Prediction of Efficient Cluster Head by Optimizer Based Clustering Algorithm for Underwater Wireless Sensor Network

Underwater Wireless Sensor Networks (UWSNs) have recently become attractive because of the possibilities to track and monitor fish, coral reefs, and other marine creatures. Many clustering algorithms can be used for UWSNs, one of the most popular being the Optimizer-Based Clustering Algorithm (OBCA). OBCA is based on a genetic algorithm (GA), which optimizes an objective value using crossover and mutation operators to search for the optimal clustering solution in the context of UWSNs. OBCA has several advantages, such as high speed, scalability, and robustness in dynamic network environments. OBCA simultaneously takes topology, node density and node mobility into consideration when forming clusters. In addition, the algorithm is able to adjust the optimal clusters when important network conditions or parameters change. OBCA has been widely studied, tested, and implemented in numerous studies. As such, the OBCA algorithm is highly recommended for the implementation of UWSNs.