronization, the communication medium between nodes and the supported

applications can all impact on the method of clock synchronization used. Therefore,

depending on the variation of methods and their applications, clock synchronization

may classify differently.

For example, when any system maintains synchronization with a standard ref-

erence clock time, it is known as absolute clock synchronization. When nodes in a

network are synchronized with respect to each other’s time, the method is known

as relative clock synchronization Tian et al. [2008]. Again, based on the variation of

the synchronization protocols we can classify time synchronization differently. Some

developed protocols for time synchronization commonly differs from each other in

some aspects again sometimes resemble each other in some other aspects Sundarara-

man et al. [2005]. For example, consider deterministic and probabilistic clock syn-

chronization. Deterministic protocols stipulate a strict upper bound on the offset

error certainty compared to probabilistic synchronization where it uses less message

transfers and, therefore, less processing overhead Arvind [1994], PalChaudhuri et al.

[2003].


However, the most popular way of classifying clock synchronization methods is

based on the time references system that is used. According to the time scale, clock

synchronization in distributed network can be classified into two main types: syn-

chronization with internal time-scale and synchronization with external time-scale.

In ad-hoc like networks such as WSN, VANET, etc., time synchronization can be

implemented locally with an internally consistent time-scale. However, VANETs are

outdoor wireless ad-hoc networks, therefore, it is also possible to deploy global time

synchronization with an external time scale.

Synchronization methods with an internal time-scale is realized through a set of

operations and message exchanges between nodes. This requires estimating both

offset and skew of the local clocks relative to each other. Hence, synchronization with

internal time-scale maintains a relative time notion with respect to each other. Such

relative synchronization is the basis of most indoor networks such as indoor wireless

sensor networks and Wifi.

Synchronization methods with an external time-scale method are implemented

with respect to an absolute or external reference time standard, such as Coordinated

Universal Time (UTC). Such an external reference time-scale is usually transmitted

and distributed by using a global radio system. Typical global radio systems include

satellite-based Global Navigation Satellite System (GNSS) and the short-wave WWVB

station [Beehler, 1981, Groves, 2013].

The next sections examine the synchronization techniques currently practiced in

different wireless networks.

2.4 Approaches to the Time Synchronization in Wireless Media

Most of the time synchronization protocols for communication networks are applica-

ble in both wired and wireless media. For example, the well adopted NTP, which is

considered as the backbone of wired computer communication networks, appears to

2.4. APPROACHES TO THE TIME SYNCHRONIZATION IN WIRELESS MEDIA 33

be implementable in wireless media with certain accuracy Elson and Romer [2003],

Ganeriwal et al. [2003]. However, although the fundamental principles are similar,

performance improvements are expected in further technology evolutions.

This section explores the prominent synchronization techniques over Wireless

Sensor Network (WSN), which provides a basis for synchronization in vehicular wire-

less networks. This is followed by presentation of the challenging issues and require-

ments of time synchronization in vehicular networks.

2.4.1 Revisiting Time Synchronization in Wireless Sensor Networks

As a VANET is a special type of mobile wireless networks, it is worth examining

the existing synchronization techniques for other types of mobile ad-hoc networks.

The focus is on wireless sensor networks (WSNs), for which considerable research

effort has been directed to time synchronization. Five main WSN synchronization

techniques are to be discussed below: time-stamp synchronization (TSS), reference-

broadcast synchronization (RBS), lightweight time synchronization (LTS), a timing-

sync protocol for sensor networks (TPSN), and flooding time synchronization proto-

col (FTSP).

Time-stamp Synchronization (TSS) is a WSN time synchronization method based

on internal synchronization on demand [Romer, 2001]. TSS does not use specific

synchronization messages for time synchronization. Instead, it uses timestamps em-

bedded in other packets to perform synchronization post-facto. The time offset is

estimated through calculation of the round-trip delay between the transmitters and

receivers. For single-hop WSNs, the average uncertainty of TSS is recorded as 200 µs.

In multi-hop networks, the maximum uncertainty of 3 ms is achieved in 5 hops.

Reference-Broadcast Synchronization (RBS) uses beacon broadcast for time synchro-

nization. In RBS, any nodes in a basic single-hop network can send a beacon to broad-

cast its time reference. A node compares its local reference time with the reference


times received from other neighbor nodes and adjusts its clock accordingly. RBS

performs both offset and rate corrections when updating the clock. Making use of

physical layer broadcasts, it does not carry explicit time-stamps. This synchronization

is enacted for the whole network.

In a multi-hop network, all network nodes are grouped into clusters. In each

cluster, a single beacon is used to synchronize all nodes in the cluster. A gateway node

is used to transfer time-stamps from one cluster to another. This helps maintain the

same reference time to compute offset and rate corrections. RBS uses the last minute

time-stamps in order to reduce random hardware delay and access delay. Its average

uncertainty is measured as 11 µs in laboratory experiments with 30 broadcasts. For

multi-hop networks with n hops, the average error grows in O(√

n). While RBS

provides comparatively high accuracy, it is subject to excessive protocol overheads.

Lightweight Time Synchronization (LTS) aims to reduce the complexity of synchro-

nization overhead [van Greunen and Rabaey, 2003]. Therefore, unlike other synchro-

nization methods, it provides with a specified precision. As a centralized algorithm,

it begins with the construction of a spanning tree for the network with n nodes. Next,

a pair-wise synchronization is performed along the n− 1 edges of the spanning tree.

The root of the spanning tree works as the reference node. It initiates all on-demand

resynchronization operations. The average synchronization error in LTS is recorded

as 0.4 s. The maximum error can reach as high as 0.5 s.

Timing-Sync Protocol for Sensor Networks (TPSN) is a network-wide synchroniza-

tion protocol based on a hierarchical approach [Ganeriwal et al., 2003]. It follows the

classical approach of sender-receiver synchronization to create a hierarchical topol-

ogy. The hierarchy maintains multiple levels in order to distinguish nodes to perform

actions. TPSN performs time synchronization through two phases. In the first phase,

a node at level 0 acts as the root node. It initiates a ‘level discovery’ broadcast message

with its identity and level in the hierarchy. Its immediate neighbors receive this mes-

sage and assign themselves level 1 below the root node. After that, each node at level

2.4. APPROACHES TO THE TIME SYNCHRONIZATION IN WIRELESS MEDIA 35

1 broadcasts a ‘level discovery’ message, which will be received by other neighbor

nodes at lower levels. This process continues until all nodes are reached by such

‘level discovery’ messages. In the second phase, all nodes synchronize their clocks to

their root or parent nodes in the tree by using a round-trip synchronization operation.

This round-trip synchronization is conducted at the MAC layer. Therefore, message-

delay uncertainties are largely eliminated. The accuracy of TPSN is considerably

high. Experimental results show that TPSN synchronization of two Berkeley motes

has reached an accuracy of 17 µs. A drawback of TPSN is the significant message

exchange overhead particularly for a large number of nodes.

Flooding Time-Synchronization Protocol (FTSP) is a hybrid time synchronization pro-

tocol built upon RBS and TPSN [Maroti et al., 2004]. In FTSP, a node with the lowest

node identity becomes the root node, which works as the reference time sender. If

this node fails, a node with the next lowest identity becomes the root node. The

root periodically floods the network with synchronization message with the reference

time. In this way, the whole network becomes synchronized. FTSP is a self-organized

algorithm. It constructs a hierarchy to perform low-level time stamping and local

clock correction. A FTSP experiment with an eight-by-eight grid of Berkley motes

shows an average error of 1.7 µs with the maximum of 38 µs per hop.

2.4.2 Why is Time Synchronization an Issue in VANETs

Time synchronization in distributed network systems is a well-recognized problem.

In wireless communication networks, time synchronization is considered as a key

element for consistent data traffic and also for accurate real-time control of message

exchanges Ghosh et al. [2011]. Many network applications require precise clock syn-

chronization among the nodes to ensure correctly ordered operations. Otherwise,

the performance of these applications and hence the network operations could be

disrupted. Over the years, the issue of time synchronization has been extensively in-

vestigated in the context of computer and telecommunication networks Bregni [2002],


Johannessen et al. [2001], Mills [1997]. Many protocols have been proposed and im-

plemented to perform time synchronization over computer and telecommunication

networks. Those protocols vary in terms of the required precision of timing and also

according to the services and types of networks. For example, in a routed network,

physical time is not a critical issue. Thus, protocols based on a routed network,

i.e., Packet-over-SONET/SDH links (POS), requires synchronization to ensure the se-

quence of the order. One the other hand, some networks require synchronization with

high time accuracy Cozzetti et al. [2011], ETSI [1 12], Scopigno and Cozzetti [2009].

For example, in pure synchronous optical networking (SONET) and synchronous

digital hierarchy (SDH) networks, the precision of time along with fixed time-division

multiplexing mechanism is mandatory.

In VANET, physical time plays an important role in many applications, which

cannot be satisfied by logical time or any kind of event ordering models. Most com-

municating interactions for time-based decisions rely upon a time-of-day clock. For

example, VANET enables traffic management on individual levels by providing com-

munication among vehicular nodes and share road information such as vehicle dy-

namics, driving intentions etc Cunha et al. [2016], Dua et al. [2014], Englund et al.

[2015]. The current status of the nodes in a VANET, therefore, needs to be determined

precisely in terms of position, speed and other real-time values. This frequently

scheduled work requires time synchronization to develop accurate and precise time

on node.

VANETs increase road safety by enabling different critical safety applications. For-

ward Collision Warning (FCW), Cooperative Collision Warning (CCW), Emergency

Electronic Brake Lights (EEBL) are a few examples that alert a driver about possible

crash scenarios ahead. In these applications, each vehicle is required to broadcast

their basic safety messages (BSM) that include vehicle location data periodically at 10

Hz. Event trigging warning messages are time sensitive and need to be transmitted

2.5. EXISTING RECOMMENDATION FOR VANET TIME SYNCHRONIZATION 37

and received orderly securing stringent delay requirements (typically 100 ms Harten-

stein and Laberteaux [2010]). If nodes clock in VANET does not have any commonly

agreed accurate time maintained among them, such periodical and event trigging

safety message from the sender may report with a past timestamped information

or with advanced timestamped information with respect to the receiver time and in

either case, those messages may be discarded after reception by the receiver nodes

considering as an outdated message. Under such circumstances, a warning message

would fail to alert drivers, thus leading to a risk of collision and other road casualties.

Time synchronization in VANET, therefore, is essential to achieve accurate and precise

time over the network Hussein et al. [2017].

Physical time is also crucial for proper bandwidth utilization and efficient channel

scheduling. Therefore, it is required that all the nodes in a VANET are able to report

the same time, regardless of the errors of their clocks or the network latency the

network nodes may have.

Furthermore, certain security measures in VANETs, such as duplication detection

and identification of session hijacking and jamming, require absolute time synchro-

nization Engoulou et al. [2014], Loo et al. [2016]. Time plays a critical role in deter-

mination of two distinct real-world events to develop traceable communication for

reconstruction of packet sequence on the channel Ben-El Kezadri and Pau [2010].

2.5 Existing Recommendation for VANET Time Synchronization

Time synchronization for VANET has been solely based on protocol IEEE 802.11p.

IEEE 802.11p is an amendment to Wireless Local Area Network (WLAN) protocol

IEEE 802.11. Therefore, the synchronization technique from IEEE 802.11 family is

naturally applicable to VANETs. In IEEE 802.11 standard family, a station (STA) can

be attached to an Access Point (AP) in a centralised mode called Basic Service Set

(BSS). It can also communicate with other STAs in decentralised ad-hoc mode called


Independent BSS (IBSS). These two modes are shown in Figure 2.9.

STA

STA

STA

STA

STASTASTA

AP

AP

STA

STA

(a) (b)

Figure 2.9: Two Modes of communications in 802.11 Standard Family.

In 802.11 networks, time synchronization is predominantly required for frequency

hopping and scheduling of sleep phases. The standard requirement of time syn-

chronization is 274 µs, which is also the threshold of out-of-synchronization [PIS-

CATAWAY, 1996]. Time synchronization within 802.11 systems rely on a Timing

Synchronization Function (TSF) timer. The TSF timer is a 64-bit hardware counter

with a resolution of 1 µs and thus is capable of performing 264 modulus counting.

It employs a local clock oscillator built on WLAN chipset with a frequency accuracy

of ±0.01%. The adjustment of the timer and hence the accuracy of synchronization

depends on the operation mode, e.g., BSS mode in centralised communications or

IBSS mode in a decentralised network. In BSS, the AP transmits beacons with TSF

timer values, and an STA sets its own TSF timer with delay usually corrected by offset

adjustment without rate correction.

In the infrastructure-based BSS mode, an AP acts as a master clock. It broadcasts

the reference time for all STAs to be time synchronized. When beacon transmits,

other data exchange operations are suspended so that the master can broadcast TSF

synchronization values to all attached STAs. The period of beacon transmission de-

pends on the network resource sharing mode as shown in the Table 2.1. In this mode,

the receiving station only accepts TSF values and updates its clock.

In the ad-hoc IBSS mode, all STAs adopt a common value, aBeaconPeriod, which


Table 2.1: Slot Time with Beacon Generation Window.

FHSS DSSS OFDM

aCWmin 15 31 15aSlotTime (µs) 50 20 9Speed (Mbps) 1 2 1 2 5.5 11 6 12 24 54Beacon length 13 8 34 22 14 12 12 8 5 4

characterises the length of a beacon interval. At the beginning of the beacon interval,

a beacon generation window forms. It consists of ω + 1 as shown in Figure 2.10. For

the station that initiates IBSS, this interval also defines Target Beacon Transmission

Times (TBTTs) in aBeaconPeriod times apart. A time zero is defined to be a TBTT. At

the TBTT event, all STAs perform the following process:

1. At TBTT, suspend the backoff timer for any pending non-beacon transmission.

The STA calculates a random delay distributed in the range [0,ω), where ω =

2*aCWmin*aSlotTime.

2. All STAs wait for the period of the random delay.

3. If the beacon is received before the expiration of the random delay timer, cancel

the remaining random delay.

4. When the random delay timer expires, STA sends beacon using its TSF timer

value as a timestamp.

5. When a station receives the beacon, it updates its TSF timer following the times-

tamp of the beacon if the beacon value is later than the station’s TSF timer.

Therefore, the TSF synchronizes timers with the fastest STA in IBSS.

The above synchronization procedure is designed for single-hop networks. Such

a procedure with TSF suffers from poor scalability and inability to handle conges-

tions. When the number of nodes increases, the node with the fastest clock faces

difficulties in successfully sending out beacon frames. As a result, its clock gradually


Beacon Interval

Beacon Generation Window(W+1 slots)

Figure 2.10: Beacon Generation Window.

drifts away from the clocks of other nodes. This problem is known as fastest node

asynchronism [Huang and Lai, 2002].

In a multi-hop network employing the native IEEE 802.11 clock synchronization

mechanism, the whole network is partitioned into multiple disjoint clock islands. If

every island is out of synchronization with one another, the time partitioning problem

appears [So and Vaidya, 2004].

Improved techniques have been proposed to address the so-called fastest node

asynchronism problem and the time partitioning problem. The basic ideas are to

enhance scalability and mitigate congestion. Two well-recognised improvements are

Adaptive TSF (ATSF) and Multi-hop TSF (MTSF).

ATSF modifies the basic 802.11 TSF. It adds a priority scheme to overcome the

fastest node asynchronism problem. This method involves maintaining and adjusting

the transmission frequency of the beacon [Huang and Lai, 2002]. When a node re-

ceives a beacon message with a larger timestamp, it reduces its beacon transmission

frequency. It keeps updating the beacon transmission frequency until it reaches the

maximum allowed value. This allows the fastest node to have a higher probability of

transmitting beacon messages.

In MTSF, each node maintains a path to the fastest node. The beacon is transmit-

ted from the fastest node to all other nodes without being suppressed anywhere in

the middle of the network. MTSF consists of two phases: a beacon window phase

and a synchronization phase [Chen et al., 2006]. In the beacon window phase, all


neighbour nodes construct a synchronization group and identify the fastest node as

the root node of the group. In the synchronization phase, root nodes are synchronized

with each other. In this way, the fastest node asynchronism problem can be avoided

together with the partitioning problem.

The average maximum clock drift with TSF is 124.5 µs for 20 nodes. It increases

to 500.2 µs when the number of nodes is 60. In comparison, MTSP performs much

better. Experimental measurements show that the average clock accuracy of MTSF is

22.4 µs for 20 nodes and 39.1 µs for 60 nodes, respectively [Cheng et al., 2006].

Such TSF-based synchronization lacks support from timing standards such as

UTC, TAI etc. The 2012 amendment of IEEE 802.11 proposes two techniques, i.e.,

Timing Advertisement (TA) and Timing Measurement (TM) mechanisms, to obtain

the support of global time [IEEE, 2011]. In TA, the external reference clocks are

attached to Access Points (APs). In TM, the frames use physical layer timestamps

to perform synchronization between AP and STA, thus reduces multi-hop errors.

However, the TA mechanism architecture requires a cascade of four clocks, which

does not direct how the external clock will be synchronized to the AP and perform

accurate time-stamping. Reference [Mahmood et al., 2015] and [Mahmood et al.,

2017] have discussed details on that issue and proposed some measures in WLAN

scenarios. VANET networks are more ad-hoc in nature compared to WLAN, where a

large portion of it relies on STA to STA but STA to AP communication. Therefore, the

feasibility of employing such a mechanism in VANET requires an extensive investi-

gation.

