| Abstract |
Tactile Internet is a fairly recent technological trend associated with the Internet-of-Things (IoT) era, with potential applications in a broad range of industrial, societal and business use cases. The real-time machine-to-machine and human-to-machine interactions (e.g., in unmanned vehicles and the underpinning infrastructure within the smart city ecosystem) in the intelligent transportation sector, for example, contribute to the potential utility of Tactile Internet in this particular sector (and the broader smart city). In the context of unmanned vehicles, such as unmanned aerial vehicles and electric (ground) vehicles, one of several key challenges to its broader utility is how to design a secure energy trading ecosystem that can be used for purposes such as charging and discharging from the supporting smart grids. Most existing approaches in the literature focused on conventional and centralized security mechanisms, which may not be applicable for energy trading in a smart city environment. Moreover, the need for real-time processing for energy trading computation is one of the essential requirements of Tactile Internet. Therefore, to address these challenges, BEST: a Blockchain-based secure energy trading scheme for electric vehicles (EVs) is proposed in this paper. Specifically, in BEST, blockchain is used to validate EVs' requests in a distributed manner; thus, ensuring resilience against the single point of failure. The miner nodes are selected to validate the requests on the basis of energy requirements, time of stay, dynamic pricing, and connectivity record, as well as other factors that are crucial for the operator at the time of operation. Moreover, to provide low latency and real-time services, software-defined networking is used as the network's backbone to transfer EVs' requests to a global software defined network controller. Finally, BEST is evaluated on the basis of the communication and computation costs incurred during various transactions between the EVs and the smart grid. A case study is also provided to demonstrate the potential deployment of BEST in energy trading. (C) 2019 Elsevier Ltd. All rights reserved. |
