Energy Efficiency Relaying Election Mechanism for 5G Internet of Things: A Deep Reinforcement Learning Technique

Recently, we have witnessed an operational deployment of 5G in big cities to increase the communication capacity of users. However, with the ever-increasing of devices in dense networks, the current 5G could fail due to its unscalable issues. Scientists in academia and industry are actively analyzing this open issue within the context of Beyond 5G (B5G) and 6G before their realistic deployment. Moreover, the high densities of devices lead to the challenges of over-energy consumption and unfair Quality of Service (QoS) caused by the distances from the base station and existing obstacles separating the communicating devices. The network relay election method can be seen as an appropriate option to overcome these challenges, but this solution should be optimized according to the environment's dynamics. Therefore, in this paper, we propose an energy-efficient election-based method that selects relays so that it maximizes the overall network lifetime while maintaining a certain level of QoS. Considering that our system is deployed in an unknown dynamic environment, we employ a deep reinforcement learning technique that aims to optimize the network relay selection, maximize the residual energy levels of devices, and ensure fair and high data rates. Our system has been tested through a discrete-event simulator, and its performances have been evaluated and compared to a set of baseline and benchmark methods. It has been shown that our proposed system outperforms existing relevant solutions.