The next sections highlight the feasibility of GNSS-based time synchronisation in

VANET.


2.6 GNSS Approaches for VANET Time Synchronization

Global Navigation Satellite Systems (GNSS) are a well-established international util-

ity for positioning, navigation and timing (PNT). The generic term ”GNSS” refers to

the USA’s Global Positioning System (GPS), Russia’s GLONASS, Europe’s GALILEO

and China’s BEIDOU navigation satellite system (BDS). It is noted that GPS, GLONASS,

Galileo and BDS use different reference time systems creating time offsets between

them. However, the offsets can be determined at the system level or user level. Any

one or more constellations can offer the same global standard UTC time. With their

worldwide coverage, continuous service, GNSS has become one of the most efficient

and standard systems for time dissemination in many applications. Many industries

such as energy, meteorology and telecommunications rely on GNSS for accurate time

synchronization in their systems and devices. The accuracy achieved by GNSS-based

time synchronization using GPS is better than 40 ns 95% of time ?. This can meet the

most restrict requirements for VANET time synchronization.

This section begins with discussing the motivation for using GNSS for VANET

time synchronization. This is followed by descriptions of GNSS models and methods

for time transfer. The challenges and solutions due to absence of GNSS signals in

vehicular environments are then summarised.

2.6.1 Motivation of GNSS-driven Time Synchronization in VANETs

Most of the earth-based time transfer techniques suffer from path delay measurement

uncertainties. In contrast, the satellite-based GNSS time transfer systems possess

measurable constant path delays. This arises because the variation of path delays

are small and due to clear, unobstructed paths to receivers. Therefore, the delay

measurements are straightforward and can be more easily calibrated compared to

any ground-based systems. In addition, the radio interferences due to weather or any

other ground-based noise have less impact in satellite-based GNSS systems.

2.6. GNSS APPROACHES FOR VANET TIME SYNCHRONIZATION 43

In telecommunication networks, GNSS is used to synchronize some major nodes

called root or server nodes outdoors. Through these root nodes, other nodes in

the system are synchronized by using other synchronization techniques, which are

mostly based on message transfer between nodes.

In contrast to telecommunication networks, VANETs are outdoor-based networks.

Except in some tunnels and blocked roads, nodes in VANETs on the road are mostly

under the coverage of GNSS signals. It is a straightforward choice for VANETs to

use GNSS for synchronization. GNSS receivers have already been used for vehicle

navigation and positioning. Nowadays, multi-GNSS constellations, more precise

GNSS services, such as space-based argumentation systems (SBAS), differential GNSS

(DGNSS) services and precise point positioning (PPP), are available for VANET de-

ployments. The GNSS-based time synchronization is indeed plausible in VANETs.

It is therefore prudent to understand how GNSS time solutions provide synchro-

nization in VANETs and what the possible solutions are when GNSS services are

absent, such as when vehicles travel in tunnels. The feasibility and accuracy of GNSS

time solutions are referred to a recent work by Hasan et al in Hasan et al. [2018a,b].

2.6.2 GNSS Time Synchronization Models for VANET

Different GNSS systems follow the same estimation principle for position, velocity

and time computing. Without loss of generality, this section discusses the theory of

GPS time, time transfer from GPS, and time propagation. It explains time synchro-

nization model by using GPS data. It also outlines possible support of synchroniza-

tion in the absence of GPS signals.

2.6.2.1 GPS Time

GPS time is one of the standard times related to UTC. It is a continuous time generated

from a precise atomic clock and maintained by some control segments. GPS time is


related to UTC by leap seconds. At present, GPS time is 18 s ahead of UTC time.

This means that the leap seconds between GPS time and UTC time are 18s. This is

indicated in USNO navy’s website tycho.usno.navy.mil/leapsec.html.

2.6.2.2 GPS Time Receiver and Time Transfer

There are a variety of GPS receivers which differ within applications, technologies

and manufacturers. Most consumer-grade GPS receivers receive single-frequency

C/A code. The clocks in GPS receivers are mostly quartz clocks. They are synchro-

nized by GPS signals. The GPS receiver clock solution is obtained from the pseudo-

range measurements. For the purpose of this work, the pseudo-range measurement

can be written as [Petovello, 2011]:

Pu = ρ + cdt + ε (2.10)

where Pu is the Pseudo-range measurement, ρ is the geometric distance between the

receiver and the satellite, c is the speed of light, dt is the receiver time offset or bias

with respect to the receive clock time tag, and ε is the sum of all errors.

It is clear from the Equation 2.10 that dt can be directly obtained from the observed-

computed (O-C) difference (Pu- ρ) if the distance is known. Taking average or weighted

average over all the O-C differences would improve the accuracy of the dt solutions.

This is the static mode for time transfer. However in a VANET, the vehicle nodes are

moving. The distances ρ are computed with the approximate vehicle states X0. The

coordinate biases of X0 can affect the accuracy of dt solution if ignored. Considering

the coordinate biases, Equation 2.10 can be rewritten as:

Pu − ρ(X0) =∂ρ0

∂X0dX + cdt + ε (2.11)

where the partial derivatives of the geometric distance ρ0 are computed with respect

http://tycho.usno.navy.mil/leapsec.html


to the 3-dimensional approximate coordinate vector X0; dX is the 3-dimensional po-

sition deviation with respect to the approximate states X0. They can be estimated

along with the clock bias dt. The least square or weighted least square procedures are

usually applied to solve the estimation problem with four or more satellites in view

[Misra and Enge, 2006].

Several techniques have been developed to transfer GPS time based on the above

equations. The simplest method is ‘time dissemination’, which is also known as ‘One

Way’ method. It predominantly aims to synchronize an on-time pulse, or to calibrate a

clock frequency source. Figure 2.11(a) illustrates the one-way concept where the clock

bias is determined by the difference between observed range Pu and computed range

ρ, namely (O-C). With more satellites in view as shown in 2.11(b), the clock bias can

be estimated from the average of all the (O-C)s. The user-position biases will affect

the clock bias dt solutions. As long as four or more satellites in view the coordinate

bias vector dX and clock bias dt can be determined with the linear Equation 2.11. A

typical clock solution accuracy with GPS-only signals is 40 ns [Hasan et al., 2018b].

A more accurate and elegant technique for GPS time transfer is ‘Common View’. As

shown in Figure 2.11(c) , it measures the clock bias difference between two receiver

oscillators using the difference of the (O-C)s between two receivers. This differencing

leads to the cancellation of satellite orbit and clock error and local ionosphere and

troposphere delays, thus providing a higher accuracy for the clock offset, saying in

the level of 10 ns. Similarly when multiple satellites are in view as shown in 2.11(d),

the common view method is equivalent to differential GPS, and determines the 3D

coordinate offsets and clock offset between two receivers. There is a highly accurate

technique for GPS time transfer called the ’Carrier-Phase’ method. In this method,

both L1 and L2 carrier phase signals are used to calculate time [Parker and Matsakis,

2004]. The timing accuracy achieved from this method is in sub-nanosecond level.

However, dual-frequency phase receivers are more costly and may be not a popular

choice for vehicle users.


(a) (b)

(c) (d)

A B A B

Figure 2.11: GPS Time Transfer.

2.6.2.3 A Simple Model for GNSS Time Synchronization

Based on the above ”one-way” and ”common-view” modes, the GNSS time synchro-

nization model is outlined as follows. An on-board GPS receiver tracks satellites.

Once the clock bias dt is obtained with a one-way time transfer, the receiver can

determine its UTC time tUTC,

tUTC = tu − dt− dtUTC (2.12)

where tu is the receiver time, which is usually the time tag of a standard time epoch;

dtUTC is the offset between GPS time and UTC time, which includes a integer term

for leap seconds and a fractional correction term calculated from GPS navigation

messages. Both are the same for different receivers at the same time. In the common

view mode, receiver B obtains the clock bias with respect receiver A, i.e., dtBA, the


receiver B’s UTC time is obtained as follows:

tBUTC = tAUTC − dtBA (2.13)

A typical GNSS receiver has an internal quartz-based oscillator that continuously

runs and follows GPS time. Generally, in the one-way time transfer, the clock update

rate can be the same as the receiver sample rate. A low-end receiver updates its

outputs at 5 to 10 Hz, while a high-end geodetic receivers sample rates can be up

to 50 Hz. However, such quartz clocks still exhibits deviations because the frequency

of each clock is different and tend to diverge from each other. This divergence is

known as clock skew. The clock drifts with respect to time is the derivative of clock

skew [Sundararaman et al., 2005]. Following [Sichitiu and Veerarittiphan, 2003] and

[Levesque and Tipper, 2016], in general, a node clock in a GNSS-synchronized dis-

tributed network over a time interval of minutes to hours can be characterised as as:

Ci(t) = di.t + bi (2.14)

where t is the time corresponding to the UTC time. di is the clock drift due to the

oscillator’s frequency differences and the result of to the environmental changes at

the node, e.g., variations in temperature, pressure and power supply voltage. bi is the

offset between the receiver local clock and the UTC time obtained from one-way time

transfer approach. This reflects the effect of hardware delays of the clock.

Any two such GNSS-synchronized clocks can be represented as:

C1(t) = d1.t + b1

C2(t) = d2.t + b2

(2.15)


They can also be related as follows:

C1(t) = Θ12C2(t) + β12 (2.16)

where Θ12 is the relative drift between two receivers and b12 is the offsets due to

the bias variations. If the two receivers are the same model, the relative offset can be

small and the drift Θ12 is 1.

To measure the time offset between two GNSS powered nodes, experiments have

been performed at physical layer on 1PPS signals generated by two GPS receivers.

The experimental results are presented in [Hasan and Feng, 2016, Hasan et al., 2018b].

It has been shown that two same-model consumer-grade GNSS receivers are capable

of synchronizing network nodes with nano-second scale timing accuracy.

2.6.3 Challenges and Solutions in Absence of GNSS Signals

Signal transmissions between satellites and receivers solely relies on the principle

of the Line-of-Sight (LOS) wave propagation technique. Drivers often experience

outages of navigations when driving through high-rise streets. This does not neces-

sarily mean loss of time synchronization. First, GNSS time solutions can be obtained

with a single satellite at reduced accuracy. The availability of valid time solutions is

much higher than the availability of valid position solution. For instance, a vehicle

experiment shows that the percentage of valid GPS+Beidou position solutions over

some Brisbane high-rise streets is 99.25%, while the percentage of a minimum of one

satellite is 100% Hasan et al. [2018b].

Secondly, some measures have been proposed as fall back solutions in the block-

ages of GPS signals. One of them is to switch from normal mode to holdover mode

using GPS Disciplined oscillator (GPSDO). GPSDO is a specially made firmware for

holdover mood. It enables the internal oscillator to predict and imitate the original

timing and frequency of the GNSS system. A GPSDO is primarily made of a phase


detector and voltage control oscillator (VCO). Its fundamental purpose is to acquire

information from the GNSS signal of satellites to control the frequency of local quartz

or rubidium oscillators. When GPS signals are unavailable, GPSDO keeps its oscilla-

tion in a stable frequency using the knowledge of its past performance.

In order to boost the performance of GPSDO, some additional technique have

been developed such as adaptive temperature and aging compensation during the

holdover period Penrod [1996]. The adaptive temperature and aging compensation

are based on a recursive implementation of linear regression. As an additional cir-

cuitry, a simple semiconductor ambient temperature sensor and an A/D converter are

used. The performance of both types improved GPSDOs are the same to some extent.

However, it is not well defined how long the independent free-running GPSDOs are

executed. Nevertheless, experiments have been conducted to test the performance

of GPSDO. An experiment was carried out over a week for holdover on 4 GPSDOs,

in which an oscillator is made of quartz and the other three are made of rubidium

Elson et al. [2002], ETSI [1 12]. After a week , the time offset from the quartz oscillator

was shown to be 82 µs. In comparison, the best time offset performance of less than

3 µs was measured for the three rubidium oscillators. This level of synchronization

accuracy is considered to be acceptable for VANET time synchronization applications.

Finally, the problem of GPS signal blockages can be addressed by incorporating

GPS synchronization with other methods. If some vehicles nodes that can view satel-

lites have GNSS time solutions, they act as root servers for synchronization of other

nodes through a non-GPS time synchronization technique. NTP-GPS is the back-

bone of general computer networks, in which the standard time hosting servers are

synchronized with GPS. For example, time synchronization based on absolute GPS

is employed in Automatic Identification System (AIS) for ships Tetreault [2005]. In

DSRC-based networks, Time advertisement (TA) has been specified in the IEEE1609.4

to provide time solutions to other devices where GNSS signals are not available.

However, to date the performance of TA for VANET is not well understood.


2.7 Summary

Communications in VANETs involve V2V and V2I communications. They form the

basis of VANETs for network connectivity and various road safety applications. Due

to the highly dynamic and mobile characteristics, precise timing and accurate mea-

surement of transmission delay become critical in VANETs. Time synchronisation

helps establish an agreed time over VANETs. It enables proper coordination and

consistency of various events throughout the networks. It also allows accurate se-

quencing and real-time control of message exchanges over the networks.

This chapter has discussed why time synchronisation is necessary for VANETs.

It has also discussed why most existing synchronisation techniques used for other

types of wireless networks are not directly satisfactory in VANETs. The discussions

are accompanied by detailed evaluations of existing time synchronisation protocols

in various distributed network systems.

Time synchronisation is a challenge in VANETs. Under certain road conditions,

VANETs require a high accuracy in time synchronisation. Some security measures

also need precise time synchronisation, which is currently not achievable in VANET

environments. Synchronisation techniques developed for general WSNs face com-

patibility issues when applied to VANETs. GNSS-driven time synchronisation is a

promising technique for VANETs.

Chapter 3

Significance and Requirement Analysis for Time

Synchronisation in VANET

Time synchronisation ensures that all nodes in a network have the same reference

clock time. The clock of any network node is imperfect thus if all node clocks agree

on a reference time at a given time, they are not capable of keeping the track over

time. Maintaining a common notion of time is necessary for VANETs for various

applications and also for the functioning of many system-level protocols. That is,

synchronising the time throughout the network is needed periodically. So far, there

has been limited work [Cozzetti et al., 2011, Morgan, 2010] on time synchronisation

and stringent timing requirements for VANET. The concepts and techniques of time,

time quality and time synchronisation in VANET have been directly adopted from

Wireless Local Area Network (WLAN) standards following IEEE 802.11p. However, a

WLAN is an infrastructure-centred asynchronous network, in which communications

are implemented among low-mobility wireless nodes through Access Points (APs).

As a WLAN is not time critical, precise time synchronisation is not required nor is it

51

52CHAPTER 3. SIGNIFICANCE AND REQUIREMENT ANALYSIS OF TIME

SYNCHRONISATION IN VANET

really achieved. In compared with WLANs, VANETs consist of both infrastructure-

based vehicle-to-infrastructure (V2I) and ad-hoc vehicle-to-vehicle (V2V) communi-

cations. The integration of V2I and V2V make VANET more challenging and time

critical than WLAN.

Time synchronisation in VANET depends not only on the synchronisation ac-

curacy but also on performance, compatibility and feasibility issues. The primary

objective of this chapter is to provide an in-depth analysis of time synchronisation

requirements in VANETs. It also discusses the importance of time synchronisation in

vehicular networking for different applications.

A general description of different time synchronisation parameters and VANET-

specific requirements is introduced below. The importance of time synchronisation in

different VANET applications along with their requirements is also discussed.

Some of the content from this chapter has contributed to the following publica-

tions:







3.1 The Need for Time Synchronisation in VANETs

Time synchronisation in distributed network systems is a well-recognised problem.

In wireless communication networks, time synchronisation is considered as a key

3.1. THE NEED FOR TIME SYNCHRONISATION IN VANETS 53

element for consistent data traffic and also for accurate real-time control of message

exchanges [Ghosh et al., 2011]. Many network applications require precise clock

synchronisation among the nodes to ensure correctly ordered operations. Otherwise,

the performance of these networks and applications are subject to disruptions. Over

the years, the issue of time synchronisation has been extensively investigated in the

context of computer and telecom networks [Bregni, 2002, Johannessen et al., 2001,

Mills, 1997]. Many protocols have been proposed and implemented to perform time

synchronisation over computer and telecommunication networks. Those protocols

vary in terms of the required precision of timing and also according to the services and

types of networks. For example, in a routed network, physical time is not a critical is-

sue. Thus, protocols based on a routed network, i.e., Packet-over-SONET/SDH links

(POS), requires synchronisation to ensure correct sequence order. One the other hand,

some networks require synchronisation with high-accuracy timing [Cozzetti et al.,

2011, ETSI, 1 12, Scopigno and Cozzetti, 2009]. For example, in pure SONET/SDH

networks, the precision of time along with fixed time-division multiplexing mecha-

nism is mandatory.

In VANET, physical time plays an important role in many applications, which

cannot be satisfied by logical time or any kind of event ordering models. Most com-

municating interactions for time-based decisions rely upon a time-of-day clock. For

example, VANETs enable traffic management on individual levels by providing com-

munication between vehicular nodes and sharing road information such as vehicle

dynamics, driving intentions etc. [Cunha et al., 2016, Englund et al., 2015]. The

current status of VANET nodes, including position, speed and time, is important

and needs to be determined precisely. That is, accurate frequently scheduled work

requires time synchronisation to develop accurate and precise time on a node.

