Intelligent-Driven Green Resource Allocation for Industrial Internet of Things in 5G Heterogeneous Networks

The Industrial Internet of Things (IIoT) is one of the important applications under the 5G mMTC scenario. To ensure the high reliability of IIoT services, it is necessary to apply an efficient resource allocation method under the dynamic and complex environment. In view of the absence of energy-efficient resource management architecture for the entire network, this paper proposes an intelligent-driven green resource allocation mechanism for the IIoT under 5G heterogeneous networks. Firstly, an intelligent end-to-end self-organizing resource allocation framework for IIoT service is given. Next an energy-efficient resource allocation model within the framework is proposed. It is then solved by an intelligent mechanism with the asynchronous advantage actor-critic (A3C)-driven deep reinforcement learning (DRL) algorithm. Through the comparison analysis of different methods and rewards under IIoT scenarios with proper parameters setting, the proposed method can achieve better performance than traditional RL methods and maintain service quality above accepted levels as well.