An Estimation of Distribution Algorithm Based Dynamic Clustering Approach for Wireless Sensor Networks
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The design of energy efficiency is a very challenging issue for wireless sensor networks (WSNs). Clustering provides an effective means of tackling the issue. It could reduce energy consumption of the nodes and prolong the network lifetime. However, cluster heads deplete more energy since they bear great load of receiving, aggregation and transmission data than sensor nodes in WSNs. Therefore, the load-balanced clustering is a most significant problem for WSNs with unequal load of the sensor nodes but it is known to be an NP-hard problem. In this paper, we introduce a new model for this problem in which the objective function is to maximize the overall minimum lifetime of the cluster heads. To solve this model, we propose a novel estimation of distribution algorithm based dynamic clustering approach (EDA-MADCA). In EDA-MADCA, a new vector encoding is introduced for representing a complete clustering solution and a probability matrix model is constructed to guide the individual search. In addition, EDA-MADCA merges the EDA based exploration and the local search based exploitation within the memetic algorithm framework. A minimum-lifetime-based local search strategy is presented to avoid invalid search and enhance the local exploitation of the EDA. Experiment results demonstrate that EDA-MADCA can prolong network lifetime, it outperforms the existing DECA algorithm in terms of various performance metrics.
KeywordsEnergy efficiency Load-balanced clustering Estimation of distribution algorithm Memetic algorithm Minimum lifetime Wireless sensor networks
This work was supported by National Natural Science Foundation of China (No. 61573277), the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. PolyU 15201414), the Fundamental Research Funds for the Central Universities, the Open Research Fund of the State Key Laboratory of Astronautic Dynamics under Grant 2016ADL-DW403, and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, the Open Projects Program of National Laboratory of Pattern Recognition. The authors would like to thank Mr. Xuan Liang and Dr. Ke Shang for their kind help and valuable suggestions. The authors are also thankful to the anonymous referees for their insightful comments and helpful suggestions which significantly improve the quality of manuscript.
Compliance with Ethical Standards
Conflict of interest
The authors have no conflicts of interest to declare.
We promise to comply with ethical standards. All authors have approved the manuscript and have contributed significantly for the paper.
This article does not contain any studies with human participants performed by any of the authors.
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