Vehicular Cloud Computing Security: A Survey

Goumidi, Hadjer; Aliouat, Zibouda; Harous, Saad

doi:10.1007/s13369-019-04094-0

Cited by 28 publications

(13 citation statements)

References 95 publications

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“…In the earliest survey of VCC, authors of [78] presented the two-tier general architecture of VCC. Services and applications are also explained in this paper.…”

Section: B Vehicular Cloud Computingmentioning

confidence: 99%

“…A comprehensive explanation about the other two categories, VuC and HC, is missing in [85]. VCC services and simulation frameworks are explored by authors of [86], resource scheduling is discussed at [87], and VCC security issues are elaborated by the authors of [78], [88]. Olariu [89], has presented a state of the art survey on promising applications and new challenges of VCC.…”

Section: B Vehicular Cloud Computingmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Review of Computing Paradigms, Enabling Computation Offloading and Task Execution in Vehicular Networks

et al. 2022

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Road safety, optimized traffic management, and passenger comfort have always been the primary goals of the vehicle networking research community. Advances in computer and communication technology have made the dream of modern intelligent vehicles a reality through the use of smart sensors, cameras, networking devices, and storage capabilities. Autonomous operation of modern intelligent vehicles requires massive computations where tasks are outsourced. The research community has proposed various computing paradigms: mobile cloud computing, vehicle cloud computing, multi-access edge computing, vehicle edge computing, vehicle fog computing, and voluntary computing-based VANET (VCBV) to move computational power close to the user and handle the delay-sensitive applications of modern intelligent vehicles. In this study, we have provided a comprehensive overview of all computing paradigms related to vehicular networks. We have presented the architectural details, similarities, differences, and key features of each computing paradigm. Finally, we concluded the study with open research challenges in vehicular networks.

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“…In the earliest survey of VCC, authors of [78] presented the two-tier general architecture of VCC. Services and applications are also explained in this paper.…”

Section: B Vehicular Cloud Computingmentioning

confidence: 99%

Section: B Vehicular Cloud Computingmentioning

confidence: 99%

A Comprehensive Review of Computing Paradigms, Enabling Computation Offloading and Task Execution in Vehicular Networks

et al. 2022

View full text Add to dashboard Cite

show abstract

“…If the link-layer provides enough information, it can be used to populate the local link set instead of the HELLO message exchange. Therefore, the delivery cycle of HELLO messages directly affects the generation of local link sets for each node in the network, which in turn affects the speed of network reconfiguration [17]. In practice, we can increase the value of the HELLP_INTERVAL parameter, to optimize the structure change, reduce the resource occupation, solve the problem of a relatively fixed number of satellites, and make the protocol more universal.…”

Section: Journal Of Sensorsmentioning

confidence: 99%

Research on Distributed In‐Vehicle Wireless Self‐Organized Routing Protocol Distribution Mechanism

Cui

Chen

2021

Journal of Sensors

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In recent years, the application of intelligent transportation systems has gradually made the transportation industry safe, efficient, and environmentally friendly and has led to a broader research prospect of vehicle wireless communication technology. Distributed vehicular self-organizing networks are mobile self-organizing networks in realistic traffic situations. Data interaction and transmission between nodes are achieved through the establishment of a vehicular self-organizing network. In this paper, a multipath routing protocol considering path stability and load balancing is proposed to address the shortcomings of existing distributed vehicular wireless self-organizing routing protocols. This protocol establishes three loop-free paths in the route discovery phase and uses the path stability parameter and load level parameter together to measure the total transmission cost. The one with the lowest total transmission cost is selected as the highest priority path for data transmission in the route selection phase, and the other two are used as alternate paths, and when the primary path breaks, the higher priority of the remaining path will continue to transmit data as the primary route. In this paper, to improve the content distribution performance of target vehicles in scenarios where communication blind zones exist between adjacent roadside units, an assisted download distribution mechanism for video-like large file content is designed in the V2V and V2I cooperative communication regime. That is, considering a two-way lane scenario, we use the same direction driving vehicles to build clusters, reverse driving vehicles to carry prefetched data, and build clusters to forward prefetched data to improve the data download volume of target vehicles in nonhot scenarios such as highways with the sparse deployment of roadside units, to meet the data volume download demand of in-vehicle users for large files and give guidance for efficient distribution of large file content in highway scenarios.

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“…Gu et al [5] proposed a two-tier data center architecture that uses excess parking lot and they have validated the high efficiency of this architecture via extensive simulations. The works [6][7][8][9] focuses on the security of the VCC system. Some research has investigated the problem of resource allocation in VCC systems.…”

Section: Related Workmentioning

confidence: 99%

A SMDP Approach to Evaluate the Performance of a Vehicular Cloud Computing System with Prioritize Requests

Ezzidani¹,

Ouammou²,

Hanini³

et al. 2021

MMEP

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In intelligent transportation systems, Vehicular Cloud Computing (VCC) is a new technology that can help ensure road security and transport efficiency. The study and evaluation of performances of a VCC is a topic of crucial interest in these environments. This paper presents a model of the computation resource allocation problem in VCC by considering heterogeneity and priority of service requests. We consider service requests from two classes, Primary service requests and Secondary service requests. We involve a Semi-Markov Decision Process (SMDP) to achieve the optimal policy that maximizes the performances of the VCC system taking into account the variability of resources, the income and the system cost. We utilize an iterative approach to achieve the optimal scheme that characterizes the action to be taken under each state. We validate our study by numerical results that show the effectiveness of the proposed SMDP-based scheme.

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Vehicular Cloud Computing Security: A Survey

Cited by 28 publications

References 95 publications

A Comprehensive Review of Computing Paradigms, Enabling Computation Offloading and Task Execution in Vehicular Networks

A Comprehensive Review of Computing Paradigms, Enabling Computation Offloading and Task Execution in Vehicular Networks

Research on Distributed In‐Vehicle Wireless Self‐Organized Routing Protocol Distribution Mechanism

A SMDP Approach to Evaluate the Performance of a Vehicular Cloud Computing System with Prioritize Requests

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