IET Intelligent Transporation Systems, SCI indexed, Impact factor: 2.5
(authors interested to submit manuscript to this special issue may feel free to contact the guest editor Vinay Chamola for any queries/ expressing intent to submit manuscript to the Special issue at email@example.com.)
The number of vehicles on the road is rapidly increasing throughout the globe. A reliable and robust transportation system is the backbone of any country’s economy. Although such a transportation network enhances the overall growth of the country, it comes with a massive trade-off in terms of accidents, traffic jams, parking issues, environmental pollution, and user security. A lot of work has been done, in the last decade, towards solving these problems with a smart and intelligent transportation systems where the vehicles can automatically and intelligently interact with each other. But, present implementations of secure and scalable protocols/algorithms for transportation systems suffer from several bottlenecks in terms of latency, privacy, security, and data leaks.
The incorporation of existing as well as upcoming state-of-the-art technologies, such as Artificial Intelligence, Blockchain, Network Function Virtualizations, Intent-based Networking, Big Data techniques, and Reinforcement Learning-based techniques into the transportation systems is a promising though yet to be explored solution. In addition to a reliable and robust transportation system for the end-users, this would make possible various novel applications that run on fast, efficient, and secure algorithms. Strong user privacy and data security can also be ensured. The aim and scope of this special issue is to seek high quality research articles both from industry and academia that aim towards the development of secure, robust, and intelligent platforms for the upcoming smart transportation systems.
Potential topics of interest include, but are not limited, to the following ones:
• Novel frameworks, protocols, and algorithms based on existing and upcoming technologies to enhance the scalability and security of smart transportation systems.
• Blockchain based security protocols and applications in smart transportation systems.
• Enhanced exploitation of the Big-data generated from smart transportation systems.
• Application of various Machine Learning-based algorithms for intelligent data analysis in smart transportation systems.
• Reinforcement-Learning based intelligent applications in vehicular ad-hoc network to predict and prevent incidents, accidents, and emergency situations.
• Software defined VANET architectures for simplifying the management for smart transportation systems.
• Computing architecture and optimization for intelligent vehicular communication in smart transportation systems.
• Using ML algorithms for Intent-based networking in smart transportation systems.
• Low latency VANET applications for data offloading and bandwidth sharing in smart transportation systems.
• Novel V2G, V2V, V2R protocols, techniques, algorithms, architectures, and frameworks for vehicular ad-hoc network using big data, ML, blockchain, and intent-based networking.