A stochastic game theory based energy-efficient device-to-device communication optimization for autonomous mobile robots over industrial IoT networks

Industrial Internet of Things (IIoT) has emerged as a novel paradigm for smart manufacturing that supports reliable and real-time network systems. The application of IIoT-based manufacturing systems handles large amounts of data across various components, including sensors and robots for industrial automation. Although routing protocols for IIoT applications have already supported various connectivity characteristics, there is still a lack of mobility. In this paper, we propose an IIoT-based device-to-device (D2D) network system that supports energy-efficient mobility of autonomous mobile robot (AMR). Moreover, the D2D network system is modeled using a stochastic game framework, which is solved using our game theory-based optimization algorithm (GTA). We apply GTA to maximize the data rate of AMR and compute the Nash equilibrium as a solution of the stochastic game theory by the best response strategy in various simulation environments. The results demonstrate that GTA can enhance performance by maximizing the average data rate, as well as maintaining the average energy consumption.