EVALUATION OF MOBILE WIMAX PERFORMANCE IN A METROPOLITAN

VEHICULAR APPLICATION

by

TONG JIN

A Thesis submitted to the

Graduate School-New Brunswick

Rutgers, The State University of New Jersey

in partial fulfillment of the requirements

for the degree of

Master of Science

Graduate Program in Electrical and Computer Engineering

written under the direction of

Professor Marco Gruteser

and approved by

________________________

________________________

________________________

________________________

New Brunswick, New Jersey

May, 2011

ii

ABSTRACT OF THE THESIS

Evaluation of Mobile WiMAX Performance in a Metropolitan Vehicular Application

By TONG JIN

Thesis Director:

Professor Marco Gruteser

Intelligent transportation systems (ITS) will enhance on-road safety, improve road

utilization, fuel efficiency and, last but not the least, allow occupants an entertainment

filled ride. They will include a plethora of applications requiring information

dissemination that will use vehicle-to-vehicle (V2V) communication, which has vehicles

form an ad hoc network to communicate with each other, and also vehicle-to-roadside

(V2R) communication, which amongst other things will provide connectivity to the

internet. The offered load in a vehicular network can be significant because of the

possibility of large vehicle densities and a variety of supported applications. Also, the

wireless propagation environment can be harsh, especially in a metropolitan area like New

York City and Los Angeles, and can significantly impact network performance. The load

requirements together with the high mobility in a vehicular network make mobile WiMAX

a suitable technology for vehicular networks. However, before WiMAX deployments are

used by vehicular networks, the performance of typical vehicular applications over

WiMAX needs to be evaluated. Also, propagation characteristics of WiMAX, especially in

relation to application performance, for example, the impact of intermittent connectivity on

data transfer over WiMAX, needs evaluation. In this work we first introduce the ParkNet

iii

system, in which vehicles collect and disseminate, over WiMAX, information regarding

roadside parking availability in a city. We implement the system and evaluate the

performance of a ParkNet deployment in Brooklyn, New York City, which includes

multiple cars and a WiMAX base station. The deployment monitors and measures the

WiMAX signal strength and data dissemination status and associated temporal and spatial

information. The data collected over multiple experiment runs conducted over many days

is used for application performance evaluation. The signal strength readings are further

used to derive a path loss model, which can be used by simulations of a mobile WiMAX

network.

iv

Acknowledgement and Dedication

I would like to acknowledge my thesis advisor Prof. Marco Gruteser providing me this

opportunity to work in such an interesting and exciting project. Thanks for his inspirational

instruction and guidance during my research in Wireless Information Network Laboratory

(Winlab) at Rutgers University. I would like to thank my co-workers, Ivan Seskar, Suhas

Mathur and Sanjit Kaul, both of whom indeed encouraged me and helped me a lot during

my research in Winlab. I feel so fruitful and enjoyable to work with them. I am also so

grateful to my colleagues Bin Zan, Sangho Oh, Nikhil.K, Janani.C, and Wenzhi Xue, for

their support and help during the whole project. Then, I would like to thank all the staffs in

Winlab and Electrical and Computer Engineering Department for their kind assistance. In

addition, I wish to thank all my friends and other people who look at me, care about me,

and emotionally support me. Finally, I would like to sincerely thank my parents and other

family members for their understanding, support, continuous inspiration and eternal love. I

cannot complete this job without their assistance, tolerance, and enthusiasm.

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Table of Contents

ABSTRACT OF THESIS ................................................................................................... ii

Acknowledgement and Dedication .................................................................................... iv

Table of Contents ................................................................................................................ v

List of Tables .................................................................................................................... vii

List of Illustrations ........................................................................................................... viii

1. Introduction ..................................................................................................................... 1

2. Background and Related Work ....................................................................................... 4

2.1 Networked Vehicular Applications and Communication Limitations ................. 4

2.1.1 Networked Vehicular Applications............................................................... 4

2.1.2 Communication Limitations ......................................................................... 5

2.2 Wireless Technologies on V2R Communication ................................................. 6

2.2.1 Open WiFi Technology................................................................................. 6

2.2.2 WiMAX Technology .................................................................................... 7

2.2.3 3G Cellular Wireless Technology ................................................................. 8

2.3 Propagation Model in Mobile Systems ............................................................... 9

2.3.1 COST-231 Hata Model ................................................................................. 9

2.3.2 Egli Model .................................................................................................. 10

2.3.3 Standord University Interim Model ............................................................ 10

2.4 Terrain Effects .................................................................................................... 10

3. ParkNet Application Overview ..................................................................................... 12

3.1 Objective and Solution Description ................................................................... 12

3.2 System Deployment ........................................................................................... 14

3.3 Onboard Platform Specification ......................................................................... 15

3.4 Delay-Tolerant Data Uploading ......................................................................... 17

4. Methodology and System Configuration ...................................................................... 20

4.1 Experiment Goals ............................................................................................... 20

4.2 Experiment Implementation ............................................................................... 21

4.2.1 Experiment Description and Configuration ................................................ 21

4.2.2 System Deployment .................................................................................... 23

4.2.3 System Specification ................................................................................... 24

4.3 WiMAX Status Monitor and Data Uploading Mechanism ................................ 26

4.3.1 OML Framework ........................................................................................ 26

4.3.2 Micro-element File Uploading Mechanism ................................................ 27

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5. Experiment Results and Analysis ................................................................................. 29

5.1 Metropolitan WiMAX Propagation Characteristics Study ................................ 29

5.1.1 Coverage Study ........................................................................................... 29

5.1.2 RSSI Statistic Information and Connection Study...................................... 31

5.1.3 Metropolitan Terrain Characteristics and Effects ....................................... 34

5.2 Path Loss Model for Mobile WiMAX in Metropolitan Environments .............. 36

5.3 Data Transfer Performance ................................................................................ 43

6. Conclusion and Future Work ........................................................................................ 48

Reference .......................................................................................................................... 50

vii

List of Tables

Table 5.1: Experimental Vehicles’ Profiles on Connection Duration .............................. 33

Table 5.2: Experimental Vehicles’ Profiles on Velocity Information .............................. 34

Table 5.3: File Transfer Duration and Number ................................................................ 45

viii

List of Illustrations

Figure 3.1: ParkNet System Architecture ............................................................................. 14

Figure 3.2: ParkNet vehicle-unit .......................................................................................... 15

Figure 3.3: Comparisons among different urban wireless applications by transfer

time-sensitivity and required data size ............................................................ 17

Figure 4.1: Experiment Routes in Brooklyn ..................................................................... 22

Figure 4.2: Schematic diagram of overall system deployment ......................................... 24

Figure 4.3: Onboard Mobile Laptop and WiMAX Base Station ...................................... 25

Figure 4.4: Satellite Picture of WiMAX Base Station Deployment ................................. 26

Figure 5.1: (a) Experiment areas vs. Coverage of WiMAX signal ................................... 30

Figure 5.1: (b) The experiment data distribution vs. received RSSI data distribution ..... 30

Figure 5.2: (a) Downlink RSSI data over distance to Base Station .................................. 32

Figure 5.2: (b) The RSSI data distribution over corresponding coordinates .................... 32

Figure 5.3: (a) The RSSI heat-map ................................................................................... 35

