Deep Learning-Enhanced Adaptive Node Placement in Wireless Sensor Networks

In order to enhance the efficacy, coverage, and energy efficiency of Wireless Sensor Networks (WSNs), this paper addresses the challenge of optimizing node positioning. We provide a unique deep learning-based strategy for adaptive node placement that makes use of cutting-edge neural network models to forecast ideal setups. We found that deep learning improves coverage ratio and energy usage over standard placement approaches, despite longer execution durations. According to the results, incorporating deep learning into node placement techniques has the potential to result in wireless sensor networks (WSNs) that are also more adaptive and efficient, which might have applications in a variety of real-world circumstances.