Security and Privacy Challenges in Connected Vehicular Cloud Computing

Masood, Arooj; Lakew, Demeke Shumeye; Cho, Sungrae

doi:10.1109/comst.2020.3012961

Cited by 62 publications

(31 citation statements)

References 150 publications

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“…To obtain the data volume as well as the data interactions of the data transfer mechanisms in one information cycle, we can plot the variation curve of the data volume with the length of the cache window. As can be seen from Figure 4, the mechanism proposed in this section significantly outperforms the other two mechanisms in the metric of video data download volume [21]. Specifically, when M < 50, the video requesting vehicle enters the communication range of the roadside unit and starts downloading video streams, and when driving away from the communication range, the video requesting vehicle will rely on the relay node to continue establishing communication links with the roadside unit, so the QDAVS mechanism and our proposed mechanism obtain significantly better data volume than the TAVS mechanism; in addition, we use the TOPSIS-based; we use the TOPSIS-based multicriteria decision method to select the optimal relay node to establish a communication link with higher download and transmission rates and thus obtain higher data download.…”

Section: Experimental Verification and Conclusionmentioning

confidence: 83%

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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Section: Experimental Verification and Conclusionmentioning

confidence: 83%

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

Cui

Chen

2021

Journal of Sensors

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“…Masood et al [49] gave a state-of-the-art review of Vehicular Cloud Computing (VCC) including its architecture, features, and services. They presented the attacker model in VCC and discussed the security and privacy issues in VCC in a layered approach: physical resource layer, V2X network layer, and vehicular cloud layer, and a complete system level.…”

Section: Related Workmentioning

confidence: 99%

“…5GVN is faced with cyber attacks [37], [46], [49] due to its open architecture. Since 5GVNs are built upon VNs of the previous communication generations, they still inherit various privacy threats.…”

Section: Privacy Threats To 5g-supported Vehicular Networkmentioning

confidence: 99%

“…A large-scale analysis of vehicular data can lead to valuable insights that address public transportation challenges and come across new scientific discoveries [43]. However, as a nontrivial combination and extension built upon existing infrastructures and technologies, it is inevitable that some issues remain in 5GVNs, especially privacy issues [44], [45], [46], [47], [48], [49], [50], [51]. SPs may have malicious or mischievous employees who steal users' data and sell them to the black market.…”

Section: Introductionmentioning

confidence: 99%

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Privacy for 5G-Supported Vehicular Networks

Zhu

Zhang

et al. 2021

IEEE Open J. Commun. Soc.

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Vehicular networks allow billions of vehicular users to be connected to report and exchange real-time data for offering various services, such as navigation, ride-hailing, smart parking, traffic monitoring, and vehicular digital forensics. Fifth generation (5G) is a new radio access technology with greater coverage, accessibility, and higher network density. 5G-supported Vehicular Networks (5GVNs) have attracted plenty of attention from both academia and industry. Geared with new features, they are expected to revolutionize the mobility ecosystem to empower a portfolio of new services. Meanwhile, the development of such communication capabilities, along with the development of sensory devices and the enhancement of local computing powers, have lead to an inevitable reality of massive data (e.g., identity, location, and trajectory) collection from vehicular users. Unfortunately, 5GVN are still confronted with a variety of privacy threats. Such threats are targeted at users' data, identity, location, and trajectory. If not properly handled, such threats will cause unimaginable consequences to users.In this survey, we first review the state-of-the-art of survey papers. Next, we introduce the architecture, features, and services of 5GVN, followed by the privacy objectives of 5GVN and privacy threats to 5GVN. Further, we present existing privacy-preserving solutions and analyze them in-depth. Finally, we define some future research directions to draw more attention and down-toearth efforts into this new architecture and its privacy issues.

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“…Today, automakers are also integrating connectivity and autonomous driving components in their vehicles to reduce travel time and improve the safety of drivers, passengers, vehicles, and the entire transportation system. However, from the perspective of the technological development path, smart cars are divided into three developments connected vehicles (CVs) [ 8 ], autonomous vehicles (AVs) [ 9 ], and electric vehicles (EVs) [ 10 ]. However, in this article, we are focus on connected vehicles.…”

Section: Introductionmentioning

confidence: 99%

Channel Allocation for Connected Vehicles in Internet of Things Services

Al-Absi

Sain

et al. 2021

Sensors

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Based on the existing Internet of Vehicles communication protocol and multi-channel allocation strategy, this paper studies the key issues with vehicle communication. First, the traffic volume is relatively large which depends on the environment (city, highway, and rural). When many vehicles need to communicate, the communication is prone to collision. Secondly, because the traditional multi-channel allocation method divides the time into control time slots and transmission time slots when there are few vehicles, it will cause waste of channels, also when there are more vehicles, the channels will not be enough for more vehicles. However, to maximize the system throughput, the existing model Enhanced Non-Cooperative Cognitive division Multiple Access (ENCCMA) performs amazingly well by connected the Cognitive Radio with Frequency Division Multiple Access (FDMA) and Time Division Multiple Access (TDMA) for a multi-channel vehicular network. However, this model induces Medium Access Control (MAC) overhead and does not consider the performance evaluation in various environmental conditions. Therefore, this paper proposes a Distributed Medium Channel Allocation (DMCA) strategy, by dividing the control time slot into an appointment and a safety period in the shared channel network. SIMITS simulator was used for experiment evaluation in terms of throughput, collision, and successful packet transmission. However, the outcome shows that our method significantly improved the channel utilization and reduced the occurrence of communication overhead.

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Security and Privacy Challenges in Connected Vehicular Cloud Computing

Cited by 62 publications

References 150 publications

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

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

Privacy for 5G-Supported Vehicular Networks

Channel Allocation for Connected Vehicles in Internet of Things Services

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