Empirical Analysis of Energy Harvesting and Clustering Methods for D2D Communication Systems

Unmanned Aerial vehicle (UAV)-based post-disaster communications face significant problems due to their energy consumption and operational range. Energy harvesting is used to overcome these problems by providing power for communication during disasters and extending the lifespan of wireless communication for networks. In order to expand the network's overall coverage and offer continuous connection during the disaster and post-disaster periods, Device-to-Device (D2D) communication and clustering methods were used. And Emergency Communication System (ECS) that uses the best Cluster Head (CH) technology to increase transfer energy effectiveness for long-term connectivity of the network. The clustering method and reinforcement learning in D2D connections are able to collect energy to extend the network lifespan with the help of UAV model. These strategies are predicted to increase the network's constancy during the emergency situations. The enhanced features with the use of the best CHs, and the path loss was significantly decreased. Hence, it reduces the computing complexity of network architecture to recover from natural disasters and also save numerous lives.