Mobile Edge Intelligence and Computing for the Internet of Vehicles (1906.00400v1)

Abstract: 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.

• The paper presents a comprehensive review of integrating edge caching, computing, and AI to optimize real-time vehicular communications.
• It introduces mobility-aware caching and MEC frameworks to reduce latency and improve task offloading in dynamic IoV environments.
• The research emphasizes federated edge AI, enabling privacy-preserving model training and efficient task partitioning in autonomous driving.

Mobile Edge Intelligence and Computing for the Internet of Vehicles

This paper, authored by Jun Zhang and Khaled B. Letaief, explores the rapidly evolving Internet of Vehicles (IoV), an emergent paradigm driven by advancements in vehicular communications and the enhancement of vehicle intelligence. It emphasizes the growing significance of integrating autonomous vehicles, the Internet of Things (IoT), and environmental interactions to usher in an era of intelligent IoV. This integration necessitates robust communication, computing, and data analytic technologies to facilitate seamless vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications.

The research systematically surveys the development in Edge Information System (EIS) architectures for intelligent IoV. EIS is highlighted as a pivotal concept, focusing on edge caching, edge computing, and edge AI. By deploying storage and computing resources at the network edge, such as radio access points, EIS supports the massive data processing and low-latency content delivery required for IoV applications.

Key Discussions and Findings

• Edge Caching: The paper discusses caching at the edge to accommodate the temporal and spatial locality of data requests in vehicular environments. It addresses techniques to overcome vehicle mobility challenges by predicting request patterns and optimizing cache placement to reduce service delay and enhance data freshness.
• Edge Computing: The necessity of mobile edge computing (MEC) arises from the latency limitations of cloud computing. MEC is presented as a crucial enabler for real-time in-vehicle data analytics, offloading resource-intensive computations from vehicles to proximal edge servers. The paper reviews resource management strategies and mobility-aware algorithms to optimize task offloading and computing resource allocation.
• Edge AI: Training and inference of AI models are studied under edge computing frameworks. Federated learning is recognized for its potential to enhance privacy-preservation in model training by maintaining data on devices. Joint device-edge AI applications allow computational tasks to be dynamically partitioned between edge nodes and vehicles, optimizing latency-sensitive tasks in autonomous driving.

Implications and Future Directions

This investigation into EIS for IoV opens numerous avenues for enhancing the efficiency and capability of connected vehicles. The implications are profound both practically and theoretically, affecting urban traffic management, vehicle safety applications, and the deployment of autonomous driving technology. The research questions traditional cloud-dependent models by positioning the edge as an intermediary, potentially revolutionizing how vehicular networks process and act on data.

Looking forward, the paper identifies open research problems such as optimizing EIS frameworks to cater to varying network conditions and developing robust security measures for edge-assisted vehicular operations. The progression of AI technologies at the edge is likely to remain a focal point, shaping the future development of intelligent IoV systems. The timely implementation of edge computing and AI in vehicular networks will ensure vehicles are not only connected but are also intelligent partners in urban ecosystems. The research invites further scrutiny into edge resource orchestration, autonomous vehicle integration, and the strategic placement of computing infrastructure to foster a resilient IoV architecture.

In summary, this paper elaborates on a comprehensive review of mobile edge intelligence and computing as fundamental to advancing IoV capabilities, proposing a multi-faceted approach to deal with the complexities of modern vehicular networks.