Abstract Wireless body area networks (WBANs) have evolved into effective options for various sports, military, and healthcare applications.
Abstract Wireless body area networks (WBANs) have evolved into effective options for various sports, military, and healthcare applications. Most of the research proposed looking at effective data collection from individuals and conventional WBANs. Energy consumption is critical in WBANs, particularly in implantable wearable sensors that are challenging to access and replace. Various energy-efficiency data collection methods are employed to send data from body sensors to the server base station (BS). To begin, this work presents an efficient algorithm named “Artificial Bee Colony Optimization (ABC) and Chicken Swarm Optimization (CSO) algorithm” to create hybrid trees for data aggregation in networks. ABC-CSO has three phases: clustering, cluster head (CH) selection, and data transmission. It categorizes the wearable sensors into groups and uses the ABC method to choose the best CH for each group. Each sensor sends data to its corresponding relay node (RN) or CH. The CSO method is used to route the aggregated data to the BS. The proposed scheme considers the biosensor routing data’s distance and residual energy. It reduces grid energy consumption and balances the energy required by various biosensors. Furthermore, the simulation outcomes demonstrate that the proposed method can handle rapidly varying WBAN architectures while maintaining a balanced energy consumption and reliability. The proposed work simulates a variety of scenarios to show the suggested algorithm’s superiority over Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), ABC, and Grey Wolf Optimization (GWO) protocols. Compared to the ACO, PSO, ABC, and GWO protocols, the proposed protocol, i.e., ABC-CSO, has increased the throughput by 26.68%, 21.87%, 18.41%, and 8.79% respectively.