VANETs can support increased road safety by enabling different safety-critical

applications. For example Forward Collision Warning (FCW), Cooperative Collision

Warning (CCW), Emergency Electronic Brake Lights (EEBL) can alert a driver about



possible crash scenarios ahead. The underlying communication messages are time

sensitive and need to be transmitted and received securely according to stringent

delay requirements (typically 100 ms [Hartenstein and Laberteaux, 2010]). If the

VANET node clocks do not maintain commonly agreed times, critical safety messages

may be accorded incorrect timestamped information or with advanced timestamped

information with respect to the receiver time and in either case, those messages may

be and erroneously discarded after reception by the receiver nodes considering as an

outdated message. Under such circumstances, a warning message would fail to alert

drivers, which may lead to collisions and casualties. Time synchronisation in VANET

is therefore essential to achieve accurate and precise time over the network [Hussein

et al., 2017].

Physical time is also crucial for proper bandwidth utilisation and efficient channel

scheduling. Therefore, it is required that all the nodes in a VANET are able to report

the same time, regardless of the possible impreciseness of their clocks or any network

latency that may be present.

3.2 Time Synchronisation Requirements in VANET

VANET is a real-time communication technology having hard and strict time bound-

aries for end-to-end transmission latency. Some applications characterise the time-

sensitive nature of VANET. First of all, VANETs are very dynamic with vehicles com-

ing in and moving out, making relative time synchronisation more difficult to achieve

and maintain in compared to other fixed networks. Secondly, for vehicle safety ap-

plications, vehicle location and velocity data, as part of Basic Safety Messages (BSM),

need to be frequently exchanged, e.g., at 10 - 100 Hz rates, between vehicles over a

single hope or multiple hops. Most importantly, the event-driven safety messages to

be transferred over VANETs are highly time-sensitive. For example, WAVE (Wire-

less Access for Vehicular Environment) messages about accidents, stop/slow vehicle

warning, Blind Spot Warning (BSW), and Emergency Electronic Brake Light (EEBL)

3.2. TIME SYNCHRONISATION REQUIREMENTS IN VANET 55

are required to be broadcast to targeted nodes within a fraction of a second. It is

understood that typical end-to-end network latency is up to 100 ms for many VANET

applications [Karagiannis et al., 2011]. Any further offset will expose vehicles in at

risk in the hazardous road environment. Thus, precise clocks that depend on time

synchronisation are required in VANETs.

A number of time synchronisation mechanisms including software protocols and

algorithms have been proposed and implemented over the years in computer and

communication networks. However, most often they are ill-suited and/or incompat-

ible with VANET requirements.

The time synchronisation mechanism for vehicular ad-hoc networks and its ap-

plications needs to address the following three criteria:

i) the degree of accuracy needed, that is, the accuracy with respect to some exter-

nal time reference,

ii) the degree of precision needed, that is, the range of accuracy that can be main-

tained at the node clock, and

iii) the availability and longevity of synchronisation mechanism, that is, whether

the system needs to stay synchronised indefinitely or establishes synchronisa-

tion on-demand.

3.2.1 Accuracy Requirement

The Timing Accuracy parameter refers to the closeness of the node time to the stan-

dard physical time such as universal coordinated time (UTC) or the standard local

time. It is a measurement value resulting from the external synchronisation of the

nodes. Let V(t) ⊆ N be a set of vehicular nodes aligned with the physical time t. In

such a communicating network, let p and q be pair of nodes such that p,q ∈ N. If α is

the required timing accuracy parameter then for each node the system should satisfy



∀t∀p ∈ V(t) : |Cp(t)− t| ≤ α (3.1)

Where α is an accuracy parameter and Cp(t) is the time maintained by the clock

of node P. VANETs are a real-world networks in which most of the communication

interactions involve time-based decisions. For example, the real-time status messages

of VANETs carry data about the position, velocity etc., which are need to deter-

mined accurately and delivered precisely on time. Communication with the outside

world via the Internet or other means requires maintaining accurate physical time for

meaningful and successful interaction. Therefore, maintaining timing accuracy with

respect to a standard reference time is a prerequisite in VANETs.

3.2.2 Precision Requirement

The term time precision pertains to the agreement and closeness of a set of results. In

a network, it refers to the closeness of the times kept at two or more nodes. It is often

called instantaneous precision and is measured as the degree or boundary of difference

between clocks. The precision requirement concerns the internal synchronisation of

nodes and a precise system should satisfy,

∀t∀p, q ∈ V(t) : |Cp(t)− Cq(t)| ≤ β (3.2)

Where β is the parameter of precision. Cp(t) and Cq(t) are the time of the node

p and q respectively. Precision is the expression of a boundary of accuracy which is

essentially a function of the Mean (a) and Standard Deviation (σ) of a set of times (t).

a = 1N ∑N−1

i=0 xi

σ =√

1N−1 ∑N−1

i=0 (xi − a)2

(3.3)


Where xi is the instantaneous time difference between nodes. In case of a single

node synchronised with an external standard clock, Equation 3.2 reverts to the ac-

curacy equation 3.1. This implies that a precise system should satisfy the specified

accuracy requirement.

The VANET spectrum is limited (75 MHz) and maintaining accurate time can

result in better spectrum utilisation. To avoid interference and accommodate time

inaccuracies between nodes, a period called Guard Interval (GI), also known as a

Guard Band, is used in synchronous and asynchronous communication channel co-

ordination protocols. A longer GI is undesirable because it generates idle time in the

transmission process, which reduces the data throughput. Thus, maintaining good

timing precision reduces the necessity of a GI or Guard Band

3.2.3 Availability Requirement

Availability is the measure of service performance, which can generally be defined as

the ability to deliver services upon demand. It captures the continuity, quality and

functionality of the service. Along with accuracy and precision, availability of time

synchronisation services is equally important in VANETs. Since communication in

a vehicular network is a progressive process, the underlying time synchronisation

processes need to be continuously updated with time.

The set of requirements for time synchronisation in VANETs can be classified into

two categories: Performance-oriented requirements and Application-oriented requirements.

The former is based on the system-wide objectives including protocol support and

compatibility with the synchronisation method, whereas the latter is based on the

needs of end-user applications.



3.2.4 Requirements for Different VANET Applications

3.2.4.1 Requirements for System Level Applications

The requirements for time synchronisation in system-level applications are essential

in VANETs and are discussed below.

Network Interoperability and Coordination refers to the ability of networks to

send and receive messages, and communicate information between inter-connected

networks, devices and nodes. It is the capacity for efficient and meaningful coordina-

tion among network nodes and components for information exchange.

Scheduling of Channels is required for efficient use of channel resources. VANET

utilises short-range communications, e.g., 5.9 GHz Dedicated Short-Range Communi-

cation (DSRC) technology, typically within a range of 1 km, to provide high data rate

and low latency. In general, a trade-off exists between the efficiency and reliability

of VANET communications. The efficiency is typically characterised by bandwidth

consumption, channel utilisation, and channel coordination. Therefore, designing an

efficient WAVE Medium Access Control (MAC) protocol is essential for improved

efficiency, enhanced Quality of Service (QoS), and reliable packet transmission. Time

synchronisation plays a crucial role in MAC coordination. Existing IEEE 802.11p

MAC uses Time Synchronisation Function (TSF) to coordinate channels. The underly-

ing medium access mechanism is Carrier Sense Multiple Access/Collision Avoidance

(CSMA/CA), which is asynchronous in nature [Hafeez et al., 2013] and is only appli-

cable to systems in which precise sub-second timing is not required. In dense VANET

scenarios, CSMA/CA does not support highly accurate time synchronisation.

Road safety is a critical objective in VANET. To avoid unpredictable events that

may cause road accidents, VANET network delays need to be small and predictable.

Effective channel scheduling will help reduce the unpredictability of VANET commu-

nications. Compared with CSMA/CA, time-slotted access protocols offer collision-

free communications with predictable dynamics. Slotted protocols, e.g., STDMA,


MS ALOHA, RR ALOHA and UTRA TDD, offer good scalability, high reliability,

and fair use of channel resources [Bilstrup et al., 2009, Bohm, 2013, Cozzetti and

Scopigno, 2011, Cozzetti et al., 2009, Cozzetti and Scopigno, 2009, Golestan et al.,

2012a, Scopigno and Cozzetti, 2009, Verenzuela et al., 2014]. However, these time

slotted protocols require precise absolute time synchronisation for time slot coordina-

tion [Lim, 2016].

A Guard Interval is used between two-time slots in slotted access protocols. Packet

propagation starts at the beginning of a new slot after a guard interval. This helps

accommodate timing inaccuracy and propagation delay. The guard interval should

be set to be bigger than the worst time synchronisation accuracy for the slotted access

mechanism to work properly. Precise absolute time synchronisation helps reduce the

guard interval greatly, thus increasing the channel slot duration significantly [Ebner

et al., 2002].

For example, the time-slotted access protocol, STDMA, is used in shipping nav-

igation through Automatic Identification System (AIS). A typical frame length of

STDMA in AIS is 2, 016 slots. It is shown [ETSI, 2012] that compressing the guard

interval by 10 µs will accommodate 45 new slots of 496 µs each. This is translated to

a noticeable increase in channel capacity, which implies that more time slots can be

used for packet delivery.

Better spectrum utilisation is considered as one of the key targets in wireless com-

munication. VANETs use a limited spectrum of 75 Mhz. Better use of this spectrum

enables to increased data throughput. In a VANET protocol stack using the “Wireless

Access for Vehicular Environment (WAVE)”, the available bandwidth is divided into

service and control channels, as shown in Figure 3.1 [Gupta et al., 2015, Li, 2010,

Morgan, 2010].

For efficient channel coordination, communicating nodes need to be synchronised.

In practical operation, the clocks of all nodes can delayed for many reasons and tend

to lose synchronisation. To accommodate the time differences among the nodes, a



Control Channel

172

175

174 176 178180 182

181184

Service Channel Service Channel

GI

5.8

50

5.8

65

5.8

75

5.8

85

5.8

95

5.9

05

5.9

15

5.9

25

5.8

55

Frequency (GHz)

SCH SCHCCH

CCH interval

CC

HS

CH

SCH interval

Guard intervalSynchronization interval

CCH inactive

SCH inactive

Start of UTC second

(a)

(b)

Figure 3.1: WAVE Spectrum: (a) Frequency and Channel Allocation; and (b) ChannelSynchronisation and Guard Interval Morgan [2010].

GP GPtransmit

GP GPtime slot #n time slot #n+1

receive

t j

t i

tij

t

t

Ni

NJ

TGP

Figure 3.2: Guard Interval Requirements Scopigno and Cozzetti [2009].

Guard Interval, which is also known as Guard Band , is used in communication

design. As shown in Figure 1 (b), a guard Interval is a period of time for separation of

two consecutive and distinct data transmissions from different users in a time-slotted

mechanism or from the same users in a frequency slotted mechanism.

The Guard Interval requirements and its relationship with time synchronisation

accuracy in VANETs are demonstrated graphically in Figure 3.2. As shown in Figure

3.2, assume that nodes Ni and Nj have time offsets of ∆ti and ∆tj, respectively, with

reference to the global standard of time. When node Nj sends a burst to node Ni, then


the observed time offset ∆tij at node Ni is estimated as

∆tij = ∆tj − ∆ti + dij/c (3.4)

where dij is the distance between the two nodes, and c is the speed of light. A

successful reception of data at node Ni from node Nj can be achieved if there is no

any overlap of communications due to time offsets. Therefore, a guard interval (TGP)

is introduced to avoid such an overlap. This requires the guard interval TGP to be

greater than the time offset ∆tij, i.e.,

∆tij < TGP (3.5)

Condition (3.5) is required for time synchronisation in wireless networks. It is seen

from this requirement that the Guard Interval can be reduced if VANETs are better

time synchronised. Moreover, since a Guard Interval is an addition to the commu-

nicating slot length, it consumes spectrum resources and consequently leads to a

longer time to transmit a message. Therefore, a reduced Guard Interval implies better

spectrum utilisation.

The impact of the guard interval on the performance of VANET services varies

with the type of the underlying communication protocol. Asynchronous wireless

communication protocols in IEEE 802.11 networks use CSMA/CA as the channel

coordination mechanism, in which a Guard Interval is used to avoid transmission

disruption due to propagation delays, echoes and data reflections. In 802.11n net-

works, cutting the guard interval by half from 800 ns to 400 ns leads to an increase in

effective data transmission rate by 11% [Perahia, 2008].

In synchronous-slotted protocols, e.g., TDMA and STDMA, a Guard Interval ac-

commodates clock inaccuracies. This enables the avoidance of message collisions

and message losses in time-slotted medium access protocols. For example, the com-

monly used frame length of STDMA in Automatic Identification System (AIS) of ship



navigation is 2, 016 slots. In such a framing, a reduction of 10 µs in Guard Interval

means that 45 new slots can be accommodated for every 496 µs, thus increasing the

channel capacity by about 9% [ETSI, 2012]. Therefore, precise time synchronisation

contributes to increased communication capacity of wireless networks.

In VANETs, network environments frequently change over time. Consider a dy-

namic scenario in which in the network density changes from a small number of

nodes (< 20) to a large number of nodes (>100). To maintain a QoS level during

this network density change, an efficient, reliable and scalable medium access control

mechanism is required that has precise time synchronisation. In another common

VANET scenario, dynamic changes occur in the location of mobile network nodes

from one geographical region to another. The fastest moving nodes can momentar-

ily connect and disconnect from node clusters. Maintenance of QoS in such highly

mobile networks requires all nodes to follow the same time standard, which can be

achieved through time synchronisation.

3.2.4.2 Requirements for Performance Enhancing Applications

In compared with many other wireless ad-hoc networks, VANETs are noticeably dy-

namic and highly mobile. In VANETs, the relative speed between two nodes can

be as high as 220 km/h. This implies that a high-speed node may only stay within

the transmission range of other nodes for a few seconds. Moreover, some VANET

applications require an extremely small end-to-end delay. For example, the maximum

acceptable end-to-end latency for pre-crash sensing warning messages is 50 ms. For

Lane Change Warning and Forward Collision Warning, it is specified to be 100 ms by

National Highway Traffic Safety Administration of US [Eze et al., 2014, Hartenstein

and Laberteaux, 2010, Rasheed et al., 2017]. Therefore, to meet the requirements of

these safety applications, accurate timing is required for deterministic and reliable

communications in VANETs.

Security is a significant concern in VANET. Session hijacking and jamming are


Tracking Node

Tracking Node

Tracking Node

Tracking Node

Communication Failure

Cyber Attack

(a) (b)

Figure 3.3: Examples of Security Issues. (a) Cyber Forensic, (b) Cyber Attack (Security). Inboth of the cases time synchronisation is important to log the events accurate and preciselyBen-El Kezadri and Pau [2010].

two communication threats for the forensic security experts and transport regulation

authorities. Precise time synchronisation is a key tool for development of traceable

and reliable communications. This allows reconstruction of packet sequences on

channels, and thus helps overcome security threats.

In cyber security domains, log files are used as sources of evidence. Analysing

communication network log files relies on having precise timestamp records, which

is only possible if the network is time synchronised. The synchronisation accuracy de-

mand, however, depends on the applications requirements. In VANETs, researchers

indicated that a fine-grained analysis of channel activity between concurrent trans-

missions requires stringent timing guarantees of 8µs for DSRC communication [Ben-

El Kezadri and Pau, 2010].

Figure 3.3 (a) (b) shows two scenarios, namely, unwanted communication failure

and cyber-attack, which are examples where a forensic analysis is required for acquir-

ing potential evidence. In both cases it can be assumed that neighbouring nodes that

are not part of the incident can serve as observer or a tracking nodes. Any roadside

unit can also be an observer. In such cases it is preferable that the tracking node is

synchronised independently of the incident node.



However, in packet-based time synchronisation (i.e. in-band message transfer

based time synchronisation) inter-contacts are required. In these cases, the contacts

should have made shortly before and after the incident. Since VANETs are highly

dynamic networks, such tracking node contacts may be not feasible. Again, if track-

ing nodes are time synchronised by malicious attacking nodes, the ensuing forensic

analyses may not be accurate as the timestamps of attacks may be compromised.

In such circumstances, any time synchronisation solutions that are independent of

explicit signalling will probably be the best candidates.

Wormhole attacks are a routing problem in mobile ad-hoc networks that confuse

routing mechanisms by generating a fake node path that is shorter than the actual

route. This is also a considerable threat for location-based wireless security systems.

A well-known approach to prevent wormhole attack is the so-called packet leash,

which also requires highly accurate clock synchronisation [Isaac et al., 2010]. In a

variety of temporal leashes the time synchronisation accuracy is higher, of the order

of a few microseconds or even hundreds of nanoseconds [Hu et al., 2003].

GPS-based relative vehicle positioning requires time synchronisation. Time-to-

Collision (TTC) on roads depends on relative locations of two vehicles. The most

stringent requirements for relative positioning accuracy is about 10 cm [Caporaletti,

2012]. Vehicle state and time information are exchanged between vehicles to compute

relative vehicle positions for safety decisions. For a vehicle traveling at the speed of

110 km per hour, a timing error of 10 ms will cause a position uncertainty of 30 cm,

which is too high for collision avoidance. To meet the accuracy requirement of 10 cm,

it is required to keep timing errors within 3 ms, giving a relative positioning error of

about 9 cm. This requirement does not seem very high. However, it emphasises that

time synchronisation is essential.

