Calls for Papers
Mobile Edge Computing for Vehicular Networks

Driven by the “connected vehicles” vision, smart vehicles are on the road equipped with advanced computation units, multiple communications technologies, intelligent sensing platforms, and human–computer interaction devices. With the aid of these equipment and technologies, vehicles can offer many new applications and services, such as active driving safety assistance, self-driving cars, smart parking, road traffic monitoring, and fleet management.

There are also new content-centric applications for drivers and passengers. These applications typically require intensive computation and demand low-delay data processing, e.g., video analytics with real-time interaction, image-aided navigation, natural language processing, and interactive gaming. Such applications pose great challenges to the existing vehicular terminals and networks, especially in terms of their computational resources.

To meet the ever-increasing computation demand in vehicular networks, Mobile Edge Computing (MEC) is emerging as a very promising solution. 

With the dramatically enhanced capacity, MEC is expected to bring a variety of benefits to vehicular networks, including ultra-low latency between smart devices/vehicles and edge cloud for real-time, interactive, and mission-critical  applications; privacy and security in  local  communications  to  access  mobile  edge  cloud, and  big data analytics at the point of capture for novel safety-oriented applications.

Despite the potentially significant benefits of MEC, many challenges need to be addressed in this new paradigm. A typical challenge is related to computation offloading which should consider mobility-awareness and incentive in MEC for vehicular networks. 

In addition, various computation tasks may have  different  resource  requirements,  including  the  computation  resources  for  task  execution  and  the  communication resources for task transmission. In this case, a joint optimization problem should consider both communications, computation and control dimensions.

The objective of this special issue is to present the latest results, insights, and perspectives on the new area of mobile edge computing for vehicular networks. We are soliciting original contributions that have not been published and are not currently under consideration by any other journals. The topics of interest include, but are not limited to:

Submissions should clearly identify how they relate to topics under consideration in this special issue. Contributions describing an overall working system and reporting real world deployment experiences are particularly of interest. 

Submitted papers should contain state-of-the-art research material presented in a tutorial or survey style. Manuscript format must adhere to the  IEEE VT Magazine submission guidelines. Articles should be about 3,000 to 4,000 words long with 5—10 figures and 10—15 references. The use of mathematical equations should be limited to three.

Submit papers using ScholarOne Manuscripts.

Important Dates

Submission Deadline: 1 June 2018
First Editorial Decision: 1 September 2018
Acceptance Notification:  1 November 2018
Final Manuscript Due:  1 December 2018
Publication: Spring 2019

Guest Editors

Yan Zhang
University of Oslo, Norway
Javier Lopez
University of Malaga, Spain
Zhen Wang
Hangzhou Dianzi University, China

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In this Issue
Message from the EiC
Message from the President:
VTS Volunteerism—Enabling Professional and Personal Growth!
Message from the 2018 IEEE VTS VP—Mobile Radio
Board of Governors Member Profile: Lajos Hanzo
Spectrum and Regulation
From the IEEE VTS Resource Center
Practical Work with Robots
From IEEE Vehicular Technology Magazine
LTE-V for Sidelink 5G V2X Vehicular Communications
Mobile Radio
5G Core Network Prototypes and 5G New Radio
Transportation Systems
Melbourne, Victoria, Expands Commuter Rail Service
Conference Report
Calls for Papers
VTC2018-Fall & VPPC 2018 in Chicago
IEEE Vehicular Technology Magazine: Special Issue on 5G Technologies and Applications
Fog/Edge Computing for Autonomous and Connected Cars
Mobile Edge Computing for Vehicular Networks