Sensor Positioning in Wireless Ad-hoc Sensor Networks Using Multidimensional Scaling Xiang Ji and Hongyuan Zha Dept. of Computer Science and Engineering,

Sensor Positioning in Wireless Ad-hoc Sensor Networks Using Multidimensional Scaling

Xiang Ji and Hongyuan ZhaDept. of Computer Science and Engineering, The Pennsylvania State University

INFOCOM 2004

Presenter: Sheng-Shih Wang

March 15, 2004

Outline

Introduction Previous Works Challenges Calculating Relative Positions With

Multidimensional Scaling Aligning Relative Positions Distributed Sensor Positioning Methods Experimental Results Conclusion

Introduction

Why the physical positions of sensors is important For object detection or tracking For communication protocol establishment

Previous Works

Global Positioning System (GPS) High cost

Other Methods Class 1

Improve the accuracy of distance estimation with different signal techniques

RSSI, ToA, TDoA, AoA

Previous Works (cont.)

Class 2 Relies on a large amount of sensor nodes with posit

ions known (i.e., beacon or anchor node) densely distributed in a sensor network

Class 3 Employ distance vector exchange to find the distan

ces from the non-anchor nodes to the anchor nodes Class 4

Locally calculate maps of adjacent nodes with trilateration or multilateration

Challenges

• Estimated distances of AC and BC are increased

Challenges (cont.)

• Estimated distances of AC and BC are increased

Challenges (cont.)

• In real world (irregular radio pattern)In real world (irregular radio pattern)The radio range of a sensoris different at different directions

Challenges (cont.)

• The complexity of the terrainleads to different signalattenuation factors andradio ranges

Calculating Relative Positions With Multidimensional Scaling

The Multidimensional Scaling (MDS) The analysis of dissimilarity of data on a set of

objects Discover the spatial structures in the data

Advantages for position estimation MDS always generates relatively high

accurate position estimation even based on limited and error-prone distance information

The Classical MDS

The classical MDS [3]

(If all pairwise distances of sensors are collected)

The Classical MDS (cont.)

The Iterative MDS

The iterative MDS

The Iterative MDS (cont.)

Known position: A,B,C,D

Unknown position: E,F

Aligning Relative Positions

To compute the physical positions of sensors

Align the relative positions to physical positions with the aid of sensors with positions known

Includes shift, rotation, and reflection of coordinates

Distributed Sensor Positioning Methods

Employ distance measurement model of RSSI

The power of the radio signal attenuates exponentially with distance

Receiver can estimate the distance to the sender by measuring the attenuation of radio signal strength

Distributed Position Estimation with Anchor Sensors

Adjacent areaAdjacent area: the sensor position are estimated with MDS

The average radio range is estimated with the hop count and physical distance

Distributed Position Estimation with Anchor Sensors (cont.)

A: Starting Anchor

D, H: Ending Anchors

Distributed Position Estimation with Anchor Sensors (cont.)

Local map

On Demand Distributed Position EstimationStudy case: one sensor’s position is needed to be estimated

Experimental Results --- Simulation Model

Simulator: Matlab 400 nodes Randomly placed 100-by-100 square region

Experimental Results --- Two Strategies

The 1st strategy The average radio range is 10

The 2nd strategy The region is equally divided into four

non-overlapped square regions Sensors have different radio ranges

The average radio ranges in different small square regions are 7, 8.5, 10, and 11.5

Experimental Results --- Evaluation Criteria

n: the total number of sensors

m: the number of anchors

A low error means good performance of the evaluated method

Physical positionsof sensors in

an adjacent area

Recovered relativepositions

(classical MDS)

After alignment(classical MDS)

Experimental Results --- Classical MDS

The increase of error rates different conditions is always slower than the increase of distance measurement error The classical MDS is robust in tolerating measurement errors of sensor distance

Experimental Results --- Iterative MDS

Error rates of sensor positioning increase when the percentage of sensorpairwise distances collected and the number of iteration increase

Experimental Results --- Iterative MDS (cont.)

When the collected pairwise distance and the number of iteration are fixed,the error rates of sensor positioning increase with the increase of distance measurement error

Experimental Results --- Results (cont.)

Errors when applying the distributed positioning method with anchor sensors to all sensorsin a square region with an uniform radio range and different distance measurement errors

The distance measurementerror rate

Experimental Results --- Results (cont.)

Errors when applying the distributed positioning method with anchor sensors to all sensorsin a square region with different signal attenuation factors (radio ranges)

Experimental Results --- Results (cont.)

Errors when applying the distributed on demand positioning method to one sensorin two square region with uniform and different signal attenuation factors, respectively

8 anchors (5% of total number of sensors)

Conclusion

The MDS-based positioning technique Compute relative positions of sensors The distributed sensor positioning method

Get the accurate position estimation Reduce error cumulation

The on demand position estimation method For one or several adjacent sensors positioning

Can work in networks with anisotropic topology and complex terrain

Advantage Effective, Efficient