The rapid advancement in wireless communication technology enables the drones to offer flexible and resilient offloading services to the mobile users in the presence of limited terrestrial infrastructure. However, the interaction between users and drones being in an open environment has raised critical security and privacy concerns. In this paper, we propose integrating the proof-of-work (PoW)-based consensus blockchain technology into the network to tackle these challenges. Specially, the drones are tasked with economically incentivizing the edge computing nodes as the miners to compete for the block generation privilege by solving cryptographic puzzles. We formulate a two-stage Stackelberg differential game to allocate edge computing resources between the drones and miners, leveraging the average reputation of miners to solve for dynamic task pricing. Additionally, the drones and miners take on the roles of leaders and followers in this game, respectively. By solving the openloop Stackelberg equilibrium, we derive the evolving trend of the optimal strategies for the system in the dynamic pricing environment. Furthermore, the numerical results substantiate the effectiveness and feasibility of this proposed scheme.

Recently, unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has emerged as a practical paradigm to enable low latency computing offloading for dispersed users in the fifth generation (5G) wireless networks. However, severe security and privacy concerns are associated with the open environment between the UAVs and edge computing nodes. In this paper, we address these challenges by integrating blockchain technology into UAV-enabled MEC networks. We present an innovative Delegated Proof of Stake (DPoS) consensus mechanism where the UAV is a primary node and verification nodes are edge computing nodes selected by the reputation mechanism. To enhance mobile users’ Quality of Service (QoS), edge computing resources need to be allocated among UAV and verification nodes. Based on this, we propose the trading mechanism for resource pricing and allocation based on the two-stage Stackelberg differential game. Meanwhile, dynamic states of user demands and verification node reputations are modeled using differential equations as constraints of the objective function at various stages to simulate adaptive service requests for users and incentivize active participation for verification nodes. Simulation results prove the effectiveness of the proposed resource trading scheme and demonstrate the equilibrium and convergence status of resource pricing and allocation for edge computing.