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
10.1145/2576768.2598311
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: https://doi.org/10.1145/2576768.2598311
