文章基本信息

标题：Fuzzy Gene Optimized Reweight Boosting Classification for Energy Efficient Data Gathering in WSN
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作者：J.Srimathi ; V.Valli Mayil
期刊名称：International Journal of Computer Networks & Communications
印刷版ISSN：0975-2293
电子版ISSN：0974-9322
出版年度：2019
卷号：11
期号：2
页码：1-18
DOI：10.5121/ijcnc.2019.11207
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：The energy is a major resource to obtain efficient data gathering and increasing network lifetime (NL). The various techniques are introduced for data aggregation, but energy optimized sensor node (SN) selection was not carried out to further enhance NL. In order to improve the energy efficient data gathering in WSN, a Fuzzy Gene Energy Optimized Reweight Boosting Classification (FGEORBC) Technique is introduced with lesser time consumption. In FGEORBC technique, the Residual Energy (RE) of SN in the WSN is computed. After calculating SN residual energy, fuzzy logic is applied to determine higher energy nodes and lower energy nodes using threshold value. For finding the optimal higher energy SNs, then Ranked Gaussian gene optimization technique is applied. If the node satisfies the fitness criterion, then the node is selected as an optimal higher energy SN. Otherwise, the rank selection, ring crossover, and Gaussian mutation process are carried out until the condition gets satisfied. After that, the sink node collects the data packets (DP) from the optimal higher energy SNs. In the sink node, Reweight Boosting Classification is carried out to classify the sensed DP and it sends to the base station (BS) for further processing. Simulation of FGEORBC technique is carried out using different parameters such as energy consumption (EC), NL, data gathering time and classification accuracy (CA) with respect to a number of SN and a number of DP. The results observed that FGEORBC technique improves the data gathering and NL with minimum time as well as EC than the state-of-the-art methods..
关键词：WSN; data gathering; residual energy; fuzzy logic; Ranked Gaussian gene optimization; data classification;Reweight Boosting Classification