A heuristic crossover enhanced evolutionary algorithm for clustering wireless sensor network

Conference paper


Oladimeji, MO, Turkey, M and Dudley, S (2016). A heuristic crossover enhanced evolutionary algorithm for clustering wireless sensor network. EvoApplications Evostar 2016. Porto, Portugal 30 Mar - 01 Apr 2016 https://doi.org/10.1007/978-3-319-31204-0_17
AuthorsOladimeji, MO, Turkey, M and Dudley, S
TypeConference paper
Abstract

© Springer International Publishing Switzerland 2016.In this paper, a Heuristic-Crossover Enhanced Evolutionary Algorithm for Cluster Head Selection is proposed. The algorithm uses a novel heuristic crossover operator to combine two different solutions in order to achieve a high quality solution that distributes the energy load evenly among the sensor nodes and enhances the distribution of cluster head nodes in a network. Additionally, we propose the Stochastic Selection of Inactive Nodes, a mechanism inspired by the Boltzmann Selection process in genetic algorithms. This mechanism stochastically considers coverage effect in the selection of nodes that are required to go into sleep mode in order to conserve energy of sensor nodes. The proposed selection of inactive node mechanisms and cluster head selections protocol are performed sequentially at every round and are part of the main algorithm proposed, namely the Heuristic Algorithm for Clustering Hierarchy (HACH). The main goal of HACH is to extend network lifetime of wireless sensor networks by reducing and balancing the energy consumption among sensor nodes during communication processes. Our protocol shows improved performance compared with state-of-the-art protocols like LEACH, TCAC and SEECH in terms of improved network lifetime for wireless sensor networks deployments.

Keywords08 Information And Computing Sciences; Artificial Intelligence & Image Processing
Year2016
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Journal citation9597, pp. 251-266
ISSN0302-9743
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-319-31204-0_17
Accepted author manuscript
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File Access Level
Open
Publication dates
Print23 Aug 2016
Publication process dates
Deposited12 Jun 2017
Accepted16 Feb 2016
ISBN9783319312033
Additional information

Conference paper published in Lecture Notes in Computer Science, 9597, 251-266.

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https://openresearch.lsbu.ac.uk/item/872qw

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