A Hybrid Quantum-driven Optimization Model for Congestion-aware and Energy-efficient Resource Allocation in IoT Networks - Bhushan, Yannam Bharath; Shivampeta, Aparna (International Journal of Sensors Wireless Communications and Control, 2025)
Introduction The increasing expansions of IoT networks enforce the adoption of efficient resource allocation, energy management, and network congestion control. Methods In this regard, this paper proposes a brand-new hybrid quantum-driven optimization model integrating Pyramid Quantum Neural Network (Py-QNN), Deep Long Short-Term Memory (DLSTM), and Multi-fragmented Jaya Puzzle Optimization (FJPO). This optimizes energy consumption to the minimum, latency to the minimum, throughput to the maximum, and network lifetime by increasing the multi-layer architecture of cluster-based communication. A comparative study with models like LEACH, PEGASIS, and Direct Transmission shows better performance. Results Simulation results show a reduction in energy consumption by up to 60%, 30-50% lower communication delay, and a throughput increase of 25%. Conclusion The proposed model is scalable and adaptable in real-time. Hence, it is suitable for large-scale dynamic IoT environments.
Keywords: routing congestion control