Design of an energy aware clustering scheme based on genetic algorithm in heterogeneous wireless sensor networks

One of the most effective techniques for energy management in wireless sensor networks is clustering. How to establish clusters and the method used to choose cluster heads are the most important factors in creating optimal clusters. In fact, appropriate cluster head selection results in longer network lifetime and more data delivery to the sink. In this paper, after exploring clustering algorithms based on computational intelligence techniques and evaluating the strengths and weaknesses of each, we provide two novel clustering schemes termed as Energy Aware Genetic Clustering Algorithm (EAGCA) and EAGCA*. In these centralized algorithms, we try to achieve an optimal clustering by using global information such as number of neighbor nodes, the energy of neighbor nodes, local distance and local traffic load on each node. Compared to some well-known clustering algorithms such as LEACH and EAERP, simulation results demonstrate that EAGCA and EAGCA* are able to create more appropriate clusters in terms of energy consumption, life time and total number of received data to the sink.