Hybrid TGN-QHHO Framework for Optimized Resource Allocation, Trust-Aware Routing, and Secure Communication in WSN-Based IoT Networks

The foundation of today's colocation systems comes from wireless sensor networks (WSNs), which enable continuous sensing, communication, and decision-making among a huge number of distributed devices.Most of the resource allocation and routing techniques still facing limitations, such as a lack of energy efficiency, high latency, low adaptability to rapidly changing trust levels, and low scalability due to a high density of devices.As traditional deep learning and heuristic (rule-based, often trial-and-error) approaches use slightly different methods of selecting parameters; therefore, they do not produce optimal performance when viewed at a system level.In this article, we propose a hybrid combination of Temporal Graph Problems (TGP) to handle spatiotemporal allocation of resources, in combination with Quantum-Inspired Harris Hawks Optimization (QHHO) for routing that not only considers trust and congestion, but also maximizes the benefits of trust composite scores (TCS) while minimizing penalties associated with nodes that have a TCS ≤ 0.35.The TGP allows the modeling of spatially and temporally varied wireless energy (EE) demand, while QHHO assists in route optimization through multiobjective fitness functions, which balance between reliability, quality (of service) and energy efficiency (EE).The real-time analysis of relative signal strength indicator (RSSI) fluctuations provides insight into the potential for attacks on wormhole nodes, with the capability of isolating compromised nodes from the network.When we simulate the nodes 50,100,150 in WSN and apply hybrid TGP/QHHO technique to achieve energy efficiency that is 0.81, throughput of 0.87 and make span is 0.37 that improve the overall performance which is tested with pervious technique such as LSTM-GRU, TGP, GCM and GAT.Our statistical validation results confirm the robustness of our approach, as we obtained: MSE (0.0021 -0.0033); RMSE (0.0458 -0.0574); R2 (0.906 -0.931); which outperformed previous systems.The integration of our Quantum-Inspired technique (QHHO) with the use of TGP has resulted in not only the enhanced resource allocation, but also enhanced adaptive learning, and Enhanced Trust Aware Routing within Dynamic the IoT.