mlsanityData to 7 Oct 2026

EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehicles

arXiv 2101.06056 ↗First posted 15 Jan 2021cs.NI cs.AI cs.DCcited by 3

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Abstract (arXiv)

Edge computing-enhanced Internet of Vehicles (EC-IoV) enables ubiquitous data processing and content sharing among vehicles and terrestrial edge computing (TEC) infrastructures (e.g., 5G base stations and roadside units) with little or no human intervention, plays a key role in the intelligent transportation systems. However, EC-IoV is heavily dependent on the connections and interactions between vehicles and TEC infrastructures, thus will break down in some remote areas where TEC infrastructures are unavailable (e.g., desert, isolated islands and disaster-stricken areas). Driven by the ubiquitous connections and global-area coverage, space-air-ground integrated networks (SAGINs) efficiently support seamless coverage and efficient resource management, represent the next frontier for edge computing. In light of this, we first review the state-of-the-art edge computing research for SAGINs in this article. After discussing several existing orbital and aerial edge computing architectures, we propose a framework of edge computing-enabled space-air-ground integrated networks (EC-SAGINs) to support various IoV services for the vehicles in remote areas. The main objective of the framework is to minimize the task completion time and satellite resource usage. To this end, a pre-classification scheme is presented to reduce the size of action space, and a deep imitation learning (DIL) driven offloading and caching algorithm is proposed to achieve real-time decision making. Simulation results show the effectiveness of our proposed scheme. At last, we also discuss some technology challenges and future directions.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 37 references that are in the map. First posted on arXiv 15 Jan 2021.

Builds on

8 of 37 references.

References · 8

  1. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  2. 2017Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperationcited by 286
  3. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 225
  4. 2013Large-scale Multi-label Text Classification - Revisiting Neural Networkscited by 35
  5. 2017Software Defined Space-Air-Ground Integrated Vehicular Networks: Challenges and Solutionscited by 8
  6. 2017A Comprehensive Survey on Fog Computing: State-of-the-art and Research Challengescited by 6
  7. 2019Ready Player One: UAV Clustering based Multi-Task Offloading for Vehicular VR/AR Gamingcited by 4
  8. 2018Air-Ground Integrated Mobile Edge Networks: Architecture, Challenges and Opportunitiescited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 0; 2026: 1.

3 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025Multiconnectivity for SAGIN: Current Trends, Challenges, AI-driven Solutions, and Opportunities
  2. 2025Joint Task Offloading and Resource Allocation in Low-Altitude MEC via Graph Attention Diffusion
  3. 2025HybridRAG-based LLM Agents for Low-Carbon Optimization in Low-Altitude Economy Networks
  4. 2023Generative AI for Space-Air-Ground Integrated Networks
  5. 2025Hierarchical Task Offloading and Trajectory Optimization in Low-Altitude Intelligent Networks Via Auction and Diffusion-based MARL
  6. 2025Joint AoI and Handover Optimization in Space-Air-Ground Integrated Network
  7. 2026A RAG-Enhanced Bi-Level Cognitive Orchestration Framework for LEO Satellite Networks ↗
  8. 2026CSI-Assisted Edge SLAM Testbed Platform for 5G Connected Unmanned Autonomous Vehicles ↗