Figure 5.3: (b) Building distribution inside RSSI coverage area ...................................... 35

Figure 5.4: NLOS downlink RSSI data over distance to Base Station ............................. 37

Figure 5.5: RSSI measurements against the logarithm of the distance............................. 39

Figure 5.6: Equation(5) with RSSI measurements against distance ................................. 40

Figure 5.7: Probability density of model error vs. Probability density function curve of

fitting Normal distribution .............................................................................. 41

Figure 5.8: Cumulative probability of model error vs. Cumulative distribution function

curve of fitting Normal distribution ............................................................... 41

Figure 5.9: NLOS Path Loss Model vs. Path Loss Model on Free Space ........................ 42

Figure 5.10: Transferred File Numbers/Number of RSSI vs. RSSI Values ..................... 43

Figure 5.11: File Transfer Area vs. RSSI Coverage Area ................................................ 44

Figure 5.12: Comparison Experiment over TCP .............................................................. 46

1

Chapter 1

Introduction

Recently, both academia and industry have paid a lot of attention to vehicular networks,

because of the significant role they are envisioned to play in future intelligent

transportation systems (ITS). ITS will utilize synergistic technologies and systems

engineering concepts to develop and improve transportation systems of all kinds [1].

They will enhance on-road safety, improve road utilization, fuel efficiency and, last but

not the least, allow occupants an entertainment filled ride. They include a plethora of

applications requiring information dissemination that will use vehicle-to-vehicle (V2V)

communication, which has vehicles form an ad hoc network to communicate with each

other, and also vehicle-to-roadside (V2R) communication, which amongst other things

will provide connectivity to the internet.

The connection approaches and wireless technologies used in vehicular networks are

often application-specific. For applications such as traffic information systems (TIS) and

collision warning, which involve close range communication between vehicles, short-

range wireless technologies based on IEEE 802.11p [33] ( the DSRC [34] standard ) are

commonly used. On the other hand, for applications that require connectivity to a

centralized server vehicle-to-roadside communication links, existing cellular networks,

open WiFi and mobile WiMAX may be used.

Worldwide interoperability of microwave access (WiMAX) is a broadband wireless

technology designed for provisioning high-speed data access over long distances. It is an

2

approach based on the IEEE 802.16 standard [2] for the metropolitan area to address the

“last mile” problem of providing connections to individual homes and offices. The IEEE

802.16 has several working groups, one of which develops the IEEE 802.16e standard

[2], more commonly known as mobile WiMAX, which standardizes the technology used

to provide wireless network access service for mobile users at speeds up to 93 miles per

hour [32]. Compared with other V2R communication technologies, due to the mobility

support at vehicular speeds and the inherent wide coverage, mobile WiMAX is a suitable

wireless technology for vehicular networks.

Before WiMAX deployments are used by vehicular networks, the propagation

characteristics of WiMAX in a typical environment, especially in relation to application

performance, for example, the impact of intermittent connectivity on data transfer over

WiMAX, needs evaluation. Also, the performance of typical vehicular applications over

WiMAX needs to be evaluated, to guide the development of future applications. In this

work, we perform an empirical evaluation of a WiMAX based vehicular system in a

metropolitan environment, using data collected from experiments that involves an actual

WiMAX installation and a real vehicular application.

We implement the ParkNet system, in which vehicles collect and disseminate, over

WiMAX, information regarding roadside parking availability in metropolises, and then

evaluate the performance of a ParkNet deployment in Brooklyn, New York City, which

includes multiple cars and a WiMAX base station. The deployment monitors and

measures the WiMAX signal strength and data dissemination status and associated

temporal and spatial information. The data collected over multiple experiment runs

conducted over many days is used for the evaluation of propagation characteristics in a

3

metropolitan environment and data transfer performance in the ParkNet system. The

signal strength measurements are further used to derive a path loss model, which can be

used as a reference by simulations of a metropolitan mobile WiMAX network.

To summary, we address three primary problems:

What are the propagation characteristics of mobile WiMAX in a metropolitan

vehicular application?

What is the propagation model of mobile WiMAX under a metropolitan

environment?

What is the data transfer performance of ParkNet application over mobile

WiMAX?

The rest of the thesis is structured as follows. Related related works are discussed in

Chapter 2. Chapter 3 describes the experiment objectives, deployment of ParkNet system,

and its data transfer requirements. In Chapter 4, we present the goals and experimental

procedure, and detail the implementation of the ParkNet system over mobile WiMAX in

Brooklyn, NYC. We present the collected measurements in Chapter 5, where we also

analyze the performance of the wireless link, and derive the path loss model for a

metropolitan environment. Finally, we conclude our work and discuss possible

extensions of our work in Chapter 6.

4

Chapter 2

Background and Related Work

An ever increasing list of possible applications that will be supported by vehicle networks

of the future has led to interest in evaluating different wireless technologies that can best

support vehicle-to-vehicle (V2V) and vehicle-to-roadside (V2R) communication. In this

chapter, we introduce some applications and their communication requirements. Current

wireless technologies used in V2R communication, like 3G, WiFi and mobile WiMAX

are also briefly described and compared in terms of coverage, connection and throughput.

Work related to propagation modeling on WiMAX technology, for example, effects of

terrain on WiMAX, is also described.

2.1 Networked Vehicular Applications and Communication Limitations

2.1.1 Networked Vehicular Applications

Networked vehicular applications have been a subject of much attention by both

academia and industry due to the significant role it plays in intelligent transportation

systems. With onboard radios, these vehicles can communicate with each other or with

roadside infrastructures to enable a variety of applications including that safety and

infotainment.

An example of a safety application is the on-coming traffic warning (OTW) application

which alerts the drivers about on-coming traffic during overtaking maneuvers that require

the driver to temporarily switch into the opposite lane of travel [1]. Another example [1]

5

is intersection violation warning (IVW), which warns the driver when violation of a red

light seems imminent. This application requires dynamic real-time information such as

the light phase and light timing which is gathered via interaction between vehicles and

roadside infrastructures.

While improving safety has been identified as one of the most critical goals of intelligent

transportation networks, the networks will also deploy applications that improve transport

efficiency, for example, by disseminating timely information about traffic flow in a

region. A recent project has attempted to address the traffic congestion issue through the

design of mobile systems that collect traffic congestion information to improve trip

planning and route finding [4]. The ParkNet system [3], described later in details, is

another example of an application that can reduce traffic congestion by providing parking

availability information and thus removing cars looking for parking slots off roads sooner

than later.

2.1.2 Communication Limitations

When we develop a networked vehicular application, it is vital to be aware of the

limitations of the underlying wireless communications, due to the technology used and/or

the physical environment of operation of the network. For example, both V2V and V2R

communication is impacted by vehicular mobility.

The communications limitations as applicable to a vehicular network include:

a. Frequent network disconnections

b. Rapid changes in link topology

c. Limited bandwidth for data aggregation.

6

We next describe the wireless technologies that have been proposed for V2R

communication and how they handle the listed limitations.

2.2 Wireless Technologies on V2R Communication

2.2.1 Open WiFi Technology

Plenty of researches have been done on WiFi-based vehicular networks. Previous results,

for example [5] [6] [7], have confirmed the feasibility of this connectivity paradigm. The

most significant factor is WiFi technology operates on an unlicensed band and does not

require regulatory approval. Also, multiple access points for civil usage lead to

potentially ubiquitous network connectivity for mobile users and permanent access to

Internet.