Localisation is a typical application in VANET. It is directly related to the challeng-

ing issue of determination of accurate vehicle positions and ranges to other vehicles.

Terrestrial radio frequency based ranging techniques, e.g., Time of Arrival (TOA)

3.3. SUMMARY 65

and Time Difference of Arrival (TDOA), are effective ways for location determina-

tion [Golestan et al., 2012b, Lee et al., 2009, Yoon et al., 2012]. They essentially involve

distance calculations involving the speed of light. This implies that a timing accuracy

of 10 ns corresponds to a distance measurement accuracy of about 3 m. If time

synchronisation is accurate to 1 ns, the ranging accuracy can be improved to 0.3

m. Therefore, highly accurate absolute time synchronisation will enable the of use

terrestrial radio ranging signals in vehicle location determination.

Overall, Figure 3.4 shows the concept tier of time synchronisation requirements

for different application with respect to the timing accuracy demand.

ns

System Level Application

Performance Enhancing Application

Timing Requirements of

Different VANET

Applications

LocalizationGuard Interval

Slotted MACSecurtiy

SchedulingNetwork Coordination

Non-Real Time/User Experience Connectivity

Figure 3.4: Concept Tier that Illustrate the Requirements of Time Synchronisation Accuracyfor Different Applications in VANET.

3.3 Summary

Time Synchronisation is a critical service in VANETs it is a fundamental require-

ment for many system-wide network applications. From basic network coordination,

channel scheduling to cyber-security, time synchronisation is important for increasing

system capacity and also in location-based services for correct location identification.

The accuracy and precision requirements, however, are different in differing applica-

tions, which can be classified into two categories: coarse grain and fine grain timing

requirements.



Table 3.1: List of Timing Accuracy Requirements for Different Applications in the Basis ofEssentialness of VANET.

Applications Timing TypesAccuracy Req./Performance IMP.

EssentialNetwork Coordination Coarse ∼ms

Scheduling of ChannelsNon-SlottedSlotted

CoarseFine

sub-mssub-µs

Relative Vehicle Positioning Coarse 3 msSecurity Fine 8 µsDesirable

Guard IntervalNon-SlottedSlotted

CoarseFine

Rate 11%AIS-10µs@45slots

Localisation Fine10 ns @ 3m1 ns @ 30cm

An overall summary of time synchronisation requirements in the category of es-

sential and desirable services are tabulated in the Table 3.1.

Chapter 4

GNSS Time Synchronisation In VANET

The previous chapter identified the importance of vehicular networks and their time

synchronisation requirements. This chapter analyses the feasibility of GNSS time

synchronisation with respect to vehicular environment compatibility, timing accuracy

achievability and signal availability. The result of a series of experiments are pre-

sented to verify the 1PPS accuracy achievable using consumer-grade low-cost GNSS

receivers.


lications:




2. K. F. Hasan and Y. Feng, ”A Study on Consumer Grade GNSS Receiver for the

Time Synchronisation in VANET,” presented at the 23rd ITS World Congress,

Melbourne, Australia, 1014 October 2016.

67

68 CHAPTER 4. GNSS TIME SYNCHRONISATION IN VANET

4.1 Motivation of GNSS-driven Time Synchronisation

Global Navigation Satellite Systems (GNSS) are regarded as an international utility

for positioning, navigation and timing (PNT). The generic term ”GNSS” refers to the

USA’s Global Positioning System (GPS), Russia’s GLONASS, Europe’s GALILEO and

China’s BEIDOU navigation satellite system (BDS). It is noted that GPS, GLONASS,

Galileo and BDS use different reference time systems creating time offsets between

them. However, the offsets can be determined at the system level or user level. Any

one or more constellations can offer the same global standard UTC time. With their

worldwide coverage, continuous service, GNSS has become one of the most efficient

and standard systems for time dissemination in many applications. Many industries

such as energy, meteorology and telecommunications rely on GNSS for accurate time

synchronisation in their systems and devices. The accuracy achieved by GNSS-based

time synchronisation using GPS is better than 40 ns 95% of time.

Most of the earth-based time transfer techniques suffer from path delay mea-

surement uncertainties. In contrast, the satellite-based GNSS time transfer systems

possess measurable constant path delays. This arises because the variation of path

delays are small and due to clear, unobstructed paths to receivers. Therefore, the

delay measurements are straightforward and can be more easily calibrated compared

to any ground-based systems. In addition, the radio interferences due to weather or

any other ground-based noise have less impact in satellite-based GNSS systems.

In telecommunication networks, GNSS is used to synchronise some major nodes

called root or server nodes outdoors. Through these root nodes, other nodes in the

system are synchronised by using other synchronisation techniques, which are mostly

based on message transfer between nodes.

In contrast to telecommunication networks, VANETs are outdoor-based networks.

Except in some tunnels and blocked roads, nodes in VANETs on the road are mostly

under the coverage of GNSS signals. It is a straightforward choice for VANETs to

4.2. FEASIBILITY OF GNSS SYNCHRONISATION IN VANET 69

use GNSS for synchronisation. GNSS receivers have already been used for vehicle

navigation and positioning. Nowadays, multi-GNSS constellations, more precise

GNSS services, such as space-based argumentation systems (SBAS), differential GNSS

(DGNSS) services and precise point positioning (PPP), are available for VANET de-

ployments. The GNSS-based time synchronisation is indeed plausible in VANETs.

It is, therefore prudent to understand how GNSS time solutions provide synchro-

nisation in VANETs and what the possible solutions are when GNSS services are

absent, such as when vehicles travel in tunnels. The feasibility and accuracy of GNSS

time solutions are discussed and examined through experiments in the sections that

follow.

4.2 Feasibility of GNSS Synchronisation in VANET

GNSS is a space-based Positioning, Navigation and Timing (PNT) utility alongside

wireless wide area networks and communications. It has a potential to bring signifi-

cant benefits to centralised and accurate time synchronisation in VANET.

4.2.1 Justification of the Feasibility

First of all, GNSS services are capable of providing absolute time-synchronisation

support over local and decentralised time synchronisation protocols [Shizhun Wang

et al., 2010]. Such external synchronisation techniques are also free from additional

message transfer delays between nodes, as shown in Figure 4.1 (a). Most time-synchronisation

techniques are designed with message exchanges between nodes. Therefore, they

rely on the data communication networks. This is shown in Figure 4.1 (b). The

performance of in-band internal time synchronisation depends on the channel ac-

tivities, the density of the communicating nodes, and the condition of the networks.

However, message delivery in such networks may suffer from a significant latency

and jitter [Chen et al., 2015, Gunther and Hoene, 2005, Loschmidt et al., 2012]. On


the contrary, GNSS based out-of-band external synchronisation techniques do not

use the communication networks in their operation. They do not use any network

bandwidth resources as all nodes are synchronised with external GNSS signals. Thus,

their performance is independent of the number of network nodes. It is worth men-

tioning that in the absence of GNSS signals, in-band time synchronisation has to be

activated for better time keeping. For instance, Timing Advertisement Frames in

DSRC-based VANET may be used to assist in time synchronisation if GNSS signals

become unavailable.

Network

Delay and

Jitter Network

GPS

Sync Link

Network

Delay and

Jitter Network

Propagation

Time

(a) (b)

Figure 4.1: Broad View of Time Synchronisation (a) In-band Time Synchronisation (b) Out-of-Band External Time Synchronisation.

The feasibility of GNSS time synchronisation is also justified by the fact that a

single visible satellite is able to provide time solutions. A GNSS receiver normally

tracks all satellites in view to obtain pseudo-range and Doppler measurements at

each frequency for Position, Velocity and Time (PVT) computing. The time states

include clock bias and clock rate. There are basically two modes for estimation of

time states: dynamic mode and static mode. The dynamic mode is used in moving

platform applications when the position is unknown. In this case, the receiver can

compute its own position and time by tracking four or more GPS satellites. The static

mode is the preferred mode for applications with a known fixed position. In this case,


the receiver can compute time bias and time rate by tracking one or more satellites.

In the dynamic mode with unknown position, the PVT estimation is performed by

solving a set of linear observation equations with the least-squares approach epoch by

epoch. In other words, the 4D states of the vehicle moving platforms are determined

without assuming knowledge of the dynamics of the receiver. The accuracy of the

state solutions depends on two factors: the user range equivalent error (URE) for

the observation accuracy, and the geometric dilution of precision (GDOP) about the

satellite geometry. In general, the PVT solutions obtained under the GDOP ≤ 6 are

considered to be valid and usable. The position and time errors are almost of the same

order of magnitude.

In the static mode, if the receiver position is known to a certain accuracy through

alternative positioning techniques or predictions, one tracked satellite at a time is still

able to provide timing information with a reasonable accuracy [Lombardi et al., 2001].

When there are fewer satellites than four in view, or the satellite geometry is very

poor, alternative positioning techniques, such as inertial measurement units (IMU),

are normally involved to determine or improve the position and velocity states. As

long as there is one satellite in view, the receivers PVT processor can calculate a

time solution. The time solution outage is always much lower than that of the us-

able PVT solution outages. In fact, most new GNSS receivers are designed to track

multiple GNSS signals, i.e. signals from GPS, GLONASS, Galileo and Beidou (BDS)

constellations. As a result, visible multi-GNSS satellites would double or quadruple

the number of GPS visible satellites. This will reduce the outage of time solutions

significantly, thus making GNSS-based time synchronisation much more feasible and

reliable than with GPS alone.

The feasibility of GNSS time synchronisation is further justified by the fact that

absolute time synchronisation with an external global time standard is particularly

suitable for VANET applications. VANET nodes operate outdoors most of the time.

The density of the nodes vary significantly from time to time or from one location to


another depending on the traffic conditions. In-band time synchronisation will suffer

from large jitter and variable latency in exchanges of synchronisation messages. This

difficulty can be easily overcome by using out-of-band external GNSS time synchro-

nisation.

Moreover, consumer-grade GNSS receivers are already mounted in most modern

vehicles for positioning and navigation. They are ready to provide GNSS timing

information for time synchronisation without additional hardware cost.

4.2.2 GNSS Timing Information

The timing information provided by GNSS is highly precise and accurate, as it is

generated from atomic clocks and maintained very stringently. In a GNSS system,

there are three scales of time, i.e., GNSS time, satellite time and standard time (such

as UTC). These times are different from each other [Misra and Enge, 2006, Scott and

Demoz, 2009]. In the satellite time transfer method, the offset between GNSS and

UTC time are transmitted to user receivers for correction. As of September 2017, UTC

is ahead of GPS by 18 s whereas international atomic time, also known in French as

temps atomique international (TAI), is lagged by 19 s as shown in Figure 4.2.

The satellite system provides UTC time to a ground receiver and is adjusted using

navigation messages as shown in Figure 4.3. In general, a typical satellite has an

18s

37s

UTC GPS TAI

UTC- Universal Coordinate Time

GPS- Global Positioning System

TAI- International Atomic time

Figure 4.2: Time Offsets Among Different Atomic Scale Standards.


atomic clock to maintain its own time tp. This time is regulated by the earth bound

control segment with GPS time tgps. Along with tgps, the control segment also uploads

navigation messages containing ∆tutc, which is regulated by the United States Naval

Observatory (USNO). A GPS receiver has its clock system with time tr. With GPS

pseudo-range measurements, the receiver can compute clock bias ∆tr with respect to

GPS time, together with the receiver position states. The ∆tr parameter is defined as:

∆tr = tr − tgps. (4.1)

Control

Segment

(tgps)

Satellite time (tp)

Receiver time

(tr)

Figure 4.3: Time Transfer Through GNSS [?].

The receiver clock bias in a receiver hardware system is also known as the instan-

taneous receiver clock offset relative to GPS [Misra and Enge, 2006]. Since this offset

can be large, GPS code measurements are known as pseudo-ranges. The clock bias

is adjusted to the GPS time when the magnitude reaches a certain limit, such as 0.1s.

As a result, a time GPS receiver adjusts the bias and obtains its UTC time using the

following relationship:

tutc = tr − ∆tr − ∆tutc, ∆tutc = tgps − tutc, (4.2)


where the offset ∆tutc between GPS time and UTC time can be obtained from nav-

igation messages. The ∆tutc parameter contains the leap seconds (currently 18s) as

shown in Figure 4.2 and a fractional part since the last leap second adjustment.

With the above GPS time transfer technique, all nodes of a network are individu-

ally synchronised with the GPS time. Then, GPS times are adjusted with an additional

UTC time offset. As a result, all nodes are synchronised with the UTC time. This is

demonstrated in Figure 4.4. As shown in Figure 4.4 (a) and (b), nodes N1 and N2 are

individually time synchronised with a satellite (tgps), and then are updated to UTC

time. Effectively, this will synchronise Node N1 and Node N2 with each other as

shown in Figure 4.4 (c).

1 1

N2

11

(a) (b)

(c)

tgpstrtutc

tgpstrtutcRTC

2 2

N1

N1 N2

RTC

(c) N N 1 2

(a) (b)

Figure 4.4: (a) (b) Nodes N1 and N2 are Individually Synchronised with GNSS and Updatedwith UTC. (c) Effectively, Two Nodes are Synchronised with Each Other via GNSS.


4.2.3 Errors of the Receiver Timing

The errors of receiver timing are now examined. Only the uncertainty of the GNSS

clock bias ∆tr will affect time synchronisation because ∆tUTC is common to all VANET

nodes. However, the basis for time transfer func intions in GNSS-based products is 1

PPS (one pulse per second) signal generated by the receivers. Such receiver generated

signal is a short logic pulse, where an edge of it is adjusted by the receiver to be

on time corresponding to the one second epoch of UTC or GPS time (GNSS time).

Errors in the time of occurrence of the 1 PPS pulses from the GNSS receiver, there-

fore, consists of three parts: 1) bias or offset due to uncompensated propagation and

hardware delay errors in the receiver/antenna system; 2) drift, which is the variation

in time over an extended period due to changes of satellites tracked over time; and

3) jitter, which is the short-term variation in timing from pulse to pulse. These error

sources are inherent in both GPS system and GPS receiver design/implementation.

The total effect of these errors is typically tens of nanoseconds to a few microseconds,

depending upon the quality of GPS receivers. Receiver manufactures usually cali-

brate the receiver bias well, yielding a timing accuracy of 10 ns or better under ideal

observational conditions. This high level of accuracy is achievable down to the reason

that the timekeeping mechanism maintained within the GPS system is repeatedly

adjusted in the system level to null out the timing errors that may generates.

A typical GNSS receiver has an internal quartz based oscillator that continuously

runs and follows the GPS time. Currently, a low-end GNSS receiver can update

position and time solution at a rate of up to 20 Hz, which corresponds to an interval

of 50 ms.

However, such a quartz-based clock deviates over time. This is because the fre-

quencies of the quartz oscillators are different. As time elapses, a quartz clock tends

to diverge from the perfect clock, i.e., the real time, and also from others in a network.

Ideally, for a perfect clock, the clock rate of change dC/dt is equal to 1. In practice, this

rate may increase or decrease due to the variation of the clock oscillator’s frequency.


In a quartz clock, this frequency variation is commonplace due to the impact of

the environmental changes at the node, e.g., variations in temperature, pressure,

and power voltage upon the local clock. In clock synchronisation terminology, this

difference in the frequencies of the practical clock and the perfect clock is known as

clock skew. The rate of change of the clock skew is known as clock drift. This means

that clock drift is the derivative of clock skew [Sundararaman et al., 2005].

Though the frequency of clock oscillator depends on ambient environmental con-

ditions and may change over time, for an extended period, e.g., minutes to hours,

the frequency of node clock can be approximated with good accuracy by an oscillator

with a fixed frequency [Levesque and Tipper, 2016, Sichitiu and Veerarittiphan, 2003].

Therefore, the clock of a node can be expressed as:

Ci(t) = di · t + bi, (4.3)

where t is the standard time of the measurement (UTC); di is the clock drift due to the

oscillator’s frequency differences resulting from environmental changes at the node

(e.g., the impact of variations in temperature, pressure, and power voltage upon the

clock); bi is the initial offset in the GNSS synchronisation framework, and can be

correlated to systematic ranging errors and hardware delays. The di and bi can be

different from node to node. However, the clock skew di is different from the drift in

GNSS timing errors offered by GNSS 1PPS outputs. It is also worth mentioning that

the clock skew can actually be estimated from GNSS Doppler measurements along

with the velocity states.

Now, any two such GNSS synchronised clocks (such as Figure 4.2(a) and Fig-

ure 4.2(b)) can be expressed as:

C1(t) = d1 · t + b1,

C2(t) = d2 · t + b2.

(4.4)

4.3. AVAILABILITY OF GNSS TIME SOLUTIONS 77

By following Figure 4.2(c), they can be related as:

C1(t) = Θ12C2(t) + β12, (4.5)

where Θ12 is the relative drift between two receivers and β12 is the offset due to

the bias variations. While the clock bias of two receivers are the same, the offset is

cancelled, implying that β12 = 0.

Following Figure 4.2(c), the overall impact of GNSS enabled synchronisation on

the networking nodes can be practically estimated by an end-to-end timing compar-

ison. The results of a series of extensive experiments which predict the accuracy of

GNSS time synchronisation are presented in Section 4.4.

