Satellite Terrestrial Networks (STN) are known to enhance the quality of service and to provide a better user experience. However, STNs are primarily utilized as wide-range relays and are characterized by a lack of effective intersatellite collaboration. The communication efficiency and quality of near real-time remote sensing tasks in 6G space-air-ground integrated networks are enhanced by the proposed Orbital Edge Computing-Collaborative Offloading Scheme (OEC-COS), which utilizes Service Function Chains (SFC) and the processing and collaboration capabilities of satellite nodes to allocate near real-time remote sensing tasks to optimal satellite nodes for execution. The remote sensing task offloading problem has been formulated as a delay minimization problem, and the advantages of the OEC-COS scheme in terms of computational resource utilization ratio and latency are validated through simulation experiments by comparing it with average orbit allocation and co-orbit allocation schemes. The proposed OEC-COS scheme achieves the lowest average task computing delay among these methods.

Abstract The low‐earth‐orbit (LEO) telecommunication constellation network is foreseen as a merging interconnection method for future sixth‐generation (6G) systems. However, how to implement constellation system composition, consider network design constraints and envision application scenarios in a low‐orbit giant constellation system, is still an open problem. In this paper, by exploring the constellation characteristics in the future LEO systems, the design of hierarchical and sub‐area network control architecture for giant constellation is first suggested. After that, a detailed research on the partition mechanism of control domain for the giant LEO constellation systems is implemented. To make the architecture design and partition mechanism feasible, the hierarchical and domain controlling flow is also discussed. Finally, a performance verification method is present with elaborate numerical results and evaluations. The results verified the correctness and effectiveness of the proposed network control architecture and partition mechanism.

Massive flood damage has garnered a lot of social attention. Due to the tension between the strong demand for generalized models and the constrained capabilities of edge devices for hydrological surveillance, this study proposes an accessible distributed hydrological surveillance (HS) and computing system with integrated end-edge-cloud (iEEC) architecture to address the issue. In order to increase the inference efficiency of the edge servers (ES), we first develop a HS model with multiple exits, aiming to exploit its network structure and inference strategy. Then, using a collaborative scheduling algorithm, we construct the iEEC pathway to decide whether to undertake edge inference or cloud invocation. With a prototype system and a simulation tool, we eventually performed a numerical analysis of the system at various scales. The accuracy reached 94.3 %, the speed reached 30.3 frames per second (FPS), and it can better handle the occurrence of hard instances compared to state-of-the-art (SOTA) approaches.

Flood damage is a devastating natural disaster that requires effective hydro-surveillance (HS) systems. However, the limited capabilities of edge servers (ES) make it challenging to develop such systems. Our study proposes an accessible, distributed HS and computing system to address flood damage. To increase inference efficiency on ES, we develop a HS model and combine it with a collaborative scheduling algorithm to construct an integrated end-edge-cloud (iEEC) computing pathway. Our system achieves 94.3% accuracy and a speed of 30.3 frames per second (FPS), outperforming state-of-the-art (SOTA) approaches, and can handle hard instances. The prototype system and simulation tool demonstrate the effectiveness of our approach at various scales.

Edge intelligence (EI) migrates data and artificial intelligence (AI) to the “edge” of a network, enhancing the high-bandwidth and low-latency of wireless data transmission with the multiplier effect of 5G and AI, greatly improving the edges’ processing speed. Through integrating EI and computer vision technology, video surveillance systems in ITS can improve the processing capability of traffic information, which improves traffic efficiency and ensures traffic safety. Accordingly, first, we propose an edge intelligence-based improved-YOLOv4 vehicle detection algorithm, introducing an efficient channel attention (ECA) mechanism and a high-resolution network (HRNet) to enhance vehicle detection ability. Second, an edge intelligence-based improved DeepLabv3+ image segmentation algorithm is proposed, replacing the original backbone network with MobileNetv2 and using the softpool method, thus reducing the network size while improving the segmentation accuracy. Experimental results show that our proposed model has a higher average precision (AP) and can improve vehicle detection accuracy from 82.03% to 86.22%. The mean intersection over union (mIOU) of the image segmentation model improves from 73.32% to 75.63%.

