scholarly journals A vehicle to vehicle relay-based task offloading scheme in Vehicular Communication Networks

2021 ◽  
Vol 7 ◽  
pp. e486
Author(s):  
Salman Raza ◽  
Muhammad Ayzed Mirza ◽  
Shahbaz Ahmad ◽  
Muhammad Asif ◽  
Muhammad Babar Rasheed ◽  
...  

Vehicular edge computing (VEC) is a potential field that distributes computational tasks between VEC servers and local vehicular terminals, hence improve vehicular services. At present, vehicles’ intelligence and capabilities are rapidly improving, which will likely support many new and exciting applications. The network resources are well-utilized by exploiting neighboring vehicles’ available resources while mitigating the VEC server’s heavy burden. However, due to the vehicles’ mobility, network topology, and the available computing resources change rapidly, which are difficult to predict. To tackle this problem, we investigate the task offloading schemes by utilizing vehicle to vehicle and vehicle to infrastructure communication modes and exploiting the vehicle’s under-utilized computation and communication resources, and taking the cost and time consumption into account. We present a promising relay task-offloading scheme in vehicular edge computing (RVEC). According to this scheme, the tasks are offloaded in a vehicle to vehicle relay for computation while being transmitted to VEC servers. Numerical results illustrate that the RVEC scheme substantially enhances the network’s overall offloading cost.

2018 ◽  
Vol 2018 ◽  
pp. 1-25 ◽  
Author(s):  
Yingying Ren ◽  
Anfeng Liu ◽  
Ming Zhao ◽  
Changqin Huang ◽  
Tian Wang

The vehicular communication networks, which can employ mobile, intelligent sensing devices with participatory sensing to gather data, could be an efficient and economical way to build various applications based on big data. However, high quality data gathering for vehicular communication networks which is urgently needed faces a lot of challenges. So, in this paper, a fine-grained data collection framework is proposed to cope with these new challenges. Different from classical data gathering which concentrates on how to collect enough data to satisfy the requirements of applications, a Quality Utilization Aware Data Gathering (QUADG) scheme is proposed for vehicular communication networks to collect the most appropriate data and to best satisfy the multidimensional requirements (mainly including data gathering quantity, quality, and cost) of application. In QUADG scheme, the data sensing is fine-grained in which the data gathering time and data gathering area are divided into very fine granularity. A metric named “Quality Utilization” (QU) is to quantify the ratio of quality of the collected sensing data to the cost of the system. Three data collection algorithms are proposed. The first algorithm is to ensure that the application which has obtained the specified quantity of sensing data can minimize the cost and maximize data quality by maximizing QU. The second algorithm is to ensure that the application which has obtained two requests of application (the quantity and quality of data collection, or the quantity and cost of data collection) could maximize the QU. The third algorithm is to ensure that the application which aims to satisfy the requirements of quantity, quality, and cost of collected data simultaneously could maximize the QU. Finally, we compare our proposed scheme with the existing schemes via extensive simulations which well justify the effectiveness of our scheme.


2014 ◽  
Vol 13 (03) ◽  
pp. 1450020 ◽  
Author(s):  
Y. Saez ◽  
X. Cao ◽  
L. B. Kish ◽  
G. Pesti

We review the security requirements for vehicular communication networks and provide a critical assessment of some typical communication security solutions. We also propose a novel unconditionally secure vehicular communication architecture that utilizes the Kirchhoff-law–Johnson-noise (KLJN) key distribution scheme.


2015 ◽  
Vol 64 (12) ◽  
pp. 5547-5562 ◽  
Author(s):  
Yi Ren ◽  
Fuqiang Liu ◽  
Zhi Liu ◽  
Chao Wang ◽  
Yusheng Ji

2018 ◽  
Vol 56 (9) ◽  
pp. 26-32 ◽  
Author(s):  
Shan Zhang ◽  
Jiayin Chen ◽  
Feng Lyu ◽  
Nan Cheng ◽  
Weisen Shi ◽  
...  

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