A HYBRID QUANTUM-INSPIRED EVOLUTIONARY ALGORITHM FOR OPTIMIZING CROSS-LAYER DESIGN IN LOW-POWER WIDE-AREA NETWORKS

Abstract

The proliferation of Internet of Things (IoT) devices necessitates efficient communication protocols for Low-Power Wide-Area Networks (LPWANs). Traditional layered network architectures often lead to sub-optimal performance due to isolated decision-making, creating a critical need for cross-layer optimization. This paper proposes a novel Hybrid Quantum-Inspired Evolutionary Algorithm (HQIEA) to jointly optimize MAC and Network layer parameters—including spreading factor allocation, transmit power, and routing paths—aimed at maximizing network lifetime and reliability while minimizing latency. Leveraging the exploration strength of quantum-inspired computation enhanced by local search, the HQIEA effectively navigates the complex solution space. Extensive simulations conducted in NS-3 demonstrate that the proposed algorithm significantly outperforms standard metaheuristics, achieving a 23% longer network lifetime and a higher packet delivery ratio than a standard QIEA. The results validate HQIEA as a superior strategy for sustainable and high-performance LPWAN planning.