A Novel Low-Complex Optimized Resource Allocation Algorithm Using GWO Optimization Technique in Energy Scavenging for WBAN

Abstract

In wireless body area networks (WBANs) powered by energy scavenging, effectively managing renewable energy is critical to ensuring delay-sensitive services. This paper proposes a novel low-complex optimized resource allocation algorithm using the Grey Wolf Optimization (GWO) technique to allocate resources, specifically energy and communication channels, and maximize user utility while guaranteeing the worst-case delay. To achieve this, firstly, a formulation of a user utility optimization problem that accounts for the stochastic nature of energy scavenging and consumption without requiring prior knowledge of these processes is performed. Utilizing GWO optimization techniques, optimization problems broke down into four sub-problems: battery management, collection rate control, transmission power allocation, and drop rate control. Additionally, the proposed algorithm’s performance is analyzed by examining the upper bounds of queues and required battery capacity. Simulation results confirm our theoretical analysis and the effectiveness of the proposed algorithm.