Download - Multi-hop-based Monte Carlo Localization for Mobile Sensor Networks Jiyoung Yi, Sungwon Yang and Hojung Cha Department of Computer Science, Yonsei University,

Multi-hop-based Monte Carlo Localization for Mobile Sensor Networks

Jiyoung Yi, Sungwon Yang and Hojung Cha

Department of Computer Science, Yonsei University, Seoul, Korea

SECON 2007

Outline

Introduction Related work Multi-hop-based Monte Carlo Localization Performance Evaluation Conclusions

Introduction Sensor positioning is a crucial part of many locati

on-dependent applications that utilize WSNs. Ex coverage, routing and target tracking

Localization can be divided into Range-based

Add additional hardware (e.g: GPS) Range-free

Location information can be obtained RSSI Time of arrival or time difference of arrival Angle of arrival measurements Probability

Introduction

Many proposed localization method typically assume static network topologies.

However, many sensor network applications demand the consideration of mobile sensor nodes. Ex. The exploration of dangerous region, fire re

scue, and environment monitor.

Introduction

Some recent works also discuss localization when dealing with mobile nodes.

Most of the studies suggest that supporting mobility can be achieved by repeating the static localization algorithm.

Localize Localize Localize Localize

t

move

Introduction

There are several challenges to designing a localization algorithm for mobile sensor networks. Many previous localization schemes for static networks

restrict environment conditions. Such as uniformly distributed anchor nodes or a fixed radio

transmission range. Since the localization is a part of the whole application,

the method cannot consume most of the resources Such as CPU, battery, and network resource.

Introduction – Monte Carlo based method

Sensor only known about its maximum speed. There are two phases in the Monte Carlo based

method localization. Prediction

Estimate the location of the sensor at this time based on previous time.

Filtering Eliminate the impossible location based on some information.

Ex. Transmission range …

Anchor nodeflooding

General nodeprediction

General nodeFiltering

Localize time

Introduction - Monte Carlo Localization

Normal node

Anchor node

iy

A

C

D

BE

F

Time=0

Anchor nodeflooding




Normal node

Anchor node

iy

A 4

B 2

C 2

D 4

E 3

F 4

A

C

D

BE

F

Anchor nodeflooding



Node i

Time=1


Normal node

Anchor node

iy

A

C

D

BE

F

Anchor nodeflooding



Time=1

Introduction - Monte Carlo Box

Normal node

Anchor node

x1y

A

C

D

BE

F

x2

The main difference between MCB and MCL is that MCB adds the maximum speed of nodes to filtering the location.

Introduction

Motivation Previous range-free localization algorithms designed

for mobile sensor networks have two major constraints. A sufficient number of anchors are required for the algorithms. The previous algorithms assume that the fixed radio

transmission range is known. These constraints are possibly lifted by DV-hop.

Goal Combine MCB and DV-Hop to propose a new

localization for mobile WSN.

Introduction - DV-Hop

y

A

CB

A, Hop n1

B, Hop n2

C, Hop n3

B, Hop n9

C, Hop n8

cA

ci : corrected factor

Multi-hop-based Monte Carlo Localization

Challenge DV-Hop only executes in isotropic sensor

networks. Mobile WSN is usually uniform networks. There should be some methods to make DV-

Hop adapted the network.


Some cases cause estimation error by DV-Hop.

Underestimation only occurs when corrected factor is too small.

0 1 51

OverestimationTransmission range=50m

Overestimation

0 1 51

S x S x


Method makes DV-Hop adapt isotropic network.


The multi-hop constraints

Multi-hop-based Monte Carlo Localization Assumption

All sensors have their own mobility. The network topology can be dynamically changed by mobile nod

es. The density of anchor nodes is low. Full network connectivity is guaranteed in spite of node mobility. Sensor field consists anchor and general nodes. General nodes are not aware of their locations Anchor nodes always know their exact positions All nodes are equally likely to move in any direction with any spee

d between 0 and vmax


Anchor nodeflooding



DV-Hop


y

A

CB

A, Hop n1

B, Hop n2

C, Hop n3

B, Hop n9

C, Hop n8

cA


x1y

A

CB

x2

Performance evaluation

Experiment Results Sensor: Tmote Sky TinyOS 21 general nodes and 4 anchor nodes

Simulation Results 400 nodes 500m x500m region Transmission range:50m

Performance Evaluation – real system

Performance Evaluation

Conclusions

The author proposed a multi-hop based Monte Carlo localization algorithm.

Compared to other Monte Carlo-based algorithm, Up to 50% errors are reduced on this work.

Performance Evaluation