Robust perception under adverse illumination demands fusing complementary RGB and thermal cues for dense scene understanding. The core challenge of RGB-Thermal semantic segmentation lies in reconciling modality heterogeneity while exploiting multi-level complementarity for robust scene parsing. Existing methods commonly adopt single-domain alignment or hierarchy-agnostic fusion to establish cross-modal features. However, these approaches neglect frequency–spatial consistency and the hierarchy-specific nature of complementary information. To address these limitations, we propose HD2FI-Net, a Hierarchical Dual-Domain Fusion–Interaction Network. Geo-semantic coupled rectification enforces cross-modal consistency via direction-aware attention and bidirectional residual rectification. Unified dual-domain fusion & interaction first performs shared frequency modulation for dual-domain alignment, then employs hybrid cross-modal attention for fine-grained fusion at deep stages. Contrastive boundary difference fusion employs parameter-free attention and differential modeling at the shallowest stage to preserve boundary details with minimal overhead. Extensive experiments on the benchmark datasets MFNet, PST900, and FMB demonstrate that HD2FI-Net outperforms several state-of-the-art methods in RGB-T semantic segmentation with superior performance and lightweight efficiency.

In highly dynamic Internet of Vehicles (IoV), hierarchical routing schemes that rely on intersections and roadside units (RSUs) face challenges in coping with the limited coverage of the millimeter-wave (mmWave) communications, while non-hierarchical routing schemes incur substantial overhead. To address this issue, this paper proposes a hierarchical network graph clustering (HNGC) method based on node adjacency similarity to reconstruct the routing search space. HNGC establishes hierarchical clustering directly according to vehicle connectivity and independent of any fixed infrastructure, which reduces the search space for routing computation and prediction. Furthermore, a hierarchical routing decision framework is designed to support the proposed adaptive clustering method, which filters out unreachable nodes before path computation and enables vehicle-side evaluation of the downstream connectivity of candidate nodes. Numerical results demonstrate that incorporating the PT-GROUT computation module and Dijkstra into HNGC improves the average packet delivery rate by 4.9% relative to the original, and decreases the average computation time by 54.1% relative to the Dijkstra.

UAV cooperative formation provides rescue and material delivery for the industrial Internet of Things (IIoT). To solve issues, such as low material distribution efficiency and poor mobility during disaster rescue, we propose a two-layer optimization mechanism-based multiple UAV dynamic task planning method (DAWN), which can cope with the problem of the global communication link unreachable caused by disasters. Specifically, we consider the global task allocation as a dynamic vehicle routing problem (VRP) and use deep reinforcement learning (DRL) to solve it so as to minimize the global flight path and energy consumption. Second, based on the current communication structure, we establish a local path planning approach based on the trust network that maximizes the regional coverage rate while minimizing the flight paths. On the basis of these two layers, an UAV formation dynamic task planning approach is realized. Experimental results prove that the proposed DAWN can obtain the optimal flight paths and achieve higher energy efficiency while providing reasonable region coverage to discover more potential tasks.

During the COVID-19 pandemic, numerous people experiencing illness or senescence choose to receive home health care (HHC) services. However, a rapid increase in patients makes it a challenge to reasonably allocate nurses to provide HHC services under the condition of a paucity of nurse resources and patient time window constraints. To solve the large-scale HHC problem, a hybrid heuristic-exact optimization algorithm is proposed with three novel contributions. First, a framework of hybrid heuristic-exact optimization is designed to solve the large-scale problem where a reasonable solution is difficult to obtain under constraints. Second, a multi-objective mixed-integer linear programming modelization is formulated to get a more diverse nurse assignment. Finally, an improved branch and bound algorithm is proposed to speed up computation for the large-scale problem. Computational results on different HHC instances from 25 to 1000 patients demonstrate that the proposed algorithm can optimize the HHC problem with more than 100 patients and can provide various assignments for different numbers of nurses, which the common algorithm cannot optimize.

With the rapid development of artificial intelligence technology, the multi-UAV cooperative search has wide applications in the field of Internet of Things, such as resource exploration, emergency rescue, intelligent transportation, etc. However, the communication network in an unknown environment may be inaccessible, and the real-time information sharing among UAVs cannot be guaranteed, resulting in the failure of cooperative search. Aiming at this issue, this article is devoted to the design of the multi-UAV flight strategy to improve the cooperative search capability in an uncertain communication environment. Specifically, a new cooperative architecture oriented to a local communication network is devised to control the observation locations of multiple UAVs in the search process, and some local communication networks are established based on the distance among UAVs to meet the requirements of the search task. On this foundation, we develop a multi-UAV cooperative search model (MCSM) with communication cost and formation benefit as an optimization function to ensure the effectiveness of multi-UAV search. Moreover, in the process of model solving, an improved sparrow search algorithm (ISSA) is presented with some different search strategies to enhance the optimization capability. To verify the superiority of the proposed method, we designed several groups of simulation experiments to analyze the performance of MCSM. Experimental results illustrate that our method can not only maintain high cooperative search accuracy but also has high stability and convergence speed.

