Weighted Adaptive Clustering Algorithm for Wireless Sensor Networks

Wireless sensor networks (WSNs) are an emerging, fast-growing technology, and the growing interest can be attributed to new applications enabled by large-scale networks. WSN applications are used in both industrial and commercial environments, some of the applications are object tracking, habitat monitoring, fire detection, traffic monitoring, and area monitoring. We concern adaptive clustering approaches to extended the life time of sensor nodes and increases the network lifetime To implement this approach we used cluster head mechanism in computing of the weight of each node in the network is one of the proposed techniques to deal with this problem. In this we propose an energy efficient and safe weighted adaptive clustering algorithm (WACA) for WSNs using a combination of five metrics. Among these metrics lie 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. We use simulation study to demonstrate the performance of the proposed algorithm. Keywords— WSN, Adaptive clustering, Weighted Adaptive Clustering Algorithm, Malicious node.