For current available version, although the maximum data rate with 802.11 a/g is 54

Mbps, its short range makes it difficult to provide long-term stable connectivity and

causes frequent handoff. The transfer throughput is constrained due to the vehicular

mobility as well. The base station comes in and goes out of range quickly, which limits

the effective information transfer time [5]. The work [8] reveals that the connectivity

procedure in drive-through WiFi is often marred by intermittent “grey” periods of very

poor connectivity due to the environment, and is hard to predict. Generally, the beginning

of the establishment period and the exit time of client shows a weak connection status [5]

[7].

Previously work has been done on improving the data transfer time by reducing the time

it takes to connect to the network and also time taken to handoff. For example, through

different intelligent strategies, [9] [10] [11] achieve fast access point selection when

7

handing off between beacons. The QuickWiFi mechanism in Cabernet [13] catches the

first access point encountered and shortens the timeouts configuration when connectivity

appears, which avoids the complex selection period and application-specific timeouts. In

addition, many upper layer solutions have been proposed to improve the performance of

WiFi-based vehicular data transfer. To some extent, data dissemination algorithm MDDV

[14], robust data transfer protocol DTP [15], and information transfer protocol VITP [16]

all improve the data transfer performance in WiFi-based vehicular networks. A recent

approach like networking coding [17] has been used to achieve the same.

2.2.2 WiMAX Technology

WiMAX technology provides both fixed and mobile Internet access, the latter of which

can support mobility in vehicular networks with possibly very few handoffs because of

its large coverage range. The mobile WiMAX is based on IEEE 802.16e standard, which

supports the soft and hard handover between base stations. It promises to open new,

economically viable market opportunities for operators, wireless Internet service

providers and equipment manufacturers. Compared with WiFi technology, the relative

high throughput, scalability and long-range features of WiMAX make it a more suitable

choice for meeting the demand of vehicular applications [2].

The mobile WiMAX provides high speed data rate, which could support up to 100Mbit/s

rates to mobile users, while the uplink throughput could reach 50Mbit/s. Operating at the

maximum range of 50 km, WiMAX technology minimizes the number of handoff and

reduces the time consumed on reconnection. Thus, it is a suitable wireless technology for

potential applications like portable mobile broadband connectivity across cities and

countries [1].

8

There has been some investigation into the performance of fixed and mobile WiMAX

under different propagation environments. [18] shows the comparison between the

simulation and experiment results obtained on throughput, and demonstrated that the

street level operating range for mobile WiMAX system can be up to 2100m. Based on the

Received Signal Strength Indication data and Signal to Noise Ratio measurements, [19]

analyzes and evaluates the physical performance of fixed WiMAX in a typical urban area

and proposes its path loss model. However, little research analyses the performance of a

vehicular network that uses WiMAX infrastructure. Also, in [35], researchers from Seoul

tested the TCP performance over mobile WiMAX by putting mobile nodes on more than

5000 vehicles. They found that the downlink TCP throughput could reach 1.9Mbps, and

the uplink throughput was up to 300Kbps.

2.2.3 3G Cellular Wireless Technology

Currently, 3G systems already offer widely available and affordable mobile Internet

access. The low cost of such cellular Internet access has made mobile data applications

very popular, including vehicular applications [20]. The main advantage of cellular

communication in vehicular application is the one hop wireless connection, which would

not constrain the capacity or the connectivity once the data has reached the 3G base

stations [1].

In comparison with WiFi technology, 3G shows stable performance on throughputs and

less variability. Besides, according to the research result [21], 3G wireless

communication is less sensitive to vehicular mobility, which is another important reason

for the employment of 3G. Nevertheless, the 3G cellular network is suitable for relative

slow and low volume data aggregation. Through comparison with mobile WiMAX, the

9

performance simulations show that planned 3G CDMA-based enhancements, EVDO and

HSPA provide lower throughput and less spectral efficiency [22].

2.3 Propagation Model in Mobile Wireless System

In wireless communication systems information is transmitted between the transmitter

and the receiver by electromagnetic waves. As electromagnetic waves propagate through

space, their interaction with the environment can lead to a significant reduction in

received power, also referred to as path loss, with increasing distance from the transmitter

[23].

Path loss may be due to many effects, including reflection, refraction, diffraction, free-

space loss, aperture-medium coupling loss, and so on. Path loss is also influenced by the

height and location of antennas, the distance between the transmitter and the receiver,

propagation medium such as air moisture, and terrain (urban, suburban or rural). It is

used in the analysis and design of the link budget of a telecommunication system. In the

context of using WiMAX for vehicular, there are some existing empirical models for path

loss prediction [24].

2.3.1 COST-231 Hata Model

The COST-231 Hata model [25] is the most famous path loss model for WiMAX

propagation. It is an extension of the Hata-Okumura model derived from the original

Okumura path loss model and is used for the prediction of WiMAX path loss in urban

settings. It was developed for usage in the 1500-2000 MHz with transmitter antenna

10

heights of 20-300m. Also, it is extended for the research in 2360-2390MHz band

WiMAX [24].

2.3.2 Egli Model

The Egli model can be used for path loss prediction in the frequency range from 3MHz-

3GHz [24]. It is suitable for mobile systems in such bands and normally used when there

is LOS between one end with fixed antenna and another with mobile antenna.

2.3.3 Stanford University Interim (SUI) Model

The Standard University Interim model [25] is suitable for prediction of path loss in

many terrain environments, such as urban, suburban and rural environments. The

working frequency of this model is 2.3GHz band [24].

2.4 Terrain Effect

A WiMAX deployment may exist in a rural, suburban, urban or a metropolitan

environment. The performance of mobile WiMAX can vary significantly across the

different environments due to differences in mobility patterns and physical signal

propagation terrain. Traffic conditions, speed limits, and the density of constructions over

the propagation path of WiMAX signal are all the important factors impacting the

operation performance. Therefore, it is typical of environment specific investigation and

signal propagation models.

In suburban environment, the data rate WiMAX offered could reach 18Mbps and

decreases over the increasing of cell radius, as illustrated in [26]. In [27], the author

derived the path loss model for suburban and campus-like environments, with respective

11

path loss exponent 3.03 and 3.53. In urban environments, [22] shows an excellent

agreement between throughput results obtained via experiments and simulation.

Compared to amount of research on mobile WiMAX in suburban and urban area,

however, there is hardly any work on mobile WiMAX performance and propagation

model in a metropolitan environment. The above are essential before planning and

implementing mobile WiMAX-based applications.

12

Chapter 3

ParkNet Application Overview

In our experiment, we employ a real-time application system called ParkNet as the

context of this urban mobile WiMAX implementation. In the chapter, we describe this

ParkNet system, on which the entire mobile WiMAX experiment is built. Besides the

brief depiction of the objective of ParkNet, both the system-level architecture and the

individual onboard equipment unit are introduced. At the end of this chapter, we state the

delay-tolerant data transmission requirement of this application, as well as the

performance issues we try to address.