Research on multiple unmanned aerial vehicles (UAVs) cooperative surveillance systems serving Internet of Things (IoT) applications, such as smart cities, precision logistics, etc., has become a hot topic. However, the target movement is unpredictable in an uncertain environment, and multiple UAVs are affected by obstacles or inaccessible regions, resulting in the decreased surveillance performance and even the loss of the target. This article is dedicated to determine the current surveillance environment through the 2-D laser scanner. At the cost of the energy consumption and the transmission unreliability, a multi-UAV cooperative path optimization (MCPO) model is designed to adjust the surveillance location of each UAV, which improves the target surveillance performance. Specifically, for different types of obstacles or inaccessible regions, we present a novel obstacle-avoidance selection strategy with two mechanisms in mind: 1) when some of UAVs encounter obstacles, but others can accurately monitor the target, a strict constraint mechanism is established to promptly adjust the surveillance location of each UAV, which ensures the accuracy of formation surveillance and 2) when all UAVs have to avoid obstacles, a fuzzy constraint mechanism is presented and combined with Lucas–Kanade (LK) method to expand the search range of the multi-UAV and enhance the flexible adjustment capability of the formation. To verify the superiority of the proposed optimization method, we develop a 3-D simulation experiment environment based on the UE4 platform and design several groups of experiments to analyze the effectiveness of MCPO. The experimental results demonstrate that MCPO can not only maintain the flight stability of multiple UAVs but also has satisfactory formation flexibility and surveillance accuracy.

UAV surveillance and tracking have attracted great enthusiasm in intelligent transportation, and various approaches have been reported up to now. However, these approaches often ignored the uncertainties in the urban environment, such as occlusion, view change, and background clutter. Ignoring these uncertain factors often leads to a reduction in surveillance performance and tracking quality. This study devotes to improving the cooperative surveillance capability of multi-UAV formation by designing different cooperative strategies in the urban environment. To be specific, a novel cooperative architecture is designed to control the observation locations of multiple UAVs throughout the formation process. For different types of interference, we introduce a novel target recognition rate of each UAV as the decision factor and design corresponding cooperative strategies to guarantee the accuracy of cooperative surveillance. Based on this architecture, we develop a vision-based method of cooperative surveillance and tracking by multiple UAVs (SMART) whose objective function is the motion cost and flight reliability of UAVs to ensure that each UAV can be in the optimal surveillance location for the target. The proposed SMART skillfully integrates the strict, elastic, and flight constraint strategies. During the execution of the multi-UAV formation, the inherent safety constraints of multiple UAVs and the designed strategies are used to solve the quadratic optimization model to adjust the locations of these UAVs. To demonstrate the superiority of our method, we conduct a 3D simulation urban environment and devise several experiments to analyze the performance of SMART on it. The experimental results demonstrate that SMART can not only maintain the high cooperative flight capability, but also provide high flexibility and fault tolerance.

Currently, cloud computing has received great attention in commerce and scientific research due to its flexibility and strong data processing capability. However, in view of the fact that the types of tasks display an upward trend as the growth of service demands, and the different types of tasks arrive at the system without regularity. Moreover, the resources deployed in cloud are insufficiency to be flexibly provisioned in the face of obvious workload fluctuations. In this article, we present a method of elastic resource provisioning using date clustering in cloud service platform. The framework of proposed method consists of three core components: tasks clustering, the amount of tasks prediction in cluster, dynamic resource provisioning and scheduling. In workload classification, we propose a clustering ensemble method, which utilizes a novel distance decision-making method to obtain the final results. Our method can effectively partition the arriving tasks into several clusters based on similarity among tasks. For each cluster, we forecast the amount of tasks arriving at next moment by prediction model based on time-series to provide reference for the follow-up resource provisioning. Afterwards, an energy-saving resource provisioning method is designed to dynamically provide resources for tasks in each cluster to meet their performance requirements. We implement the experiments in Google cloud traces dataset and the results show that our method achieves 92.3, 91.2 percent, and 3679.2 kW$ \cdot$·h respectively in terms of guarantee ratio, resource utilization and total energy consumption, which demonstrates the effectiveness of proposed method for dynamic resource provisioning.

As an unsupervised data mining method, clustering can extract valuable information in complex and redundan-t network data analysis. However, the existing methods are sensitive to the selection of initial cluster centers, and cannot automatically determine the number of clusters, which fails to adapt to various types of network data. To solve these issues, this paper proposes a method of adaptive clustering ensemble based on uncertain entropy decision-making. Firstly, K-means is used as the base clustering algorithm of clustering ensemble, and several base clustering members are randomly generated according to different the number of clusters, and the members with high stability and quality are selected as clustering ensemble inputs. Furthermore, the uncertainty of clusters in the base clusterings are calculated based on the information entropy criterion, and then the co-association matrix is established. The obtained co-association matrix is transformed into a distance matrix by Bhattacharyya distance among data samples. Finally, we use the distance matrix as the input of the density peaks (DP) algorithm, and further calculate the final clustering result. The experimental results on real-world datasets illustrate that the proposed method has better performance than other clustering methods.

In intelligent transportation system, UAV surveillance plays an important role, and it has wide applications in traffic detection and order management, etc. However, the interference of extensive buildings and inaccessible regions in the urban environment directly lead to the failure of the surveillance task. Aiming at this issue, this paper proposes a method of multi-UAV UAV Cooperative Obstacle Avoidance and Surveillance (COAS). The ellipse tangent method is used to avoid obstacles for the interference of urban obstacles. Furthermore, taking into account the cooperation of multi-UAV formation, the cooperative model based on moving cost and formation stability is established. Due to the timeliness requirement of multi-UAV cooperative surveillance task, we use a sparrow search algorithm with fast convergence speed and strong optimization capability to solve the cooperative model. Finally, the simulation experimental results in an urban environment with obstacle information demonstrate the effectiveness of the proposed method in tackling the issues of cooperative obstacle avoidance and target surveillance.

A cyber security incident in SCADA systems can cause the disruption of physical process, and may result in significant economic loss, environmental disasters or even human casualties. To exploit the feature of the physical process and find the potential attacks, this paper presents and analyzes a new class of cyber-physical attacks, named false logic attacks, against the logic of control process in SCADA systems. In addition, it proposes a model for false logic attacks, which is useful for analyzing how attacks can affect the physical system. An experiment is performed to illustrate the concepts, and the effect of false logic attacks are also discussed.