RJCC: Reinforcement-Learning-Based Joint Communicational-and-Computational Resource Allocation Mechanism for Smart City IoT

With the fast development of smart cities and 5G, the amount of mobile data is growing exponentially. The centralized cloud computing mode is hard to support the continuous exchanging and processing of information generated by millions of the Internet-of-Things (IoT) devices. Therefore, mobile-edge computing (MEC) and software-defined networking (SDN) are introduced to form a cloud-edge-terminal collaboration network (CETCN) architecture to jointly utilize the communicational and computational resources. Although the CETCN brings many benefits, there still exist some challenges, such as the unclear operation mode, low utilization of edge resources, as well as the limited energy of terminals. To address these problems, a reinforcement learning-based joint communicational-and-computational resource allocation mechanism (RJCC) is proposed to optimize overall processing delay under energy limits. In RJCC, a $Q$ -learning-based online offloading algorithm and a Lagrange-based migration algorithm are designed to jointly optimize computation offloading across multisegments and on edge platform, respectively. The simulation results show that the proposed RJCC outperforms the delay-optimal, energy-optimal, and edge-to-terminal offloading algorithm by 42%–74% in long-term average energy consumption while maintaining relatively low delay.