Efficient on-demand multi-node charging techniques for wireless sensor networks

Khelladi, Lyes; Djenouri, Djamel; Rossi, Michele; Badache, Nadjib

Abstract

This paper deals with wireless charging in sensor networks and explores efficient policies to perform simultaneous multi-node power transfer through a mobile charger (MC).The proposed solution, called On-demand Multi-node Charging (OMC), features an original threshold-based tour launching (TTL) strategy, using request grouping, and a path planning algorithm based on minimizing the number of stopping points in the charging tour. Contrary to existing solutions, which focus on shortening the charging delays, OMC groups incoming charging requests and optimizes the charging tour and the mobile charger energy consumption. Although slightly increasing the waiting time before nodes are charged, this allows taking advantage of multiple simultaneous charges and also reduces node failures. At the tour planning level, a new modeling approach is used. It leverages simultaneous energy transfer to multiple nodes by maximizing the number of sensors that are charged at each stop. Given its NP-hardness, tour planning is approximated through a clique partitioning problem, which is solved using a lightweight heuristic approach. The proposed schemes are evaluated in offline and on-demand scenarios and compared against relevant state-of-the-art protocols. The results in the offline scenario show that the path planning strategy reduces the number of stops and the energy consumed by the mobile charger, compared to existing offline solutions. This is with further reduction in time complexity, due to the simple heuristics that are used. The results in the on-demand scenario confirm the effectiveness of the path planning strategy. More importantly, they show the impact of path planning, TTL and multi-node charging on the efficiency of handling the requests, in a way that reduces node failures and the mobile charger energy expenditure.

• Journal Article Type: Article
• Acceptance Date: Oct 9, 2016
• Online Publication Date: Oct 11, 2016
• Publication Date: Mar 15, 2017
• Deposit Date: Jan 21, 2020
• Publicly Available Date: Jan 22, 2020
• Journal: Computer Communications
• Print ISSN: 0140-3664
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 101
• Pages: 44-56
• DOI: https://doi.org/10.1016/j.comcom.2016.10.005
• Keywords: Sensor networks; Wireless energy transfer; Mobile charger scheduling; Magnetic resonance coupling
• Public URL: https://uwe-repository.worktribe.com/output/5193065
• Publisher URL: https://doi.org/10.1016/j.comcom.2016.10.005