Nowadays, connected vehicles are able to collect up to 170 measurements (speed, temperature, fuel consumption, etc.) from on board built-in sensors and transmit them to an infrastructure, usually by 4G/5G wireless communications. This raises many opportunities to develop new and innovative telematics services including, among others, driver safety, customer experience, location-based services, dealer services, infotainment, etc. It is expected that there will be roughly 2 billion connected cars by the end of 2025 on the world’s roadways, where each of which can produce up to 30 terabytes of data each day. This huge amount of data, whereas it offers interesting commercial opportunities, it emphasizes however the development of sophisticated computation frameworks, in particular parallel and distributed ones, for collecting, gathering and analyzing the generated data.
Most notable, among the challenges facing connected vehicles applications, is the infrastructure’s ability of real-time or near real time processing in order to enable new and innovative services. In fact, a broad range of applications, more precisely safety application (e.g., early alert on the presence of freezing rain on roads), is based on a new emerging communication paradigm, known as “car-to-car communication via infrastructure”. The performance, and even the existence, of this paradigm is highly dependent on the infrastructure’s ability to collect information, process and gather it and finally deliver it (i.e., sending back) to cars within acceptable delays. Even the latter depend on the target applications, they are mostly required to be very short to meet real-time or near real-time delays. This is true specifically for safety applications.
The objective of this special issue is to explore recent advances and future trends in developing, as well as deploying innovative applications related to connected vehicles. It aims to provide a forum for researchers and practitioners to foster exchange of research ideas, approaches, theories, practices and industrial experiences to resolve challenging issues associated with managing big data generated from connected vehicles, whilst considering their intrinsic requirements.
This special issue seeks high-quality and original technical contributions as well as industrial experiences. Submissions will be judged on their originality, significance, clarity, relevance, and technical correctness. High-quality survey papers are also welcome. Submitted papers must not be under consideration in any other venue(s).
Scope of the special issue:
Topics of interest are (but not limited to):
Stream computing for connected vehicle’s big data
Parallel and distributed Big Data architectures for connected vehicles
VANET Big Data for Predicting Traffic Congestion
IoT connected vehicles data analytics
Cloud computing systems for connected vehicles
Massive parallel-processing systems for connected vehicle’s big data
Architectures (software and hardware) for managing connected vehicle data
Dynamic resource, service and context management for connected vehicle applications
Quality of Service/Experience (QoS/QoE) provisioning for connected vehicle’s applications
Formal verification techniques for protocols and applications of connected vehicles.
5G communication architecture and protocols for V2I and I2V
Performance evaluation of connected vehicles big data platforms
Big Data Analysis for Vehicular Social Networks (VSN)
Transportation visualization based on VSN big data
Mobility modeling and big data mining in Connected and Autonomous Vehicles
Human mobility prediction and visualization leveraging big data
AI based techniques for connected and autonomous vehicles
Data privacy and security for connected vehicular networks
Authors should follow the Journal of Parallel and Distributed Computing manuscript format described at the journal site: https://www.elsevier.com/journals/journal-of-parallel-and-distributed-computing/0743-7315/guide-for-authors. Manuscripts that extend research published previously (e.g., in conference or workshop proceedings) will only be considered if they include at least 30% of significantly new material; the submission of such manuscripts must be accompanied by a “Summary of Differences” letter explaining how the authors extended their previously published work. All manuscripts and any supplementary material should be submitted through Elsevier Editorial System (EVISE), available at: https://www.evise.com/evise/jrnl/YJPDC. The authors must select “VSI: CV meet BigData” when they reach the “Article Type” in the submission process.
Submission deadline: December 15th, 2020
First round of Reviews: March 15th, 2021
Revised paper submission: May 1st, 2021
Final decision: July 30th, 2021
Publications: Late 2021
University of Franche-Comte, France
University of Exeter, UK
IBM Zurich Research Laboratory, Switzerland