Building a Real Distributed Sensor Network: Experiences and Challenges

1. CMPSCI 791L: Sensor Networks Experiences Building a Real Distributed Sensor Network Victor R. Lesser Computer Science Department University of Massachusetts, Amherst September 12, 2003

2. Acknowledgements • Bryan Horling • Roger Mailler • Jiaying Shen • Dr. Regis Vincent (SRI) • http://mas.cs.umass.edu/~bhorling/papers/02-14.ps.gz • http://mas.cs.umass.edu/~bhorling/papers/00-49.ps.gz

3. Outline • An example DSN problem • Issues in Distributed Resource Allocation • An example of one approach

4. Distributed Sensor Network Challenge Problem • Small 2D Doppler radar units (30’s) • Scan one of three 120 sectors at a time • Commodity Processor associated with each radar • Communicate short messages using one of 8 radio channels • Triangulate radars to do tracking

5. Representative of Distributed Sensor Network Issues • Need for Coordination/Distributed Resource Allocation • Multiple sensors need to collaborate on tasks • View objects of interest from multiple angles with different types of sensors • Sensing time windows need to be closely aligned • Environmental Dynamics • Sensor configuration changes as target moves • Potential for Resource Overloads • Multiple target in overlapping sensor regions • Limited Communication Channels

6. Representative of DSN Issues, cont. • Soft Real-time • Limited time window for sensing • Must anticipate where target is moving in order to effectively allocate sensor resources • Time for coordination affects time for sensing • Distribution: communication latency/limited bandwidth precludes global knowledge/control • distributed data fusion • Scalability: need to be able to handle large numbers of sensor nodes • Robustness: local failures should not induce global collapse • Handle uncertain information, sensor/processor/communication failures

7. Legend radar 802.11b (0,1,2,4Mb) Fractional T1 (100K) Real-Time Tornado Tracking Internet2 supercomputers

8. Control: what to sense, when Weather/Computation/Sensor Integrated Control Hazardous Weather Detection algorithms Determine initial conditions for near-term dynamic forecasting models (NWP) Quality Control (clutter removal, de-aliasing) Retrieval of 3D wind, other fields signal processing radars Assimilation, Multiple Doppler analysis (more Compete gridding Resource database weather-algorithm-provided utility functions

9. How to Allocate Processing/Sensing Tasks • Avoid processing overloads • Avoid communication overloads • Have information/processing co-located • Avoid failure of network based on single location failure • Allocate sensing so that as many targets can be tracked with reasonable fidelity • Allocate processing/sensing so that real-time constraints can be met

10. Additional Questions • What tasks can be assigned statically which have to be dynamically allocated • When do static and dynamically made decisions need to be revisited • What is the appropriate context for making these decision • What decisions can be made locally • What decisions need to made with in a non-local context • Is this context fixed or dynamically evolved

11. Sensor Processing Issues • Integrating Target Acquisition with Target Tracking • Re-acquiring lost targets • Data-Correlation Issues • Recognizing which data belongs to which target • Handling Uncertainty in Sensor Information • How to make resource allocation issues in face of faulty sensor data

12. Tasks, Processes and Agents • Issue of Autonomy -- Locus of Control • How much leeaway is allowed in what goals to pursue, how to do them, who to interact with, what resources to use, … • Where are these decisions being made • How decentralized are these decisions • How dynamic/context-dependent these decisions are

13. Soft vs. Hard Real-Time • There are not catastrophic effects if events are occasionally not interpreted correctly • If lose sight of target for a few time steps and then reacquire generally okay • Computation/Sensing after the deadline may still have some value • Reduction in certainty of target location

14. How to Evaluate a Sensor Network • Communication Locality • Information and Processing Bottlenecks • Organizational Control Overhead • Overall Effectiveness • ……. What’s Best -- Multi-attributed Evaluation?

15. One Approach from an MAS perspective • Decompose environment to form a partitioned organization. • Each partition (sector) will contain a set of sensor nodes, each with its own controlling agent. • Individual sectors are relatively autonomous. • Specialize members of the agent population to dynamically take on multiple, different goals/roles. • Individual agents become “managers” of different aspects of the problem. • Managers form high-level plans to address their goals, and negotiate with other nodes to achieve them.

16. Sector Manager Tracking Manager Scanning Agent Tracking Agent Sectored-Based Agent OrganizationAgents Multiplex among Different roles

17. Sector Manager Tracking Manager Scanning Agent Tracking Agent Organizationally-Structured Communication among Agents DrA DrQ DrR TB RR TD PTC RB PC DA TBU ES

18. Sensors Conflicting Scanning Tasks from different Sector Managers Locally resolved by agent connected to sensor -- SRTA agent Tracking Tasks wanting same sensor resources Negotiation among track managers -- SPAM protocol Communication Communication Degradation due to lack of Locality Track manager migration among sectors Communication Channel Overload Sector manager assignment of track manager roles Processors Data Fusion Overload/Knowledge locality Sector manager assignment of data fusion/track manager roles Multiplexing Roles -- SRTA agent Managing Conflicted Resources:Sensors, Processors, Communication

19. Centralizing Information in Sector ManagerHandling Data Correlation with Multiple Tracks • Targets are represented by uncertainty bounds • Bounds are affected by speed of target and age of supporting measurements • Bounds are shared with sector manager, who in turn shares them with other track managers • Sector manager • Uses target uncertainty bounds to determine if new target detections (from scanning) are known targets • Data from known target detections are used to focus attention of relevant track manager • Track managers • Uses amplitude lobe intersections to estimate position in times of need • Prevents data fusion if estimated resolved position is within another target’s bounds • Throws out ambiguous measurements which intersect another target’s bounds

20. Fault Tolerance • Node information is propagated through the use of directory services (x, y, orientation, etc.). • Sensors provide sector managers with their information. • Track managers query sector managers for sensor details. • This information is cached for future use at each step • The directory held in sector manager maintains historical query information • New data are analyzed for relevance to those queries • Relevant information is automatically propagated to the query source • This process quickly updates agents’ beliefs, allowing them to adapt to change

21. Major Issues in This Approach • What is an appropriate organization for agents • Scalability and Robustness • Self-Organization and Adaptation • What is the protocol for distributed resource allocation • Soft Real-Time, Graceful Degradation, Efficient • What is the structure of an agent architecture that supports • Agents functioning in an organizational context • Agents implementing complex distributed resource protocols • Agents operating under soft real-time constraints

22. Some Final Thoughts • Can not isolate one set of issues from another • Strict layering of issues does not seem to work • There is no one best approach • Very sensitive to characteristics/capabilities of sensors, quality of sensor data, the character of required sensor fusion, amount and type of processing required, system objectives, communication and processing capabilities, environment …