In wireless sensor networks (WSNs), major optimization issue includes the saving of energy. In order to make an energy efficient network, clustering techniques are used. In hierarchical based WSNs, Cluster Head (CH) selection plays a major role. CH needs to collect the information from its members and the information has to be aggregated and forwarded to BS. So it requires additional energy. The lifetime of a network is affected due to the wrong selection of CH. So, suitable optimization algorithms have to be used to select the CH in order to make a network which is energy efficient. In this research, K-means and Ant Lion Optimization algorithms are combined to form a hybrid clustering technique for doing cluster analysis in an optimum way. Ant Lion Optimization (ALO) is a stochastic global optimization model and it is termed as called HKALO. The proposed K-means clustering-based routing protocol is energy efficient and based on radio parameters and condition of the channel, optimum packet size are considered. Individual node’s energy consumption is reduced by this technique and enhances the network’s lifetime. The transmission of data between cluster head to cluster member and cluster head to base station are done by assuming different levels of power. In terms of lifetime of a network and throughput, the proposed algorithm shown better performance when compared to conventional clustering algorithm as shown by simulation results.
Volume 11 | 11-Special Issue