With the popularization of mobile devices, mobile crowdsensing (MCS) has become a paradigm with broad application prospects. However, traditional MCS face numerous challenges, such as surges in network traffic and infrastructure failures. To address these issues, we leverage flexible and low-cost Unmanned Aerial Vehicles (UAVs) in the MCS framework. UAV-assisted crowdsensing (UCS) provides an innovative approach to data collection that effectively addresses problems such as insufficient network coverage and congestion. In the UCS framework, UAVs can serve not only as temporary base stations (BSs) but also participate in collecting data and processing tasks. Nevertheless, the lack of adequate incentive mechanisms may lead both UAVs and mobile users to be reluctant to participate in sensing tasks. Therefore, this paper aims to investigate hierarchical incentive mechanisms for UCS. Considering the freshness of the collected data and the benefits of the platform, we adopt the Age of Information (AoI) metric to measure the quality of data. To ensure AoI of data, we model the incentive mechanisms from both the UAV and user perspectives, and we formulate them as single-dimensional and multi-dimensional contract-based incentives under scenarios of information asymmetry. Furthermore, we derive the optimal contract scheme under the constraints of individual rationality and incentive compatibility. Finally, experimental results confirm the effectiveness of the proposed contract design and maximize the utility of the model owner.

In UAV-assisted Multi-access Edge Computing (MEC) systems, UAV trajectories and resource pricing directly determine system utility - improper UAV positioning will lead to increased delay and energy costs for users, while inappropriate pricing will result in insufficient task offloading or UAV overload. This paper aims to optimize UAVs' trajectories, pricing strategies and users' offloading decisions, enabling UAVs to move to suitable locations and provide computing offloading services at appropriate prices, thereby reducing delay and energy, and enhancing the utilities of both UAV and users. The user utility specifically consist of throughput, energy consumption, delay penalties, and expenditures on purchasing computing resources. The UAV's utility consists of computing energy consumption, delay penalties, and revenue from selling computing resources as incentives. We formulate the pre-offloading problem to solve users' offloading selection, and transform the problem into a multi-user non-cooperative game using game theory while proving the existence of Nash equilibrium. Then we model the interaction between UAV and users using Stackelberg game model and prove the existence of Stackelberg equilibrium. We use multi-agent deep reinforcement learning (MADRL) and propose DRL and Game Theory-based Trajectory Optimization and Task Offloading (DGTT) algorithm to solve UAVs' trajectories and obtain the Stackelberg equilibrium solution. We sequentially solve for the pricing strategy and offloading decisions, thereby obtaining the optimized utilities of both UAV and users. Finally, we conduct simulation experiments to verify the feasibility of DGTT algorithm, along with comparative experiments that demonstrate our proposed DGTT algorithm's excellent performance in optimizing UAV and user utilities, while reducing system energy consumption and delay.

Space networks composed of Low Earth Orbit (LEO) satellites play a significant role in Earth observation systems. LEO satellite constellations, which provide wide-ranging coverage, continuously observe the Earth and transmit the observed data to satellites in higher orbits or ground stations for further analysis, benefiting from their more powerful computation capacity. In such an observation and analysis network, the timeliness of data update and the quality of analysis are crucial factors, both constrained by limited energy resources. In this paper, we jointly optimize the timeliness and analysis quality by constructing an update scheduling problem with a trade-off between Age of Information (AoI) and accuracy. Considering the unpredictable and changeable environment dynamics, we formulate this problem into a Markov decision process and propose an algorithm based on deep reinforcement learning to obtain an optimal policy that effectively tackles the tradeoff problem. Simulation results demonstrate that our algorithm can jointly reduce AoI and increase accuracy, outperforming benchmark algorithms in different cases.