In this paper, we study the unmanned aerial vehicle (UAV)-enabled opportunistic data collection in wireless sensor networks (WSNs). The UAV performing remote missions is expected to collect data from the WSN during the return flights. Due to the specified task and safety restrictions, flight trajectory and time of the UAV are strictly constrained, resulting in the limited coverage ability in the data collection process. Moreover, the unknown distribution of active sensors makes it difficult for ground sensors and the UAV to complete the off-line optimization of flight mode and transmission. To tackle these problems, we develop an air-ground collaborative online planning method. On one hand, ground sensors actively form terrestrial transmission clusters to improve the data upload efficiency. After analyzing the line-of-sight (LoS) reliability and transmission correlation, we construct a coalition formation game model for the clustering of ground sensors. We discuss the equilibrium property of the game model, which can be achieved by the proposed distributed coalition formation algorithm. On the other hand, to avoid conflicts during the data collection, a data upload protocol is designed. We further discuss various flight speed planning schemes based on different detection capabilities of the UAV. The simulation results show that the performance of ground coalition-based air-ground collaborative online optimization is much better than that of the unilateral data collection by the UAV. Moreover, UAV flight online planning can further improve data uploading efficiency.

Device-to-device (D2D) communication is a promising technology for 5G wireless communication system. Since D2D communication usually reuses the resource of cellular users, interference management and resource allocation of D2D are the mainly research directions in D2D communication underlay cellular networks. The physical-layer security problem of D2D link is rarely under studied. In this paper, we develop the Stackelberg game frame to model D2D underlay communication in cellular networks. We analyze the equilibrium solution and optimize the power control and channel access of D2D link to maximize the achievable rate of cellular users and the physical-layer secrecy rate of D2D link. We propose a power control and channel access algorithm of D2D link then. Finally, we perform numerical simulations to study the performance of the proposed algorithm.

In this paper, we study joint relay selection and the power control optimization problem in an anti-jamming relay communication system. Considering the hierarchical competitive relationship between a user and jammer, we formulate the anti-jamming problem as a Stackelberg game. From the perspective of game, the user selects relay and power strategy firstly which acts as the leader, while the jammer chooses power strategy then that acts as follower. Moreover, we prove the existence of Stackelberg equilibrium. Based on the Q-learning algorithm and multi-armed bandit method, a hierarchical joint optimization algorithm is proposed. Simulation results show the user’s strategy selection probability and the jammer’s regret. We compare the user’s and jammer’s utility under the proposed algorithm with a random selection algorithm to verify the algorithm’s superiority. Moreover, the influence of feedback error and eavesdropping error on utility is analyzed.