Application of Deep Neural Networks in Multi-Hop Wireless Sensor Network (WSN) Channel Optimization

Abstract Optimizing communication channels in multi-hop wireless sensor networks (WSNs) is critical for improving network efficiency, energy consumption, and data transmission reliability. Traditional optimization methods often rely on heuristic algorithms, which may struggle with dynamic network conditions and high-dimensional feature spaces. This paper explores the application of deep neural networks (DNNs) to optimize WSN channel allocation and routing strategies. By leveraging deep learning, the model learns adaptive transmission policies that minimize interference, reduce latency, and enhance overall network performance. The proposed framework integrates reinforcement learning techniques with convolutional and recurrent architectures to capture spatial-temporal variations in channel quality. Experimental results demonstrate that the DNN-based approach outperforms conventional methods in terms of throughput, energy efficiency, and network stability under varying traffic loads and environmental conditions. These findings highlight the potential of deep learning for real-time, intelligent WSN channel optimization.