Cognitive Internet of Vehicles: Motivation, Layered Architecture and Security Issues

Hasan, Khondokar Fida; Kaur, Tarandeep; Hasan, Md. Mhedi; Feng, Yanming

doi:10.1109/sti47673.2019.9068070

Cited by 23 publications

(9 citation statements)

References 23 publications

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“…Apart from the Internet in the era of Web 2.0, the Internet of Vehicles (IoV) has emerged as an outlet which can merge the traditional media (e.g., television, radio) and social media (e.g., infotainment applications). The evolved cognitive IoV architecture is proposed to further enhance the functionalities of intelligent cognition, control, decision-making and security in autonomous driving scenarios [38,39]. The intelligent connected vehicles (ICVs) become a platform to create and offer content for the consumers.…”

Section: Mobile-first Strategymentioning

confidence: 99%

Internet of Vehicles Empowered Mobile Media: Research on Mobile-Generated Content (MoGC) for Intelligent Connected Vehicles

Jin

Zhai

et al. 2021

Sustainability

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With the rapid growth of mobile media, large quantities of mobile content have been generated by moving entities. Some content generation patterns become popular for users and professional organizations, e.g., user-generated content, professionally-generated content, machine-generated content. However, due to the limitations of device types and functions, it is necessary to explore the new production tools and further inspire the content potential in mobile scenarios. According to the production capacity supported by Internet of Vehicles, intelligent connected vehicles (ICVs) emerge as new content generated devices in sustainable cities. In this paper, we propose the concept of Mobile-Generated Content (MoGC) as a new part of production patterns. First, we analyze the relationship between MoGC and existing patterns from the perspectives of entity and workflow. Second, the unique functionality and social property of MoGC are revealed, i.e., ICVs play the role of middle platform with data and technology offices. The current dominant discourse created by the professional institutions (e.g., media agency or governing authority) will transfer to the vehicle users. In this way, the content generation system has been further enriched in omni-media environments through efficiently integrating productivity tools and resources. Besides, MoGC not only contributes to social governance by enlarging the news source and coverage, but also strengthen the personal discourse in mobile scenarios.

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Section: Mobile-first Strategymentioning

confidence: 99%

Internet of Vehicles Empowered Mobile Media: Research on Mobile-Generated Content (MoGC) for Intelligent Connected Vehicles

Jin

Zhai

et al. 2021

Sustainability

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“…ML can take control of vehicles to enable error-free driving. CIoV allows cloudbased ML into a transportation system for security risks and privacy issues [37]. In CIoV cognition and control layer, ML provides strategic services for different function levels, i.e., driving behaviors, health monitoring, and pattern and emotion analysis, in addition to network resource allocation and optimization.…”

Section: Machine Learning Algorithms In the Iov Networkmentioning

confidence: 99%

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Ali

Hasan

Hassan

et al. 2021

Security and Communication Networks

120

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Recently, interest in Internet of Vehicles’ (IoV) technologies has significantly emerged due to the substantial development in the smart automobile industries. Internet of Vehicles’ technology enables vehicles to communicate with public networks and interact with the surrounding environment. It also allows vehicles to exchange and collect information about other vehicles and roads. IoV is introduced to enhance road users’ experience by reducing road congestion, improving traffic management, and ensuring the road safety. The promised applications of smart vehicles and IoV systems face many challenges, such as big data collection in IoV and distribution to attractive vehicles and humans. Another challenge is achieving fast and efficient communication between many different vehicles and smart devices called Vehicle-to-Everything (V2X). One of the vital questions that the researchers need to address is how to effectively handle the privacy of large groups of data and vehicles in IoV systems. Artificial Intelligence technology offers many smart solutions that may help IoV networks address all these questions and issues. Machine learning (ML) is one of the highest efficient AI tools that have been extensively used to resolve all mentioned problematic issues. For example, ML can be used to avoid road accidents by analyzing the driving behavior and environment by sensing data of the surrounding environment. Machine learning mechanisms are characterized by the time change and are critical to channel modeling in-vehicle network scenarios. This paper aims to provide theoretical foundations for machine learning and the leading models and algorithms to resolve IoV applications’ challenges. This paper has conducted a critical review with analytical modeling for offloading mobile edge-computing decisions based on machine learning and Deep Reinforcement Learning (DRL) approaches for the Internet of Vehicles (IoV). The paper has assumed a Secure IoV edge-computing offloading model with various data processing and traffic flow. The proposed analytical model considers the Markov decision process (MDP) and ML in offloading the decision process of different task flows of the IoV network control cycle. In the paper, we focused on buffer and energy aware in ML-enabled Quality of Experience (QoE) optimization, where many recent related research and methods were analyzed, compared, and discussed. The IoV edge computing and fog-based identity authentication and security mechanism were presented as well. Finally, future directions and potential solutions for secure ML IoV and V2X were highlighted.

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“…It fundamentally aims at bridging the transportation system such as a vehicle, road infrastructure and the social world such as human demand, awareness and social behavior and so on. It also aims to enable smart network operation and optimization, resource allocation, emergency responses and intelligent service provisioning [5,7,15,16].…”

Section: A Overview Of C-iovmentioning

confidence: 99%

Security, Privacy, and Trust of Emerging Intelligent Transportation: Cognitive Internet of Vehicles

Hasan¹,

Overall²,

Ansari³

et al. 2022

Next-Generation Enterprise Security and Governance

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The recent advancement of cloud technology offers unparallel strength to support intelligent computations and advanced services to assist with automated decisions to improve road transportation safety and comfort. Besides, the rise of machine intelligence propels the technological evolution of transportation systems one step further and leads to a new framework known as Cognitive Internet of Vehicles (C-IoV). The redefined cognitive technology in this framework promises significant enhancements and optimized network capacities compared with its predecessor framework, the Internet of Vehicles (IoV). CIoV offers additional security measures and introduces security and privacy concerns, such as evasion attacks, additional threats of data poisoning, and learning errors, which may likely lead to system failure and road user fatalities. Similar to many other public enterprise systems, transportation has a significant impact on the population. Therefore, it is crucial to understand the evolution and equally essential to identify potential security vulnerabilities and issues to offer mitigation towards success. This chapter offers discussions framing answers to the following two questions, 1) how and in what ways the penetration of the latest technologies are reshaping the transportation system? 2) whether the evolved system is capable of addressing the concerns of cybersecurity? This chapter, therefore, starts presenting the evolution of the transportation system followed by a quick overview of the evolved CIoV, highlighting the evolved cognitive design. Later it presents how a cognitive engine can overcome legacy security concerns and also be subjected to further potential security, privacy, and trust issues that this cloud-based evolved transportation system may encounter.

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Cognitive Internet of Vehicles: Motivation, Layered Architecture and Security Issues

Cited by 23 publications

References 23 publications

Internet of Vehicles Empowered Mobile Media: Research on Mobile-Generated Content (MoGC) for Intelligent Connected Vehicles

Internet of Vehicles Empowered Mobile Media: Research on Mobile-Generated Content (MoGC) for Intelligent Connected Vehicles

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Security, Privacy, and Trust of Emerging Intelligent Transportation: Cognitive Internet of Vehicles

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