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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/577669
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dc.contributor.authorBiju Paul
dc.contributor.authorN. Kumar
dc.date.accessioned2023-11-06T02:46:17Z-
dc.date.available2023-11-06T02:46:17Z-
dc.date.issued2016-07
dc.identifier.issn0128-7680
dc.identifier.otherukmvital:82901
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/577669-
dc.descriptionEnergy is a main issue in wireless sensor networks (WSN) that function on limited power supplies like batteries. Minimisation of energy utilisation is a promising area of research in WSN. However, energy utilisation of partitioning data still remains a demanding issue. With the objective of reducing the energy utilisation partitioning multi-hop wireless communications, the Energy-Efficient Traffic Renovate Partitioning (EETRP) method is proposed in this paper. The network traffic partitioning method, phantom partitioning, is an efficient technique for practical conditions. The partitioning using double cut methods yields desirable results with significant reduction in energy utilisation. Renovating by the EETRP method provides enhanced data gathering by means of centroid mean point gathering. Crucial sites are recognised and connectivity is reestablished in data aggregation. The results prove that the EETRP method achieves significant improvement in performance over state-of-the-art methods in terms of network-connectivity rate, data-gathering accuracy, network-traffic rate, energy-utilisation rate, node-renovating efficiency and network-partitioning time.
dc.language.isoen
dc.publisherUniversiti Putra Malaysia Press
dc.relation.haspartPertanika Journal of Social Sciences & Humanities
dc.relation.urihttp://www.pertanika.upm.edu.my/regular_issues.php?jtype=2&journal=JST-24-2-7
dc.subjectEnergy-efficient traffic renovate partitioning
dc.subjectPartition gaps
dc.subjectPhantom partitioning
dc.subjectWireless sensor networks
dc.titleData gathering protocol for reducing energy utilisation in a wireless sensor network
dc.typeJournal Article
dc.format.volume24
dc.format.pages381-392
dc.format.issue2
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