3.1 Objective and Solution Description

An increasing number of vehicles imposes a plethora of related problems, one significant

of which is the parking issue. In many places, especially in big urban areas, obtaining the

parking availability information has been becoming a knotty problem that concerns more

and more people and government departments. In comparison with complex indoor

parking garages and expensive fees in large parking lots, street parking has advantages

such as low price, more flexible time slot control, and quicker arrival and departure.

Therefore, in large densely populated metropolitan area, roadside parking is often treated

as the first choice, both economically and ecologically.

However, in these urban areas like New York City, Boston, and Los Angeles, congestion

and traffic delays are also due to parking. The drivers usually slow down to search for an

13

available street parking slot, which may affect the movement of traffic flow and even

hold up the traffic. As a consequence, redundant trips caused by parking space searching

equivalently increase the vehicle density on a certain area. According to a recent study

[30], researchers found that, over the course of a year, vehicles looking for parking in a

small district in Los Angeles created the equivalent of 38 trips around the world. Also,

these extra trips produced 730 tons of carbon dioxide and burnt 47,000 gallons of

gasoline [31].

One key factor playing a significant role in excess parking vehicle miles is a lack of

information about street parking occupancy. Due to lacking of such information, it is also

possible for municipal governors to make bad arrangements that lead to contradictions

between increasing demand on available parking slots and limited roadside parking slots

supply. Detailed parking information would allow travelers to make wiser decisions on

parking slot installation in advance, which reduces the related economic and ecological

impact.

ParkNet is designed and implemented to address the problem associated with parking

availability information. It is a low-cost mobile system comprising vehicles with onboard

sensors that collect roadside parking availability information while driving by. By

leveraging the mobility of vehicles that regularly comb an urban area, such as

government vehicles, taxicabs, and buses, this sensing platform would report the roadside

status periodically. Through effective wireless communication links, the parking

occupancy information is aggregated and processed at a central server, which builds a

real-time parking map and disseminates it to the clients who request the parking

information.

14

3.2 System Deployment

The ParkNet architecture adopts a mobile sensing approach with ultrasonic rangefinders

and GPS to detect road-side parking availability. It consists of a set of sensors-equipped

vehicles and a centralized parking estimation server with powerful processing capability.

Figure 3.1: ParkNet System Architecture

As illustrated in Figure 3.1, these vehicles report their sensor readings to the server while

driving by. After the server combines information obtained from one or multiple vehicles

on the same road segment, it processes these data by using probabilistic detection

algorithm and creates an estimate of road-side parking availability. Vehicles can report

their data over a cellular uplink and opportunistic use of Wifi or WiMAX connections is

also possible. Then, the parking availability information would be distributed to end user

systems or over the Internet, similarly to the various dissemination channels for road

traffic congestion information.

15

3.3 Onboard Platform Specification

In Figure 3.2, it depicts the basic onboard unit in ParkNet, which contains three principle

parts including an onboard mobile PC, an ultrasonic rangefinder, and a GPS device. Both

the GPS device and the ultrasonic sensor are connected to the mobile PC by USB cables.

With necessary CPU processing ability and hard disk capacity, the on-board PC controls

the data collecting and coming data streaming. Besides, a wireless adapter like WiFi

802.11 a/b/g PCI card or WiMAX adapter is also prerequisite, which is responsible for

the wireless network connection and data transmission.

Figure 3.2: ParkNet vehicle-unit (contains mobile PC connecting with a GPS device and an

ultrasonic sensor, which is mounted on the vehicle’s passenger side)

16

Sensor

A Maxbotic WR1 waterproof ultrasonic sensor is selected in ParkNet system. It is

magnet-mounted on the passenger side of the vehicle, which keeps passenger-side facing

and detects the parking status.

This sensor emits ultrasonic waves every 50 ms at a frequency of 42 KHz. It provides a

single range reading from 12 to 255 inches every cycle, which corresponds to the

distance to the nearest obstacle it detects. If no obstacle is detected, the sensor reading

shows the maximum range of 255.

GPS

In our onboard unit, we use a Garmin 18-5Hz GPS device with 12 parallel channels that

continuously tracks and uses up to 12 satellites. It keeps providing 5 fresh GPS location

readings per second. It also contains an enabled real-time WAAS correction of errors less

than 3 meters.

Both the ultrasonic sensor and the GPS device provide data in serial format, which is

accessed via an USB serial port on a computer. The sensor measurements at each vehicle

are time-stamped and location-stamped with inputs from a 5Hz GPS receiver, producing

the following sensor records:

<Kernel-time, sensor reading, latitude, longitude, speed>

Through wireless communication, onboard PCs transmit a collection of these

measurements to the parking estimation server where data from mobile sensors is

continuously aggregated and processed using probabilistic detection algorithms.

17

3.4 Delay-Tolerant Data Uploading

In a wireless network, the performance is measured in two fundamental ways: bandwidth

and latency. Accordingly, the selection of a wireless technology in an application is

related to the demand of its transferred data size and delay tolerance. On comparison with

other applications shown in Figure 3.3, as an environment sensing application system,

ParkNet has its own data collection and transfer requirements. Traffic-related delay

tolerance on data transfer and continuous small data streaming on data collection are the

notable data characteristics of ParkNet.

Figure 3.3: Comparisons among different urban wireless applications by transfer time-sensitivity

and required data size

Traffic-related delay tolerance: Figure 3.3 depicts the comparison of different

applications concerning the tolerance of data transfer delay. The real-time

communication among multi-users in a conference call requests a demanding requirement

on latency, which asks for a real-time data transfer or second-scale latency. On the

18

contrary, the final aim of large file downloading service is to finish the file downloading

successfully, which needs lowest time sensitivity. Comparatively speaking, vehicular

monitoring efforts have a moderate demand on data transfer delay. Due to the rapid

changing traffic condition, traffic monitoring systems require a relative higher demand on

data transfer and updating than ParkNet. In the contrast, ParkNet system allows a relative

higher updating delay-tolerance with respect to the slow variable status of parking spaces

in an urban area. In addition, the ability of delay tolerance depends on the change of

roadside parking status, which varies with the traffic flow. Thus, considering about the

cost saving, it is unnecessary and hardly for wireless signal to cover the entire vehicle

driving routes. The sensing cars only need dump the data through wireless network after

a certain time offline data collecting.

Continuous small size data streaming: Different from video stream or image files,

vehicular sensing systems usually have small files, as shown in Figure 3.3. In ParkNet,

the sensing vehicles keep collecting roadside status information continuously. As it is

mentioned above, ParkNet system collects 20 times per second measurement containing

sensor reading with corresponding temporal and spatial information, which results in that

the data stream is only approximate 1kbit/s to 2kbit/s. However, before the data is

dumped into the central server, the total onboard data keep increasing while the

measurement is accumulated offline, which may sometimes reach several mega bytes.

Therefore, ParkNet system requires a reliable wireless network supplying a certain

throughput, which could support possible mega byte scale data transfer during limited

transfer time caused by vehicular movement.

19

Consequently, because of the information delay tolerance of ParkNet system, the sensing

vehicles in this system are not required to connect to the network or upload data all the

running time. In other word, this characteristic of ParkNet reduces the demand on density

of wireless access point or base station, which only need guarantee the coverage of

wireless signal on a certain district with highest sensing vehicle density. On the other

hand, accumulated client measurement collected offline requires sufficient uplink

throughputs during the limited network connectivity duration due to the vehicle’s

movement.

