mlsanityData to 7 Oct 2026

Mobile Edge Intelligence and Computing for the Internet of Vehicles

arXiv 1906.00400 ↗First posted 2 Jun 2019cs.NI eess.SPcited by 22

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Abstract (arXiv)

The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent advancements in vehicular communications and networking. Advances in research can now provide reliable communication links between vehicles, via vehicle-to-vehicle communications, and between vehicles and roadside infrastructures, via vehicle-to-infrastructure communications. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications, which will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. These trends will bring about an era of intelligent IoV, which will heavily depend upon communications, computing, and data analytics technologies. To store and process the massive amount of data generated by intelligent IoV, onboard processing and Cloud computing will not be sufficient, due to resource/power constraints and communication overhead/latency, respectively. By deploying storage and computing resources at the wireless network edge, e.g., radio access points, the edge information system (EIS), including edge caching, edge computing, and edge AI, will play a key role in the future intelligent IoV. Such system will provide not only low-latency content delivery and computation services, but also localized data acquisition, aggregation and processing. This article surveys the latest development in EIS for intelligent IoV. Key design issues, methodologies and hardware platforms are introduced. In particular, typical use cases for intelligent vehicles are illustrated, including edge-assisted perception, mapping, and localization. In addition, various open research problems are identified.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jun 2019.

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Citing papers per year: 2021: 3; 2022: 6; 2023: 3; 2024: 4; 2025: 6.

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  1. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  2. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Designcited by 47
  3. 2025Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Modelscited by 44
  4. 2022A Survey on Collaborative DNN Inference for Edge Intelligencecited by 23
  5. 2024DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approachcited by 17
  6. 2022Trustworthy Federated Learning via Blockchaincited by 15
  7. 2022Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verificationcited by 14
  8. 2023A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Servicescited by 14
  9. 2022Resource-Constrained Edge AI with Early Exit Predictioncited by 10
  10. 2022Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Reviewcited by 7
  11. 2024Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Surveycited by 6
  12. 2024Resource Management for Low-latency Cooperative Fine-tuning of Foundation Models at the Network Edgecited by 4
10 more
  1. 2025PartialLoading: User Scheduling and Bandwidth Allocation for Parameter-sharing Edge Inferencecited by 4
  2. 2024Combining Federated Learning and Control: A Surveycited by 2
  3. 2025Enhancing Disaster Resilience with UAV-Assisted Edge Computing: A Reinforcement Learning Approach to Managing Heterogeneous Edge Devicescited by 2
  4. 2023Semantics-enhanced Temporal Graph Networks for Content Popularity Predictioncited by 1
  5. 2025Aligning Task- and Reconstruction-Oriented Communications for Edge Intelligencecited by 1
  6. 2025Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detectioncited by 1
  7. 2021Learning Based Task Offloading in Digital Twin Empowered Internet of Vehiclescited by 0
  8. 2022Watch from sky: machine-learning-based multi-UAV network for predictive police surveillancecited by 0
  9. 2023Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigationcited by 0
  10. 2025Diffusion Learning with Partial Agent Participation and Local Updatescited by 0