Providing Locality Information to Smart Sensor Networks

Tim Mead

Supervisor: Charles Greif

Overview Intro / Aim Smart Sensor

Networks Ptolemy Smart Sensor

Hardware The Problem

Multidimensional Scaling

Findings Outcomes Future Work Conclusions

Introduction and Aim

Project involves the task of calculating the relative locations of nodes in a Smart Sensor network, based on detected inter-node distances

A full simulation is devised, not just a specific implementation

Smart Sensor Networks What is a Smart Sensor network?

An array of small, self-powered, processors with the ability to acquire data from a number of sources, as well as communicate with other nodes

Roots lie with the early ’90s Intel/Berkeley “Smart Dust” project

Recent research focused on efficiency and miniaturization

Research involves very recent technologies such as MEMS

Deputy Dust!

Smart Sensor Networks (con’t)

What can they be used for? The monitoring of data over a

distributed space: inside a home, over a factory, crops in a field

29-Palms Experiment. Six nodes dropped from a UAV, which were used to detect ground vehicles. UAV then flew past the nodes and queried them for their findings.

Ptolemy

What is Ptolemy? A modeling and simulation suite, covering a number

of domains, including wireless

Why Model? Modeling allows for retargeting, reuse and formal

validation and verification

Why Simulate? Simulation reduces development time, by allowing

developers to simulate the entire system, without having to construct prototypes

The need for Verified Software Why V&V?

Systems are ‘turn key’ – once they’re out in the field, they can’t easily be collected and reprogrammed.

Design and Verification of Embedded Systems

Thomas A. HenzingerUniversity of California, Berkeley

Collaboration between two research groups at Berkeley

Ptolemy in Use

Ptolemy and TinyOS Ptolemy was designed to support TinyOS What is TinyOS?

A lightweight, event-driven real-time OS Manages ad-hoc wireless communication Designed for smart sensors

TinyOS is implemented on the sensors using gcc/Atmel cross-compiler with nesC language (extension of C)

Smart Sensor Hardware Processor:

Atmel processor with Flash memory Wireless RF interface:

310 / 916 MHz Sensor acquisition hardware:

Light, temperature, pressure, acceleration sensors

Real-time operating system: TinyOS

For location detection: Audio receiver / transmitter

Commercial Smart Sensor Hardware: Crossbow

Crossbow ‘mote’

Audio sensor board

PC interface and programmer board

Local Developments in SSN’s

Dr Peter Corke, with Qld CSIRO, has developed the ‘Flecks’

Similar to overseas units Runs TinyOS Uses Atmel processor

Cheaper, more readilyavailable

The Problem: Positional Determination

Why do nodes want to know their position?

Much more powerful processing of acquired data by being able to correlate against position

Opens the door to smarter network routing algorithms, saving power and reducing errors

How can the position be determined? Systems such as GPS are too cost prohibitive Using a combination of high- and low-speed propagating

signals allows the inter-node distances to be determined

Positional Determination (con’t)

What to with do with distances? Inter-node distances can be transformed into relative

positions

How to transform? Conventional methods utilise triangulation-like

systems, but limit themselves to 3 pieces of information

Multidimensional scaling utilises a greater body of information, to provide more reliable results, particularly when data is missing or corrupt

Multidimensional Scaling

Began in the area of psychology, for grouping and correlation

Later adapted to statistics, for reducing dimensionality

Iteratively, works on minimising a loss function:

2/1

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2

1

2

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n

ijij

n

i

n

ijijij

d

d

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Multidimensional Scaling (con’t)

Findings Ptolemy’s “building block” system

inadequate for complex decision-making and iterative logic

The wireless building blocks are well designed and extensible Allows for basic terrain simulation to be

easily added Proper simulation of audio effects, such as

reflection and diffraction requires complex FE methods

Findings (con’t)

Multidimensional scaling: Requires a minimum of 3 known positions to

determine 2D positions Holds up well with missing distance data Handles spurious data

with appropriate weights

Outcomes

Ptolemy simulation and model of nodes in an environment Simulates network communication Produces a matrix

of inter-nodedistances andweights

Outcomes (con’t)

javaMDS Basic metric,

weighted MDScalculator with asimple graphicaloutput

Future Work Immediate future:

Improvement of the Ptolemy model and synthesis of downloadable code (for Atmel)

Improved Data Acquisition More Efficient Communication

If we know where surround nodes are, we know how far away they are, so we can attenuate the power output accordingly.

More Efficient Packet Sending If we know where nodes are, any how far they can

communicate, we can determine the optimal communication pathway between two nodes.

Long-term future: Integration of T. Henzinger’s Verification and

Validation tools

Conclusions A low-cost system for providing locality information

to Smart Sensor networks was devised Ptolemy: A visual programming environment in

which solutions for Smart Sensor networks can be developed

Simulation can be performed prior to production of code

V&V will be able to be performed in the same suite Extensions to all sorts of areas, such as Peter

Corke’s Fleck nodes

Positional Determination (con’t)

Calculation of node position using triangulation utilises only three distances

Positional Determination (con’t)

Calculation of node position using multidimensional scaling utilises all available data