Game-Theoretic Learning-Enabled Multi-UGV Fairness-Aware and Timely Data Collection in Industrial WSNs
Nan Qi, Shuqi Wang, Daolong Wu, Lei Zhang, Luliang Jia, Chang Tian, Ming Zhou Zhan · IEEE Internet of Things Journal · 2025
In agricultural and food production, sensors are widely used for real-time monitoring of the production process. These sensors transmit data to access points (APs) in wireless sensor networks (WSNs), forming an Internet of Things-empowered advanced production paradigm. Due to limited power, sensors have constrained transmission ranges, necessitating unmanned ground vehicles (UGVs) to assist in timely sensor data collection. A critical problem is the intelligent coordination among multiple UGVs to realize safe path planning, as well as fair and timely data collection. However, it encounters the following challenges: 1) real-time monitoring introduces the dynamics in the volume of sensor data; 2) unknown obstacles, such as mobile packaging containers and vehicles, complicate safe path planning and fair data collection in WSNs; and 3) inefficient action explorations deteriorate action selection. To address these challenges, we propose a multiagent path planning algorithm based on coalition formation game and Bayesian optimization (BO) (MAPP-CFGBO) to optimize UGVs paths and sensor association in industrial WSNs. First, we construct a dynamic data caching model and design a fairness index. Second, a cooperative communication coalition formation (C3F) algorithm is proposed to facilitate cooperation among UGVs. Next, the safe path planning problem is solved with our proposed BO algorithm, which addresses challenges 2 and 3. Extensive simulations are performed. Compared with the benchmark algorithms, the proposed algorithm improves the fairness of communication services by$\rm 39.20{\,}\% $and increases the amount of collected data by$\rm 142.07{\,}\%$.