Multi-space crowd sensing has emerged as a promising paradigm for 3D urban perception. However, it faces critical challenges including space coupling, task heterogeneity, and dynamic resource availability. To address these issues, the Multi-Space Fairness Task Allocation (MSFTA) problem is formulated, aiming to maximize task completion while ensuring fairness across spatial dimensions. The problem is proven to be NP-hard, and a dynamic collaborative optimization framework is proposed. Within this framework, a Multi-Space Clustering QuadTree Voronoi Partition (MCQVP) is developed for fine-grained multi-dimensional partitioning by leveraging DBSCAN and quadtree structures. In addition, a Group Urgency-Based Multi-Shortest Path (GUBMSP) scheduler is incorporated to prioritize time-sensitive task groups via urgency-aware critical paths. Furthermore, a Fairness-Aware Pareto Multi-Objective Ant-Q Learning (FA-PMOAQL) allocator is introduced to integrate Q-learning and ant-colony optimization under fairness-aware multi-objective guidance. These designs establish a unified framework that not only improves task allocation efficiency through multi-space partitioning and urgency-driven scheduling, but also ensures equitable resource utilization by embedding fairness into the learning process. Comparison experiments on Tokyo and New York datasets demonstrate that the proposed approach achieves up to 12.8% higher task completion rate compared with baseline algorithms, while maintaining relatively low runtime. In cross-layer scenarios, the completion rate improves by 20% when agent resources increase, and under heavy task loads it sustains competitive performance with only moderate decline.

Mobile crowdsourcing is rapidly growing with Artificial Intelligent of Things. At the same time, the type and complexity of the tasks requested by the requester change and diversify. Therefore, how to design allocation algorithms for the situation of increasing task complexity is particularly critical. In this paper, to cope with this problem, the idea of worker coalition collaboration and reputation evaluation mechanisms are introduced into it. A two-stage allocation based on same-region and cross-region is performed in the divided regional grid. In the first stage, multi-worker and multi-task allocation is realized by combining the reverse auction theory based on the workers’ historical reputation value, which motivates the workers with high reputation value to choose their tasks and contribute data with high sensed quality. The second stage utilizes genetic algorithms to select a coalition of workers for cross-region sensed execution for tasks that do not meet sensed quality requirements. This process will provide additional payoff incentives to compensate for travel costs within the worker coalition, increasing the number of tasks completed and maximizing social welfare. Finally, multiple comparison experiments on the real dataset Yelp are conducted for validation.

The evolution of 5G and 6G technologies has boosted mobile network speed, reduced delays, and widened coverage, empowering Mobile Crowd Sensing (MCS) to overcome surface and terrain obstacles. However, this advancement brings new hurdles for online task assignment. While most MCS methods suit surface applications, they struggle with allocation in complex environments. This paper focuses on MCS in varied settings like surface, air, and high altitude. Currently, planning-based task assignment works better for one-to-one or one-to-many scenarios, with limited options for many-to-many situations. Improving platform utility, attracting top-quality crowd workers, and enhancing task completion efficiency are vital. To tackle these challenges, the paper introduces a spatial division algorithm using 3D Voronoi diagrams for complex environments. This algorithm utilizes task coordinates to delineate assignment spaces. Additionally, it introduces a two-stage many-to-many online task assignment algorithm (MOTA) that forecasts crowd workers’ arrival probabilities and combines auction-based incentives with differential evolution algorithms. MOTA ensures efficient matching of workers and tasks within spatio-temporal constraints, balancing both parties’ interests. Finally, comparative experiments on real datasets assess the proposed MOTA algorithm’s usability and effectiveness based on overall gain, running time, task count, and assignment rate.

As a new paradigm to solve problems by gathering the intelligence of crowds, mobile crowdsourcing has become one of the hot spots in academic and industrial fields. Task requester, platform, and crowd workers are stakeholders in mobile crowdsourcing, which inevitably leads to conflicts of interest. In order to solve this problem, this article constructs a three-party evolutionary game model among task requester, platform, and crowd workers. This model also considers the collusion between crowd workers and the platform to make it more realistic. Then, the replication dynamics method is utilized to analyze the evolutionary stability strategy. The strategies of rewards and penalties are given to avoid free-riding and false-reporting problems. Finally, the stability of the equilibrium point in the three-party game system is verified through simulation experiments, and the effective methods to motivate each player to choose a trusted strategy are given.