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Anagnostopoulos, Christos; Hadjiefthymiades, Stathes; Kolomvatsos, Kostas (2016)
Publisher: Association for Computing Machinery, Inc.
Languages: English
Types: Article
We present a robust, dynamic scheme for the automatic self-deployment and relocation of mobile sensor\ud nodes (e.g., unmanned ground vehicles, robots) around areas where phenomena take place. Our scheme aims\ud (i) to sense environmental contextual parameters and accurately capture the spatio-temporal evolution of a\ud certain phenomenon (e.g., fire, air contamination) and (ii) to fully automate the deployment process by letting\ud nodes relocate, self-organize (and self-reorganize) and optimally cover the focus area. Our intention is to\ud ‘opportunistically’ modify the previous placement of nodes to attain high quality phenomena monitoring. The\ud required intelligence is fully distributed within the mobile sensor network so that the deployment algorithm\ud is executed incrementally by different nodes. The presented algorithm adopts the Particle Swarm Optimization\ud technique, which yields very promising results as reported in the paper (performance assessment). Our\ud findings show that the proposed algorithm captures a certain phenomenon with very high accuracy while\ud maintaining the network-wide energy expenditure at low levels. Random occurrences of similar phenomena\ud put stress upon the algorithm which manages to react promptly and efficiently manage the available sensing\ud resources in the broader setting.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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