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 Crowd Sensing (MCS) is a widely adopted approach for data collection across diverse applications. However, as the number of tasks and participants in MCS continues to grow, task allocation and dynamic pricing challenges have become increasingly complex. Existing research primarily focuses on single-task allocation problems, often overlooking the diversity and complexity inherent in multi-task, multi-worker scenarios. To address these challenges, this paper proposes the Enhanced Heuristic Search with Tabu and Local Search (EH-STLS) algorithm, alongside a Kolmogorov-Arnold Deep Q Network (KDQN) model, both grounded in a Stackelberg game framework. The EH-STLS algorithm employs a multi-objective optimization framework that combines tabu search and local search strategies to improve the efficiency of worker-task matching while ensuring high task quality. The KDQN model views task publishers as leaders and treats crowdsourcing platforms, encryption agencies, and workers as followers to achieve optimal dynamic pricing and utility allocation. Extensive experiments on synthetic datasets generated from real-world data reveal that the proposed methods substantially outperforms the baseline algorithms regarding task allocation quality and pricing efficiency, achieving up to a 21.7% increase in task quality and a 16.4% improvement in pricing effectiveness.