Ontology type: schema:ScholarlyArticle
2019-03-16
AUTHORSMohammed M. Ahmed, Essam H. Houssein, Aboul Ella Hassanien, Ayman Taha, Ehab Hassanien
ABSTRACTThe sink nodes in large-scale wireless sensor networks (LSWSNs) are responsible for receiving and processing the collected data from sensor nodes. Identifying the locations of sink nodes in LSWSNs play a vital role in term of saving energy. Furthermore, sink nodes have extremely extra resources such as large memory, powerful batteries, long-range antenna, etc. This paper proposes a multi-objective whale optimization algorithm (MOWOA) to determine the lowest number of sink nodes that cover the whole network. The major aim of MOWOA is to reduce the energy consumption and prolongs the lifetime of LSWSNs. To achieve these objectives, a fitness function has been formulated to decrease energy consumption and maximize the network’s lifetime. The experimental results revealed that the proposed MOWOA achieved a better efficiency in reducing the total power consumption by 26% compared with four well-known optimization algorithms: multi-objective grasshopper optimization algorithm, multi-objective salp swarm algorithm, multi-objective gray wolf optimization, multi-objective particle swarm optimization over all networks sizes. More... »
PAGES1-17
http://scigraph.springernature.com/pub.10.1007/s11235-019-00559-7
DOIhttp://dx.doi.org/10.1007/s11235-019-00559-7
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