Mobile crowdsensing (MCS) is a new data collection paradigm profiting from the human-centric cyber social computing. However, due to the humans' uncontrollable mobility, it raises severe concerns of data redundancy and poor data quality. In this paper, we propose a novel data gathering architecture based on mobile edge computing (MEC), which distributes computing resources [edge nodes (ENs)] in the sensing scenarios close to the mobile users, and thus enables significant improvements to handle users' frequent location changes and reduce the specified quantity of sensing tasks. Based on the MEC-enhanced architecture, we design a quality-aware sparse data collection (QSDC) algorithm in the MCS systems. In the ENs' part, QSDC exploits the implicit correlation (IC) among the sensing data to reduce the data redundancy and selects the appropriate users' group to ensure the spatiotemporal coverage of sensing grids. In the cloud server part, QSDC leverages the compressive sensing to recover the sensing data in the whole sensing area with high data quality. Extensive experiments verify the performance of QSDC based on real data sets under different experiment settings and demonstrate the effectiveness and availability of QSDC.

Traditional Internet of Things (IoT) networks have limited coverage and may experience failures due to natural disasters affecting critical IoT devices, making it difficult for them to provide communication services. Therefore, how to establish network communication service more efficiently in the presence of fault points is the problem we solve in this paper. To address this issue, this study constructs a hierarchical multi-domain data transmission architecture for an emergency network with unmanned aerial vehicles (UAVs) employed as core communication devices. This architecture expands the functionality of UAVs as key network devices and provides a theoretical basis for their feasibility as intelligent network controllers and switches. Firstly, the UAV controllers perceive the network status and learn the spatio-temporal characteristics of air-to-ground network links. Secondly, a routing algorithm within the domain based on federated reinforcement distillation (FedRDR) is developed, which enhances the generalization capability of the routing decision model by increasing the training data samples. Simulation experiments are conducted, and the results show that the average communication data size between each domain controller and the server is approximately 45.3 KB when using the FedRDR algorithm. Compared to the transmission of parameters through federated reinforcement learning algorithms, FedRDR reduces the transmitted parameter size by approximately 29%. Therefore, the FedRDR routing algorithm helps to facilitate knowledge transfer, accelerate the training process of intelligent agents within the domain, and reduce communication costs in resource-constrained scenarios for UAV networks and has practical value.

With the increasing popularity of cab services such as Didi and Uber, cities are faced with the challenge of high carbon emissions and traffic congestion. Ride-sharing services, as a novel green mode of transportation, have emerged as a key technology in smart transportation for addressing these problems. The implementation of ride-sharing is predicated on an accurate ridership demand forecasting model, which can effectively prevent vehicle resource waste, alleviate traffic congestion, and reduce carbon emissions. In this paper, a periodic attentional graph convolutional spatio-temporal network model (PAG-TSN) is proposed to predict regional ridership demand. Specifically, the model is trained using a large amount of GPS data and user demand data collected by the travel service provider. PAG-TSN consists of two parts: the bicomponent attention graph convolution model (BAT-GCN) and the periodic attentional gated recurrent unit model (PA-GRU). The former uses GCN to extract spatial features from pointwise and edgewise graphs; the latter uses the spatial feature vectors extracted from the former with external information as input, and uses GRU to extract temporal features from feature data of different periods, and finally uses attention mechanism and POI requirement correlation to integrate the extracted spatio-temporal information to derive prediction results. Extensive experiments and evaluations on the CD2Date and XA2Date datasets show that PAG-TSN outperforms other baseline models in accurately predicting regional ridership demand, with MAPE and RMSE values of 0.1147 and 5.56, respectively.

The ever-more-capable smart mobile phone gave birth to a novel sensing paradigm, participatory sensing. In the application environment of mobile participatory sensing networks, mobile equipment is usually weakly connected. Due to uncertainty of connection, mobile nodes sometimes need encounter opportunities to accomplish data communication and transmission. However, the participants’ reluctance would diminish their enthusiasm if there is no incentive mechanism. To address this nontrivial issue, we propose reputation-based incentive schemes to motivate participants to disseminate reliable data in participatory sensing system, named RIDD, while minimizing incentive cost for maintaining sufficient number of reliable participants. When an intended receiver receives the data packet from a participant, the receiver authorizes the participant by an acknowledgment message within an encryption code automatically generated by the data packet, which serves as a proof of successful data delivery. RIDD evaluates participants using reputation degree calculated according to the encryption code, encouraging reliable participants to keep being interested in the participatory service with rewards. We conduct simulations in different scenarios. The results show that RIDD remarkably increases the winning probability of participants who disseminate accurate data and reduces the cost for retaining sufficient number of reliable participants.

The proliferation of uncrewed aerial vehicles (UAVs) in 6G-enabled edge computing presents a dual challenge of ensuring secure data transmission and performing accurate object detection on resource-constrained platforms. This article proposes a Synergistic Encryption and Detection framework (SED-UAV) that integrates a lightweight chaotic image encryption algorithm, lightweight, block-based, and improved chaotic image encryption algorithm (LB-ICA), and an enhanced small object detector, YOLO-LFP. The LB-ICA algorithm leverages an improved chaotic map and a plaintext-aware key mechanism using SHA-256. It achieves a key space of over $2^{100}$ and an average encryption speed of 20.3 ms per block, providing robust security against differential and chosen-plaintext attacks with high efficiency suitable for UAVs. For detection, the YOLO-LFP model enhances the YOLOv8 baseline with a hybrid attention module, an adaptive feature fusion strategy, and a dedicated small object head. Experimental results on the VisDrone2019 benchmark show YOLO-LFP achieves a state-of-the-art performance of 44.7% mAP@0.5, significantly outperforming existing models. This work provides a comprehensive solution for deploying secure, real-time computer vision applications on UAV platforms.