Mobile edge computing for big-data-enabled electric vehicle charging

As one of the key drivers of smart grid, EVs are environment-friendly to alleviate CO2 pollution. Big data analytics could enable the move from Internet of EVs, to optimized EV charging in smart transportation. In this article, we propose a MECbased system, in line with a big data-driven planning strategy, for CS charging. The GC as cloud server further facilitates analytics of big data, from CSs (service providers) and on-the-move EVs (mobile clients), to predict the charging availability of CSs. Mobility-aware MEC servers interact with opportunistically encountered EVs to disseminate CSs' predicted charging availability, collect EVs' driving big data, and implement decentralized computing on data mining and aggregation. The case study shows the benefits of the MEC-based system in terms of communication efficiency (with repeated monitoring of a traffic jam) concerning the long-term popularity of EVs

Electric Vehicle Charging Recommendation and Enabling ICT Technologies: Recent Advances and Future Directions

The introduction of Electric Vehicles (EV) will have a significant impact on the sustainable economic development of urban city. However, compared with traditional gasoline-powered vehicles, EVs currently have limited range, which necessitates regular recharging. Considering the limited charging infrastructure currently available in most countries, infrastructure investments and Renewable Energy Sources (RES) are critical. Thus, service quality provisioning is necessary for realizing EV market. Unlike numerous previous works which investigate "charging scheduling" (referred to when/whether to charge) for EVs already been parked at home/Charging Stations (CSs), a few works focus on “charging recommendation” (refer to where/which CS to charge) for on-the-move EVs. The latter use case cannot be overlooked as it is the most important feature of EVs, especially for driving experience during journeys. On-the-move EVs will travel towards appropriate CSs for charging based on smart decision on where to charge, so as to experience a shorter waiting time for charging. The effort towards sustainable engagement of EVs has not attracted enough attention from both industrial and academia communities. Even if there have been many charging service providers available, the utilization of charging infrastructures is still in need of significant enhancement. Such a situation certainly requires the popularity of EVs towards the sustainable, green and economic market. Enabling the sustainability requires a joint contribution from each domain, e.g., how to guarantee accurate information involved in decision making, how to optimally guide EV drivers towards charging place with the least waiting time, how to schedule charging services for EVs being parked within grid capacity. Achieving this goal is of importance towards a positioning of efficient, scalable and smart ICT framework, makes it feasible to learn the whole picture of grid: - Necessary information needs to be disseminated between stakeholders CSs and EVs, e.g., expected queuing time at individual CSs. In this context, how accurate CSs condition information plays an important role on the optimality of charging recommendation. - Also, it is very time-consuming for the centralized Global Controller (GC) to achieve optimization, by seamlessly collecting data from all EVs and CSs, The complexity and computation load of this centralized solution, increases exponentially with the number of EVs. This paper summaries the recent interdisciplinary research works on EV charging recommendation along with novel ICT frameworks, with an original taxonomy on how Intelligent Transportation Systems (ITS) technologies support the EV charging use case. Future directions are also highlighted to promote the future research

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems

Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather challenging to make timely and accurate decisions of vehicle behaviors. Moreover, in the presence of mobile wireless communications, the privacy and security of vehicle information are at constant risk. In this context, a new paradigm is urgently needed for various applications in dynamic vehicle environments. As a distributed machine learning technology, federated learning (FL) has received extensive attention due to its outstanding privacy protection properties and easy scalability. We conduct a comprehensive survey of the latest developments in FL for ITS. Specifically, we initially research the prevalent challenges in ITS and elucidate the motivations for applying FL from various perspectives. Subsequently, we review existing deployments of FL in ITS across various scenarios, and discuss specific potential issues in object recognition, traffic management, and service providing scenarios. Furthermore, we conduct a further analysis of the new challenges introduced by FL deployment and the inherent limitations that FL alone cannot fully address, including uneven data distribution, limited storage and computing power, and potential privacy and security concerns. We then examine the existing collaborative technologies that can help mitigate these challenges. Lastly, we discuss the open challenges that remain to be addressed in applying FL in ITS and propose several future research directions

Vehicular-Publish/Subscribe (V-P/S) communication enabled on-the-move EV charging management

Recently, the charging management for Electric Vehicles (EVs) on-the-move has become an emerging research problem in urban cities. Major technical challenges here involve intelligence for the selection of Charging Stations (CSs) to guide drivers’ charging plans, as well as the corresponding communication infrastructure for information dissemination between the power grid and EVs. In this article, a Vehicular- Publish/Subscribe (P/S) communication framework, in conjunction with Public Transportation Buses (PTBs) is provisioned to support on-the-move EV charging management. Benefiting from low privacy sensitivity, we propose a fully distributed charging management scheme concerning the driving intention. Results demonstrate a guidance for the provisioning of V P/Scommunication framework, concerning EV drivers’ experience including charging waiting time and total trip duration. Also, the benefit of V-P/S communication framework is reflected in terms of the communication efficiency. Open research issues of this emerging research area are also presented

Towards Cyber Security for Low-Carbon Transportation: Overview, Challenges and Future Directions

In recent years, low-carbon transportation has become an indispensable part as sustainable development strategies of various countries, and plays a very important responsibility in promoting low-carbon cities. However, the security of low-carbon transportation has been threatened from various ways. For example, denial of service attacks pose a great threat to the electric vehicles and vehicle-to-grid networks. To minimize these threats, several methods have been proposed to defense against them. Yet, these methods are only for certain types of scenarios or attacks. Therefore, this review addresses security aspect from holistic view, provides the overview, challenges and future directions of cyber security technologies in low-carbon transportation. Firstly, based on the concept and importance of low-carbon transportation, this review positions the low-carbon transportation services. Then, with the perspective of network architecture and communication mode, this review classifies its typical attack risks. The corresponding defense technologies and relevant security suggestions are further reviewed from perspective of data security, network management security and network application security. Finally, in view of the long term development of low-carbon transportation, future research directions have been concerned.Comment: 34 pages, 6 figures, accepted by journal Renewable and Sustainable Energy Review