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An innovative hyperheuristic, gaussian clustering scheme for energy-efficient optimization in wireless sensor networks

dc.contributor.authorAroba, Oluwasegun Juliusen_US
dc.contributor.authorNaicker, Nalindrenen_US
dc.contributor.authorAdeliyi, Timothyen_US
dc.date.accessioned2023-07-20T13:35:40Z
dc.date.available2023-07-20T13:35:40Z
dc.date.issued2021-02-11
dc.date.updated2023-06-30T10:02:01Z
dc.description.abstractEnergy stability on sensor nodes in wireless sensor networks (WSNs) is always an important challenge, especially during data capturing and transmission of packets. The recent advancement in distributed clustering algorithms in the extant literature proposed for energy efficiency showed refinements in deployment of sensor nodes, network duration stability, and throughput of information data that are channelled to the base station. However, much scope still exists for energy improvements in a heterogeneous WSN environment. This research study uses the Gaussian elimination method merged with distributed energy efficient clustering (referred to as DEEC-Gauss) to ensure energy efficient optimization in the wireless environment. The rationale behind the use of the novel DEEC-Gauss clustering algorithm is that it fills the gap in the literature as researchers have not been able to use this scheme before to carry out energy-efficient optimization in WSNs with 100 nodes, between 1,000 and 5000 rounds and still achieve a fast time output. In this study, using simulation, the performance of highly developed clustering algorithms, namely, DEEC, EDEEC_E, and DDEEC, was compared to the proposed Gaussian Elimination Clustering Algorithm (DEEC-Gauss). The results show that the proposed DEEC-Gauss Algorithm gives an average percentage of 4.2% improvement for the first node dead (FND), a further 2.8% improvement for the tenth node dead (TND), and the overall time of delivery was increased and optimized when compared with other contemporary algorithms.</jats:p>en_US
dc.format.extent12 pen_US
dc.identifier.citationAroba, O.J.; Naicker, N. and Adeliyi, T. 2021. An innovative hyperheuristic, gaussian clustering scheme for energy-efficient optimization in wireless sensor networks. Journal of Sensors. 2021: 1-12. doi:10.1155/2021/6666742en_US
dc.identifier.doi10.1155/2021/6666742
dc.identifier.issn1687-725X
dc.identifier.issn1687-7268 (Online)
dc.identifier.urihttps://hdl.handle.net/10321/4897
dc.language.isoenen_US
dc.publisherHindawi Limiteden_US
dc.relation.ispartofJournal of Sensors; Vol. 2021en_US
dc.subject0303 Macromolecular and Materials Chemistryen_US
dc.subject0306 Physical Chemistry (incl. Structural)en_US
dc.titleAn innovative hyperheuristic, gaussian clustering scheme for energy-efficient optimization in wireless sensor networksen_US
dc.typeArticleen_US

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