Deep-Reinforcement-Learning-Based Energy-Efficient Resource Management for Social and Cognitive Internet of Things

Abstract

Internet of Things (IoT) has attracted much interest due to its wide applications, such as smart city, manufacturing, transportation, and healthcare. Social and cognitive IoT is capable of exploiting social networking characteristics to optimize network performance. Considering the fact that the IoT devices have different Quality-of-Service (QoS) requirements [ranging from ultrareliable and low-latency communications (URLLCs) to minimum data rate], this article presents a QoS-driven social-aware-enhanced device-to-device (D2D) communication network model for social and cognitive IoT by utilizing social orientation information. We model the optimization problem as a multiagent reinforcement learning formulation, and a novel coordinated multiagent deep-reinforcement-learning-based resource management approach is proposed to optimize the joint radio block assignment and the transmission power control strategy. Meanwhile, the prioritized experience replay (PER) and the coordinated learning mechanisms are employed to enable communication links to work cooperatively in a distributed manner, which enhances the network performance and access success probability. The simulation results corroborate the superiority in the performance of the presented resource management approach, and it outperforms other existing approaches in terms of meeting the energy efficiency and the QoS requirements.