In urban area, except for lacking of available authorized open WiFi access points, the

data transfer time of WiFi based vehicular network often negatively affected by frequent

handoff between APs and link build-up duration. In addition, intense density of high

building clusters also results in the large NLOS path loss and brings up the difficulties on

WiFi access point deployment. By comparison, longer effective coverage radius reduces

the number of WiMAX base stations, as well as the handoff frequency and buildup time

on link reconnection. Furthermore, the deployment of WiMAX base stations becomes

easier in metropolitan area. Comparing with 3G technology, the mobile WiMAX

performs a throughput benefit with data-centric traffic and spectral efficiency advantages,

as well as fewer base station needed on a desired data density. [18]

Therefore, mobile WiMAX meets the data uploading requirements of ParkNet system.

On the other hand, the analysis result of mobile WiMAX performance monitored on this

implementation could also provide further direction for other urban vehicular simulations

and wireless technique selection in real application.

20

Chapter 4

Methodology and System Configuration

The ParkNet system was modified to add network monitoring mechanisms that are used

to evaluate the performance of mobile WiMAX in metropolitan conditions. The system,

including multiple cars and a WiMAX base station, was deployed in Brooklyn, New

York City. In this chapter we first present the goals and requirements of our work, and

then describe the system architecture and also detail implementation of the specific

mechanisms that were used for network monitoring.

4.1 Experiment Goals

Through the implementation of ParkNet system over WiMAX in Brooklyn, NYC, we are

attempting to answer the following questions:

What is the propagation characteristics of mobile WiMAX in a metropolitan

environment

Specifically, the principal propagation characteristics that we focus on include the

connection performance between the base station and mobile client nodes, the

coverage of mobile WiMAX signal, and the distribution of received signal

strength.

What is the propagation model of mobile WiMAX in a metropolitan environment

Due to the special terrain characteristics in a metropolitan environment, like dense

building clusters, we want to derive a path loss model for a metropolitan

21

environment like that in New York City. The model can be used as a reference for

future WiMAX experiments and also to evaluate any vehicular applications that

use WiMAX.

How is the performance of data dissemination in ParkNet system over mobile

WiMAX

The propagation characteristics of WiMAX in relation to application performance

in the ParkNet system, for example, the impact of intermittent connectivity on

data transfer over WiMAX, needs evaluation. Based on the evaluation, we may

figure out what file transfer strategy works well over mobile WiMAX, and even

propose a better file transfer mechanism for similar applications.

4.2 Experiment Implementation

4.2.1 Experiment Description and Configuration

We implemented and deployed the ParkNet system on vehicles which collected parking

data in Brooklyn area, New York City. Brooklyn is a metropolitan district with its typical

characteristics including high density of building clusters, rigid speed limits from 25 mph

to 35mph, and traffic congestion. In addition the metropolis sees a large demand for

parking and hence requires street parking availability information. The wireless

propagation environment, city like traffic mobility together with street side parking make

Brooklyn an ideal choice for evaluating the performance of ParkNet over mobile

WiMAX.

The experiments were carried out at different times of a day and over multiple days and

were thus able to capture the varied traffic conditions encountered in Brooklyn area, New

22

York City. During our performance measurement campaign, 8 cars each with an onboard

Samsung NC10 notebook connected with GPS devices and sensors drove along different

routes in Brooklyn. 10 routes were selected for our experiments. The vehicles follow all

traffic rules and are always a part of the flow of traffic along the routes, thus emulating

the drive of a typical vehicle in the city. Due to different experimental routes and

different traffic conditions in New York City, each vehicle spent 30 to 180 minutes to

repeat its routes several times so as to populate the experimental coverage map.

Figure 4.1: Experiment Routes in Brooklyn

Figure 4.1 shows the 10 test routes selected in our experiment, which are represented in

different colors. Some of the routes are close to the WiMAX base station and others are

relatively farther. In total, the experiments covered about 3.6 square kilometers of area;

23

and the sum of running distance for all the experimental vehicles was more than 50

kilometers.

Meanwhile, the WiMAX link status and vehicular movement information along with a

timestamp were measured and stored continuously. The measurements include WiMAX

connectivity, downlink received signal strength indicator (RSSI), working frequency,

vehicle speed, and location information like latitude and longitude. In addition, each

measurement has a unique system timestamp for synchronization across the various

components in the measurement setup.

4.2.2 System Deployment

All the equipment in this system including base station and client nodes are commercial

products. The figure below depicts the high level system architecture of the experiment

setup used to carry out measurements in Brooklyn. To the basic ParkNet system, we add

wireless link detection to the experiment. Besides the basic ParkNet measurements, the

vehicles also capture real-time WiMAX status information. The information together

with timestamps helps monitor the availability of the mobile WiMAX connection and the

ongoing data transfer in real-time. The collected information is used to analyze the

performance of WiMAX during the experiment.

24

Figure 4.2: Schematic diagram of overall system deployment

These experimental measurements were captured using an onboard PC connected to the

WiMAX base-station. Cars upload the GPS and sensor data from their ParkNet

application together with WiMAX status information over their wireless connection to

the WiMAX base station. At the WiMAX end the received measurements are separated

and sent to their corresponding servers for further processing.

4.2.3 System Specification

Client Side Specification

The basic client part unit onboard is the same as that in the ParkNet system. Other than

the equipment like GPS device and ultrasonic Sensor, the onboard laptop PC has a 1.6

GHz Intel Atom N270 Processor with 1GB memory and 160GB hard disk space. The

10.2” size greatly enhances its mobility and portability (see Figure 4.3 left). The

experiment data was captured using a laptop computer with Intel mobile WiMAX Link

25

5150 adapter. This Intel mobile WiMAX adapter is an IEEE 802.16e wireless network

adapter that operates in the 2.5 GHz spectrum for mobile WiMAX data communication.

It provides flexible and convenient connectivity, and delivers data rates up to 13 Mbps

downlink and 3 Mbps uplink.

Figure 4.3: Onboard Mobile Laptop (left); and WiMAX Base Station (right)

Base Station Specification

The base station (BS) used in our system is the NEC PasoWings Mobile WiMAX base

station (see Figure 4.3 right), which is operation at 2.59GHz and designed for end-to-end

service delivery. It was mounted on the roof of a building that is about 15 meters tall. The

building is located at the coordinate of (40.694491, -73.986) and half-embraced by other

taller constructions, but not in the direction towards which the base station antenna's

beam is pointed. During the experiment, the transmit power is configured to 32dBm, with

22dBi transmission antenna gain. The directional transmitting antenna points to the south

and has a beam width of 120 degrees, as shown in Figure 4.4.

26

Figure 4.4: Satellite Picture of WiMAX Base Station Deployment

4.3 WiMAX Status Monitor and Data Uploading Mechanism

4.3.1 OML Framework

In order to monitor the status of WiMAX signal and connection, we introduce the OML

framework in our system, which runs parallel to our ParkNet implementation. OML is a

flexible software framework for measurement collection from any source, such as

statistics about network traffic flows, CPU and memory usage, or multiple readings from

different sensors.

Generally, OML has two main components, the OML client that is responsible to perform

the measurement and the OML server that stores the measurements inside a database. In

our experiment, the WiMAX status measurements are collected locally at a vehicle,

which is the client side of the OML measurement setup, and stored in a local file.