4.3 Availability of GNSS Time Solutions

Availability may be defined as the capability of a system to provide usable service

within a specified coverage area. GNSS provides well-recognised and accepted tools

for Position, Navigation and Timing (PNT) estimation and its signals should theo-

retically be available from any regions of the world. However, receiving satellite

signals relies on having near Line of Sight (LOS) propagation, therefore, the signal

are restricted by different obstacles, like buildings, trees and other obstructions etc.

Since vehicular environments are mostly outdoor based, it is plausible to cover by

the signal under general circumstances and scenarios. However, there are still certain

places where signal availability can be restricted and thus become a potential issue to

consider. For instance, signal within urban areas and under tunnels.

Chapter 6 presents the results of experiments and investigation of GNSS signal

availability within Brisbane urban high-rise areas. The aim of these experiments was

to understand, to what extent the obstruction of GNSS signals by physical structures

reduces the availability of GNSS position and time solutions. Consumer-grade re-

ceivers were used to collect the data and versatile RTKLIB GNSS data processing


software was employed to process and analyse the acquired data.

From the obtained experimental results, it was found that position outages occur

due to signal path obstructions, and incorrect position solutions result due to weak

satellite geometry. However, accessing multiple satellite constellations did improve

availability substantially as there was always a visible satellite present. Table 4.1

summaries that visibility of GNSS satellites in three cases: four or more satellites,

1 to 3, and null (< 1). The visibilities for a minimum of 1 satellite in the three same

constellations were 100%, 99.98% and 100%, respectively. This shows that having

access to multiple constellations reduces the impact of signal path obstructions on the

availability of GNSS time services. Thus, this experiment demonstrates the feasibility

of GNSS time synchronisation in the high-rise urban areas, particularly with multi-

GNSS constellations.

Table 4.1: The Number of Satellites Available with Different GNSS Services Constellation.

GNSS System NSAT ≥ 4 NSAT = (1 to 3) NSAT < 1GPS 77.32% 22.68% 0%BDS 82..93% 17.05% 0.02%BDS+GPS 99.25% 0.75% 0%

4.4 Synchronisation Accuracy of 1 PPS Signals

This section presents the an experiment and results that were conducted to assess how

accurately consumer-grade GPS receivers are able to synchronise their clocks with 1

PPS signals in VANET.

4.4.1 Characteristics of 1 PPS GNSS Signal

Acquiring the one pulse per second or 1PPS signal is important for the operation and

application of GPS receivers. This is an electrical signal having a width of less than

4.4. SYNCHRONISATION ACCURACY OF 1 PPS SIGNALS 79

one second that precisely repeats every second. Maintaining precise timing perfor-

mance is vitally important to the operation of GPS receivers. To maintain a precise

pulse rate, consumer grade GPS receivers use a high-precision crystal or rubidium

oscillator that can synchronise itself to the common time stamp generated by the

atomic clock of GPS satellites. In practice, the received 1PPS signal suffers from jitter

with respect to ideal 1PPS signals that are generated by the highly accurate satellite

atomic clocks. Along with GPS ephemeris, signal conditions due to orbit paths,

receiver oscillator drift, cable connections and device interconnections can contribute

to the jitter [Texas Instruments, 2012, Witherspoon and Schuchman, 1978].

A comparison between an ideal GPS 1PPS signal’s rising edge with a typical

receivers 1 PPS signal rising edge is shown in Figure 4.5 [Texas Instruments, 2012].

This theoretical figure reflects the result of mitigating jitter to produce an accurate

timing signal at the receiver’s end.

Rising Edge Threshold

Rising Edge Delayed Rising Edge

Δ t

Time

Amplitude

Figure 4.5: Ideal (GPS signal) and Practical (Produced Signal by GPS receiver) 1PPS SignalPulses.


4.4.2 Clock Accuracy of Low Cost GPS Receivers

The GPS time, that is sent and controlled by the GPS system, is generated by highly

precise atomic clock. This GPS time is usually not adjusted with the Universal Co-

ordinated Time (UTC), and therefore some offsets always remain between them. Ac-

cording to the data recoded in July 2015, GPS time is ahead of UTC by 18s [QPS, 2015].

Another very accurate timing system, the International Atomic Time (TAI) that uses

200 caesium atomic clocks in over 50 national laboratories worldwide, also differs

from GPS by 19s. This offset usually adjust by adding leap seconds [Behrendt et al.,

2006, Lewandowski et al., 1999, 1993].

For GNSS timing support, GPS uses four atomic on board clocks that are usually

accurate to few nanoseconds (ns) of each other [Behrendt et al., 2006]. GPS sends

two frequencies are known as L1(1575.42 MHz) and L2(1227.6 MHz) [Lombardi et al.,

2001]. L1 frequency carries civilian code has a time accuracy specifications of 340ns in

the scale of 2 standard deviations, and in practice it provides 35ns accuracy [Behrendt

et al., 2006].

Fundamentally, Consumer grade receiver units consist of an antenna unit, an elec-

tronic receiver unit with a correlator in order to lock to the satellite signals, a reference

time oscillator and a counter called time interval counter (TIC) to measure the arrival

time of received signals and the GPS-sent signals. Six-kinds of delays are encoun-

tered: one the usual offset of the on-board satellite clock, two the propagation delay

between the satellite and antenna, three the inherent signal delay in antenna unit, four

the delay from antenna to the receiver input, five the receiver-processing delay and six,

the time offset of the reference clock [De Jong and Lewandowski, 1997]. The first two

kinds of delays rely on the GPS system, whereas the remainder of the delays depend

on the receiver performance. Therefore, both GPS time accuracy and GPS receiver

module accuracy individually depends on various issues that needs to be considered

to demonstrate the overall accuracy of the timing and synchronisation accuracy of

GNSS systems. According to [Behrendt et al., 2006], the statistical probability of the


accuracy of commercial GPS clocks range from 50 ns to 1 ms. This is a 1 standard

deviation (1σ) rating. A typical comparison between GPS time accuracy (34 ns, 2 σ)

and GPS receiver module accuracy (50 ns, 1 σ) is shown in Figure 4.6.

Figure 4.6: Standard Deviation Rating.

In practice, GPS timing accuracy can vary considerably can be seen from the

above figure and depends upon the manufacturer. Considerable research [Bogovic,

2013, Bullock et al., 1997, King et al., 1994, Mumford, 2003] has been conducted to

study 1PPS signal timing performance. However, their results vary with application,

manufacturer and type GPS receiver module.

In 1997, [Bullock et al., 1997] compared 1PPS signal timing for a GPS receiver with

a caesium atomic clock. Motorola UT Oncore GPS receivers were used in their study.

They observed a maximum offset of 236.5 ns with a standard deviation of 37.1 ns

(average) in position-hold mode, and a maximum spread of 538.6 ns with a standard

deviation of 78.5 ns (average) in position-fix mode.

Instead of comparing timing with a precise atomic clock, the relative offset be-

tween identical receivers (Motorola Oncore UT) has been investigated by [Bogovic,

2013]. They observed standard deviations of <13 ns in navigation-mode and <8 ns

in position-hold mode, respectively.


Conversely, in 2003, [Mumford, 2003] conducted another research on 1PPS signals

of three inexpensive GPS receivers from different manufacturers. He reported a typ-

ical maximum relative offset of 1450-1500 ns between two different receivers with a

typical jitter of 140 ns.

4.4.3 Experimental Design

The experiments described below employed GPS receivers from U-Blox (MAX-M8Q,

NEO-6T, Evaluation Series-6 EVK-6H-0-001 models) and Furuno (VN-872 model),

which are low-cost and consumer-grade. Both U-Blox and Furuno use Temperature

Compensated Crystal Oscillator (TCXO) that have excellent stability over a broad

range of temperatures. The receivers are equipped with a Pluto+ RPT5032A model

quartz oscillator. The clocks in these GPS receivers are designed with an advanced

temperature compensation circuit. According to their technical specifications, they

offer sub 0.1 ppm frequency stability over an extended temperature range (55◦C to

105◦C).

The experimental setup is shown in Figure 4.7. Two GPS receivers, along with two

identical antennas were connected to a high resolution oscilloscope, 200MHz Agilent

Technology DSO-X-2024A. The oscilloscope was used to measure the offset between

the 1 PPS signals of the receivers. A process to calibrate and record experimental

data was developed using Lab-View software on a computer system as shown in

Figure 4.7. The experiment was conducted on the 13th floor of a building at QUT

under an unobstructed open sky.

4.4.4 Data Acquisition

The experiment measurement and data acquisition method consisted of two phases.

In the First Phase, the timing offset was measured between identical receivers

(i.e., the same model from the same manufacturer). With identical receivers, it was


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Dia

gram

ofEx

peri

men

talS

etup

for

Sync

hron

isat

ion

Ass

essm

ento

fGPS

Rec

eive

rs.


expected that their clock oscillators and temperature compensation circuitry should

be exactly the same.

In the Second Phase, timing offset measurements were acquired between different

receiver models from different manufacturers. In this phase, we conducted the varia-

tion in two ways to understand the receivers performances, One the timing offsets

were measured between two receivers from entirely different manufacturers; (i.e.

vendors) two, the timing offsets were measured between two receivers of different

models from the same manufacturers.

In each case, the time offset was measured with respect to the single pulse wave-

form provided by the receivers as the 1 PPS signal output. The width and/or the rise

time together with the time delay between the signals were measured and compared

using the oscilloscope. For repetitions of the same experiments, the calibration of

the oscilloscope was controlled by developed LabView software. In particular, a pro-

cess cycle was developed and implemented using LabView to control the oscilloscope

settings and to record the data into a database. This process enabled the width of the

pulses to be measured and recorded every second. The logged data was subsequently

analysed using a MATLAB data analyser. The experiments were carried out under the

temperature (18◦C-25◦C).

4.4.5 Result Analysis

The results of a series of experiments are presented in this section and grouped ac-

cording to the type of GNSS receiver. The standard deviation (STD) and root-mean-

square of the offsets (RMS), for four different types of datasets (according to offset

time durations of 5 minutes, 1 hour, 10 hours and 24 hours) are presented in the

following.


Figure 4.8: Pulses Showing Time Difference Between TwoWaveforms of Identical Receivers.

4.4.5.1 Time Offset Between Receivers of the Same Model

This sub-section reports the results of a series of experiments conducted with identical

receivers and receivers from two manufacturers, Ublox and Furuno. In the first stage

of the experiment, results from identical Ublox (NEO-6T) receivers are presented and

analysed. Then the results for the Furuno (VN-872) GNSS receiver are presented.

Figure 4.8 shows observations from the oscilloscope of the exact time differences

between two waveforms of identical receivers (Ublox-Ublox). As the main interest is

to measure the relative time differences of the 1PPS signal, this waveform indicates

the exact time differences about the threshold level. This threshold level was calcu-

lated at a trigger level of 800mV at which the value of ∆t was 22ns.

Figure 4.9a shows plots of the time offset and jitter observed over 5 minutes

between two receivers of the same model and from the same vendor. It can be seen

from this figure that the differences between the 1 PPS outputs of the two receivers

vary randomly over time. It is observed from this data set that the peak value of

|offset+jitter| is 30 ns. The mean value of the offset measurement is calculated as 3.6

ns. Figure 4.10 shows the statistical distribution of the observed data. The distribution


-40

-30 -20

-10 -3.60 10 20 30 40

Time Offset (ns)

01

45

6

Instant OffsetMin

MaxAvg

23

Elapsed Time (Minutes)

(a)Time

OffsetD

istributionofD

atafor

5M

ins.

05

10

15

20

25

30

35

40

45

50

55

Elapsed T

ime (H

ours)

-60

-40

-20 0 20

40

Time Offset (ns)

ub

lox-u

blo

x (same m

od

el) 1ho

ur

Instant offsetM

oving Average

(b)Time

OffsetD

istributionofD

atafor

1hour.

01

23

45

67

89

10

Elap

sed T

ime (H

ou

rs)

-80

-60

-40

-20 0

20

40

60

80

Time Offset (ns)

ub

lox-u

blo

x (s

am

e m

od

el) 1

0 h

ou

rs

Instan

t offset

Mo

vin

g A

verag

e

(c)Time

OffsetD

istributionofD

atafor

10H

ours.

02

46

81

01

21

41

61

82

02

22

4

Elap

sed T

ime (H

ou

rs)

-80

-60

-40

-20 0

20

40

60

80

Time Offset (ns)

ub

lox

-ub

lox

(sa

me

mo

de

l) 24

ho

urs

Instan

t offset

Mo

vin

g A

verag

e

(d)Time

OffsetD

istributionofD

atafor

24H

ours.

Figure4.9:

PlotoftheD

ifferentDatasets

ofRelative

Time

Offsets

Between

Two

IdenticalReceivers

(Same

Manufacturer

Same

Model.


of measured data corresponds to a Gaussian distribution. The standard deviation is

equal to ± 12.67 ns for 1σ.

-50 -40 -30 -20 -10 0 10 20 30 40 50Offset

0

5

10

15

20

25

30

35

Density

Offset/ErrorNormal distribution

Figure 4.10: Gaussian Distribution of Dataset 5 Minutes thatRepresents STD of 12.67 ns for 1σ..

Table 4.2 summarises the observed of peak, mean, standard deviation (STD), and

root mean square (RMS) of observed time offsets from four independent experimental

sessions of 5 min, 1 hour, 10 hour and 24 hour durations. The results show that the

peak values vary between 30 ns to 90 ns, with standard deviations ranging from 9 ns

to 13 ns. Consistent RMS values between 10 ns and 13 ns were also observed. These

statistics match the accuracy and precision specifications of the GNSS receiver.

Table 4.2: Relative Time offsets of Different Datasets in the Timerange of Nanosecond Between Receivers of Same Model (Ublox-Ublox).

Test Session Peak Mean STD RMS5 min ±30 3.6 12.67 12.671 hr ±90 4.15 12.58 13.210 hrs ±40 3.8 9.73 10.424 hrs ±60 1.75 12.2 12.2

The observed time offset of the 1PPS solutions between two receivers of the same

model over 24 hours are shown in Figure 4.9d. As shown in Table 4.2, in this test case,

the STD, mean value and peak value of the time series are 12.2 ns, 1.75 ns, and ±60


Table 4.3: Relative Time Offsets of Different Datasets in the TimeRange of Nanosecond Between Receivers of Same Model (Furuno-Furuno).

Test Session Peak Mean STD RMS5 min ±17 8.5 3.5 9.11 hr ±20 5.6 3.8 4

10 hrs ±21 2.12 4.1 4.324 hrs ±21.1 1.67 4.8 5.1

ns, respectively. These results indicate a consistent match between the pulse signals

from the two receivers.

Similar experiments were conducted with the GNSS receiver manufactured by

Furuno. Two identical Furuno VN872 model receivers were used in this experiment.

The Furuno receiver was selected in the experiment because it is widely used in

ship navigation, such as the Automatic Identification System (AIS) in the Brisbane

city ferry network. In AIS, GNSS timing signal is already been used for system

synchronisation.

The time offset results from this experiment are tabulated in Table: 4.3. From the

table, the maximum peak can be identified from the dataset of 24 hrs, whereas, the

mean value is remarkably low at 1.67 ns. From all of this datasets it can be seen that

the performance of Furuno in terms of STD and RMS is slightly better than of Ublox.

However, in all cases, the result indicates, the consistent match between 1PPS signals

from identical receivers.

4.4.5.2 Time Offset Between Receivers of Different Models

Similar experiments were performed with two different model GPS receivers from

two vendors. The observed peak, mean, STD and RMS results of the experiments are

summarised in Table 4.4. It can be seen that tabulated values are much larger than

those in Table 4.2 measured from two GPS receivers of the same model, however, a

maximum offset of under 200 ns was again observed.


0 60 120 180 240 300

Elapsed Time (Sec)

-5

0

5

10

15

20T

ime

Off

set (

ns)

Furuno-Furuno (same model) 5 mins

Max = 17 ns

Min = 0 ns

Instant offsetMoving Average

(a) Short Term Time Offset Recorded Between Receivers of Same Models (5 mins).

0 2 4 6 8 10 12 14 16 18 20 22 24

Elapsed Time (Hours)

-25

-20

-15

-10

-5

0

5

10

15

20

25

Tim

e O

ffse

t (ns

)

Furuno-Furuno Same Receiver 24h

Max = 21.1 ns

Min = 21.9 ns


(b) Long Term Time Offset Recorded Between Receivers of Same Models (24 hours).

Figure 4.11: Plot of the two datasets (5 minutes and 24 hours long) time offsetsbetween two identical receivers manufactured by Furuno (Model VN 872). (a) 5 minsdata represents a maximum of 17 ns deviation along with 3.5 ns STD and 9.1 ns RMS.(b) 24 hours data represents a maximum of 21.1 ns deviation with 4.8 ns STD and 5.1 nsRMS.

The offsets measurements over 24 hours are depicted in Figure 4.12. The STD

value is measured to be 30 ns. The mean offset is 6.9 ns and peak values are +80 ns

and −180 ns. The mean values over a moving data window of 2 hours show that the

offset is consistently within ±30 ns.

The results of another experiment between different model receivers but same


Table 4.4: Relative Time Offsets From Different Datasets in theTime Range of Nanosecond Between Receivers of Different Model(Ublox-Furuno)

.

