Document Type
Conference Proceeding
Abstract
Mesh network topologies are becoming increasingly popular in battery powered wireless sensor networks, primarily due to the extension of network range and resilience against routing failures. However, multi-hop mesh networks suffer from higher energy costs, and the routing strategy directly affects the lifetime of nodes with limited energy sources. Hence while planning routes there are trade-offs to be considered between individual and system-wide battery lifetimes. We present a novel multi-objective routing optimisation approach using evolutionary algorithms to approximate the optimal trade-off between minimum lifetime and the average lifetime of nodes in the network. In order to accomplish this combinatorial optimisation rapidly and thus permit dynamic optimisation for self-healing networks, our approach uses novel k-shortest paths based search space pruning in conjunction with a new edge metric, which associates the energy cost at a pair of nodes with the link between them. We demonstrate our solution on a real network, deployed in the Victoria & Albert Museum, London. We show that this approach provides better trade-off solutions in comparison to the minimum energy option, and how a combination of solutions over the lifetime of the network can enhance the overall minimum lifetime. © 2014 ACM.
DOI Link
Publication Date
2014-07-12
Event
GECCO '14: Genetic and Evolutionary Computation Conference
Publication Title
Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation
Publisher
ACM
Embargo Period
2024-11-22
Recommended Citation
Rahat, A., Everson, R., & Fieldsend, J. (2014) 'Multi-objective routing optimisation for battery-powered wireless sensor mesh networks', Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, . ACM: Available at: 10.1145/2576768.2598311
