An energy-aware clustering approach based on the k-means method for wireless sensor networks

This paper describes a new approach to build energy aware clusters for Wireless Sensor Networks (WSN). The algorithm behind is based on the k-means method which is well known as a clustering technique and widely used in several fields of engineering and research. K-means clustering tends to find clusters of comparable spatial extent (density clustering). We try to enhance the clustering process by selecting nodes as clusters that are centric and have a high level of energy. This will give the same QoS results as given by the K-means approach with a reduction of energy consumption and a prolongation of the lifetime of the sensor network. For the simulation purposes, we have implemented our approach on the OLSR routing protocol which has been selected by our research team as a test bed routing protocol. We have compared our new approach (OLSR-Kmeans-Energy) with a previous work we developed and where we use a Max-Min heuristic (OLSR-MaxMin-Energy) to enhance QoS parameters and the lifetime of the WSN. Our new approach seems to give better results than the MaxMin approach. © Springer Science+Business Media Singapore 2016.