With the rapid development of 5G and 6G communications in recent years, there has been significant interest in space–air–ground integrated networks (SAGINs), which aim to achieve seamless all-area, all-time coverage. As a key component of SAGINs, flying ad hoc networks (FANETs) have been widely used in the agriculture and transportation sectors in recent years. Reliable communication in SAGINs requires efficient routing algorithms to support them. In this study, we analyze the unique communication architecture of FANETs in SAGINs. At the same time, existing routing protocols are presented and clustered. In addition, we review the latest research advances in routing algorithms over the last five years. Finally, we clarify the future research trends of FANET routing algorithms in SAGINs by discussing the algorithms and comparing the routing experiments with the characteristics of unmanned aerial vehicles.

Autonomous driving systems (ADSs) have the potential to enhance safety through advanced perception and reaction capabilities, reduce emissions by alleviating congestion, and contribute to various improvements in quality of life. Despite significant advancements in ADSs, several real-world accidents resulting in fatalities have occurred due to failures in the autonomous driving perception modules. As a critical component of autonomous vehicles, LiDAR-based perception systems are marked by high complexity and low interpretability, necessitating the development of effective testing methods for these systems. Current testing methods largely depend on manual data collection and labeling, which restricts their ability to detect a diverse range of erroneous behaviors. This process is not only time-consuming and labor-intensive, but it may also result in the recurrent discovery of similar erroneous behaviors during testing, hindering a comprehensive assessment of the systems. In this paper, we propose and implement a fuzzing framework for LiDAR-based autonomous driving perception systems, named LDFuzz, grounded in metamorphic testing theory. This framework offers the first uniform solution for the automated generation of tests with oracle information. To enhance testing efficiency and increase the number of tests that identify erroneous behaviors, we incorporate spatial and semantic coverage based on the characteristics of point cloud data to guide the generation process. We evaluate the performance of LDFuzz through experiments conducted on four LiDAR-based autonomous driving perception systems designed for the 3D object detection task. The experimental results demonstrate that the tests produced by LDFuzz can effectively detect an average of 7.5% more erroneous behaviors within LiDAR-based perception systems than the optimal baseline. Furthermore, the findings indicate that LDFuzz significantly enhances the diversity of failed tests.

In the 6G era, the proliferation of smart devices has led to explosive growth in data volume. The traditional cloud computing can no longer meet the demand for efficient processing of large amounts of data. Edge computing can solve the energy loss problems caused by transmission delay and multi-level forwarding in cloud computing by processing data close to the data source. In this paper, we propose a cloud–edge–end collaborative task offloading strategy with task response time and execution energy consumption as the optimization targets under a limited resource environment. The tasks generated by smart devices can be processed using three kinds of computing nodes, including user devices, edge servers, and cloud servers. The computing nodes are constrained by bandwidth and computing resources. For the target optimization problem, a genetic particle swarm optimization algorithm considering three layers of computing nodes is designed. The task offloading optimization is performed by introducing (1) opposition-based learning algorithm, (2) adaptive inertia weights, and (3) adjustive acceleration coefficients. All metaheuristic algorithms adopt a symmetric training method to ensure fairness and consistency in evaluation. Through experimental simulation, compared with the classic evolutionary algorithm, our algorithm reduces the objective function value by about 6–12% and has higher algorithm convergence speed, accuracy, and stability.

With the tremendous advancement of deep learning and communication technology, Vehicle-to-Everything (V2X) cooperative perception has the potential to address limitations in sensing distant objects and occlusion for a single-agent perception system. V2X cooperative perception systems are software systems characterized by diverse sensor types and cooperative agents, varying fusion schemes, and operation under different communication conditions. Therefore, their complex composition gives rise to numerous operational challenges. Furthermore, when cooperative perception systems produce erroneous predictions, the types of errors and their underlying causes remain insufficiently explored. To bridge this gap, we take an initial step by conducting an empirical study of V2X cooperative perception. To systematically evaluate the impact of cooperative perception on the ego vehicle's perception performance, we identify and analyze six prevalent error patterns in cooperative perception systems. We further conduct a systematic evaluation of the critical components of these systems through our large-scale study and identify the following key findings: (1) The LiDAR-based cooperation configuration exhibits the highest perception performance; (2) Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication exhibit distinct cooperative perception performance under different fusion schemes; (3) Increased cooperative perception errors may result in a higher frequency of driving violations; (4) Cooperative perception systems are not robust against communication interference when running online. Our results reveal potential risks and vulnerabilities in critical components of cooperative perception systems. We hope that our findings can better promote the design and repair of cooperative perception systems.

Self-Supervised Learning (SSL) has attracted increasing attention in remote sensing image understanding because it can learn transferable representations from unlabeled images. However, two issues remain insufficiently examined in collaborative SSL for remote sensing. First, when high-ratio masking removes entire small objects or structurally informative regions, the remaining visible patches may provide insufficient evidence for semantically coherent reconstruction. Second, heterogeneous self-supervised objectives may exhibit different loss scales, gradient magnitudes, and convergence behaviors such that fixed coefficients can produce uneven branch contributions during training. To address these issues, this paper proposes Context-Consistency Enhanced Collaborative Self-Supervised Learning (C²DSSL) for remote sensing image understanding. C²DSSL introduces a teacher–student context-consistency constraint, in which multi-scale reconstruction features from the complete teacher observation serve as contextual targets for the student network when reconstructing the corresponding masked observation. In addition, gradient-sensitive dynamic weighting uses temporally smoothed loss-gradient magnitudes as empirical signals to adjust the relative contributions of heterogeneous self-supervised objectives. Under the evaluated settings, adding the context-consistency constraint improves KNN representation evaluation, UCMerced classification, Potsdam semantic segmentation, and DOTA oriented object detection, while maintaining the Baseline performance on the Million-AID subset. Dynamic weighting shows task-dependent effects, including a performance gain on the Million-AID subset classification task when combined with CCL.

Faults in the three-dimensional detection and multi-object tracking components of autonomous driving systems (ADSs) often manifest as cross-frame anomalies that single-frame confidence or neuron-coverage signals cannot adequately characterize. This paper proposes SGPrior, an offline method for prioritizing light detection and ranging (LiDAR) test sequences through scene-graph reasoning. SGPrior converts frame-wise detection and tracking outputs into attributed scene graphs. A finite-state machine activates consistency assertions whose operators construct an assertion graph for comparison with the current ADS-output graph. Normalized Hamming distances between aligned graph encodings are aggregated into a sequence-level priority score. Experiments on KITTI, nuScenes, and Waymo show that, under a 30% test-analysis budget, SGPrior selects subsets with lower multiple object tracking accuracy (MOTA), multiple object tracking precision (MOTP), and higher order tracking accuracy (HOTA), and more false positives and false negatives than the baselines. It achieves an average percentage of faults detected (APFD) of 0.957–0.958 with substantially lower cost than neuron-coverage ranking. Retraining the detectors in six detector–tracker combinations with SGPrior-selected sequences also outperforms equal-size random selection on held-out data. These results suggest that, after perception inference, SGPrior can help testers allocate a limited inspection or annotation budget to sequences that expose more detection and tracking errors. The benefit concerns the selection of sequences for analysis, rather than a reduction in the inference required to process the candidate pool.