Hybrid meta-heuristics for the optimization of the deployment of Wireless Sensor Networks (WSN)

EasyChair Preprint 2105

Abstract

Over the past decade, Wireless Sensor Networks (WCNs) have attracted the attention of most research communities. Their miniature size, as well as their decreasing cost and non-destructive aspect, make them among the most adopted techniques in multiple sectors of activity: industry, health, autonomous vehicles, etc. Added to this, the new industrial revolution (i.e.: industry of the future or industry 4.0) increases the interest in this technique and relaunches the debate on several issues related to their implementation (network design, cycle planning, node positions, etc.). Among these issues, network design and the identification of optimal node positions remains a very complex task that requires the mobilization of experts with a wide range of skills (electronics, IT, network, optimization, etc.). This involves high costs related to planning and testing phases, before the implementation of the final solution (node deployment). In order to avoid these costs, upstream network design must be considered.

Keyphrases: Réseaux de capteurs sans fil, fitness approximation, fitness inheritence, hybridation, métaheuristique, neural networks

@booklet{EasyChair:2105,
  author    = {Mohamed Amin Benatia and M'Hamed Sahnoun and Anne Louis and David Baudry},
  title     = {Hybrid meta-heuristics for the optimization of the deployment of Wireless Sensor Networks (WSN)},
  howpublished = {EasyChair Preprint 2105},
  year      = {EasyChair, 2019}}