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dc.contributor.authorRahat, AAMen
dc.contributor.authorEverson, RMen
dc.contributor.authorFieldsend, JEen
dc.date.accessioned2018-10-19T19:58:05Z
dc.date.available2018-10-19T19:58:05Z
dc.date.issued2014-01-01en
dc.identifier.isbn9781450326629en
dc.identifier.urihttp://hdl.handle.net/10026.1/12585
dc.description.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.

en
dc.format.extent1175 - 1182en
dc.language.isoenen
dc.titleMulti-objective routing optimisation for battery-powered wireless sensor mesh networksen
dc.typeConference Contribution
plymouth.publication-statusPublisheden
plymouth.journalGECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conferenceen
dc.identifier.doi10.1145/2576768.2598311en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/00 Groups by role
plymouth.organisational-group/Plymouth/00 Groups by role/Academics
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Computing, Electronics and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
dc.rights.embargoperiodNot knownen
rioxxterms.versionofrecord10.1145/2576768.2598311en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.typeConference Paper/Proceeding/Abstracten


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