Accurate, Dynamic, and Distributed Localization of Phenomena for Mobile Sensor Networks

Abstract

We present a robust, dynamic scheme for the automatic self-deployment and relocation of mobile sensor nodes (e.g., unmanned ground vehicles, robots) around areas where phenomena take place. Our scheme aims (i) to sense environmental contextual parameters and accurately capture the spatiotemporal evolution of a certain phenomenon (e.g., fire, air contamination) and (ii) to fully automate the deployment process by letting nodes relocate, self-organize (and self-reorganize), and optimally cover the focus area. Our intention is to “opportunistically” modify the previous placement of nodes to attain high-quality phenomenon monitoring. The required intelligence is fully distributed within the mobile sensor network so the deployment algorithm is executed incrementally by different nodes. The presented algorithm adopts the Particle Swarm Optimization technique, which yields very promising results as reported in the article (performance assessment). Our findings show that the proposed algorithm captures a certain phenomenon with very high accuracy while maintaining the networkwide energy expenditure at low levels. Random occurrences of similar phenomena put stress upon the algorithm which manages to react promptly and efficiently manage the available sensing resources in the broader setting.

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Index Terms

• Hardware

  • Communication hardware, interfaces and storage

    • Sensors and actuators

• Mathematics of computing

  • Mathematical analysis

    • Mathematical optimization

      • Continuous optimization

        • Bio-inspired optimization

• Theory of computation

  • Design and analysis of algorithms

    • Parallel algorithms

      • Self-organization