Balancing Energy Consumption in Heterogeneous Wireless Sensor Networks Using Genetic Algorithm

In a heterogeneous Wireless Sensor Network (WSN), factors such as initial energy, data processing capability, etc. greatly influence the network lifespan. Despite the success of various clustering strategies of WSN, the numerous possible sensor clusters make searching for an optimal network structure an open challenge. In this paper, we propose a Genetic Algorithm based method that optimizes heterogeneous sensor node clustering. Compared with five state-of-the-art methods, our proposed method greatly extends the network life, and the average improvement with respect to the second best performance based on the first-node-die and the last-node-die is 33.8% and 13%, respectively. The balanced energy consumption greatly improves the network life and allows the sensor energy to deplete evenly. The computational efficiency of our method is comparable to the others and the overall average time across all experiments is 0.6 seconds with a standard deviation of 0.06.