Autonomous and connected cars have the potential to provide a safer and greener transportation system for the public. With advances in wireless communications, machine learning, and sensing technologies, autonomous and connected cars are becoming a reality.
Many potential applications (e.g., augmented reality for information providing through a heads-up display and accident avoidance in autopilot) requires significant computing power to process data generated by the vehicle sensors for near-real-time responses. Upgrading the on-board computers is one option at relatively high cost.
Another solution is cloud computing, where the traditional centralized approach suffers from long latency and unstable connections in a vehicular environment and may congest the backhaul of the network with a large amount of data.
Recently, fog computing (or “edge computing”), an evolved architecture that migrates the computing from the cloud to the edge of the network, has emerged for next-generation (5G) communication networks.
Fog/edge computing can be a more suitable solution for enhancing computational capabilities in vehicular networks via moving the intelligence closer to the vehicles. Processing can be done at road-sided units, wireless access points/base stations at the edge of the network, or even other vehicles as they are not running computationally intensive applications at all times.
Despite all the possibilities offered by fog/edge computing for autonomous and connected cars, the highly dynamic network topology and the tremendous number of cars on roads introduce new technical challenges.
For example, how can we achieve reliable and low-latency vehicle-to-vehicle and vehicle-to-infrastructure communications? How can we efficiently manage computing resources of the fog/edge network? Also, information/network security and user privacy issues are important and require in-depth exploration.
This special section intends to collect the latest research findings in addressing the key challenges, such as those mentioned above, and the future directions in leveraging fog/edge computing for autonomous and connected cars.
Topics of Interest
We solicit papers covering novel results on recent research in fog/edge computing for autonomous and connected cars. The topics include, but not limited to, the following:
New architectures and systems design
Protocol design and networking
Resource allocation and management
Modeling and performance analysis
Machine learning, deep learning for intelligent management and control
QoS and QoE provisioning
Reliability and low-latency communications
Energy and scalability issues
Security and privacy issues
Implementation and testbed
Authors should follow the guidelines in “Information for Authors” in the IEEE Transactions on Vehicular Technology under Information for Authors. Submit papers using ScholarOne Manuscripts.
Manuscript submission: 1 June 2018
First editorial decision: 1 September 2018
Revised manuscript due: 15 October 2018
Final editorial decision: 1 December 2018
Final papers due: 20 December 2018
Publication: February 2019
Prof. Ai-Chun Pang, National Taiwan University, Taiwan
Dr. Edward Au, Huawei Technologies, Canada
Prof. Bo Ai, Beijing Jiao Tong University, China
Prof. Weihua Zhuang, University of Waterloo, Canada