Organization-Based Tracking

1. Organization-Based Tracking

2. Tracking • Given a sensor network, use the sensors to determine the motion of one or more targets • Canonical domain for DSNs - much of what we have seen so far is applicable • data routing, query propagation, wireless protocols • Typically requires more cooperation among entities than other examples we have seen • Compare: “is there an elephant out there?” vs. “where has that particular elephant been?”

3. Tracking Challenges • Data dissemination and storage • Resource allocation and control • Operating under uncertainty • Real-time constraints • Data fusion (measurement interpretation) • Multiple target disambiguation • Track modeling, continuity and prediction • Target identification and classification

4. Tracking Domains • Appropriate strategy depends on the sensors’ capabilities, domain goals and environment • Requires multiple measurements? • Bounded communication? • Target movement characteristics? • No single solution for all problems • For example… • Limited bandwidth encourages local processing • Limited sensors requires cooperation

5. Why Not Centralized? • Scale! • Data processing combinatorics • Resource bottleneck (communication, processing) • Single point of failure • Ignores benefits of locality

6. Why Not (fully) Distributed? (i.e. everyone tracks) • Redundant information and computation • Can increase uncertainty • Lack of unified view • High communication costs • (exception: overhearing [Fitzpatrick 2003])

7. Organization-Based Tracking • Use structure, roles to control data and action flow • Can be static, or dynamically evolved • [Brooks 2003]: Spontaneous coalition formation • [Horling 2003]: Partitions, mediated clustering • [Li 2002]: Hierarchical information fusion • [Yadgar 2003]: Hierarchical teams • [Wang 2003]: Roles and group formation • [Zhao 2002]: Geographic groups

8. Distributed Target Classification and Tracking in Sensor Networks Richard Brooks, Parameswaran Ramanathan, & Akbar Sayeed

9. Problem Domain • Single target • Fixed, acoustic sensors • Requires multiple measurements • Limited ad-hoc wireless network • Track and classify target • (classification, which uses a supervised learning technique, is not discussed here)

10. Location-Centric Tracking Control and data flow at each node: • Initialization: disseminate sensor information • Receive candidates: describe approaching targets • Local detections: gather measurements • Merge detections: form track, compare candidates • Determine confidence: estimate uncertainty • Estimate track: predict future target location • Transmit track: notify relevant sensors

11. CPA Magnitude Time Location-Centric Tracking • “Closest point of approach” (CPA) measurements • Target detection causes cell formation • Cells formed around the target’s estimated location • Intended to include relevant sensors • Manager is selected • Node with greatest signal strength • Manager collects local CPA’s • Linear regression over CPA node locations

12. Location-Centric-Tracking • Estimated location compared to prior tracks • Projections from candidate tracks • Cell created for track in new area • Size is a function of target velocity • Track information propagated to cell • Tracking repeats…

13. Location-Centric Advantages • Avoids combinatorial explosion of track association • Centralized: n targets, n candidate locations = n2 • Distributed: 1 target, n candidate locations = n • Reduces communication costs (multi-hop ad hoc) • Saves energy

14. Results

15. Using and Maintaining Organization in a Large-Scale Sensor Network Bryan Horling, Roger Mailler, Mark Sims and Victor Lesser Multi-Agent Systems Lab University of Massachusetts

16. Problem Domain • Fixed doppler radars • Requires multiple, coordinated measurements • Multiple targets • Shared 8-channel RF communication

17. Sensor Characteristics • Hardware • Fixed location, orientation • Three 120° radar heads • Agent controller • Doppler radar • Amplitude and frequency data • One (asynchronous) measurement at a time

18. Organizational Control • Use organization to address scaling issues • Environment is partitioned • Constrains information propagation • Reduces information load • Exploits locality • Agents take on one or more roles • Limits sources of information • Facilitates data retrieval • Other techniques also built into negotiation protocol and individual role behaviors

19. Typical Node Layout • Nodes are arranged or scattered, and have varied orientations. • One agent is assigned to each node.

20. Partitioning of Nodes • The environment is first partitioned into sectors. • Sector managers are then assigned.

21. Competition for Sensor Agents • Sector members send their capabilities to their managers. • Each manager then generates and disseminates a scan schedule.

22. Track Manager Selection • Nodes in the scan schedule perform scanning actions. • Detections reported to manager, and a track manager selected.

23. Managing Conflicted Resources • Track manager discovers and coordinates with tracking nodes. • New tracking tasks may conflict with existing tasks at the node.

24. Data Fusion (Track Generation) • Tracking data sent to an agent which performs the fusion. • Results sent back to track manager for path prediction.

25. Sector Manager Tracking Manager Scanning Agent Tracking Agent Protocol Usage Map Protocols DrA DrQ DrR TB RR TD PTC RB PC DA TBU ES

26. Sector Size • This one parameter affects many things… • Sector manager load • Smaller sector –› smaller manager directory • Larger sector –› better sector coverage • Track manager actions • Smaller sector –› fewer update messages • Larger sector –› fewer directory queries • Communication distance, agent activity, RMS error, message type counts • Empirical evaluation of varying this parameter

27. Experimental Setup • Radsim simulator • 36 sensors • 1-36 equal sized sectors • 4 mobile targets • 10 runs per configuration • Hypothesis: sector size of 6-10 agents is best

28. Communication Characteristics • Larger sectors with more agents leads to less messaging overall • Less tracking control • Fewer directory queries • More sectors to query • More tracking data

29. Load Disparity • Large sectors increase SM comm. load • More messages to handle • Greater disparity - SM is a “hotspot” • Greater disparity in activity load • Average action totals are constant

30. Domain Metrics • Communication distance increases with larger sectors • Track migration triggered by boundaries • …but better RMS error • More measurements due to lower control overhead

31. What’s Best? • This would vary, depending on sensor and environmental characteristics • Find inflection point in graphs’ intersection • Empirical evidence supports sector size from 5-10 sensors

32. Conclusions • Specific results are domain-specific • However, this demonstrates that organizational controls can affect performance • General notions of locality, information bottlenecks, organizational control overhead