A Quantum-Inspired Bat and Harris Hawks Optimization Algorithm for Heterogeneous Wireless Sensor Networks

Abstract

Data aggregation is one major problem in heterogeneous wireless sensor networks (WSNs) where nodes possess varying sensing, computation, and communication capabilities. In order to fulfill the requirements of energy efficiency, latency, and optimization of the network lifetime, we introduce the QIBOA_HHO_Hybrid protocol, which is a mix of the Quantum-Inspired Binary Optimization Algorithm (QIBOA) and the Harris Hawks Optimization (HHO) algorithm. The hybrid protocol synergistically blends QIBOA's quantum-inspired parallel search to gain faster convergence with HHO's adaptive exploitation methods to optimize routing and clustering decisions dynamically. By prioritizing the most important energy-aware cluster head (CH) selection based on proximity and residual energy, the protocol balances the load and minimizes energy consumption. Simulation results verify that QIBOA_HHO_Hybrid outperforms conventional protocols SEP, DEEC, Z-SEP, and PSO-ECSM, with less latency, more throughput, and more network lifetime. By fusing quantum optimization while simulations suggest a compromise with energy efficiency and latency compared to some existing protocols, adaptive clustering, and HHO's cooperative predation-inspired methods, scalability and reliability are enhanced in dynamic environments, and it is a trusted solution to large-scale heterogeneous WSNs.