Energy optimized Routing Protocol For Heterogeneous Wireless Body Area

Abstract

newlineThe advancement of technology, particularly with 5G and the Internet-of-Things (IoT), newlinehas led to the development of Wireless Body Area Networks (WBAN). Such networks newlineshow significant applications in healthcare sectors. However, a key issue in their newlinepractical deployment is the efficient management of energy. As sensors in WBANS newlinecontinuously sense and transmit data, they face limitations in energy resources. newlineTherefore, optimizing energy utilization in these networks is crucial for their effective newlineoperation. newlineTo address this, the research first proposes a Modified Grey Wolf Optimization with QLearning newline(MGWOQL) algorithm for WBAN. The effectiveness of MGWOQL is newlinedemonstrated through comparisons with other optimization algorithms, highlighting its newlinesuperior convergence in optimizing the fitness function. However, the performance of newlineMGWOQL with increasing users leads to slow convergence towards solution. To newlineovercome these limitations, the research extends to a Multi-Objective Quantum- newlineInspired Grey Wolf Optimization (MOQIGWO) to achieve energy-efficiency, heat newlinedissipation control and congestion control. This novel protocol leverages quantum newlinecomputing concepts like superposition and entanglement to achieve a balance between newlineexploitation and exploration in routing decisions, resulting in faster convergence and newlineenhanced results. MOQIGWO introduces a hierarchical three-layered communication newlinemodel for WBANs, consisting of intra-WBAN communication, inter-WBAN newlinecommunication, and a centralized collecting node for remote access. The results of the newlineanalysis show that increasing the density of nodes in a WBAN can improve energy newlineefficiency. The Thermal-Aware, Energy-Efficient, Congestion-Aware Routing Protocol newline(TECRP) technique shows improved performance over other methods like the Cuckoo newlineSearch Optimization Algorithm by 9.92% and the Energy Efficient Sustainable network newlineusing Network Optimisation Technique (EES-NOT) by 0.42%.