Safety of Mobile Wireless Sensor Networks Based on Clustering Algorithm

Safety of Mobile Wireless Sensor Networks Based on Clustering Algorithm

Amine Dahane (Intelligent Systems Research Laboratory, University of Sciences and Technology of Oran, Oran, Algeria), Nasr-Eddine Berrached (Intelligent Systems Research Laboratory, University of Sciences and Technology of Oran, Oran, Algeria) and Abdelhamid Loukil (Intelligent Systems Research Laboratory, University of Sciences and Technology of Oran, Oran, Algeria)
DOI: 10.4018/IJWNBT.2016010105

Abstract

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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2. A Survey Of Clustering Schemes

In this section, a review of some approaches of clustering in ad-hoc networks and WSNs is provided. A summary of all the examined algorithms is presented in Table 1. Using this table, we want to further clarify the main features of different types of clustering approaches. Furthermore, this way of presenting makes easier the comparison between various techniques. The first column specifies the algorithm name. The second column specifies the authors of the proposed approach. The other columns are devoted to the most representative grouping functions with which we can easily see the basic differences and common aspects of different approaches. The meaning of each column is explained in the following text:

Table 1.
A Survey of clustering schemes

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