Meanwhile, the OML client at the vehicle keeps detecting the network connectivity and

27

starts to send data over a TCP connection as soon as the connectivity is obtained. When

the connectivity is down, the client side pauses data sending and waits for the next

connection status. After the measurements successfully reach the OML server, they will

be stored in OML database in the chosen format. The data collecting frequency is one

sample per second.

4.3.2 Micro-element File Uploading Mechanism

The roadside parking information collected by the ParkNet system is not time critical.

However, it needs to be received by the parking server within a reasonably short period

of time. Else, the parking availability information may be too stale to be useful. The

vehicles in the system that are used to collect information about the availability of

parking lots may lose WiMAX network connectivity intermittently. If connectivity is lost

during a parking information file transfer the portion of file transferred will have to be

retransmitted on re-connection to the WiMAX network, thus wasting transfer time and

reducing transfer efficiency. This calls for an effective file transfer mechanism that is

reliable and uses the connection efficiently in between possible breaks in network

connectivity. The file transfer mechanism wants to transmit as many complete files as

possible while WiMAX coverage is available.

To achieve this aim, we introduce the micro-element file transfer mechanism here. Under

the mechanism, we first split the large file to be transferred into fixed size small pieces

with sequential index numbers, each of size 20KB. File uploading process keeps

attempting to run SCP operation all the time to detect if the network is built-up. If the

connectivity is set up, the mechanism starts to send small files one by one sequentially

while keeping track of the successfully uploaded files. When the network connection

28

goes down during the file transfer, the SCP process aborts the current file transfer.

However, due to the small size of this file, aborted transfer only adds a tiny additional

delay to the total transfer time, especially when compared to a strategy where a whole file

transfer is attempted at once. Compared with other existing resuming uploading

mechanisms, our design guarantee the sequential file transfer, which means the earlier

collections would be transferred earlier. In this way, these measurements could be

processed first on the server side. In summary, this mechanism enhances the file transfer

efficiency over an intermittently connected wireless communication link.

29

Chapter 5

Experimental Results and Analysis

In this chapter, we present experimental measurements from a real WiMAX based

application deployment. The performance of the wireless link is analyzed and path loss

model for a metropolitan environment is derived from the measurements.

We conduct an empirical evaluation of a WiMAX-based vehicular system, in a

metropolitan environment, using data collected from experiments that involve an actual

WiMAX installation and the ParkNet vehicular application. All performance analysis,

proposed models and conclusions are derived from a fairly extensive set of measurements

that were collected over varied metropolitan traffic conditions over multiple days. This

gives us the confidence that the insights from the study apply to a typical metropolitan

area like New York City and are not restricted to specific experiment scenarios.

5.1 Metropolitan WiMAX Propagation Characteristics Study

5.1.1 Coverage Study

RSSI depicts the received signal strength on client side. In the WiMAX system, RSSI is

the relative received signal strength in a wireless environment, and has arbitrary units.

RSSI is an indication of the power level being received by the antenna. Therefore, the

higher the RSSI value is (or less negative in our case), the stronger the signal. In our

experiment, we measure the RSSI value every second on the client and store them with

current timestamp and GPS coordinates. On the basis of these GPS coordinates and

30

related downlink RSSI values, we can infer the WiMAX coverage, which reflects the

communication range of the base station, and also understand the distribution of the RSSI

values.

(a) (b)

Figure 5.1: (a) Experiment areas vs. Coverage of WiMAX signal. (b) The experiment data

distribution vs. received RSSI data distribution.

Figure 5.1(a) shows the experiment information of our running system on Google Maps.

As is shown, the experimental vehicles responsible for data covered the light orange area.

The dark red area near base station indicates the unique region over which WiMAX RSSI

could be sensed and measured. In the rest of the area that the vehicles covered, no link

related information was obtainable by them. Only when the RSSI value is greater than -

98dBm, the Intel WiMAX adapter embedded in client nodes could detect the signal,

connect to the WiMAX base station, and then transfer measured RSSI values. Figure

5.1(b) represents the real RSSI measurements accumulated during the experiment.

When compared with the entire 2000m * 1800m experiment area, WiMAX signal

coverage only occupies a small area of 500m * 1km around WiMAX base station that

31

has a directional antenna with 22dBi gain. Owing to the effect of tall building clusters in

urban experiment area, the Non Line-Of-Sight (NLOS) propagation results in the shorter

coverage range than the general coverage results on mobile WiMAX indicated in [19]

[22]. In the next subsection, we will discuss the statistic of the obtained RSSI

measurements and quantitively analyze the results.

5.1.2 RSSI Statistic Information and Connection Study

We can calculate the distance between the current measurement location and the base

station through the coordinate information provided by GPS data, and then map the RSSI

measurement with corresponding distance. This could be obtained by using the formula

below:

, (3.1)

where the Latitude’ and Longitude’ are the latitude and longitude that have been

translated to UTM standard with distance measured in meters, the subscripts represent the

base station and measurement location, and H0 is the height of the building height of the

building on which the base station is mounted, which is 16 meters in our case. According

to formula (3.1), we may plot the distribution of RSSI measurements against

corresponding distance values, as shown in Figure 5.2.

32

(a) (b)

Figure 5.2: (a) Downlink RSSI data over distance to Base Station.

(b) The RSSI data distribution over corresponding coordinates.

Figure 5.2 (a) shows the downlink RSSI data over distance to WiMAX base station, the

range of which varies from -40 dBm to -98 dBm. Figure 5.2 (b) depicts the area where

the RSSI data is distributed and could be received by client nodes in the car. When the

signal strength is weaker than the threshold -98 dBm, its RSSI value cannot be detected

and measured. At this detection sensitivity level, the WiMAX coverage extended up to

590 meters, which is smaller than kilometer or so of propagation distance revealed in

previous research results [2] and [22]. In [22], researchers from Norway ran the

vehicular experiment over mobile WiMAX on 3.5GHz, with 28dBm transmitter power

and 14dBi antenna gain. The result showed that the coverage could reach 1000m in urban

area and 2km in suburban area. Compared with these research outcomes, reduction in

coverage distance mainly results from the difference in the transmission environment,

which in our case is a metropolitan area. Terrain characteristic in New York City, such as

dense constructions, negatively impacts WiMAX propagation and has a significant effect

on implementation of applications using mobile WiMAX. Also, some outliers in the red

33

rectangle don’t match the trend of measurements distribution. By projecting these

measurements on the map, we may find that they are collected under the Line of Sight

condition, where has the higher signal strength than those from other space with same

distance.

Minimum

Connection Time

Maximum

Connection Time

Mean Connection

Time

Profile #1 79.2 s 1145.1 s 402.5 s

Profile #2 128.6 s 276.7 s 2027 s

Profile #3 36.9 s 1527.9 s 389.5 s

Profile #4 151.6 s 1193.3 s 522.0 s

Profile #5 156.8 s 410.3 s 2283.5 s

Profile #6 310.7 s 475.2 s 393.0 s

Profile #7 3.83.9 s 3.83.9 s 3.83.9 s

Profile #8 529.5 s 529.5 s 529.5 s

Profile #9 207.7 s 292.8 s 250.2 s

Profile #10 1183.2 s 1183.2 s 1183.2 s

Total 36.9 s 1527.9 s 402.6 s

Table 5.1: Experimental Vehicles’ Profiles on Connection Duration

34

Table 5.1 shows the duration of each connection, the minimum of which is only 36.9

seconds. These duration values could help us plan the WiMAX base station deployment

in future implementation.

