Network Security and Privacy Challenges in Smart Vehicle-to-Grid

This paper investigates the physical-layer security for an indoor visible light communication (VLC) network consisting of a transmitter, a legitimate receiver and an eavesdropper. Both the main channel and the wiretapping channel have nonnegative inputs, which are corrupted by additive white Gaussian noises. Considering the illumination requirement and the physical characteristics of lighting source, the input is also constrained in both its average and peak optical intensities. Two scenarios are investigated: one is only with an average optical intensity constraint, and the other is with both average and peak optical intensity constraints. Based on information theory, closed-form expressions of the upper and lower bounds on secrecy capacity for the two scenarios are derived. Numerical results show that the upper and lower bounds on secrecy capacity are tight, which validates the derived closed-form expressions. Moreover, the asymptotic behaviors in the high signal-to-noise ratio (SNR) regime are analyzed from the theoretical aspects. At high SNR, when only considering the average optical intensity constraint, a small performance gap exists between the asymptotic upper and lower bounds on secrecy capacity. When considering both average and peak optical intensity constraints, the asymptotic upper and lower bounds on secrecy capacity coincide with each other. These conclusions are also confirmed by numerical results.Index Terms-Gaussian noise, physical-layer security, secrecy capacity, visible light communications.

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“…where X * denotes the optimal input, and its corresponding PDF is f X * (x). According to (13) and (14), we have…”

Section: B Upper Bound On Secrecy Capacitymentioning

confidence: 99%

“…According to [32], a lower bound of C B can be easily obtained as (12) in [29], and an upper bound of C E can be easily obtained as (13) in [29]. Then, submit (12) and (13) in [29] into (A.1), eq. (7) can be derived.…”

Section: Appendix a Proof Of Lower Bound (7) In Theoremmentioning

confidence: 99%

Physical-Layer Security for Indoor Visible Light Communications: Secrecy Capacity Analysis

Wang

Liu

Wang

et al. 2018

IEEE Trans. Commun.

104

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“…For example, a self-driving car is expected to generate about one gigabyte of data per second, and continuously offloading such an amount of data to the edge network is not realistic. Privacy is another concern that can prevent centralized ML for most sensor data collected by IoT devices, e.g., smart meters [82] or electric vehicles [83]. While local processing of IoT data is an alternative, often a single device is limited in terms of both available data and computation power.…”

Section: Learning At the Wireless Edgementioning

confidence: 99%

Machine Learning in the Air

Gündüz

Kerret

Sidiropoulos

et al. 2019

IEEE J. Select. Areas Commun.

199

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Thanks to the recent advances in processing speed, data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story -ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles. While recent years have seen a flurry of research activity in exploiting ML tools for various wireless communication problems, the impact of these techniques in practical communication systems and standards is yet to be seen. In this paper, we review some of the major promises and challenges of ML in wireless communication systems, focusing mainly on the physical layer. We present some of the most striking recent accomplishments that ML techniques have achieved with respect to classical approaches, and point to promising research directions where ML is likely to make the biggest impact in the near future. We also highlight the complementary problem of designing physical layer techniques to enable distributed ML at the wireless network edge, which further emphasizes the need to understand and connect ML with fundamental concepts in wireless communications.

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“…3) Security of V2G Technology: Similar to EV charging, V2G demands secure payment protection. Due to the nature of V2G service (e.g., regulation and peak load mitigation), V2G payment needs to be fast, anonymous, and secure [229]. From the EV's perspective, its identity and location data should be unlinkable to AGs; service-related information (e.g., battery status, service preference, etc.)…”

Section: Information Management For V2g Servicesmentioning

confidence: 99%

“…From the operators' perspective, they need to protect users' privacy and acquire appropriate data for efficient operation. [229] presents a novel network security architecture for secure V2G payment. The trade-off in terms of the operator is studied in [230], where the payment mechanism is a registration and data maintenance process based on blockchain.…”

Section: Information Management For V2g Servicesmentioning

confidence: 99%

Energy and Information Management of Electric Vehicular Network: A Survey

Chen

Wang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

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The connected vehicle paradigm empowers vehicles with the capability to communicate with neighboring vehicles and infrastructure, shifting the role of vehicles from a transportation tool to an intelligent service platform. Meanwhile, the transportation electrification pushes forward the electric vehicle (EV) commercialization to reduce the greenhouse gas emission by petroleum combustion. The unstoppable trends of connected vehicle and EVs transform the traditional vehicular system to an electric vehicular network (EVN), a clean, mobile, and safe system. However, due to the mobility and heterogeneity of the EVN, improper management of the network could result in charging overload and data congestion. Thus, energy and information management of the EVN should be carefully studied. In this paper, we provide a comprehensive survey on the deployment and management of EVN considering all three aspects of energy flow, data communication, and computation. We first introduce the management framework of EVN. Then, research works on the EV aggregator (AG) deployment are reviewed to provide energy and information infrastructure for the EVN. Based on the deployed AGs, we present the research work review on EV scheduling that includes both charging and vehicle-to-grid (V2G) scheduling. Moreover, related works on information communication and computing are surveyed under each scenario. Finally, we discuss open research issues in the EVN.

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Network Security and Privacy Challenges in Smart Vehicle-to-Grid

Cited by 113 publications

References 10 publications

Physical-Layer Security for Indoor Visible Light Communications: Secrecy Capacity Analysis

Physical-Layer Security for Indoor Visible Light Communications: Secrecy Capacity Analysis

Machine Learning in the Air

Energy and Information Management of Electric Vehicular Network: A Survey

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