Particle Swarm Optimization for Emission Source Localization in Sensor Networks


This paper evaluates different strategies for solving the problem of emission source localization in an environment monitored by a distributed wireless sensor network. Typical application scenarios in which this problem could arise include emergency response and military surveillance. A nonlinear least squares method is developed to model the problem of estimation of the emission source location and the intensity at the source. Three solution approaches are considered to solve the problem, namely, a least squares solver (LSQ), particle swarm optimization (PSO), and differential evolution (DE). On the estimation error performance metric, in most cases the LSQ and PSO solutions achieve about equally good quality results, better than that of the DE solution. On execution time performance, however, the PSO algorithm is found to consistently outperform LSQ, by up to a factor of 20. This is an important criterion for selection of algorithms for embedded applications such as in sensor networks. Finally, the paper remarks on further acceleration of the PSO solution using distributed processing and hardware techniques.

  • Abstract
  • I. Introduction
  • II. Problem Modeling
  • III. Solution Strategies
  • IV. Test Configurations and Simulation Results
  • V. Discussions and Conclusion
  • References

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