Severe challenges in terms of real-time processing and low-energy transmission brought by the development of Internet of Things （IoT） and the proliferation of mobile terminal devices are face by compute-intensive tasks. Particularly in multi-Unmanned Aerial Vehicle （UAV） -assisted Mobile Edge Computing （MEC） scenarios， communication links are constrained by obstacle blockages and UAV trajectories in complex 3D environments， and latency and energy consumption pressures are further exacerbated. For the scenario of multiple UAVs providing computational offloading services to ground users， an optimization model was established to minimize the weighted sum of the system's maximum task completion latency and total energy consumption， so as to optimize the users' discrete offloading decisions and the UAVs' continuous 3D trajectories jointly. To solve the problem of mixed （discrete-continuous） action space and strong decision coupling， a heterogeneous multi-agent algorithm UOUM （User Offloading and UAV Mobility co-optimization） was proposed. In the algorithm， under a heterogeneous multi-agent deep reinforcement learning framework， dedicated network architectures were designed for the two types of heterogeneous agents： users and UAVs. And differential reward mechanism was introduced to quantify marginal contributions of the agents， thereby solving the multi-agent credit allocation problem. Concurrently， an Artificial Potential Field （APF） was innovatively integrated as a differentiable physical constraint into the agent learning framework， so as to ensure safe obstacle avoidance for the UAVs. Simulation results show that compared to three benchmark methods （Only User Offloading optimization （OUO）， Only UAV Trajectory optimization （OUT）， and a standard heterogeneous multi-agent reinforcement learning （H-MARL） using only a global reward mechanism））， UOUM has advantages in various scenarios with different numbers of users， UAVs， and obstacle densities. Compared with H-MARL， UOUM has the final convergence reward improved by approximately 28.6% on average， and achieves strong environmental adaptability in terms of latency control， energy optimization， and safe obstacle avoidance.

In the real-time and complex network environment， how to motivate workers to participate in tasks and obtain high-quality perception data is the focus of spatio-temporal crowdsourcing research. Based on this， a spatio-temporal crowdsourcing’s online incentive mechanism based on quality perception was proposed. Firstly， in order to adapt to the real-time characteristics of spatio-temporal crowdsourcing， a Phased Online selection of workers Algorithm （POA） was proposed. In this algorithm， the entire crowdsourcing activity cycle was divided into multiple stages under budget constraints， and workers were selected online in each stage. Secondly， in order to improve the accuracy and efficiency of quality prediction， an Improved Expected Maximum （IEM） algorithm was proposed. In this algorithm， the task results submitted by workers with high reliability were given priority in the process of algorithm iteration. Finally， the effectiveness of the proposed incentive mechanism in improving platform utility was verified by comparison experiments on real datasets. Experimental results show that in terms of efficiency， compared with the Improved Two-stage Auction （ITA） algorithm， the Multi-attribute and ITA （M-ITA） algorithm， Lyapunov-based Vickrey-Clarke-Groves （L-VCG） and other auction algorithms， the efficiency of POA has increased by 11.11% on average， and the amount of additional rewards for workers has increased by 12.12% on average， which can encourage workers to move to remote and unpopular areas； In terms of quality estimation， the IEM algorithm has an average improvement of 5.06% in accuracy and 14.2% in efficiency compared to other quality estimation algorithms.

By setting up a network model of data aggregation and using a bit-hop metric to quantify energy costs in wireless sensor networks, we formally analyze the problem of area-based data aggregation and obtain that data aggregation will definitely be energy efficient when the relative difference between the average lengths of the two shortest paths, which are from source sensor nodes to the sink and to the aggregation node respectively, is not less than the ratio of data correlation to the number of source sensor nodes. Simulation results show that over 50% energy cost can be saved under the given conditions.