1. Nov 1 - 2 2005: Review MeetingACCLIMATE Instrumenting Wireless Sensor Networks for Real-Time Surveillance Songhwai Oh Advisor: Shankar Sastry EECS UC Berkeley

2. Nov 1 - 2 2005: Review MeetingACCLIMATE Building Comfort, Smart Alarms Great Duck Island Elder Care Fire Response Factories Wind Response Of Golden Gate Bridge Vineyards Redwoods Instrumenting the world Soil monitoring

3. Nov 1 - 2 2005: Review MeetingACCLIMATE Challenges = Research Opportunities applications service network system architecture data mgmt Monitoring & Managing Spaces and Things technology MEMS sensing Power Comm. uRobots actuate Miniature, low-power connections to the physical world Proc Store

4. Nov 1 - 2 2005: Review MeetingACCLIMATE Limited capabilities of a sensor node –Limited supply of power –Short communication range –High transmission failure rates –High communication delay rates –Limited amount of memory and computational power Inaccuracy of sensors –Short sensing range –Low detection probabilities –High false detection probabilities Inaccuracy of sensor network localization Challenges = Research Opportunities mica2dot mag ultrasound acoustic

5. Nov 1 - 2 2005: Review MeetingACCLIMATE Tracking in Sensor Networks Representative application of sensor networks –Event detection –Communication –Sensor fusion and estimation –Sensor management –Decision making, etc. Applications –Surveillance and security –Search and rescue –Disaster and emergency response system –Pursuit evasion games –Inventory management –Spatio-temporal data collection –Visitor guidance and other location-based services

6. Nov 1 - 2 2005: Review MeetingACCLIMATE Multiple-Target Tracking (MTT) in Sensor Networks Model uncertainty –Unknown number of targets –Unknown target initiation and termination times Measurement noise and inconsistency –Noise, False alarms, Packet losses, Delays Data association problem Real-time –Timely outputs required for control applications (e.g., pursuit evasion games)

7. Nov 1 - 2 2005: Review MeetingACCLIMATE Yet Another Complication: Binary Sensors Why binary sensors? –Sensor output is too noisy to correlate signal strength with range –Simple detection code –1-bit to communicate Provides coarse measurements Difficult to use them directly to initiate, maintain and terminate tracks We use spatial correlation to fuse binary measurements into finer position measurements Needs an efficient fusion algorithm

8. Nov 1 - 2 2005: Review MeetingACCLIMATE Problem

9. Nov 1 - 2 2005: Review MeetingACCLIMATE Previous Work: Multiple-Target Tracking (MTT) in Sensor Networks Traditional – computationally intensive –[Chong et al. 1990] Distributed multitarget multisensor tracking Classification-based – multiple single-target tracking problems –[Li et al. 2002] Detection, classification and tracking of targets –[Shin et al. 2003] A distributed algorithm for managing multi- target identities in wireless ad-hoc sensor networks –[Liu et al. 2004] Distributed state representation for tracking problems in sensor networks Ad-hoc – not robust –[Liu et al. 2003] Distributed group management for track initiation and maintenance in target localization applications No general algorithm suited to sensor networks

10. Nov 1 - 2 2005: Review MeetingACCLIMATE Outline Multiple-target tracking (MTT) algorithm –Multi-sensor fusion algorithm –Markov chain Monte Carlo data association (MCMCDA) Results from the final experiment of the Network Embedded Systems Technology (NEST) project

11. Nov 1 - 2 2005: Review MeetingACCLIMATE MTT Overall Architecture FusionMCMCDA Controller

12. Nov 1 - 2 2005: Review MeetingACCLIMATE “ Simple ” Multi-Sensor Fusion

13. Nov 1 - 2 2005: Review MeetingACCLIMATE Multi-Sensor Fusion: Likelihood Detections Likelihood

14. Nov 1 - 2 2005: Review MeetingACCLIMATE Multi-Sensor Fusion: Threshold Likelihood after threshold But it requires detections from all sensors to account false alarms Instead we compute the likelihood if there are at least n d detections Likelihood

15. Nov 1 - 2 2005: Review MeetingACCLIMATE Multi-Sensor Fusion: Position Estimation Black circle: position estimate

16. Nov 1 - 2 2005: Review MeetingACCLIMATE MTT Overall Architecture FusionMCMCDA Controller

17. Nov 1 - 2 2005: Review MeetingACCLIMATE MTT Problem: General Setup

18. Nov 1 - 2 2005: Review MeetingACCLIMATE Solution Space of Data Association Problem (a) Observations Y (b) Example of a partition  of Y