Test Session Peak Mean STD RMS5 min ±55 10.6 9.1 311 hr ±45 8.6 26.4 29

10 hrs ±60 11.8 28 29.324 hrs ±180 6.9 30 31.4

0 2 4 6 8 10 12 14 16 18 20 22 24


-200

-150

-100

-50

0

50

100

Tim

e O

ffse

t (ns

)


Figure 4.12: Time Offset Between Receivers of Different Models over a Long Period.

manufacturer are now presented; one is a receiver with a PPS output for which the

manufacturer does not claim that it is for timing applications, and the other one is a

receiver with a PPS output for which the manufacturer claims that it is designed to be

used for dedicated timing applications. In this experiment, interestingly, large offsets

between the two receiver PPS signals were observed. One receiver was observed

to always lead the other with a significant and constant offset. Table 4.5 shows the

recorded offset over 24 hours. It can be seen that the maximum peak is 764 ns with

mean value of 610 ns. Considering the mean value as the constant bias, we processed

the data by subtracting 610 ns form the dataset ended up with a peak value of 154 ns

with a mean of .5ns, STD 21.9 and RMS 21ns as shown in the Table 4.6.

This implies a constant timing offset bias was present due to the calibration and/or


0 2 4 6 8 10 12 14 16 18 20 22 24


450

500

600

700

800

450

500

600T

ime

Off

set

(ns)

Max = 764 ns

Min = 507 ns


Figure 4.13: Time Offset Between Receivers of Different Models Over a Long Period.

Table 4.5: Relative Offset in ns Between Receivers of Different Models.

Test Session Peak Mean STD RMS24 hrs ±764 610 21.9 610

0 2 4 6 8 10 12 14 16 18 20 22 24


-200

-150

-100

-50

0

50

100

150

200

Tim

e O

ffse

t (ns

)

Max = 154 ns

Min = -103 ns


Figure 4.14: Time Offset Between Receivers of Different Models Over a Long Period.

algorithm within the two particular receivers. It is expected that such a bias could be

removed by a further calibration process.


Table 4.6: Relative Offset in ns Between Receivers of Different Models.

Test Session Peak Mean STD RMS24 hrs ±154 .5 21.9 21

4.5 Summary

It is observed the results of experiments that using the same model of GPS receivers

in a network enables more accurate time synchronisation than using different model

GPS receivers. In comparison with the experimental results from GPS receivers of the

same model, time synchronisation errors almost doubled when GPS receivers from

different vendors are used. This is observed from the above-described practical exper-

iments, and is not claimed for all VANET scenarios. It is inferred that GPS receivers

from different vendors may not adopt the same error models and mitigation algo-

rithms in their receiver navigation processors. It is understood that the amplification

of relative PVT errors can be minimised if the interoperability requirement for vehicle

GNSS receivers is addressed appropriately [ARRB-Project-Team, 2013]. Nevertheless,

the observed timing errors of tens of nanoseconds can be accommodated for most

VANET applications with strict time synchronisation accuracy requirements.

Consumer-grade receivers are low-end GNSS devices. Such receivers use C/A

code (Single band (L1) Coarse Accusation code) for PVT solutions [Misra and Enge,

2006]. Clocks in these receivers with inexpensive quartz oscillators are responsible for

receiver clock skews, drifts and noises. In our experiments, we have recorded relative

time offsets between two consumer grade receivers. The maximum time offset in our

tests is 180 ns, which is the combined effect of individual receiver hardware delays,

clock variations, and noises. It is worth mentioning that this relative-receiver time

offset is not the timing parameter to be used for further positioning and velocity

calculation but is proposed as a measure of time synchronisation capacity between

GNSS receivers that support VANET applications and services.

Chapter 5

GNSS Synchronisation with On-board Devices

The previous chapter identified promising aspect of GNSS time synchronisation in

vehicular networks and assessed feasibility through compatibility, availability and

accuracy experiments. In this chapter, the aim is to integrate GNSS time synchronisa-

tion into on-board portable devices and examine achievable network-wide synchroni-

sation accuracy. Therefore, this chapter starts with a review of previously-suggested

time synchronisation techniques. It discusses the challenges faced by the suggested

techniques and proposes an architecture for GNSS services. Subsequently, a series of

experiments and validation is performed to demonstrate the achievable accuracy of

the system.


lications:

1. K. F. Hasan, Y. Feng, and Y.-C. Tian ”Exploring the Potential and Feasibility of

Time Synchronisation using GNSS Receivers in Vehicle-to-Vehicle Communica-

tions,” In Proceedings of ITM 2018, Reston, VA, Jan 29-2 Feb, 2018.

93

94 CHAPTER 5. GNSS SYNCHRONISATION WITH ON-BOARD DEVICES

5.1 Problem Definition and Solution Approach

The concept of Vehicle to Everything (V2X) communication refers to the message

transfer between vehicles and any other objects in an Intelligent Transportation Sys-

tem (ITS). In connected vehicle technology, communications includes interactions

between vehicles, vehicles with infrastructure including Road Side Units (RSU) to

receive network coordinating messages and Internet services, with smart power grids

to charge vehicles or at home for supplementary charge support. This communication

also extends to communication devices to connect pedestrians, cyclist, etc. as shown

in Figure 5.1.

V2V

V2P

V2D

V2

I

V2I (V2H, V2G )

Figure 5.1: VANET Communication: Vehicle to Everything Scenario.

The basic communication, however, is considered as Vehicle to Vehicle (V2V)

and Vehicle to Infrastructure (V2I) communication. The enabling technology of V2V

and V2I communication is known as Dedicated Short Range Communication (DSRC)

technology. The 75 MHz spectrum in the 5.9 GHz frequency band has been allo-

cated to DSRC communication technology, which offer low latency communication

5.1. PROBLEM DEFINITION AND SOLUTION APPROACH 95

1 4

32

5

6

V2I scenario V2V scenario

Figure 5.2: VANET Communication: Vehicle to Infrastructure (V2I), and Vehicle to Vehicle(v2v) Communication.

links in vehicular environments. In respect to the variation of V2V and V2I, it can

be seen that V2I is infrastructure oriented that resembles a generic WLAN infras-

tructure having access points (AP) and stations (STAs). The stations are connected

to an Access Points to form a Basic Service Set (BSS)1. In contrast, the V2V relies

on ad-hoc communications, where there is no fixed infrastructure as explained in

Figure 5.2. In VANET, the idea of time-synchronisation concepts were adopted from

the existing synchronisation practices in WLANs (i.e., Wi-Fi). The VANET standard

802.11p is an amendment of 802.11, which is the generic WLAN standard. The time-

synchronisation mechanism in 802.11 relies on the Time Synchronisation Ftion unc-

tion (TSF) timer, which is essentially a register in MAC layers capable of performing

216 modulus counting with a resolution of 1 s, that update with the received broadcast

messages (i.e., beacon or probe messages) timing information. Such messages are

transmitted by a network element that recognises the sources of the common clock

for that particular BSS. In infrastructure mode of operation, APs are considered as

the sources of a common clock. In ad-hoc mode, station nodes (STA) send time

frame messages to enable synchronisation each other. A number of TSF-based time-

synchronisation method have been proposed and implemented in WLANs. The TSF

is a register responsible for coordinating the channel access of the wireless medium, it

1Basic Service Set (BSS) is a group of IEEE 802.11 stations coordinated and configured by an AccessPoint (AP) to communicate with each other over using wireless medium [Kenney, 2011]


Application

TSF Register

Application

TSF Register

RSU/AP OBU/STATA

Frame

Ref clock

1 2

34

TA

Frame

(a)

(b)

Data 12:00 am Hardware Medium

TSF

Data 12:00 am Hardware

TSF

(c)

Applications

Figure 5.3: Existing Problem with Time-Advertisement-based Time Synchronisation. (a)BSS Communication, RSU Sending Beacon Containing TA Frame for Synchronisation. (b)TA frame is Transmitting from RSU to OBU. (c) Time Development (transfer) in TA process.

is, however, not meant to be used by the application layer of the system for any other

system and application purposes. In order to support time synchronisation across

other layers and applications, a 2012 IEEE 802.11 amendment proposed a method

called Timing Advertisement (TA).

In Timing Advertisement (TA) mechanisms, the AP sends beacon messages, which

contain time advertisement frames having both the time of the local clock and the

offset between the local clock and the global standard of time as ’time value’ and

’time error’ fields, respectively. The beacon or the probe messages can also use these

5.1. PROBLEM DEFINITION AND SOLUTION APPROACH 97

1 2

34

TA

Frame

Figure 5.4: Undefined Situation Using TA Mechanism in Pure Ad-hoc Communication.

two parameters to introduce time synchronisation under a single BSS. STA node re-

vives the signal and updates calculating the timing information sent across as shown

in Figure 5.3. However, it is clear that such synchronisation mechanisms depend

entirely on the in-band data communication between AP and STAs, thus relying on

accurate computational performance and also adding overhead to the transferable

payloads. In VANET, a significant part of network nodes are used for ad-hoc V2V

communications. In comparison, infrastructure-based V2I communications resem-

ble AP to STA communications, where the synchronisation signals are provided by

the infrastructure. This make VANET communications significantly different from

WLANs.

Along with the time-transfer primitives TA, the 2012 IEEE 802.11 standard in-

troduces another mechanism, known as the Timing Measurement (TM) frame, that

allows individual STA (i.e., communicating nodes) to measure the relative offset be-

tween STAs and take any action necessary. At this point, it is not clear that how

this relative time measurement would be calculated and what degree of accuracy can

be achieved. The 2016 IEEE 802.11 standard, introduces another frame, named Fine

Timing Measurement (FTM), to maintain estimation of relative-time offsets between


AP and/or STAs. The standard recommends that this time offset is achieved through

Round-Trip-Time (RTT) calculations by regular transfer of messages between commu-

nicating nodes. In view of communication mode, the timing advertisement works on

forward broadcasting messages, whereas Fine Timing Management (FTM) is based

on back and forth message transmission mechanism between network elements. At

this stage, the conditions under which the above methods would be employed for

network synchronisation remain unclear. In addition, the performance of TA and TM

methods have been difficult to confirm, as work has been scarcely reported by the

scientific research community [Mahmood et al., 2017].

In a vehicular network, the WAVE MAC standard IEEE 802.11p adopts time-

synchronisation principles following IEEE 802.11 family standard, as stated above.

In association with 802.11p, WAVE defines a MAC extension (IEEE 1609.4) as shown

in Figure 5.5 to enable multi-channel operation in a vehicular context. This time-

synchronisation extension accommodates any open technology that can provide a

common time such as GPS [SCC32, 2006]. However, it is not clear how overall system-

level time synchronisation in WAVE devices would be achieved and what level of

accuracy can be maintained. In infrastructure mode, a node can be synchronised

with a RSU, but in ad-hoc based V2V communications, it remains unclear whether

such communications are feasible and can be studied. A number of available of ad-

hoc MANET protocols for WSN are discussed in Chapter 2. But those protocols are

ill-suited to VANET, given its high-mobility, short-range communications, and end-

to-end latency requirements.

Instead of TSF synchronisation, a complete GNSS-time synchronisation method

for VANETs is proposed as shown in Figure 5.6. In this method, individual nodes

obtain synchronisation using external GNSS receivers as their reference clocks. This

enables both applications and TSF registers for multi-channel operations to be syn-

chronised with an external GNSS reference clock.

5.2. RELATED WORK 99

PhysicalPLME

MAC

LLC

MLME Ext

WSMPUDP/TCP

IPv6

Upper Layers

Managment Plane Data Plane

Man

agem

ent E

ntit

y

Multi Channel Operation

802.11p

802.11p

MLME

1609.4

Figure 5.5: Wireless Access for Vehicular Environment (WAVE) Layers.

5.2 Related Work

Previous researchers have envisaged the possibility of GNSS time integration in VANET.

As mentioned in the literature survey, references [Scopigno and Cozzetti, 2009] and

[Cozzetti et al., 2011] have proposed the idea of GNSS time integration into vehic-

ular network scenarios. Reference [Cozzetti et al., 2011], suggested a tight-coupling

model, in which navigational data and timing data from a GNSS system are inte-

grated into on-board communication module of an intelligent transportation system

(ITS ) for road navigation and synchronisation. Although this article argued the

system integration possibility, it did not mention any experiments that demonstrate

its accuracy and feasibility.

Some European researchers have carried out scholarly work on GPS integration

in interconnected vehicle solutions. Under the Fleetnet Project [Enkelmann, 2003],

decentralised time synchronisation for vehicular ad hoc networks have been studied,

in which the concept of GPS time integration with slotted MAC protocols has been

proposed [Franz et al., 2005]. Sjoberg et al. 211 investigated 802.11-based MAC limita-

tions and proposed Self Organised Time Division Multiple Access (STDMA) in place

of Carrier Sense Multiple Access (CSMA) [Bilstrup et al., 2009, Bilstrup et al., 2010,


Application

TSF Register

OBU/STA

Application

TSF Register

OBU/STA

GPS Ref clock GPS Ref clock

System 12:00 am Hardware Medium

TSF

Data 12:00 amHardware

TSF

GPS GPS

OBU/STA OBU/STA

Figure 5.6: Proposed Solution. GNSS Time Synchronisation to TSF Register ThroughApplication Layer.

Sjoberg et al., 2011]. The efficiency of a TDMA-based access schedule requires precise

and accurate time synchronisation, and therefore, they suggested the use of GPS as

an external time source. [Morgan, 2010] proposed synchronising DSRC Network

Interface Controllers (NICs) with GNSS signals. He suggested that the DSRC NIC

Clocks adopt the phase and frequency of the PPS signal from GPS receivers. But

there were no clear guidelines mentioned about the DSRC layer in which GNSS time

would be integrated.

Network synchronisation experiments using GPS-PPS timing signals have been

conducted by previous researchers. Recently [Guo and Crossley, 2017] described an

experiment that uses GNSS signals for time synchronisation in a power transmission

sub-station. Although the major portion of the electric power station network is based

indoors, they blended GPS and PTP to achieving a complete timing solution. In

contrast, VANETs are mostly outdoor networks, where GNSS signals are expected to

be widely available. [Fraleigh et al., 2001] conducted an experiment in an IP backbone

5.3. TESTBED DEVELOPMENT 101

network where they have used special network card DAG for synchronisation with a

GPS reference clock. [Mangharam et al., 2007] proposed the concept of synchronising

the operating system clock to a GPS PPS signal in vehicular environments. This idea

is in-line with the work presented herein, however, their experiments achieve sub-

200µs local synchronisation accuracy.

5.3 Testbed Development

A testbed was developed using a single board computer (SBC), namely, a Raspberry

pi-3, running a Linux-based operating system, Jessie (kernel 4.4). The SBC was se-

lected because of its simplicity and ease of integration with vehicle communication

systems. It provides a GPIO port, which enables a straightforward way to integrate

an external GPS module with the system. This involved integrating a PPS-enabled

GPS receiver from UBLOX (Model: Ublox MAX-M8Q).

The selected GPS receiver has two distinct outputs: GPS data which contains

NMEA sentences and a PPS pulse signal. The PPS-API driver was installed and

enabled on a node. Its primary function was to timestamp external events with a

high resolution. In this experiment, it was used for connecting GPS receivers with the

node. The kernel of the operating system was configured with the PPS-API patch.

The PPS-API fetches the PPS signal from DCD pin of the serial port, evaluates the

offsets between the system clock and the PPS reference clock and passes it to the time

daemon. In addition, the kernel of the operating system was compiled to fetch the

driver closer to the daemon. A time daemon was used for locking the system clock

with reference clocks. It also monitored various timing statistics. There are several

time daemons that can be used for this experiment. The Network Time Protocol

Daemon (NTPD) was used herein. The NTPD is sensitive to the availability of GPS

connections. When a GPS signal is disrupted, the NTPD exhibits deflections in its

output. Statistical results were recorded by generating log files. Since the PPS pulse

does not contain any information about the absolute time (i.e., second, minute, hour,


Driv

ers P

PS

AP

IN

ME

A

hw

clo

ck

syste

m c

lock /

swclo

ck

Ref c

lock

start

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D

GP

S

PPS N

TP

Ap

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atio

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12

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e In

terfa

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Abstra

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ayers

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Ke

rne

l Space

Ap

plic

atio

ns

(a)

(b)

NT

PD

MA

C

Ref c

lock

1 2 34

5

Figure5.7:

Com

municating

Node

developmentusing

aG

NSS

Receiver.(a)Schem

aticD

iagramofthe

Processto

Synchronisethe

SystemC

lockofthe

LinuxBased

Nodes

(Raspberry

Pi-3)tothe

GN

SSR

eceiver.(b)LayeredV

iewofthe

Developm

ent.This

FigureShow

sD

ifferentClock

Timing

andalso

DifferentJitter

throughouttheSystem

.

5.4. RESULT ANALYSIS AND PERFORMANCE EVALUATION 103

Rpi Node GPS Receiver

Power

Supply

Antenna

System Setup

Antenna cable

Figure 5.8: Schematic Diagram of the Experimental Setup on Moving Node to CollectData on Real-time Vehicular Environment Including Urban, Suburb, Highway, etc.

