Joint optimization of networking and computing resources for green M2M communications based on DRL
Recent advances in Internet of Things (IoT) provide plenty of opportunities for various areas. Nevertheless, the machine-to-machine (M2M) communications-based IoT develops rapidly but suffers from extra energy consumption, large data transmission latency as well as overmuch network cost, because various of machine-type communication devices (MTCDs) are deployed in the network. To meet the requirements of energy efficient M2M communications, in this paper, we introduce a promising technology named as mobile edge computing (MEC), and propose a performance optimization framework with MEC for M2M communications network based on deep reinforcement learning (DRL). According to dynamic decision process by DRL, the appropriate access networks and the computing servers can be determined and selected with the minimum system cost, which includes lower network cost, time cost and energy consumption for data transmission and computing tasks execution. Extensive simulation results with different system parameters show that our proposed framework can effectively improve the system performance for M2M communications compared to the existing schemes.
|Keywords||Deep reinforcement learning, Energy efficiency, Machine-to-machine communications, Mobile edge computing, Performance optimization|
|Conference||2019 IEEE Global Communications Conference, GLOBECOM 2019|
Li, M. (Meng), Yang, L. (Le), Yu, F.R, Wu, W. (Wenjun), Wang, Z. (Zhuwei), & Zhang, Y. (Yanhua). (2019). Joint optimization of networking and computing resources for green M2M communications based on DRL. In 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings. doi:10.1109/GLOBECOM38437.2019.9013366