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.

With the explosive growth of mobile data, Mobile Crowd Sensing (MCS) has become a popular paradigm for large-scale data collection. The difficulty of data collection and the gaps in workers’ sensing capabilities are key factors to consider in worker recruitment and task assignment. To address these issues, we designed a two-stage cooperative incentive mechanism. In the first stage, a shelving level is introduced to assess task difficulty. Tasks are divided into high-quality and low-quality groups based on quality scores, while workers are categorized into high-ability and low-ability groups based on their historical performance. The Improved Chaotic Particle Swarm Optimization (ICPSO) algorithm is then applied to generate optimal task combinations. In the second stage, we address benefit distribution among cooperating workers by introducing time-dependent rewards and employing Stackelberg game theory to analyze the optimal completion time for both high-ability and low-ability workers. This analysis determines the optimal reward distribution and ability value allocation, with proof of the existence and uniqueness of a Nash equilibrium. Comparative experiments conducted on real-world datasets demonstrate that our cooperative task mechanism outperforms existing task bundling methods, validating its rationality and effectiveness.

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.

In this paper, a novel task offloading architecture called Flex-MEC is proposed, which achieves efficient task allocation and scheduling (TAS) between MEC servers. By adding metadata before task data, we redesign the offloading process in Flex-MEC, the TAS planning can be conducted without finishing the task data receiving. Once planning is done the task data can be directly forwarded to the allocated server and executed. This reduces latency compared to the traditional way of transmitting, planning, forwarding and executing sequentially. For TAS planning, a multi-server multi-task allocation and scheduling (MMAS) problem is formulated to maximize the MEC system profit. The MMAS problem is proven as an NP-complete problem, thus is challenging to solve. Then, a distributed scheme and a centralized scheme are proposed to solve the MMAS problem with low complexity. In the distributed scheme, the MMAS problem is converted into a non-cooperative game and the existence of Nash Equilibrium (NE) is proven and a low complexity response update algorithm is proposed to converge to NE. And the centralized scheme is based on a greedy idea and runs on a MEC controller in a centralized way. Verified by experiments, these two schemes can achieve better performance than compared schemes.

In wireless sensor networks (WSNs), many applications require a high reliability for the sensing data forwarding to sink. Due to the lossy nature of wireless channels, achieving reliable communication through multihop forwarding can be very challenging. Broadcast technology is an effective way to improve the communication reliability so that the data can be received by multiple receiver nodes. As long as the data of any one of the receiver nodes is transmitted to the sink, the data can be transmitted successfully. In this paper, a cross‐layer optimization protocol named Adaptive transmission Power control based Reliable data Forwarding (APRF) scheme by using broadcast technology is proposed to improve the reliability of network and reduce communication delay. The main contributions of this paper are as follows: (1) for general data aggregation sensor networks, through the theoretical analysis, the energy consumption characteristics of the network are obtained. (2) According to the case that the energy consumption of near‐sink area is high and that in far‐sink area is low, a cross‐layer optimization method is adopted, which can effectively improve the data communication by increasing the transmission power of the remaining energy nodes. (3) Since the reliability of communication is improved by increasing the transmission power of the node, the number of retransmissions of the data packet is reduced, so that the delay of the packet reaching the sink node is reduced. The theoretical and experimental results show that, applying APRF scheme under initial transmission power of 0 dBm, although the lifetime dropped by 13.77%, delay could be reduced by 40.37%, network reliability could be reduced by 10.08%, and volume of data arriving at sink increased by 10.08% compared with retransmission‐only mechanism.

An Adaptive Transmission Range Based Topology Control (ATRTC) scheme is proposed to reduce delay and improve reliability for data collection in delay and loss sensitive wireless sensor network. The core idea of the ATRTC scheme is to extend the transmission range to speed up data collection and improve the reliability of data collection. The main innovations of our work are as follows: (1) an adaptive transmission range adjustment method is proposed to improve data collection reliability and reduce data collection delay. The expansion of the transmission range will allow the data packet to be received by more receivers, thus improving the reliability of data transmission. On the other hand, by extending the transmission range, data packets can be transmitted to the sink with fewer hops. Thereby the delay of data collection is reduced and the reliability of data transmission is improved. Extending the transmission range will consume more energy. Fortunately, we found the imbalanced energy consumption of the network. There is a large amount of energy remains when the network died. ATRTC scheme proposed in this paper can make full use of the residual energy to extend the transmission range of nodes. Because of the expansion of transmission range, nodes in the network form multiple paths for data collection to the sink node. Therefore, the volume of data received and sent by the near‐sink nodes is reduced, the energy consumption of the near‐sink nodes is reduced, and the network lifetime is increased as well. (2) According to the analysis in this paper, compared with the CTPR scheme, the ATRTC scheme reduces the maximum energy consumption by 9%, increases the network lifetime by 10%, increases the data collection reliability by 7.3%, and reduces the network data collection time by 23%.