Fine-grained and accurate network flow measurements are essential for various network management tasks. In recent years, the evolution of programmable networks enables flow measurement on the switch. However, limited hardware resources on programmable switches drive the shift of measurement from a single switch to network-wide coordinations. This paper aims to optimize the allocation strategy of flow measurement among switches under the objective of measurement coverage and accuracy in network-wide measurement scenarios. We design a Graph Neural Network model, NeuralMon, that can model and solve the above problem precisely. NeuralMon converts network topologies and network flows into a hypergraph and transforms the flow measurement task allocation problem into a node classification problem. NeuralMon is effective in learning the task allocation solution from the network topologies and flows directly. Even on untrained real-world network topologies, NeuralMon still provides excellent performance.

In recent years, with the rapid proliferation of smart devices, Mobile Edge Computing (MEC) has been regarded as a promising technique that provides computing services in proximity to end-users. To improve the performance of MEC systems, Collaborative Edge Computing (CEC) is proposed to balance the load among cooperative edge servers. In practice, however, edge servers belong to different MEC service providers (SPs) and they have no incentive to help others. To encourage the cooperation between self-interested SPs, in this paper, we propose a profit-sharing incentive mechanism based on the Shapley value. In addition to the desirable properties such as efficiency and fairness, we also proved that our mechanism induces optimal offloading strategies and provides every SP an incentive to join the coalition. To protect the private information of SPs, we defined an aggregate profit function for each SP and showed that revealing this function is sufficient to calculate the profit allocation. Simulation results demonstrate that the system performance and SPs' revenue are substantially improved under cooperation.

With the rapid development of intelligent transportation, various computation-intensive applications have e-merged to improve the safety, efficiency, and comfort on the road. However, due to the mobility and resource dynamics, it is still a challenge for the resource-constrained vehicles to timely process computation-intensive tasks. Fortunately, the computation offloading in the Internet of Vehicles (IoV) greatly eases the contradiction between resource constraints and computing requirements. In this paper, we first present a collaborative computing architecture based on Edge-Cloud (EC) and Mobile-Edge-Platooning-Cloud (MEPC). Then, considering the priority of the Delay-Sensitive Tasks (DSTs), preemptive scheduling is introduced to deal with the hybrid tasks, comprised of DSTs and Delay-Tolerant Tasks (DTTs). Finally, a computation offloading problem based on the collaborative EC-MEPC architecture is established by jointly optimizing the decision-making and resource allocation issue. To solve the above problem, a distributed computation offloading and resource allocation algorithm is designed to achieve the optimal solution. Simulation results show that the proposed collaborative computing architecture and the distributed algorithm can effectively improve the delay and energy consumption performance of this system.

With the increasing demand for high speed and low latency services on the Internet of Vehicles, researches on wireless networks in intelligent connected vehicles (ICVs) with communication and caching capability have attracted much attention. Content retrieving in ICVs is subject to performance degradation as a result of channel fading and intermittent network connectivity. The emerging fifth-generation (5G) networks are promising in supporting the needs of data transmission and alleviating the communication problems in ICVs. Specifically, to improve the users' quality of experience (QoE) and reduce the access delay of content retrieval, it helps to leverage in-network caching in on-board units and small cell base stations (SBSs). In this paper, we propose a cooperative caching scheme based on content popularity and transmission power restriction for inhomogeneous ICV, which pre-caches content files at SBSs to significantly reduce content retrieval delay. In specific, we model the proposed system as a cache management problem and attain optimal QoE by allocating proper transmission power for each content file. Using extensive simulations, we demonstrate that the proposed solution can effectively provide service for ICVs with high QoE in different scenarios.