Optimization of Cluster Head Selection Using Bacterial Foraging Algorithm for Energy-Efficient Routing in Wireless Body Area Networks

Optimizing cluster head (CH) selection in Wireless Body Area Networks (WBANs) using the Bacterial Foraging Optimization (BFO) algorithm. The approach aims to enhance network performance by minimizing energy consumption, balancing node load, and ensuring reliable communication. The process begins with the initialization of bacteria, representing potential CH candidates, which are randomly distributed within the network. A multi-objective fitness function evaluates each bacterium based on residual energy, proximity to the sink, and data load distribution. The BFO algorithm employs chemotaxis for exploring the solution space, guided by both directed and random movements. The reproduction phase ensures that the most efficient bacteria propagate their traits to subsequent generations, while the elimination-dispersal phase prevents stagnation by introducing diversity in the solution space. The iterative process continues until a stopping criterion, such as convergence or maximum iterations, is met. The optimal CHs are selected based on the fitness evaluation, ensuring energy efficiency and balanced operation within the WBAN.