Harnessing Quantum Convolutional Neural Networks for Enhanced Coverage Hole Detection and Efficient Routing in Iot-Assisted Wsns

WSNs are an essential part of many applications, such as environmental monitoring, healthcare, and security systems, that collect and transmit important data. However, WSNs suffer from several challenges that make them inefficient, such as energy consumption, detection of coverage holes, and optimization of routing paths. Such problems can have serious effects on the performance and life of the network. Traditional solutions are incapable of balancing energy usage, coverage hole detection, and routing efficacy. This paper introduces an integrated approach toward improving the performance of WSNs by addressing energy efficiency, coverage hole detection, and routing optimization. The proposed system will integrate Dandelion Optimizer (DO) for energy-efficient cluster head selection, Quantum Convolutional Neural Network (QCNN) for high-accuracy coverage hole detection, and War Strategy Optimization (WSO) for dynamic routing optimization. DO improves the network lifespan by balancing energy consumption. Meanwhile, QCNN achieves 98.9% accuracy in coverage hole detection. WSO improves routing efficiency by 25%, reduces latency, and optimizes network parameters. This hybrid approach is an important improvement of WSN performance to ensure long-term operational stability and reliability. The results are based on the impact that integrated methods have on improving network performance and overcoming energy inefficiency, coverage gaps, and poor routing issues while ensuring long-term stability of the network with high overall performance.