Abstract—This study proposes a genetic algorithm-based (GA-based) adaptive clustering protocol with an optimal probability prediction to achieve good performance in terms of lifetime of network in wireless sensor networks. The proposed GA-based protocol is based on LEACH, called LEACH-GA herein, which basically has set-up and steady-state phases for each round in the protocol and an additional preparation phase before the beginning of the first round. In the period of preparation phase, all nodes initially perform cluster head selection process and then send their messages with statuses of being a candidate cluster head or not, node IDs, and geographical positions to the base station. As the base station received the messages from all nodes, it then searches for an optimal probability of nodes being cluster heads via a genetic algorithm by minimizing the total energy consumption required for completing one round in the sensor field. Thereafter, the base station broadcasts an advertisement message with the optimal value of probability to the all nodes in order to form clusters in the following set-up phase. The preparation phase is performed only once before the set-up phase of the first round. The processes of following set-up and steady-state phases in every round are the same as LEACH. Simulation results show that the proposed genetic-algorithm-based adaptive clustering protocol effectively produces optimal energy consumption for the wireless sensor networks, and resulting in an extension of lifetime for the network.
Index Terms—Adaptive clustering protocol, clustering head, genetic algorithm, optimal probability, lifetime.
Jenn-Long Liu is with the I-Shou University, Kaohsiung, 84001 Taiwan (corresponding author to provide phone: +886-7-6577711 ext. 6579; fax: +886-7-6578491; e-mail: jlliu@isu.edu.tw). Chinya V. Ravishankar is with the Department of Computer Science & Engineering, Riverside, CA 92507 USA (e-mail: ravi@cs.ucr.edu).
Cite: Jenn-Long Liu and Chinya V. Ravishankar, "LEACH-GA: Genetic Algorithm-Based Energy-Efficient Adaptive Clustering Protocol for Wireless Sensor Networks," International Journal of Machine Learning and Computing vol. 1, no. 1, pp. 79-85, 2011.