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 location-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 rescue, 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. move Localize Localize Localize Localize t

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 … Localize time Anchor node flooding General node prediction General node Filtering

Introduction - Monte Carlo Localization Anchor node flooding General node prediction General node Filtering Normal node F D A Anchor node E Time=0 B C i y

Introduction - Monte Carlo Localization Anchor node flooding General node prediction General node Filtering Time=1 Normal node D F A Anchor node Node i E B A 4 B 2 C D E 3 F C y i

Introduction - Monte Carlo Localization Anchor node flooding General node prediction General node Filtering Time=1 Normal node D F A Anchor node E B C y i

Introduction - Monte Carlo Localization Anchor node flooding General node prediction General node Filtering Time=2 Normal node D F A Anchor node E B C y i

Introduction - Monte Carlo Box The main difference between MCB and MCL is that MCB adds the maximum speed of nodes to filtering the location. Normal node D F A Anchor node B E C y x1 x2

Introduction Motivation Goal 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 B, Hop n9 C, Hop n8 ci : corrected factor A B C y A, Hop n1 B, Hop n2 C, Hop n3 cA

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.

Multi-hop-based Monte Carlo Localization Some cases cause estimation error by DV-Hop. Underestimation only occurs when corrected factor is too small. Overestimation Transmission range=50m Overestimation S x S x 1 51 1 51

Multi-hop-based Monte Carlo Localization Method makes DV-Hop adapt isotropic network.

Multi-hop-based Monte Carlo Localization

Multi-hop-based Monte Carlo Localization

Multi-hop-based Monte Carlo Localization 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 nodes. 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 speed between 0 and vmax

Multi-hop-based Monte Carlo Localization DV-Hop Anchor node flooding General node prediction General node Filtering

Multi-hop-based Monte Carlo Localization B, Hop n9 C, Hop n8 A B C y A, Hop n1 B, Hop n2 C, Hop n3 cA

Multi-hop-based Monte Carlo Localization y x1 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 – real system

Performance Evaluation – real system

Performance Evaluation

Performance Evaluation

Performance Evaluation

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.

End

Performance Evaluation

Performance Evaluation