Acoustic Target Tracking Using Tiny Wireless Sensor Devices

1. Acoustic Target Tracking Using Tiny Wireless Sensor Devices Qixin Wang, Wei-Peng Chen, Rong Zheng, Kihwal Lee, and Lui Sha Dept. of CS, UIUC

2. Introduction Context • Delay based sound source locating algorithm, requires large number of redundant sensors for accuracy. -Tiny wireless sensors to real-world acoustic tracking applications. • Tracking only impulsive acoustic signals, such as foot steps, sniper shots etc. No concept of tracking motion.

3. Challenges: Partial info at one sensor site Inaccuracy and unreliability of sensors Effective use of scarce wireless bandwidth Solutions: Sensor clustering and coordination Redundancy for robustness Quality-driven (QDR) networking. Info. flow oriented v.s. raw data flow oriented. Introduction

4. Introduction Scenario Sensor Router Cluster Head Sink/Pursuer Cluster Head Sink/ Pursuer

5. System Overview • System Architecture • Acoustic target tracking subsystem Sensor (mica motes) Sensors belong to clusters with singular cluster head. Cluster head knows the locations of its slave sensors. Raw data gathered from sensors are processed in cluster head to generate localization results Cluster Head (mono-board computer)

6. System Overview • Communication Subsystem: route back the reports generated by cluster heads to sink Sink cluster covered area cluster head router (mica motes)

7. Acoustic Target Tracking Subsystem • Use RBS Time Synch (error  30s). • Onset Detection (on sensors) • Small sliding window to compute moving average of acoustic signal magnitude. • Use threshold to detect onset time t0. • Record one buffer load of data, then post-process.

8. Acoustic Target Tracking Subsystem Locate sound src loc. Detected intersted sound Broadcast sound signature ClusterHead: • Cross Correlation (to find out delays) Cross-correlation to detect local arrival time Report local arrival time SlaveSensor:

9. Acoustic Target Tracking Subsystem • Sound Source Locating & Evaluation of Quality Rank (main idea) • Throw away apparently erroneous sensor readings. • Let A = cluster’s monitored area, sound src location = argpAmin{|d(p) - ds|},where d(p) is the hypothetical sensors’ sound arrival time vector, while ds is the actual one. |·| is an error measurement function.

10. Acoustic Target Tracking Subsystem • In practice, we cannot check every location in A, instead, we apply a grid with 33inch2 granularity onto A, and only check those grid points. • Quality Rank = percentage of d(p)’s elements that falls outside  boundary of ds .

11. Communication Subsystem • Quality-driven(QDR) Redundancy Suppression and Contention Resolution • Redundant clusters may report same event’s location. Good for reliability reasons. • Quality Rank is used to suppress inferior reports and only report high quality rank localization reports to data sink

12. Acoustic Target Tracking Subsystem • Quality Rank is also used for contention resolution along the routes (with CSMA as MAC) to let higher quality reports get to data sink earlier:Tbackoff = QualityRank  interval + random

13. Experiment • Locations of sensors and sound sources in a single cluster

14. Experiment • Examples of localization results for different sound source locations

15. Experiment • Average error vs. sound source locations. Note sound source is a 4inch speaker

16. Experiment

17. Experiment • % of reports within 3-inch error range: higher quality rank, higher creditabi-lity

18. Experiment • Quality-driven (QDR): Effect of various interval on the percen-tage of suppressed reports

19. Experiment • Effect of Quality-driven(QDR) Suppose info/bit is fixed; the smaller Quality Rank, the better the quality.

20. Conclusion • Acoustic target tracking using tiny wireless devices with satisfying accuracy is possible. • Quality Rank can be used to decide the quality of tracking result • Quality-driven redundancy suppression and contention resolution is effective in improving the information throughput.