5.1.3 Metropolitan Terrain Characteristics and Effects

Vehicle Mobility:

Because of the inherent wide coverage and mobility support of mobile WiMAX, it

minimizes the rate of handover and data loss. The effect is more severe in suburban area

rather than urban area, owing to the higher speed limit and lower traffic density. In

Brooklyn area at NYC, frequent traffic light, speed limits, and congested traffic flow

reduces vehicular mobility and hence the Doppler spread.

Minimum Speed Maximum Speed Mean Speed

Profile #1 0 m/s 10.29 m/s 2.03 m/s

Profile #2 0 m/s 9.09 m/s 1.54 m/s

Profile #3 0 m/s 12.21 m/s 1.39 m/s

Profile #4 0 m/s 11.89 m/s 1.35 m/s

Profile #5 0 m/s 13.78 m/s 1.87 m/s

Profile #6 0 m/s 13.06 m/s 1.28 m/s

Profile #7 0 m/s 13.27 m/s 1.67 m/s

Profile #8 0 m/s 10.72 m/s 1.84 m/s

Profile #9 0 m/s 13.24 m/s 2.66 m/s

Profile #10 0 m/s 9.17 m/s 1.07 m/s

Total 0 m/s 13.78 m/s 1.67 m/s

Table 5.2: Experimental Vehicles’ Profiles on Velocity Information

35

Table 5.2 shows the car profiles of testing vehicles. As it can be seen, during the entire

experiment, none of the vehicles reach 14 meter/second and the mean speed is limited to

1.67 meter/second, which is less than that in a usual urban area [28].

Terrain Effects:

Figure 5.3 (a) below depicts the heat map of RSSI values, which represents all the RSSI

values obtained through interpolation over the experiment area by using different colors.

The received signal strength decreases gradually from warm colors to the colder ones.

(a) (b)

Figure 5.3: (a) The RSSI heat-map. (b) Building distribution inside RSSI coverage area

The RSSI values at same levels are contoured with dash lines, showing the general RSSI

distribution over the selected environment. As we can see, the RSSI attenuation gradually

becomes larger with the increasing distance from base station; but the variance is

relatively slower in vertical and horizontal direction. In contract, on the left flank area,

dense contour lines represent the faster fading that shortens the coverage radius. This

phenomenon could be explained through the comparison with Figure 5.3 (b), which

36

displays the distribution of constructions projected on Google Map, over the same area,

in Figure 5.3 (a). The dark black regions represent the buildings and the light blue lines

represent the testing routes where RSSI values are measured. The measurements with

different colors depict various RSSI levels. In our experiment configuration, the base

station is placed on the top of a 16 meters building, which is surrounded by nearby tall

buildings. Hence, when the vehicular client nodes run following the blue routes, the paths

of wave propagation are blocked by the construction clusters at most of time, resulting

into the NLOS propagation environment that greatly lowers the effective received power

and reduces the transmission distance. As a result of being impacted by continuous high

obstacles, signal strength sharply attenuates, especially on the lower left area. However,

since the area on the top left is next to the on which the base station is installed, the

measurements collected in horizontal direction experience Line-Of-Sight (LOS)

propagation conditions and perform better on transmission range and signal fading rate.

In addition, the directional antenna radiates greater power in vertical downward direction,

allowing for increased performance on transmit and receive on that area, as shown by the

heat map.

Therefore, from the analysis above, we can conclude that the metropolitan terrain

environment and geographical position characteristics introduce a mixing propagation

condition including both NLOS and LOS, the former being predominant.

5.2 Path Loss Model for Mobile WiMAX in Metropolitan Environments

In wireless communication systems, information is transmitted between the transmitter

and the receiver by electromagnetic waves. As electromagnetic waves propagate through

37

space, its continuous interaction with environment causes the path loss, which is the

reduction in its power density.

We want to derive a path loss model for a metropolitan environment like that in New

York City. The model can be used as a reference for future WiMAX experiments and also

to evaluate any vehicular applications that use WiMAX.

During most of the experiment time the vehicles observe NLOS conditions. The vehicles

observe LOS communication only along a few streets in the vicinity of the WiMAX base

station. Hence, the path loss model under NLOS condition has a crucial value on urban

network planning. We delete the RSSI collections from LOS streets and only consider the

measurements under NLOS conditions to obtain the path loss model. The pruned

measurements are shown in Figure 5.4.

Figure 5.4: NLOS downlink RSSI data over distance to Base Station.

We aim at getting a path loss model of the form:

38

PL(dB) = A+Blog10(d) (1)

where A is PLd0, which is the reference path loss value at d0=1 meter distance; d is the

distance from the transmitter to receiver; and B is 10n; n is the path loss exponent, which

indicates the rate of propagation path loss with respect to distance. When the environment

propagation characteristic is close to free space propagation or has fewer clusters, the

path loss exponent value is about 2. In urban environment, the path loss exponent is

between 2 and 4. Therefore, the path loss at a given location with respect to the reference

distance d0, the equation (1) could be expressed as:

PL(dB) = PLd0 + 10nlog10(d/d0) (2)

When a graph of path loss against logarithm of distance is plotted, the path loss exponent

n could be determined by calculating the slope of the line that corresponds to the linear

least squares fit that minimizes error in prediction by the fit. The path loss value at

reference distance d0 can be obtained as well.

To derive the path loss value at a given location with respect to the path loss at a

reference distance, we need make use of the relationship between path loss values and

existing measured plentiful of RSSI data with the same location. A path loss model can

be derived from link budget equation of communication system:

RSSI = Tx + Gt – PL + Gr (3)

where the left side of equation is the received signal strength (dBm); on the right side of

equation, Tx is the transmitter power (dBm); Gt is the transmitter antenna gain (dBi), Gr

is the receiver antenna gain (dBi), and PL is the path loss (dB) in communication

environment. Thus, from equation (3), the path loss mode equation could be written as:

39

PL = Tx + Gt + Gr - RSSI (4)

Figure 5.5: RSSI measurements against the logarithm of the distance

The RSSI related to the distance between WiMAX Base Station and the mobile client

nodes provides valuable information related to the power loss in this WiMAX

communication system. Figure 5.5 shows the RSSI measurements versus the logarithm of

the distance between Base Station and clients, excluding abnormal measurements. A

straight line should be drawn through the points in this figure so as to confirm the path

loss equation (4). We use Least Square (LS) regression analysis to determine the slope

and other parameters of the path loss line, which is given by:

RSSI = -7.84-30.08*log10(d) (5)

Where d is the distance between Base Station and client nodes, and the RSSI is denoted

in dBm.

40

Figure 5.6: Equation (5) with RSSI measurements against distance.

Also, we can calculate the prediction error between collected RSSI and the result on

equation (5) by using:

Error = RSSI measured – RSSI (6)

After using the statistical analysis approach of distribution fitting, we can get a normal

distribution curve fitting this error data. The normal distribution N (0, 57.1) is a good fit.

