Transfer Learning for Efficient Node Placement in Dynamic Wireless Sensor Networks

Transfer learning is employed in this paper to introduce a novel method for node allocation in dynamic Wireless Sensor Networks (WSNs). In dynamic situations, traditional node placement techniques typically face challenges in terms of flexibility and efficiency. We propose a transfer learning approach that utilizes information from past network deployments to improve the design of node placement techniques in novel situations. In comparison to traditional approaches, the suggested method exhibits substantial enhancements in coverage ratio, energy efficiency, and execution time. The findings of our study suggest that transfer learning has the potential to significantly enhance network performance, thereby presenting a viable approach for dynamic WSNs. Improved network management and flexibility in different settings are among the ramifications of this study.