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Towards optimized one-step clustering approach in wireless sensor networks

Merabtine, Nassima; Djenouri, Djamel; Zegour, Djamel-Eddine; Bounnssairi, Adel; Rahmani, Khaled

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Authors

Nassima Merabtine

Djamel-Eddine Zegour

Adel Bounnssairi

Khaled Rahmani



Abstract

This paper introduces a nonlinear integer programming model for the clustering problem in wireless sensor networks, with a threefold contribution. First, all factors that may influence the energy consumption of clustering protocols, such as cluster-heads selection and distribution, are considered implicitly in the model. Second, an innovative fitness function that directly maximizes the WSN lifetime is proposed. Finally, a global optimum of the whole network lifespan clustering schemes is targeted. The proposed model is then solved by a particle swarm optimization meta-heuristic based algorithm. This algorithm follows a centralized one-step off-line approach, in which the clustering schemes of the whole network lifetime and their associated durations are computed at the initialization of the network operations. The one-step approach is enabled by an energy prediction mechanism that allows to reduce the costs of the periodic network re-clustering. Simulation results show clear enhancement in network lifespan and number of received data packets as compared to some state-of-the-art clustering approaches.

Citation

Merabtine, N., Djenouri, D., Zegour, D., Bounnssairi, A., & Rahmani, K. (2021). Towards optimized one-step clustering approach in wireless sensor networks. Wireless Personal Communications, 120, 1501–1523. https://doi.org/10.1007/s11277-021-08521-0

Journal Article Type Article
Acceptance Date Apr 10, 2021
Online Publication Date May 3, 2021
Publication Date 2021-09
Deposit Date Apr 22, 2021
Publicly Available Date May 4, 2022
Journal Wireless Personal Communications
Print ISSN 0929-6212
Electronic ISSN 1572-834X
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 120
Pages 1501–1523
DOI https://doi.org/10.1007/s11277-021-08521-0
Public URL https://uwe-repository.worktribe.com/output/7283556

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