Bargain Game Based Secure Content Delivery in Vehicular Networks

2020 ◽  
pp. 111-129
Author(s):  
Zhou Su ◽  
Yilong Hui ◽  
Tom H. Luan ◽  
Qiaorong Liu ◽  
Rui Xing
Keyword(s):  
Vehicular Networks ◽  
Content Delivery

scholarly journals Secure and DoS-Resilient Fragment Authentication in CCN-Based Vehicular Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Sangwon Hyun ◽  
Hyoungshick Kim
Keyword(s):  
Vehicular Networks ◽  
Denial Of Service ◽  
Content Delivery ◽  
Dos Attacks ◽  
Promising Alternative ◽  
Content Centric Networking ◽  
Transmission Unit ◽  
Communication Environments ◽  
Maximum Transmission

Content-Centric Networking (CCN) is considered as a promising alternative to traditional IP-based networking for vehicle-to-everything communication environments. In general, CCN packets must be fragmented and reassembled based on the Maximum Transmission Unit (MTU) size of the content delivery path. It is thus challenging to securely protect fragmented packets against attackers who intentionally inject malicious fragments to disrupt normal services on CCN-based vehicular networks. This paper presents a new secure content fragmentation method that is resistant to Denial-of-Service (DoS) attacks in CCN-based vehicular networks. Our approach guarantees the authenticity of each fragment through the immediate fragment verification at interim nodes on the routing path. Our experiment results demonstrate that the proposed approach provides much stronger security than the existing approach named FIGOA, without imposing a significant overhead in the process. The proposed method achieves a high immediate verification probability of 98.2% on average, which is 52% higher than that of FIGOA, while requiring only 14% more fragments than FIGOA.

Collaborative Content Delivery in Software-Defined Heterogeneous Vehicular Networks

2020 ◽  
Vol 28 (2) ◽  
pp. 575-587 ◽  
Author(s):  
Yilong Hui ◽  
Zhou Su ◽  
Tom H. Luan
Keyword(s):  
Vehicular Networks ◽  
Content Delivery

Information-centric cost-efficient optimization for multimedia content delivery in mobile vehicular networks

2017 ◽  
Vol 99 ◽  
pp. 93-106 ◽  
Author(s):  
Changqiao Xu ◽  
Wei Quan ◽  
Athanasios V. Vasilakos ◽  
Hongke Zhang ◽  
Gabriel-Miro Muntean
Keyword(s):  
Vehicular Networks ◽  
Content Delivery ◽  
Multimedia Content ◽  
Multimedia Content Delivery ◽  
Cost Efficient

Cooperative content delivery in UAV-RSU assisted vehicular networks

2020 ◽  
Author(s):  
Ahmed Al-Hilo ◽  
Moataz Samir ◽  
Chadi Assi ◽  
Sanaa Sharafeddine ◽  
Dariush Ebrahimi
Keyword(s):  
Vehicular Networks ◽  
Content Delivery

scholarly journals A Mobility Prediction-Based Relay Cluster Strategy for Content Delivery in Urban Vehicular Networks

2021 ◽  
Vol 11 (5) ◽  
pp. 2157
Author(s):  
Shaoqi Yue ◽  
Qi Zhu
Keyword(s):  
Vehicular Networks ◽  
Success Probability ◽  
Content Delivery ◽  
Statistical Characteristics ◽  
Trajectory Prediction ◽  
Urban Road ◽  
Cluster Strategy ◽  
Roadside Unit ◽  
Multiple Vehicles ◽  
High Dynamic

In recent years, cache-enabled vehicles have been introduced to improve the efficiency of content delivery in vehicular networks. However, because of the high dynamic of network topology, it is a big challenge to increase the success probability of content delivery. In this paper, we propose a relay strategy based on cluster’s prediction trajectory for the situation of no cache near the request vehicles. In our strategy, the roadside unit (RSU) divides vehicles into clusters by their prediction trajectory, and then proactively caches contents at a cluster that will be about to meet the request vehicle. In order to decrease the probability of unsuccessful content delivery caused by communication duration that is too short between the request vehicle and content source vehicle, RSU caches content chunks at multiple vehicles in a cluster. By letting the request vehicle communicate with vehicle-caching content chunks one by one, our strategy enlarges the communication duration and increases the success probability. Our strategy also maximizes the success probability by optimizing the number of vehicles selected to cache content chunks. Besides, based on statistical characteristics of vehicles’ speed, we derive the formula of success probability of content delivery. The simulation results show that our strategy can increase the success probability of content delivery, as well as decrease time delay, for example. For example, we increase the success probability by about 20%. Since the trajectory prediction-based cluster-dividing mechanism can improve clusters’ stability at intersections, this method is well suited for urban road scenarios.

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