19. Nov 1 - 2 2005: Review MeetingACCLIMATE Two Possible Solutions to Data Association Problem

20. Nov 1 - 2 2005: Review MeetingACCLIMATE Markov Chain Monte Carlo (MCMC) A general method to generate samples from a complex distribution For some complex problems, MCMC is the only known general algorithm that finds a good approximate solution in polynomial time [Jerrum, Sinclair, 1996] Applications: –Complex probability distribution integration problems –Counting problems (#P-complete problems) –Combinatorial optimization problems Data association problem has a very complex probability distribution

21. Nov 1 - 2 2005: Review MeetingACCLIMATE MCMC Data Association (MCMCDA)* Start with some initial state  1 2   *[Oh, Russell, Sastry 2004]

22. Nov 1 - 2 2005: Review MeetingACCLIMATE MCMC Data Association (MCMCDA) Propose a new state  ’ » q(  n,  ’ ) q:  £ 2  ! [0,1], proposal distribution q(  n,  ’ ) = probability of proposing  ’ when the chain is in  n propose nn ’’ q(  n,  ’ ) is determined by 8 moves:

23. Nov 1 - 2 2005: Review MeetingACCLIMATE MCMC Data Association (MCMCDA) If accepted, If not accepted,  n+1 =  ’  n+1 =  n Accept the proposal with probability  (  ) = P(  |Y), Y = observations

24. Nov 1 - 2 2005: Review MeetingACCLIMATE MCMC Data Association (MCMCDA) Repeat it for N steps 

25. Nov 1 - 2 2005: Review MeetingACCLIMATE MCMC Data Association (MCMCDA) Repeat it for N steps 

26. Nov 1 - 2 2005: Review MeetingACCLIMATE MCMC Data Association (MCMCDA) Repeat it for N steps 

27. Nov 1 - 2 2005: Review MeetingACCLIMATE Optimality in the Limit But how fast does it converge?

28. Nov 1 - 2 2005: Review MeetingACCLIMATE Polynomial-Time Approximation to Joint Probabilistic Data Association* *[Oh, Sastry 2005]

29. Nov 1 - 2 2005: Review MeetingACCLIMATE Overall Architecture MTT FusionMCMCDA Controller Multi-agent coordination algorithm Minimize time to capture all evaders Robust Minimum Time Control (MTC)

30. Nov 1 - 2 2005: Review MeetingACCLIMATE Simulation: Multiple-Target Tracking & Pursuit Evasion Games in Sensor Networks

31. Nov 1 - 2 2005: Review MeetingACCLIMATE NEST Final Experiment: MTT Demo Goal –Track an unknown number of multiple targets using a sensor network of binary sensors without classification information –Coordinate multiple pursuers to chase and capture multiple evaders in minimum time using a sensor network Done in simulation due to physical and time constraints

32. Nov 1 - 2 2005: Review MeetingACCLIMATE NEST Final Experiment: Summer 2005

33. Nov 1 - 2 2005: Review MeetingACCLIMATE NEST Final Experiment: Sensor Node Telos B mote 8MHz TI MSP430 microcontroller RAM: 10kB; Flash: 48kB Chipcon CC2420 Radio: 250kbps, 2.4GHz, IEEE 802.15.4 standard compliant Radio range of up to 125 meters Trio Sensor Board Features a microphone, a piezoelectric buzzer, x-y axis magnetometers, and four passive infrared (PIR) motion sensors Solar-power charging circuitry Trio Node

34. Nov 1 - 2 2005: Review MeetingACCLIMATE NEST Final Experiment: System Software –TinyOS –Deluge Network reprogramming –Drip and Drain (Routing Layer) Drip: disseminate commands Drain: collect data –DetectionEvent Multi-moded event generator –Multi-sensor fusion and multiple-target tracking algorithms

35. Nov 1 - 2 2005: Review MeetingACCLIMATE NEST Final Experiment: Demo

36. Nov 1 - 2 2005: Review MeetingACCLIMATE Current and Future Work Sensor networks –Robust distributed tracking algorithm –Robust tracking against malicious attacks –Performance analysis and metrics for sensor networks Camera networks Distributed multiple-target tracking and identity management

37. Nov 1 - 2 2005: Review MeetingACCLIMATE Distributed multiple-target tracking and identity management*: an application of MCMCDA *[Oh, Hwang, Roy, Sastry, 2005]

38. Nov 1 - 2 2005: Review MeetingACCLIMATE Summary Sensor networks –Individual sensor nodes are incapable and inaccurate –But the aggregation of spatially spread sensors can provide accurate estimates using spatio-temporal correlation System-level approach –Multi-sensor fusion may provide incorrect and inconsistent position reports –The inconsistency in position reports are fixed by the MCMCDA tracking algorithm using temporal correlation –Adaptive control system