Challenges and future directions for security and privacy in vehicular fog computing

Nazih, Ossama; Benamar, Nabil; Lamaazi, Hanane; Chaoui, Habiba

doi:10.1109/3ict56508.2022.9990869

Cited by 3 publications

(4 citation statements)

References 43 publications

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“…Drivers can access various real-time applications through the application and services layer, including fuel feedback, health detection, and environmental monitoring [48]. It provides the user various services, including connectivity, information, data processing and storage, and entertainment.…”

Section: Applications and Servicesmentioning

confidence: 99%

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Toward Secure and Trustworthy Vehicular Fog Computing: A Survey

Nazih,

Benamar,

Lamaazi

et al. 2024

IEEE Access

Self Cite

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The integration of fog computing in vehicular networks has led to significant advancements in road safety, traffic control, entertainment, and comfort services. Vehicular Fog Computing (VFC) emerges as an optimistic solution, offering a pathway to responsive service requests. VFC uses either onboard vehicle computers or vehicles as fog infrastructure between the underlying networks and the cloud, addressing the limitations of centralized data processing in traditional cloud computing. However, VFC faces security vulnerabilities due to the open nature of its deployment. In this survey, we explore the security threats confronting VFC and review existing solutions related to their detection and mitigation capabilities. This paper provides a comprehensive overview of the foundational concept of Fog Computing, VFC architectures, and their critical role in various intelligent computing applications. Moreover, it spotlights the trust and security concerns in deploying VFC and real-time big data analytics within the vehicular environment. This survey identifies pressing issues and outlines potential research directions, offering insights for the research community to address while designing secure VFC architectures.

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Section: Applications and Servicesmentioning

confidence: 99%

“…Fog nodes can still offer customers localized processing for a quicker response [63]. Figure 4 illustrates the three models used for managing trust in a VFC environment [48]. In the dynamic landscape of VFC, a game-changing reputation-based service provisioning system unfolds.…”

Section: B Trust Management In Vfcmentioning

confidence: 99%

Toward Secure and Trustworthy Vehicular Fog Computing: A Survey

Nazih,

Benamar,

Lamaazi

et al. 2024

IEEE Access

Self Cite

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“…Fog computing additionally facilitates the integration of machine learning and artificial intelligence algorithms, which can consequently improve the decision-making capabilities of connected vehicles [117]. For example, trajectory prediction, traffic prediction, and accident prevention are some of the applications that can be improved with the integration of machine learning algorithms.…”

Section: Edge and Fogmentioning

confidence: 99%

“…For example, trajectory prediction, traffic prediction, and accident prevention are some of the applications that can be improved with the integration of machine learning algorithms. Additionally, fog computing can also help to improve the scalability of vehicular networks by processing and making decisions locally, thereby reducing the need for central processing [117].…”

Section: Edge and Fogmentioning

confidence: 99%

The Evolution of Intelligent Transportation Systems: Analyzing the Differences and Similarities between IoV and IoFV

Herich,

Vaščák

2024

Drones

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The Internet of Vehicles (IoV) and the Internet of Flying Vehicles (IoFV) are integral components of intelligent transportation systems with the potential to revolutionize the way we move people and goods. Although both the IoV and IoFV share a common goal of improving transportation efficiency, safety, and sustainability, they possess distinct characteristics and face unique challenges. To date, the existing literature has predominantly focused on specific aspects of either the IoV or IoFV, but a comprehensive review comparing and contrasting the two domains is still lacking. This review paper aims to address this gap by providing an in-depth analysis of the key differences between the IoV and IoFV systems. The review will examine the technological components, network infrastructure, communication protocols, data management, objectives, applications, challenges, and future trends associated with both domains. Additionally, this paper will explore the potential impact of technologies such as artificial intelligence, machine learning, and blockchain. Ultimately, the paper aims to contribute to a deeper understanding of the implications and potential of these technologies, both in the context of transportation systems and beyond.

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