Its mean value is 0, and its variance is 57.1. In the figures 5.7 and 5.8 we show the

probability distribution function and the cumulative distribution function with

corresponding error data statistic quintiles.

41

Figure 5.7: Probability density of model error vs. Probability density function curve of fitting

Normal distribution

Figure 5.8: Cumulative probability of model error vs. Cumulative distribution function curve of

fitting Normal distribution

42

In our system, the transmission power is 32 dBm and the transmitter antenna gain is 22

dBi. According to path loss model equation (2), we may put the reference path loss value

at 1 meter point to form the full path loss model:

PL = 61.84 + 30.08*log10(d) (7)

Where d is the distance between base station and client node, and the path loss is denoted

dB. Also, the path loss exponent is obtained to be 3.008.

Figure 5.9: NLOS Path Loss Model (upper one),

vs. Path Loss Model on Free Space (lower one).

Figure 5.9 indicates the comparison between NLOS path loss model that we get from

experiment results and the one assuming free space. The path loss exponent in urban area

reflects the negative effects of high density tall constructions clusters between WiMAX

base station and onboard client receivers.

43

As the above analysis procedure shows, the derived empirical NLOS Path Loss Model of

metropolitan mobile WiMAX is presented on equation (7), whose error follows the

normal distribution N (0, 57.1).

5.3 Data Transfer Performance

In this mobile WiMAX implementation, when employing the micro-element file transfer

approach, the client node makes all the effort to transfer the small fixed-size file during

the network connectivity. After the connection times out or the TCP link breaks, it aborts

the current uploading assignment and keeps attempting to re-connect to the WiMAX

network until next successful connectivity is built up; and then it continues to upload the

remaining files. During our entire experiment, we successfully transferred 608 files each

of size 20KB.

Figure 5.10: Transferred File Numbers/Number of RSSI vs. RSSI Values

44

Figure 5.10 above illustrates the number of transferred files grouped based on received

RSSI values, comparing with corresponding number of RSSI measurements. As we can

see, the red columns represent the number of RSSI measurements falling into associated

RSSI value range; and the blue ones show the number of successfully transferred files

under a certain RSSI value. According to the figure, we find that most of successful file

transfers happened when the RSSI was above -75dbm, and a few files are transferred

between -79dbm and -75dbm. It is inferred that SCP operation would be able to build the

TCP connection when the signal strength is larger than a certain threshold value like -79

dBm.

Figure 5.11: File Transfer Area vs. RSSI Coverage Area

45

Figure 5.11 compares the RSSI coverage area (red) with file transferring area (black).

The maximum distance that file transfer succeeded reaches 396 m.

Time #1 Time #2 Time #3 Time #4 Time #5

time file # time file # time file # time file # time file #

Profile #1 133 s 6 7 s 1 109 s 26 11 s 5 296 s 61

Profile #2 163 s 7 257 s 111

Profile #3 128 s 5 24 s 5 37 s 7 1391s 115

Profile #4 43 s 2 127 s 39 1027s 66

Profile #5 161 s 7 116 s 38 150 s 55

Profile #6 80 s 4 297 s 50

Profile #7 309 s 148

Profile #8 244 s 94

Profile #9 214 s 118 113 s 9

Profile #10 746 s 52 145 s 22

Table 5.3: File Transfer Duration and Number

Although micro-element file transfer mechanism effectively avoids possible frequent

aborting of file uploading during large file transfers when using SCP, due to break in

network connectivity, it introduces a large amount of overhead caused by frequent

connection attempts and TCP handshake. Every time when an experiment vehicle passes

through WiMAX coverage area, it makes best effort to connect to the network and

transfer a certain number of files during that time, the results of which are depicted in

Table 5.3. As the table shows, the efficiency of file transfer is very low, which has the

46

average throughput of only 70kbps and maximum value of 91kbps. In other words, only

0.43 file is transferred per second.

Figure 5.12: Comparison Experiment over TCP

After doing a comparison experiments, we tested the data rate performance in different

transfer mechanisms using SCP operation. As is shown in Figure 5.12, the blue curve

represents the large file transfer over one SCP operation. Then, we split the file into small

pieces with 20KB size each to get the red curve. The black curve used the same

mechanism in Brooklyn test. We find that the data rate of black curve keeps at 70Kbps all

the time. This result is produced due to the dynamic behavior of TCP. It takes some

amount of time to increase sending rate to the maximum bandwidth. Hence, when the file

size is small, the file transfer is already completed before TCP reaching the maximum

link throughput. Although blue curve shows the better maximum link throughput, it costs

47

more time on file transfer and brings into higher risk of transfer abortion. Therefore, we

would like to achieve a tradeoff between the high throughput and risk of transfer abortion

caused by increasing transfer time. To obtain such tradeoff, a fast reaching throughput

with small pieces is preferable in the future design. For example, UDP is a good choice

instead of TCP, when the reliable transfer mechanism and congestion control could be

achieved on the upper layer.

48

Chapter 6

Conclusion and Future Work

In this work, we describe the implementation of ParkNet system, a WiMAX-based

vehicular application in Brooklyn area, New York City. Also, we analyze and evaluate

the physical performance of mobile WiMAX in the metropolitan environment using a

large amount of measurements collected over the course of extensive experimentation

using a real world WiMAX deployment and multiple cars.

Through the analysis of the terrain characteristics in Brooklyn, we find that the sensing

vehicles in ParkNet system predominantly observe Non Line of Sight (NLOS)

propagation conditions, due to the dense tall building constructions between the base

station and client receivers. In addition, the low speed limit and congested traffic

conditions in the experimental area reduce vehicular mobility; the speed never exceeds 14

meter/second; and the mean speed is constrained to 1.67 meter/second.

From the downlink RSSI measurements we collected, the range of which varies from -40

dBm to -98 dBm, we observed that WiMAX coverage extends up to 590 meters under

this terrain environment, which is smaller than kilometer-scale propagation distance

observed in other propagation environments. We also derived the analytical path loss

model describing the WiMAX signal propagation in the metropolitan environment. As

the analysis in Chapter 5 shows, the derived empirical NLOS Path Loss Model of

metropolitan mobile WiMAX is described by a complementary error CDF that has a

normal distribution ~ N(0,57.1). The path loss exponent is obtained to be 3.008.

49

During our entire experiment, we successfully transferred 608 files of which each was

20KB large. Most of these transfers happened when the RSSI was greater than -75dBm,

and a few were done when the RSSI was in between -79dBm and -75dBm. It is inferred

that secure copy process (SCP operation) would be able to build the TCP connection

when the signal strength is larger than a certain threshold value of about -80 dBm. File

transfer is hard to complete under an RSSI of -79dBm.

We analyze the effect of micro-element file transfer mechanism, and show that a file is

uploaded before TCP is able to reach maximum link throughput. Therefore, in spite of the

avoidance of temporal cost caused by data transfer abortion, the uplink throughput of

mobile WiMAX in this application is lower than most of previous work. Thus, in the

future, we plan to modify the file transfer mechanism to balance the data size and risk of

file transfer abortion. Also, we may use UDP to replace TCP in ParkNet data

dissemination process, and design reliable file transfer mechanism on upper layer. Last

but not the least, in addition to throughput, the latency performance of data transfer can

be measured and analyzed.

50

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