IoT Communication Resource Management Method Based on Intelligent Reflective Surface Collaboration Mechanism

Intelligent Reflecting Surface (IRS) technology can effectively enhance the system capacity and energy efficiency of collaborative IoT systems. However, existing resource management methods suffer from slow convergence, susceptibility to local optima, and high computational complexity when dealing with large-scale reflector configurations. This paper proposes a collaborative IoT communication resource management method for IRS based on the Quantum Marine Predators Algorithm (QMPA). By integrating the quantum revolving door mechanism with the cooperative search behavior of marine predators, the method achieves joint optimization of transmission power and phase shift matrices through three evolutionary phases: exploration, parallel predation, and predation. This ensures rapid convergence and robust global exploration with strong stability. Through dynamic position updates and adaptive quantum evolution, the algorithm effectively avoids local optima, achieving high-precision resource allocation. This method enables accurate, robust, and energy-efficient resource management in complex and dynamic network environments. Its superior convergence performance and adaptability make it highly suitable for large-scale IoT deployments and real-time engineering applications, providing a reliable foundation for intelligent sensing, environmental monitoring, and autonomous control.