A Genetic Algorithm-Based, Dynamic Clustering Method Towards Improved WSN Longevity

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• 作者：Xiaohui Yuan ; Mohamed Elhoseny…
• 关键词：Wireless sensor networks ; Genetic algorithms ; Clustering ; Energy consumption
• 刊名：Journal of Network and Systems Management
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：25
• 期：1
• 页码：21-46
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Computer Communication Networks; Computer Systems Organization and Communication Networks; Communications Engineering, Networks; Information Systems and Communication Service; Operation Research/Decis
• 出版者：Springer US
• ISSN：1573-7705
• 卷排序：25

文摘

The dynamic nature of wireless sensor networks (WSNs) and numerous possible cluster configurations make searching for an optimal network structure on-the-fly an open challenge. To address this problem, we propose a genetic algorithm-based, self-organizing network clustering (GASONeC) method that provides a framework to dynamically optimize wireless sensor node clusters. In GASONeC, the residual energy, the expected energy expenditure, the distance to the base station, and the number of nodes in the vicinity are employed in search for an optimal, dynamic network structure. Balancing these factors is the key of organizing nodes into appropriate clusters and designating a surrogate node as cluster head. Compared to the state-of-the-art methods, GASONeC greatly extends the network life and the improvement up to 43.44 %. The node density greatly affects the network longevity. Due to the increased distance between nodes, the network life is usually shortened. In addition, when the base station is placed far from the sensor field, it is preferred that more clusters are formed to conserve energy. The overall average time of GASONeC is 0.58 s with a standard deviation of 0.05.