Route.

day, month and year). It just produces a tick every second, which marks the beginning

and/or ending of an arbitrary second. A GPS NMEA driver was used to receive

NMEA sentences and obtain the real physical time, which was then locked to the

PPS signal. Another external time source such as the Internet wall clock could be

employed, but NMEA receiver outputs were used here to make the node independent

and standalone.

A systematic development approach is shown in Figure 5.8. In Figure 5.8 (a) the

system development among different clock systems integrated with GPS ref clock

is shown. Equivalent layered development is shown in Figure 5.8 (b) where the

development space both in user space and kernel space is indicated. The figure also

shows different clock timing and different jitter throughout the system.

5.4 Result Analysis and Performance Evaluation

The overall experiment involved three distinct stages. In the first stage, a laboratory

experiment was conducted to measure the timing accuracy exhibited by a GPS re-

ceiver and the integration capabilities of a host node to synchronise it’s clock with

it. The measurements included the accuracy of the time from NMEA data, the PPS


0 50 100 150 200 250 300 350 400 450 500

No. of Observation in 10h

-0.02

-0.01

0

0.01

0.02

Offse

t (s

)

Time from RS232 messages

Max = 9.2 ms

Min = -8.7 ms

Instant offset/JitterMoving AverageAverage

0 200 400 600 800 1000 1200 1400


-0.02

-0.01

0

0.01

0.02

Offse

t (s

)

Time from RS232 messages

Max = 9.2 ms

Min = -20.07 ms


Figure 5.9: System Clock Synchronised with GPS Data Signal.


Algorithm 1 GPS Synchronised Node Development on RPi

1: Start2: Initial Environment Setup

Require: picocom, ntp, libcap-devinstall← picocominstall← ntpinstall← libcap-dev

3: Freeing Serial Port from Consolestop← [email protected]← [email protected]

4: Update and Upgradeinstall← apt-getupdate← apt-getdist-upgrade← apt-gethold-upgrade← ntp

5: Configuring the Noderemove← console=ttyAMA0,115200include← pps-gpio (modules)include← dtoverlay=pps-gpio (config)include← gpiopin=18 (config)

6: Enabling NTPD that supports PPSRequire: ntp-4.2.8p10, ntpstat, ntpdate,libssl-dev

install← ntp-4.2.8p10install← ntpstateinstall← ntpdateinstall← libssl-dev

7: NTPD compilation with PPSRequire: bc, ncurses-dev

install← bcinstall← bc ncurses-devgit clone← address: https://github.com/raspberrypi/linuxenable← Old Idle Dyntics configenable← PPS kernel consumer support

8: Configuring NTPDEnable Local Server, stratum-10server← 127.127.1.0fudge← 127.127.1.0Enable PPSserver← 127.127.22.0 mode 17 iburst true preferfudge← 127.127.22.0 flag1 1Enable GPSserver← 127.127.28.0 mode 17 iburst true preferfudge← 127.127.28.0Enable GPS-PPSserver← 127.127.20.0 mode 17 iburst true preferfudge← 127.127.20.0 flag1 1

9: Setting up Ethernetset← IP

10: End


output and the accuracy achievable by locking them together. In the second stage, the

developed node was integrated into a car and a field test was conducted. This enabled

the assessment of the developed GNSS-based time synchronisation system in terms of

signal availability and environmental parameters including vehicular speed. In the

third stage, laboratory experiments were conducted with three synchronised GNSS

nodes to determine relative time accuracy between them.

5.4.1 Node Clock Synchronisation With GNSS Receiver

To evaluate and analyse the performance of GNSS time integration within the devel-

oped node, long-term laboratory tests and on-road field tests were also conducted.

A series of long-term timing-accuracy measurements were conducted in a laboratory

environment. The overall evaluation process followed two steps. In the first step,

signals were recorded from individual GPS receivers, i.e., the timing information

received from RS232 messages (through a UART port) and PPS signals (from DCD

pins), which are plotted in Figure 5.9 and Figure 5.10, respectively. In the second step,

both GPS message times and the PPS signal are bound together to lock the system

clock. The resulting clock frequency is locked to the PPS ticks and the real physical

time received through the RS232 NMEA messages.

To understand the effect of temperature on the systems reception of the RS232

message stream, experiments were conducted in two different environments. One

experiment was conducted in a temperature-controlled room with 16◦C (top plot of

the Figure 5.9) and the other was conducted at normal room temperature (20◦ −

25◦C) as shown in bottom plot Figure 5.9. It can be seen from the log data that

temperature does impact on the message stream performance. In the low temperature

environment, low average offsets of 75 µs were recorded over 10 hours.

The second graph of the Figure 5.9, shows a 5.2 ms time-offset variation over a

24 hour period. In both cases, significant and random time offsets can be observed.

From the dataset logged at room temperature, the maximum recorded offset was +9.2


0 200 400 600 800 1000 1200


-2

-1.5

-1

-0.5

0

0.5

1

1.5

2O

ffset

(s)

PPS ticks at Node

Max = 1.11 s

Min = -1.82 s


Mean= -42ns

Figure 5.10: System Clock Synchronised with PPS Ticks.

ms (-20.7 ms in negative scale). The positive and negative offset values show that the

GPS times and system clocks can alternately lead or lag each other. The average value

of 5.2 ms is high enough in practice and therefore does not meet the requirements for

many vehicle network applications.

The later part of the first stage evaluation involved another long-term measure-

ment of PPS signals. This results are plotted in Figure 5.10. Compared with the GPS

RS232 data stream, it can be seen that the PPS clock is highly accurate and exhibits an

average offset of 42 ns. From the 24-hour-measurement duration, a maximum of 1.11

µs (1.82 µs in negative scale) can observed. Note that VANETs are real time networks,

where physical time plays a significant role in many applications. The PPS signal does

not carry any timing information. Therefore, depending exclusively on PPS signals

does not provide a timing solution for vehicular environments. Consequently, a

secondary source is required for accurate estimation of the physical time, i.e., the time

of day (TOD). Some researchers [Ben-El Kezadri and Pau, 2010, Guo and Crossley,

2017, Mangharam et al., 2007] have conducted experiments in which TOD timing

information is sourced from Internet, which requires coverage by another network to

provide Internet access. However, as VANETs are outdoor networks, the provision


0 200 400 600 800 1000 1200 1400 1600 1800 2000


-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5O

ffset

(s)

GPS PPS time locked at Node

Max = 2.16 s

Min = -2.06 s


Mean= 127ns

Figure 5.11: System Clock Synchronised with Both GPS PPS Signal.

of continuous Internet access is not practical on-board and should not be considered

as a part of the solution. Therefore, the experiment described herein combined the

TOD (i.e. UTC) from GPS RS232 data streams with a PPS signal. Figure 5.11 shows

the performance of the developed GPS-PPS-locked clock system over 24 hours. It

can be seen that the Peak-to-Peak maximum offset is 4.22µs (2.16µs+2.06µs). That is,

any two such networks can be synchronised with an accuracy of 4.22 µs (max). The

statistical distribution of the observed offsets is shown in Figure 5.12. It can be seen

that the distribution of the measured data corresponds to Gaussian distribution. The

standard deviation equal to 0.69µs.

Clock stability and noise level can be characterised by the so-called Allan Variance

(AVAR). The Allan variance is a recognised tool for time-domain frequency stability

measurement and can be derived from the expected value < . > of normalised fre-

quency y. It is calculated over a period of time (τ) and can be expressed as [Loschmidt

et al., 2012];

σ2y =

12〈(yn+1 − yn)

2〉 (5.1)


-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

Offset (s) 10-6

0

50

100

150

Den

sity

Offsets

Normal Distribution

Figure 5.12: Statistical Distribution of Measured Time Difference.

Equation 5.1 is a characterisation of clock stability. It provides an estimate of sta-

bility due to the inherent noise processes with the clock. The calculated Allan variance

serves as an indicative parameter of clock stability and a low value indicates high

stability. The Allan Deviation (ADEV) is the square root of the AVAR is expressed as:

σy(τ) =√

σ2y (τ) (5.2)

There are different, alternate methods for estimating the ADEV. Overlapped ADEV

methods provide better noise estimation performance than non-overlapped ADEV

calculations. It averages the normalised frequency over blocks of n data samples. For

this experiment, the Overlapped ADEV was estimated in MATLAB and the (σy) is

plotted in Figure 5.13. The noise template (κτµ) slope and sum of tangents (∑ κτµ) are

also plotted. From the slope analysis, white (thermal) noise (µ=-.5), pink (Electronic)

noise (µ=0) and red (Angle rate) noise (µ=.5) are calculated, which indicate that the

developed GPS-PPS-locked clock system exhibits good stability.


100 101 102 103

[s]

10-9

10-8

10-7

10-6

y

Allan deviation [overlapping]

y( )

k k

k - 5 11.2301 897.0000 2.1288e-07

0 897.0000 897.0000 7.1077e-09 0.5 897.0000 897.0000 2.3732e-10

µ τ min τ max

Noise Identification

Figure 5.13: Illustration of Clock Stability and Noises using Allan Deviation (Log-Log scale).

5.4.2 Field test of GNSS Time Synchronisation

The results of a group of experiments conducted on different road scenarios are plot-

ted in Figure 5.14. It is interesting to see that the clock binding performance on roads

is better than in the afore-mentioned laboratory experiments. The observed maxi-

mum offset between reference GPS and system clocks was 1.62µs, with an average of

533 ns over 30 minutes of logged data within suburban (40-60 km/hr speeds) traffic

scenario. In highway traffic (with 80-100 km/hr speeds), the observed maximum

offset was 2.04µs, with an average of 495ns over 30 minutes of logged data. In a

mixed suburban and city environment (40 to 60 km/hr speeds), a maximum 2.17µs

deflection and a peak-to-peak difference of 4.07µs (max) were observed. It is assumed

that better signal availability (through the GPS RS232 data stream) led to the overall

performance improvement. These results also suggest that higher vehicle speeds (up

to 100 km/hr) do not result in significant performance degradation.


0 20 40 60 80 100 120 140 160 180

No. of Observation in 30 mins

-2

-1

0

1

2

Offs

et (s

)

10-6 Suburb 40-60 km/hr

Max = 1.62 s

Min = -1.49 s

PPS-GPS Mean

0 20 40 60 80 100 120 140 160


-3

-2

-1

0

1

2

3

Offs

et (s

)

10-6 Highway 80 km/h

Max = 2.04 s

Min = -2.38 s

PPS-GPS Mean

0 50 100 150 200 250

No. of Observation in 2 hours

-3

-2

-1

0

1

2

3

Offs

et (s

)

10-6 Mixed Environment with suburb and city

Max = 1.90 s

Min = -2.17 s

PPS-GPS Mean

533 ns

495 ns

372 ns

Figure 5.14: GPS-PPS Enabled Clock in Different Road Scenarios.


C1 C2

C3

Figure 5.15: Schematic Diagram of the Experimental Setup to Measure the Time Offsetsbetween Two GNSS Synchronised Computing nodes. Here, Node C3 is Acting as a Server toSend Messages to the Other Two Clients, C1 and C2.

5.4.3 Network Synchronisation with GNSS

This phase of the experiment investigates the timing accuracy between two GNSS-

synchronised nodes that were developed according to the phase one experiment de-

tailed above. The experimental setup involved one server node and two client nodes.

The nodes were situated sufficiently close to each other to communicate wirelessly, as

shown in the Figure 5.15.

Figure 5.15 depicts a server node C3 sending UDP broadcast messages that are

received by clients C1 and C2, which are both synchronised with identical-model

GNSS receivers. The central idea is that, the server broadcasts messages to identically-

configured receivers, which are assumed to received at the same time. As they receive

the message at the same time, the nodes individual time-stamps would yield their

5.5. SUMMARY 113

clock times. In order to send and receive UDP messages, a simple socket program was

written in python. The tcpdump was used to record the time-stamps and compare the

clock times. The experiment was implemented using the Raspberry Pi-3 integrated

built-in wireless support (802.11n).

Time-stamps were collected from the received packets of individual nodes in three

datasets: 10 packets, 100 packets per second and 300 packets per minute.

To understand the comparative distributional characteristics the results of the

experiments are presented in the box-plot provided in Figure 5.16. For the 10-packet

dataset, it can be seen that there is an inter-quartile spread of approximately 6 µs,

a median offset of 1 µs and a maximum offset recorded here is 8 µs. Similarly, the

100-packet dataset shows a 5 µs inter-quartile spread, a median offset of 1.5 µs and

a maximum offset of 6.5 µs. The 300-packet dataset, yields more variation, namely,

offsets ranging from 7.5 µs to 10.75 µs , although the 50% (inter-quartile) values reside

between 1.5 µs to -3.6 µs. The median value is minimal at 250 ns.

To understand individual differences between packets, the 300 packet dataset is

plotted in Figure 5.17. From the figure, the presence of outliers can be seen over 8 to 10

µs. In this experiment it is assumed that the receiver systems are identical, therefore,

the timing jitter and time-stamping should be same. In practice, small parametric

differences can exist within the receiver components. Thus, the measured offsets are

not exact but only approximate the time offsets between the nodes.

5.5 Summary

This chapter has presented an experimental verification of a GNSS solution for stable

and accurate time synchronisation in vehicular networks. It started with a review of

previously-proposed VANET time-synchronisation mechanisms and explained their

limitations. Subsequently, an application-layer GNSS time integration is proposed,

where the system clock frequency is guided by both the PPS signal and real physical


0

-5

-10

5

10

15

-15

10 Packets 100 Packets 300 Packets

Mic

rose

co

nds

Figure 5.16: Box-plot of the Time Offsets Between Two GNSS Synchronised Node Developedon Rpi. Three boxes were Generated from 3 sets of data: 10-packet, 100-packet and 300-packetdatasets.

0 50 100 150 200 250 300 350

Number of Packets

-12

-10

-8

-6

-4

-2

0

2

4

6

Tim

e O

ffse

t (m

icro

seco

nd)

Time offset between two GNSS synchronized nodes


Figure 5.17: Offsets Between Individual Packets and their Moving Average from the Datasetof 300 Packets.

5.5. SUMMARY 115

time obtained from the GPS RS232 data stream. The operation of a mobile node that

exploits the developed GPS-PPS-time-synchronisation system is also described. The

results of a series of experiments are then presented which determine the accuracy

and precision of the timing signal.

The experimental results from both laboratory and field tests show that a PPS-

enabled-GPS integration implemented on an on-board unit (OBU) can achieve tight

coupling with UTC at a maximum clock deviation of 2.16 µs. From the experiments

described in the previous chapter it is understood that a GNSS receiver is capable of

providing UTC synchronisation with an accuracy of 40 ns. However, when GNSS

receivers are integrated with communications devices having different physical and

operating layers, timing jitter is generated, which results in synchronisation offsets.

On a Raspberry pi-3 platform, it was possible to provide 2 µs synchronisation accu-

racy with a median value of around 500 ns. Since there is no direct way to accurately

compare the clock of two communication nodes while avoiding jitter, an experiment

was conducted in which two identical systems time-stamped the same events. It was

assumed that the communication paths were symmetric, therefore, messages sent

from one server node are identically received at two client nodes. This experiment

showed that two-GNSS-synchronised nodes exhibit a relative time offset of sub-10

µs. Larger time offsets would probably be experienced in practice due to the presence

of some timing jitter. Overall, it has been demonstrated that the developed GPS-

PPS-time-synchronisation system can better support vehicular networks than existing

systems.


Chapter 6

Synchronisation in Occasional loss of GNSS

Signals

The previous chapters addressed the feasibility, integration and performance of GNSS

time synchronisation systems for vehicular communication networks. This chapter

analyses time synchronisation performance limitations during GNSS time solution

outages using data collected from vehicles travelling along high-rise streets in the

Brisbane CBD. The analyses show that indicates that time drift solution can be suc-

cessfully handled and potential strategies can be developed to bridge time-solution

outages. A laboratory experiment also conducted to understand and mitigate the drift

rate of node clocks during the absence of GNSS signals such as the tunnel scenario.

Some content from this chapter has contributed to the following publications:




117

118CHAPTER 6. SYNCHRONISATION IN OCCASIONAL LOSS OF GNSS SIGNALS

6.1 Understanding Service Availability

VANET is a highly dynamic and time-sensitive network. The nodes and network

elements in a VANET periodically send beacon signals to communicate updates about

status information on the road. To maximise communication efficiency, all nodes need

to maintain common time synchronisation. However, GNSS-based time synchroni-

sation relies on receiving signals from overhead satellites, which requires near Line

of Sight (LOS). Poor GNSS signal reception can occur in buildings, tunnels, under

trees and around other obstacles which are frequently encountered in urban road

scenarios. Therefore, it is important to understand the efficacy of GNSS-based time-

synchronisation systems operating in GPS-challenged environments.

GPS is a recognised tool for PNT (Positioning, Navigation, and Timing) solution.

With the standard positioning services (SPS) with pseudo-range measurements, the

precision depends on the number of satellites visible at the receiver also depends

on the geometry. The satellite/user geometry measurement metric is Dilution of

Precision (DOP). The DOP relates the geometric strength of navigational satellites

of GPS systems on the accuracy of a navigation solution.

To understand the issue of availability of a GNSS based time synchronisation

system, an experiment was conducted along a cluttered road in the Brisbane CBD.

In this experiment, a vehicle travelled past high-rise buildings, under tree canopies

and short overpasses. The vehicles paths are shown in Figure 6.1.

