Game theory-based energy efficiency optimization model for the Internet of Things

This paper conducts a game-theoretic based optimization study on the energy efficiency of the Internet of Things (IoT). For the problem that wireless sensor node information in the IoT requires the selection of a suitable backbone access point for energy efficiency optimization, this paper first establishes a mathematical model for the system-level energy optimization of the sensor node free-choice access point problem and then proposes a game model based on the concept of cooperation and the corresponding utility function, and after theoretical analysis. Among the new connections in the future, more than 30% are suitable for carrying by the cellular network, so the network challenges brought by mobile operators are huge. The paper then studied the current mainstream Internet of Things technology in the development of mobile networks-the application principles and key technologies of Narrow Band Internet of Things (NB-IoT), combined with the current wireless network optimization and maintenance work, and studied How does the Internet of Things become the focus of the industry and actively lay out and promote industrial development for operators, and how does NB-IoT move from a concept to small-scale commercial use, become the operator's leading role in promoting the standard industry, and hope to become an industry leader It is proved that the best access point allocation scheme is the optimal equilibrium point of the proposed game. Then a non-correlated parallel learning algorithm is proposed, according to which the system can converge to the optimal equilibrium point with a very low probability after learning, which is the optimal solution of the proposed system energy efficiency optimization problem, and achieve the global optimal system energy efficiency. Compared with other models, our model has improved efficiency by about 12% and accuracy by about 8%, and it can be applied in practice.