Distributed sensor network for real time tracking

ABSTRACT

In this paper we describe our solution to a real-time distributed resource allocation application involving distributed situation assessment. The hardware configuration consists of a set of reconfigurable sensors at fixed locations, each having local processing and low-bandwidth communication capabilities with other sensor nodes. The objective is to track objects moving in the environment in real-time as best as possible, given uncertainty and constraints on sensor loads, communication, power consumption, action characteristics, and clock synchronization. Once the target is detected, the sensors must communicate and cooperate so that, within a given window of time, the data needed to triangulate the position of the target can be collected. Our solution to this problem decomposes the environment into a number of sectors, where individual sensor nodes in a sector are specialize dynamically to address different parts of the goal. We describe our solution to this problem in detail, including the high-level architecture and a number of the more interesting implementation challenges. Results and future direction are also covered.

REFERENCES

• 1: K. S. Decker and V. R. Lesser. Quantitative modeling of complex environments. International Journal of Intelligent Systems in Accounting, Finance, and Management, 2(4):215-234, Dec. 1993. Special issue on "Mathematical and Computational Models of Organizations: Models and Characteristics of Agent Behavior".
• 2: John R. Graham , Daniel McHugh , Michael Mersic , Foster McGeary , M. Victoria Windley , David Cleaver , Keith Decker, Tools for Developing and Monitoring Agents in Distributed Multi-Agent Systems, Revised Papers from the International Workshop on Infrastructure for Multi-Agent Systems: Infrastructure for Agents, Multi-Agent Systems, and Scalable Multi-Agent Systems, p.12-27, June 03-07, 2000
• 3: B. Horling et al. The taems white paper, 1999. http://mas.cs.umass.edu/res-earch/taems/white/.
• 4: B. C. Horling, A Reusable Component Architecture for Agent Construction TITLE2:, University of Massachusetts, Amherst, MA, 1998
• 5: V. Lesser and D. Corkill. The distributed vehicle monitoring testbed: A toolforinvestigating distributed problem solving networks. AI Magazine, 4(3):15-33, 1983.
• 6: V. R. Lesser and L. D. Erman. Distributed interpretation: A model and an experiment. IEEE Transactions on Computers, C-29(12):1144-1163, Dec. 1980.
• 7: R. G. Smith. The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Transctions on Computers, 29(12):1104-1113, 1980.
• 8: K. Sycara, K. Decker, and M. Williamson. Middle-agents for the internet. In Proceedings of IJCAI-97, January 1997.
• 9: Régis Vincent , Bryan Horling , Victor Lesser , Thomas Wagner, Implementing soft real-time agent control, Proceedings of the fifth international conference on Autonomous agents, p.355-362, May 2001, Montreal, Quebec, Canada [doi>10.1145/375735.376329]
• 10: T. Wagner , A. Garvey , V. Lesser, Criteria-Directed Heuristic Task Scheduling TITLE2:, University of Massachusetts, Amherst, MA, 1997

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE

Additional Classification:
  D. Software
  D.3 PROGRAMMING LANGUAGES
      D.3.2 Language Classifications

          Nouns: Java

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.9 Robotics
          Subjects: Sensors

General Terms:
Design, Languages, Measurement, Performance, Theory