Figure 6.2 illustrates the different sorts of obstacles encountered on the vehicle

paths, such as high-rise building in Figure 6.2 (a) and (b), a short overpass in Fig-

ure 6.2 (c) and trees in Figure 6.2 (d). All of them are potential obstacles of barrier to

the GNSS signals.

The primary objective of this study to understand that to what extent the shielding

of GNSS signals by different urban structures on and around the roads impacts on

6.1. UNDERSTANDING SERVICE AVAILABILITY 119

the availability of GNSS time solutions. In this experiment, consumer-grade GNSS-

receivers were used to record data at the rate of 10 Hz. The open source RTKLIB was

used to process the collected data. In this experiment, we particularly focused on two

GNSS constellations, GPS and BDS (Beidou).

Figure 6.3 (a) (b) and (c) show the vehicle tracks reported by GPS, BDS and GPS+BDS,

respectively. It is evident from the individual GNSS vehicle tracks that some discon-

tinuities are present. The discontinuities are the result of position outages due to the

signal blockages and incorrect position solutions due to the weak geometry. Figure 6.4

plots the number of GPS and BDS satellites that were visible throughout the vehicle

trajectories over elevations of 10 degrees or more. It can be seen that there was at

least one satellite in view throughout the vehicles travel. For the constellations of

GPS, BDS and GPS+BDS, respectively, Table 6.1 summaries the percentages of the

satellite visibility in three cases: the number of satellites is more or equal to 4 (NSAT

≥ 4), between 1 and 3 (NSAT = 1 3) and is zero (NSAT < 1).

The Geometric Dilution of Precision (GDOP) values for NSAT ≥ 4 are listed in

Table 6.1 (b). From the table it can be observed that the GDOP ≤ 6, the parameters

of 4D states (for both position and time) in our case of high-rise urban areas are

low, i.e., 49.6%, 33.59% and 80.25% with GPS, BDS and GPS+BDS constellations,

respectively. The visibility for a minimum of 1 visible satellite for the same three

constellations are 100%, 99.98%, and 100% respectively. These results imply that

the availability of GNSS time solutions can be much higher than the availability of

positioning solutions within the high-rise streets, provided that the position states

can be fixed to a certain accuracy with alternative technologies. Overall, the use of

GNSS for time synchronisation in VANET is more feasible in urban areas than for

vehicle positioning in VANET.


(a)TopV

iewofthe

Projectedarea

inBrisbane

CBD

.(b)Selected

Route

tocollectD

atain

BrisbaneC

BD.The

Red

Marks

Shows

theD

ataPoints

onthe

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Figure6.1:

GPS

Challenged

Environmentin

Urban

Concept.Sun

anEnvironm

enthasD

ifferentSortofSignalBlockage/Lim

itingelem

ents,forexam

ple,High

Rising

Building,Trees,ShortOver

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6.1. UNDERSTANDING SERVICE AVAILABILITY 121

(a)T

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ith

Hig

hR

isin

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(b)S

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BDSGPS GPS+BDS

Figure 6.3: Vehicle tracks of of GPS, BDS and GPS+BDS on High Rising Urban roads inBrisbane.

5:16 5:20 5:25 5:30 5:35

2468

10

1214161820

GPS + BDS

No

of S

atel

lites

Elapsed Time

Figure 6.4: The number of satellites under the signal coverage of BDS and GPS.

Table 6.1: The number of satellites available with different GNSS service constellation.

GNSS System NSAT ≥ 4 NSAT = (1 to 3) NSAT < 1GPS 77.32% 22.68% 0%BDS 82..93% 17.05% 0.02%BDS+GPS 99.25% 0.75% 0%

Table 6.2: GDOP with Sifferent GNSS Services.

GNSS System NSAT< 4 GDOP 6 6 GDOP > 6GPS 22.68% 49.61% 27.71%BDS 17.07% 33.59% 49.34%BDS+GPS 0.75% 80.25% 19.00%

6.2. FALL BACK SOLUTIONS DURING SIGNAL OUTAGE 123

6.2 Fall Back Solutions During Signal Outage

Undoubtedly, the GPS-based time-synchronisation can be one of the most accurate

sources for time dissemination in open environments, and particularly road commu-

nication networks. However, there are still the possibility of complete signal outages

that can occur while travelling in tunnels and under bridges. In such challenged

environments, GPS signal may be intermittent or entirely absent for a while. To fos-

ter a better understanding of GPS-challenged environments and to propose solution

measures, two scenarios are considered below:

1. a signal blockage that causes the number of satellites to be less than four, but

there is always at least one (NSAT=1 to 3), and

2. when there are no satellite signals at all (NSAT=0).

6.2.1 Number of Satellites 1 to 3 (0<NSAT< 4)

It was observed that a high percentage of 1 to 3 visible satellites can occur in high-

rise urban areas. Dropping the number of visible satellites can happen from time to

time - depending on the road clutter conditions. To determine the impact of dropped

satellites, an experiment was conducted in a mixed-road environment. This experi-

ment involved developing a mobile node using the Raspberry Pi 3 (RPi3) platform,

and synchronising it with the GPS signal, as illustrated in Figure 6.5. The RPi3 used

the Linux operating system, Jessie. A consumer grade GNSS receiver manufactured

by Ublox was connected to its GPIO port to synchronise the node clocks with the re-

ceiver. The RPi3 was selected for the experiment because of its simplicity like a DSRC

device, (it is programmable and movable). An ntpd daemon was used to monitor

and record the timing performance on the road. The node was mounted in a car that

travelled for over 2 hours in different road conditions within the Brisbane CBD. The

vehicle environment included dense urban, suburban and highway scenarios and the

speed varied from 40 km/hr to 100 km/hr.


0 20 40 60 80 100 120 140 160 180


-2

-1

0

1

2

Offset (s

)

10-6 Suburb 40-60 km/hr

Max = 1.62 s

Min = -1.49 s

PPS-GPS Mean

0 20 40 60 80 100 120 140 160


-3

-2

-1

0

1

2

3

Offset (s

)

10-6 Highway 80 km/h

Max = 2.04 s

Min = -2.38 s

PPS-GPS Mean

0 50 100 150 200 250

No. of Observation in 2 hours

-3

-2

-1

0

1

2

3O

ffset (s

)10-6 Mixed Environment with suburb and city

Max = 1.90 s

Min = -2.17 s

PPS-GPS Mean

533 ns

495 ns

372 ns

Figure 6.5: Performances of a Node Clock Integrated with GNSS in a Mixed RoadEnvironment Including Urban, Sub-urban, Low speed and High Speed Areas.

As shown in Figure 6.5, the effects of momentarily reverting to 1-3 satellites is

reflected in the peak-to-peak offset variation of the timing solution. Note that, during

momentary periods when only 1 to 3 satellites are available, the vehicle position states

may not be updated. The projection of the position errors on the line-of-sight paths

follows the direction cosine rule. In the worst case, the position error of 300 m leads

to 1s clock bias. The Figure 6.5 shows the clock error variation between - 2.17 and 1.90

s, while the average deviation of the node clock from the GPS clock is -372 ns. Thus,

GPS can provide valid time solutions during periods where only 1 to 3 satellites are

in view, provided that clock errors of about 2s meet VANET timing requirements.

6.2.2 Number of satellite is Zero (No visible satellites)

Consider the second scenario where no satellites are visible (NSAT=0) for an extended

period. A two-step experiment was conducted to understand the consequences of

complete GNSS signal losses. In this experiment, three Rpi3 were individually con-

nected to three Ublox consumer-grade GNSS receivers as shown in Figure 6.6. Ini-

tially, in Step 1, all of the three nodes were synchronised with GNSS signals. The

6.2. FALL BACK SOLUTIONS DURING SIGNAL OUTAGE 125

node with Clock C3 was used for the server of the reference clock for comparing

the time differences among the three GNSS synchronised systems. Later, in Step 2,

the node with Clock C1 was disconnected from the GNSS antenna to mimic a GNSS

signal loss. The Figure 6.7 shows how the node clock C1 is drifts without the benefit

of a received time-synchronisation signal. Figure 6.7 shows the overall result of the

clock drift over a 4-hour duration.

C1 C2

C3

Ste

p 2

Ste

p 1

Figure 6.6: Schematic Diagram of the Experimental Set-up Between Three Nodes. NodeC3 is used to Record the Data where All of the Nodes are Synchronised with Identical GPSreceivers. GPS Lost is Testing by Removing GPS Device from the Node C1.

0 20 40 60 80 100 120 140 160 180 200


-600

-500

-400

-300

-200

-100

0

100

200

Offs

et (

s)

Node Clock drift while GPS signal lost

Clock C1 (GPS lost)Clock C1 (Linearized)Clock C2 (GPS Guided)

Figure 6.7: Plot of the Clock Drift Recorded over 4 hours.


5 8 10 15 20 25 30 35 40 45 50 55 60(1h) 65 70 75


-90

-80

-70

-60

-50

-40

-30

-20

-10

0

5O

ffset

(s)

Node Clock drift while GPS signal lost

Clock C1 (GPS lost)Clock C1 LinearizedClock C2 (GPS Guided)

Figure 6.8: Plot of the Clock Drift Recorded over 4 hours.

Figure 6.8 provides a closer view of the clock drift results over a 1 hour period.

It can be seen that from about 8 minutes that the GNSS signal is absent. Over the

next hour a drift of 80 s arises. The blue line represents the original drift with respect

to elapsed time, and the red line is their moving average. From the moving average

track, it follows that the trend of the drift is close to linear. That is, the clock drift is

predictable over short periods. Thus, a predicted clock drift can bridge the timing

outages when vehicles travel through tunnels where GNSS signals are completely

blocked. This is at least feasible in Australia, as the longest tunnel in Australia is 5.25

km. If the speed limit is 60 km/h, the signal outage could be 5.25 minutes. Figure 6.8

indicates that the clock offsets would increase 7 to 8 s over 5 to 6 minutes. The offsets

can be further reduced if a prediction model is used.

6.3 Summary

This chapter has focused on the availability aspects of GNSS time synchronisation in

vehicular environments. The vehicle-based experimental results have shown that the

availability of a minimum of 1 visible GPS satellite is 100% within Brisbane high-rise

CBD areas, while the availability of valid 4D position and time solutions was as low

6.3. SUMMARY 127

as 49.6%. Hence the availability of GNSS time solutions can be much higher than

the availability of positioning solutions within high-rise locations. The analysis of a

vehicle-tracking experiment indicates that the impact of user position biases due to

GNSS positioning outages on the clock errors is within acceptable range. Indeed, if

the clock errors of about sub 10 µs meet VANET timing requirements, GPS is con-

sidered still offering valid time solutions during periods where 1 to 3 satellites are

in view. If GNSS signals are completely lost, such as within tunnels, the suggested

fall-back solution is to use the predicted clock solution to bridge the outages for time

synchronisation in VANET. This is at least feasible in Australia, where the longest

tunnel is 5.25 km. In this case, signal outages of 5 to 6 minutes can lead to the clock

offsets 7 to 8 µs. The offsets can be further reduced if a prediction model is used.


Chapter 7

Conclusions and Recommendations

The research findings and the original contributions of this study presented on this

thesis report are summarised in this chapter. Some additional research issues are also

presented here as the possible future works.

7.1 Summary of the Research

Time synchronisation is a critical and important issue in vehicular networks. Ve-

hicular networks are fundamentally decentralised in nature, therefore, the network

nodes need to use a time synchronisation method to maintain accurate and precise

time. The research described herein addresses the needs and applications of time

synchronisation in vehicular networks and contributes timing techniques that can be

provided by GNSS systems.

The thesis begins with a survey of the theory and practices of time synchronisa-

tion in different decentralised networks. This surveys various time synchronisation

protocols for emerging wireless, ad-hoc networks from a range of perspectives, in-

cluding precision, accuracy, cost and complexity. An analysis of time synchronisation

protocols is presented, which serves as a guide for evaluating and tailoring protocols

specific to applications in various road networks, and understanding the difficulties

and prospects of time synchronisation for vehicular networks. The analysis identifies

129

130 CHAPTER 7. CONCLUSIONS AND RECOMMENDATIONS

time-sensitive applications and requirements of vehicular networks. According the

findings, there are numerous applications that are sensitive to accurate and precise

time. The identified time-sensitive applications are subdivided into essential and

desirable categories, in which a stringent timing requirement is defined as sub-10

µs. The performance of existing time synchronisation practices in different networks

is examined and the feasibility of GNSS time synchronisation is specifically investi-

gated.

A feasibility analysis is presented to provide an understanding of the accuracy,

precision and availability offered by consumer-grade, low-cost, GNSS receivers. These

receivers are commonly used in modern vehicles for navigation purposes. It is found

that such receivers can provide around 30 ns timing accuracy in favourable envi-

ronments. An investigation was conducted into timing synchronisation accuracy for

applications involving the integration of GNSS receivers with on-board devices. This

investigation found that sub-10µs accuracy is achievable, which can meet vehicle net-

work application requirements. However, results of experiments reveal that the dif-

ference between achievable accuracy and the capacity of the GNSS receiver is mostly

due to the integration jitter, which occurs and accumulates in the system interfaces

and different communication stack layers, can degrade the available accuracy.

The experiments described herein serve to compare an existing time synchronisa-

tion solution and a developed GNSS time synchronisation solution for vehicular net-

works. The analyses address the limitations of the Time Advertisement (TA) scheme

of IEEE 802.11p in vehicular environments, and address the feasibility of using GNSS

time, which has been proposed as a source for standard UTC time in IEEE 1609.4. In

consideration of the outdoor and dynamic nature of VANET nodes, vehicle-based

experiments were conducted to investigate GNSS signal availability within urban

environments. The experimental results show that the availability for a minimum

of 1 visible GPS satellites is 100% in the Brisbane high rising CBD areas, while the

availability of valid 4D position and time solutions was as low as 49.6%. This implies

7.2. SUMMARY OF THE CONTRIBUTION 131

that the availability of GNSS time solutions can be much higher than the availability

of positioning solutions in the high-rising streets. Clearly the compatibility, accuracy,

compatibility and availability results described herein demonstrate that GNSS time

synchronisation is a promising method for vehicular network applications.

7.2 Summary of the Contribution

The main contribution of this thesis is to develop a time synchronised cooperative

vehicular network using the support of Global Navigation Satellite System (GNSS)

time synchronisation mechanism. A great deal of time synchronisation is being done

with technology-based wired and wireless communication. But they are not always

compatible or ill-suited with highly dynamic network VANET.

To understand the time sensitiveness of emerging cooperative vehicular network,

in the first place, this research work contributes to identifying different applications in

connected vehicular technology that relies on accurate and precise time and presented

them with their requirement threshold. This contribution to the knowledge helps to

recognise the importance of precise time synchronisation in the vehicular networks.

The research work demonstrates the efficacy of GNSS system to provide time syn-

chronisation solution for the vehicular network. In this part, the specific contribution

includes a thorough feasibility analysis of GNSS time synchronisation mechanism by

conducting and validating different setup variations with different real-time experi-

ments.

Another important contribution of this research work is to identify the shortcom-

ings of existing TSF time synchronization solutions proposed in VANET. This con-

tribution also extends to the demonstration of the capability of GNSS time solution

using DSRC like devices.

The other area of significant contribution is the demonstration of the potential

of the multi-GNSS constellation in signal availability for the timing solution. This


research work conducts several real-time experiments with multi-GNSS enabled con-

sumer grade receivers in different obstructed road scenario to prove the improvement

of the availability of signal reception with the help of he advancement of multi-GNSS

constellations. Overall, this thesis proposed a complete solution of time synchronisa-

tion for the vehicular ad-hoc network using GNSS timing support.

7.3 Future Works

This thesis is primarily concerned investigating GNSS time synchronisation and in-

tegration for vehicular networks. However, several unresolved issues remain, which

prompt recommendations for continuing research. This section, describes some areas

of interest for potential future research.

1. Many security applications depend on maintaining accurate time synchronisa-

tion with respect to a standard time-scale such as UTC. Some security issues

have been described for different wired and wireless communication networks,

including vehicle, power, financial and telecommunication networks. It is sug-

gested that further analyses of vehicular network security are undertaken to

manage safety vulnerabilities and possible terrorist attacks. In our work, we

have addressed few of the security issues, but another level of investigation is

important which we are considering as one of the future steps of the research.

2. It has been identified that the GNSS receiver integration within on-board de-

vices creates additional jitter. This results in timing uncertainties of a few mi-

croseconds between the practical timing accuracy offered by GNSS systems

and the achievable on-board accuracy. It is suggested that the jitter within

different integration layers is analysed further in the interest of reducing the

timing uncertainty.

3. In the absence of GNSS signals, the unguided quartz-based clock oscillators

7.3. FUTURE WORKS 133

drift and deviate with respect to the reference time. Although quartz clocks

are influenced by environment factors such as temperature, opportunities may

exist to use prediction models to improve the clock performance. For example,

it may be possible to train a clock system with past corrections to predict the

future corrections.

4. The scope of time synchronisation studied in this work is limited to a DSRC-

centric analyses. An alternative V2X communication interface candidate, LTE-

V, is increasing in popularity. The application of GNSS time synchronisation to

LTE-V and ensuing performance investigations are suggested for future work.

Similarly, the rapid growth of the IOT devices (Internet of Things) and their

applications, represent follow-on opportunities for GNSS time synchronisation

studies.


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