The main concern of clustering approaches for mobile wireless sensor networks (WSNs) is to prolong the battery life of the individual sensors and the network lifetime. For a successful clustering approach the need of a powerful mechanism to safely elect a cluster head remains a challenging task in many research works that take into account the mobility of the network. The approach based on the computing of the weight of each node in the network is one of the proposed techniques to deal with this problem. In this paper, we propose an energy efficient and safe weighted clustering algorithm (ES-WCA) for mobile WSNs using a combination of five metrics. Among these metrics lies the behavioral level metric which promotes a safe choice of a cluster head in the sense where this last one will never be a malicious node. Moreover, the highlight of our work is summarized in a comprehensive strategy for monitoring the network, in order to detect and remove the malicious nodes. We use simulation study to demonstrate the performance of the proposed algorithm.

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Clustering approaches for mobile wireless sensor networks (WSNs) tend to extend the battery life of the individual sensors and the network lifetime. Taking into account the mobility of the network, a powerful mechanism to safely elect a cluster head is a challenging task in many research works. As a proposed technique to deal with such problem, the approach based on the computing of the weight of each node in the network is chosen. This paper is intended to propose a new algorithm called “S-WCA” for safety of mobile sensor networks based on clustering algorithm using a combination of five metrics. Among these metrics lies the behavioral level metric which promotes a safe choice of a cluster head in the sense where this last one will never be a malicious node. Moreover, a summary of the highlight of the authors' work is provided in a comprehensive strategy for monitoring the network, so as to detect and remove the malicious nodes. Simulation study is used to demonstrate the performance of the proposed algorithm.

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