Security and Privacy for Vehicular Networks

Submission Deadline: 15 August 2018

IEEE Access invites manuscript submissions in the area of Security and Privacy for Vehicular Networks.

In recent years, Intelligent Transportation System (ITS) and vehicles, especially cars, have developed a lot. More and more sensors and communication technologies (e.g., cloud computing) are integrated with cars, which opens up a new design space for vehicular-based applications. Prospectively, vehicular networks are envisioned to support vehicular-based, road-based and traffic-based data sensing, transmitting and processing for ITS applications, and eventually evolve towards a new paradigm, named Vehicular Networks (VNs), which bundle the characteristics of networks into vehicular networks. The social features and mobility patterns of vehicles are used to design efficient and effective message forwarding methods. In common, network analysis techniques (such as node centrality, similarity, and community structure) are employed to explore the vehicles’ attributes and behavior, discover their relationships, and study their implications.

Although VNs are expected to have wide-range applications in future ITS services, considerable technical issues are challenging and need to be solved. The performance of content dissemination in VNs heavily relies on the communication between vehicles, and the behaviors of human beings inside. similar to an online networks, both malicious behaviors could exist and users’ privacy could be exposed in VNs. Therefore, as a crucial content transmission and processing platform for ITS, VNs should inherently ensure security and privacy from cyber physical systems to users. For example, the ways to protect location privacy in vehicular networks have been actively studied in recent years, as the locations of vehicles are frequently used for authentication during the data transmission process, which makes it easier for attackers to launch attacks by threaten the location privacy of vehicles. Mitigating security attacks and protecting individual privacy by developing security-aware techniques are pressing needs for researchers in academia and industry.

The objective of this Special Section in IEEE Access is to collect articles on the state of the art and practices of vehicular networks. In particular, we are soliciting theoretical and applied research in security and privacy solutions including algorithms, modeling, technologies and applications.

The topics of interest include, but are not limited to:

• Trust establishment and measurement for vehicular networks
• Access authentication for vehicular networks
• Analysis and optimization for modeling attacks for vehicular networks
• Attack resistant in vehicular networks
• Individual privacy protection in vehicular networks
• Architecture, strategies and/or algorithms for security-based vehicular networking
• Protocols, scheduling, and/or designs for security-based vehicular networking
• Data storage, and data offloading for security-aware vehicles
• Security and privacy-based method and protocols for Internet of Vehicles
• Security and privacy solutions for vehicular cloud computing
• Security handoff scheme between different network access for vehicles
• Standards, policy and regulation for V2X communication systems considering security and privacy
• Security-based hardware, devices and designs for V2X communication systems
• New technologies and research trends
• Case studies and testbeds

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

Associate Editor: Zhaolong Ning, Dalian University of Technology, China

Guest Editors:

1. Ruilong Deng, University of Alberta, Canada
2. Liangtian Wan, Nanyang Technological University, Singapore
3. Lei Mo, INRIA Rennes – Bretagne Atlantique, France
4. Mohammad S. Obaidat, Fellow of IEEE, Fordham University, USA

Relevant IEEE Access Special Sections:

1. Intelligent Systems for the Internet of Things
2. Privacy Preservation for Large-Scale User Data in Social Networks
3. Trusted Computing

IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: zhaolongning@gmail.com