Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Network

1. Dynamic Clustering forAcoustic Target Tracking inWireless Sensor Network Wei-Peng Chen, Jennifer C. Hou, Lui Sha

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

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

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

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

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

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

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

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

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

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

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

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(CHi) / total # of sample points

20. Initial Distance Calibration and Tabulation • Sensors do similarly • Each sensor Sj pre-computes for different • ri: energy reading from CHi • rj: energy reading of Sj • : conditional probability that target locates in V(Sj) given

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 (CHi)

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

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

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 • rj higher than all others’ energy readings; or • Sj is a Voronoi neighbor of the sensor that reports the largest signal strength

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

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

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

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. 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. 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