Relative distance identification in "Smart Dust" networks for environmental modelling

  • Authors:
  • Graham A. Rollings;David W. Corne

  • Affiliations:
  • School of Engineering, Computer Science and Mathematics, University of Exeter, UK;School of Mathematical and Computer Sciences, Heriot-Watt University, UK

  • Venue:
  • AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
  • Year:
  • 2008

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Abstract

Smart dust (wireless sensor) networks have many applications in environmental monitoring. In many such applications, the data gathered by the motes is only useful in combination with knowledge of the motes' positions, and this needs to be estimated in cases where they are airborne, or otherwise not statically fixed. GPS based location solutions are prohibitively expensive and also present some operational difficulties, so it is important to find others ways to determine location. This is usually termed 'Localisation', a problem made more complex where the position and velocity of the motes are subject to external forces. We investigate this by exploring the use of optimisation algorithms to find relative distance configurations that minimise a straightforward error function based on the Received Signal Strength Indication (RSSI). In this paper we focus on simulating noise-free conditions; although less realistic, this provides information about whether or not this problem can be satisfactorily solved at all in reasonable time, and, if so, for what sizes of mote network will this be possible. We find that for the range of mote clusters sizes considered as a static field model the results are indeed sufficiently encouraging to indicate the need to continue the research.