DISTRIBUTED ENTROPY ENERGY-EFFICIENT CLUSTERING ALGORITHM FOR HETEROGENEOUS WIRELESS SENSOR NETWORK BASED CHAOTIC FIREFLY ALGORITHM CLUSTER HEAD SELECTION

In Wireless Sensor Networks (WSN) the energy of Sensor nodes are not certainly sufficient. In order to optimize the endurance of WSN it is essential to minimize the utilization of energy. Moreover an eminent method to develop the endurance of WSN is to aggregate the WSN with higher energy, which is called as Head of group, called as Cluster Head (CH). For intra-cluster and inter-cluster communication CH becomes dependent. For complete WSN, the Energy level of CH extends its life of cluster. The complicated job is identifying the energy utilization amount of heterogeneous WSNs while evolving cluster algorithms. Based on Chaotic Firefly Algorithm CH (CFACH) Selection, the formulated work is named as “Novel Distributed Entropy Energy-Efficient Clustering Algorithm” in short DEEEC for HWSNs. The formulated DEEEC Algorithm, which is CH, has two main stages. In the first step, the identification of temporary CHs, along with its entropy value which is found using the correlative measure of residual and original energy. Along with this, in the cluster algorithm, the rotating epoch and its entropy value must be predicted by each of its sensor node automatically. In the next stage, if any member in the cluster having larger residual energy shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs, which is determined by the above two stages which is meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produces good results respect to the energy and increased lifetime, when it is correlated with the current traditional clustering protocols, which are used in the Heterogeneous WSNs.