Cognitive WSN Control Optimization for Unmanned Farms Under the Two-Layer Game

With the deep integration of modern technology and agricultural production, the development of unmanned precision agriculture has become a breakthrough for agricultural upgrading. Unmanned farms rely on multi-source data provided by dense wireless sensor networks (WSN), which causes greater pressure on the scarce spectrum resources. Combining fuzzy clustering and game theory, this paper studies a joint optimization method for cognitive wireless sensor networks (CWSN) to optimize agricultural data transmission and make full use of network resources. Two objective functions are designed to maximize the network energy efficiency and minimize the transmit power, so as to optimize the transmit power and transmission channel in CWSN. Different optimization objectives are regarded as different players, which turns the multi-objective optimization into a game decision-making problem. By defining the influence model of design variables on different objective functions, fuzzy clustering is adopted to divide the strategy subspace for each player. Then, this paper studies a two-layer cooperative game algorithm. Each node chooses the transmit power and channel in the strategy space based on their price function. The game scheme is obtained by a multi-round two-layer cooperative game. The experimental results show that compared with NSGA-ò, the reinforcement learning (RL) algorithm, and the non-cooperative game method, the joint optimization algorithm can reduce network interference and improve network QoS.