文章基本信息

标题：Improved multi-objective weighted clustering algorithm in Wireless Sensor Network
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作者：Hicham Ouchitachen ; Abdellatif Hair ; Najlae Idrissi 等
期刊名称：Egyptian Informatics Journal
印刷版ISSN：1110-8665
出版年度：2017
卷号：18
期号：1
页码：45-54
DOI：10.1016/j.eij.2016.06.001
出版社：Elsevier
摘要：In Wireless Sensor Networks (WSNs), the network's performance is usually influenced by energy constraint. Through a well-designed clustering algorithm, WSN's energy consumption can be decreased evidently. In this paper, an Improved Multi-Objective Weighted Clustering Algorithm (IMOWCA) is proposed using additional constraints to select cluster heads in WSN. IMOWCA aims at handling a WSN in some critical circumstances where each sensor satisfies its own mission depending on its location. In addition to fulfill its mission, the sensor tries to improve the quality of communication with its neighboring nodes. Our proposed algorithm divides the network into different clusters and selects the best performing sensors based on residual energy to communicate with the Base Station (BS). IMOWCA uses four critical parameters: EC"i: Energetic Characteristic of sensor i, DD"i: Degree Difference of sensor i, DC"i: Sum of distances between sensor i and its neighbors and DM"i: Mission distance of sensor i. To balance the consumed energy in different formed clusters, a Base Station Genetic Algorithm (BGA) is developed. Simulation results demonstrate that the proposed algorithms are advantageous in terms of convergence to the appropriate locations and efficients in regard to energy conservation in WSNs.
关键词：Wireless Sensor Networks ; Clustering ; Energy ; Mission cost ; Genetic algorithm