1. 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

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

3. 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

4. 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.

5. 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

6. 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.

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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.

14. Introduction - DV-Hop B, Hop n9 C, Hop n8 ci : corrected factor A B Cy
A, Hop n1
B, Hop n2
C, Hop n3 cA

15. 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.

16. 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

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

18. Multi-hop-based Monte Carlo Localization

19. Multi-hop-based Monte Carlo Localization

20. Multi-hop-based Monte Carlo Localization
The multi-hop constraints

21. 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

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

23. 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

24. Multi-hop-based Monte Carlo Localizationy x1 x2

25. 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

26. Performance Evaluation – real system

27. Performance Evaluation – real system

28. Performance Evaluation – real system

29. Performance Evaluation

30. Performance Evaluation

31. Performance Evaluation

32. Performance Evaluation

33. 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.

34. End

35. Performance Evaluation

36. Performance Evaluation