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Edge Learning for the Internet of Vehicles– Algorithms, Frameworks and Applications

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Edge Learning for the Internet of Vehicles– Algorithms, Frameworks and Applications Edge Learning for the Internet of Vehicles– Algorithms, Frameworks and Applications Internet of Vehicles (IoV) concept has emerged from Internet of Things (IoT), referring to a large number of connected vehicles. IoV focuses on information exchange between vehicles-to-anything (V2X) in near real-time using IEEE 802.11p as well as cellular technologies. The advances in IoV are expected to lead towards a true smart city that has Intelligent Transportation System (ITS) which provides a number of services. These include: traffic crash data collection and trend analysis for safer roads, intelligent traffic management through safe lane changing and intersection crossing, health emergency, outing, parking, infotainment, and mobile Internet services. Machine learning (ML) techniques are anticipated to be significantly useful for the development of a smart IoV environment. Some examples are: traffic prediction-based path and data routing, vehicular blockchain, congestion, load balancing, cyber-physical attack mitigation, resource management based-on e.g. energy-efficiency, virtualization, and distributed learning. High complexity of the IoV environment can be addressed by online/offline learning mechanisms (e.g., supervised, unsupervised, and reinforcement). In addition, near real-time requirements of the majority of services emerging from ITS require low-latency solutions. Thus, edge learning will play a key role in bringing intelligent, efficient, and creative applications and systems for the vehicular ecosystem. For instance, caching the contents at the network edge for intelligent information processing can be built on machine learning techniques to enhance the efficiency of IoV. Considering all of these exciting areas, this special issue aims to bring together research efforts on IoVs that benefit from ML and edge computing. The topics of interest include but are not limited to: Intelligent Road safety and accident mitigation in IoV ML-based edge-supported location management mechanisms of IoV Driver safety and assistance using deep learning in IoV Intelligent Information dissemination and retrieval in IoV ML-based edge caching mechanisms for IoV Intelligent Autonomous driving in IoV Intelligent Traffic management system for small/large rural/urban areas in IoV Smart Health emergency handling in IoV Intelligent content-centric mechanisms in IoV using edge learning Theft mitigation and rescue in IoV using machine learning Public parking and monitoring in IoV using ML Mobile Internet usage in IoV Blockchain applications in IoV enabled by edge learning Security based-on edge learning in IoV Privacy mechanisms supported by IoV edge Smart mobility management in IoV using edge learning Infotainment in IoV using edge learning Novel frameworks supporting edge learning in ITS Intelligent edge-based algorithms for IoV Field trials/Testbed implementations in IoV supported by edge learning Original, high-quality contributions that are not yet published or that are not currently under review by other journals or peer-reviewed conferences are sought. Papers will be peer-reviewed by independent reviewers and selected based on originality, scientific quality and relevance to this Special Issue. The guest editors will make final decisions about the acceptance of the papers. Important dates Paper submission due: Jun. 31, 2019 First-round notification: Sep. 30, 2019 Revision submission: Nov. 30, 2019 Notification of final decision: Jan. 20, 2019 Submission of final paper: Feb. 30, 2020 Publication date: Mar. 2020 Guest Editors Muhammad Maaz Rehan (Managing Guest Editor) COMSATS University Islamabad, Pakistan [email protected] | [email protected] Mubashir Hussain Rehmani Cork Institute of Technology, Ireland [email protected] Sedjelmaci Hichem Orange Labs, Châtillon, France [email protected] Melike Erol-Kantarci University of Ottawa, Canada [email protected] Akhilesh Thyagaturu Intel, USA [email protected] .
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