Energy efficient clustering algorithms for wireless sensor networks: novel chemical reaction optimization approach

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• 作者：P. C. Srinivasa Rao ; Haider Banka
• 关键词：Clustering ; NP ; hard ; Chemical reaction optimization ; Wireless sensor networks
• 刊名：Wireless Networks
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：23
• 期：2
• 页码：433-452
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Communications Engineering, Networks; Computer Communication Networks; Electrical Engineering; IT in Business;
• 出版者：Springer US
• ISSN：1572-8196
• 卷排序：23

文摘

Clustering has been accepted as one of the most efficient techniques for conserving energy of wireless sensor networks (WSNs). However, in a two-tiered cluster based WSN, cluster heads (CHs) consume more energy due to extra overload for receiving data from their member sensor nodes, aggregating them and transmitting that data to the base station (BS). Therefore, proper selection of CHs and optimal formation of clusters play a crucial role to conserve the energy of sensor nodes for prolonging the lifetime of WSNs. In this paper, we propose an energy efficient CH selection and energy balanced cluster formation algorithms, which are based on novel chemical reaction optimization technique (nCRO), we jointly called these algorithms as novel CRO based energy efficient clustering algorithms (nCRO-ECA). These algorithms are developed with efficient schemes of molecular structure encoding and potential energy functions. For the energy efficiency, we consider various parameters such as intra-cluster distance, sink distance and residual energy of sensor nodes in the CH selection phase. In the cluster formation phase, we consider various distance and energy parameters. The algorithm is tested extensively on various scenarios of WSNs by varying number of sensor nodes and CHs. The results are compared with original CRO based algorithm, namely CRO-ECA and some existing algorithms to demonstrate the superiority of the proposed algorithm in terms of energy consumption, network lifetime, packets received by the BS and convergence rate.