1. Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Network Wei-Peng Chen, Jennifer C. Hou, Lui Sha

2. 2 Outline Introduction to sensor network Technical background for the system The dynamic clustering algorithm Limitations of the system Conclusion

3. 3 Sensor Network Nodes in the network  Sensor to sense physical environment  On-board processing, limited capability  Wireless communication  Limited power from batteries

4. 4 The Network The network  2 kinds of nodes: source and sink  Wireless network Berkeley motes use CSMA MAC  Ad-hoc type Multi-hop routing Nodes sleep periodically

5. 5 Data Dissemination Some research questions  How to coordinate sensors?  How to route data?  How to do in-network data fusion?  What to do with congestion?  How to do the above efficiently… in terms of energy? in terms of time? We need distributed solutions

6. The Acoustic Target Tracking System

7. 7 Types of Clusters Static Clusters  Fault tolerance?  Information Sharing? Dynamic Clusters  Formation triggered by events.

8. 8 Localization in acoustic tracking Time delay based  Susceptible to estimation error in time synchronization and echo effect Energy based  More robust.

9. 9 Acoustic tracking Sensing energy level of signals Sound analysis, classification and data fusion

10. 10 Acoustic tracking Cluster Head Sets up the timer for willingness to be active Waits for the response Then takes the appropriate action

11. 11 Energy-based Localization Signal strength decreases exponentially with propagation distance : received signal strength in the i th sensor : strength of an acoustic signal from the target : target position yet to be determined : known position of the i th sensor : attenuation coefficient : white Gaussian noise

12. 12 Energy-based Localization With a pair of energy readings   Target is closer to sensor i than to sensor j j i

13. 13 Energy-based Localization Voronoi diagram  2-D space divided into Voronoi cells  V(p i ) : Voronoi cell containing node p i  V(p i ) contains all points closer to p i than to any other p j  r i larger than all neighbors’ readings only if target in V(p i )

14. 14 Network Characteristics Network structure: 2-layer hierarchy  Static backbone of sparse cluster heads  Dense sensors for detecting targets Radio transmission range = 2 * signal detection range  Ensure 1 cluster at a time  Ensure nodes in a cluster hear each other directly

15. 15 The Dynamic Clustering Algorithm 4 component mechanisms  Initial distance calibration and tabulation  Cluster head (CH) volunteering  Sensor replying  Reporting of tracking results

16. 16 Idea of the Algorithm Objective: minimize messages sent in the network and avoid collisions Given an energy reading, estimate distance from target Using Voronoi diagram, estimate probability that target is in my Voronoi cell In CH volunteering and sensor replying process  Nodes with high probability speak quickly  When you hear a higher energy reading from others, you give up speaking

17. 17 Initial Distance Calibration and Tabulation Each sensor to know 2-D coordinates of all other sensors in its transmission range Each CH constructs a Voronoi diagram for neighboring CHs Each sensor (including CH) constructs a Voronoi diagram for neighboring sensors

18. 18 Initial Distance Calibration and Tabulation Each CH i pre-computes for different d   Target on the circle centered at CH i with radius d  : conditional probability that target locates within V(CH i ) given d  3 cases…

19. 19 Three Cases d < radius of inner circle: d > radius of outer circle: In between:  Take sample points on the circle  Check location of each point  Estimate as # of sample points inside V(CH i ) / total # of sample points

20. 20 Initial Distance Calibration and Tabulation Sensors do similarly Each sensor S j pre-computes for different   r i : energy reading from CH i  r j : energy reading of S j  : conditional probability that target locates in V(S j ) given

21. 21 CH Volunteering Distributed election algorithm CH closest to target should be elected Solicitation packet  Request to form cluster and volunteer to be the cluster head  Contains signal signature  Contains signal strength detected by CH ( CH i )

22. 22 CH Volunteering Random delay-based broadcast mechanism  CH i detects a signal, estimates d, checks  Sets a back-off timer with back-off time  CH i does not broadcast solicitation packet until timer expires  If during back-off, hears other solicitation packets with higher energy readings, gives up volunteering

23. 23 The problem of choosing the CH Two phase random-delay broadcast mechanism Relies on: Signal Strength(energy packet) Relies on Signature of acoustic sound. A signature packet contains detailed signature information.

24. 24 Two phase broadcast mechanism A CH sets its back-off timer with value D for energy packet After expiry energy packet broadcast and set the timer again After expiry broadcast the signature packet If you hear in either first or second round, with larger signal strength or detect a signature packet, do not volunteer

25. 25 Sensor Replying Sensor S j receives a solicitation packet Matches signal signature with buffered data Upon a match, calculates signal strength r j Attempts to send a reply using similar delay-based mechanism

26. 26 Sensor Replying Random delay-based broadcast mechanism  Calculates, checks  Sets back-off timer with back-off time  If during back-off, hears other reply packets, records the sensor that reports largest signal strength  When timer expires, sends reply packet if r j higher than all others’ energy readings; or S j is a Voronoi neighbor of the sensor that reports the largest signal strength

27. 27 Reporting Tracking Results CH receives replies from sensors Sufficient number of replies:  A reply from S j with largest signal strength  Replies from all S j ’s Voronoi neighbors Takes location of S j as location of target Sends result to sink through static backbone

28. 28 Simulation Surveillance area: 180 x 180 36 CHs and 288 sensors Sink at (0,0)

29. 29 Approaches 1 st approach : Complete algorithm 2 nd approach: only one phase, it sends the complete signature 3 rd approach: both phases but does not employ distance calibration.

30. 30

31. 31 Limitations Limited application space  Not applicable to general monitoring applications without “target”  Signals must attenuate with propagation distance 1 cluster for 1 signal  Signals may come simultaneously  Multiple clusters may form simultaneously causing more collisions

32. 32 Limitations Energy inefficiency  Radio transmission range = 2 * signal detection range  Can be improved by considering multi-hop routing  Signals at any position must be detected by at lease 1 CH Tradeoff of sensor density and energy efficiency

33. 33 Conclusion Data dissemination in sensor network Dynamic clustering triggered per signal More research on:  Collision behavior between clusters  Multi-hop routing  Time efficient data dissemination

34. Discussion Thank you