P2O: A Parallel Processing-based Offloading Mechanism for Improving Response Time in Intelligent Transportation Applications
Laya Mohammadi, Vahid Khajehvand, Esmaeil Zeinali, Khashayar Salehi Nobandegani · 2024
Given the increasing number of vehicles and the substantial volume of data collected from the environment, there is a need for solutions to efficiently process computational tasks in intelligent transportation applications, such as traffic control. This is essential to accelerate critical decision-making, prevent accidents and undesirable road incidents, reduce urban traffic, and enhance safety. Vehicle Cloud Computing (VCC) and Vehicle Edge Computing (VEC) in intelligent transport environments have faced challenges such as task offloading delays and high response times in delay-sensitive applications. Therefore, Vehicle Fog Computing (VFC), leveraging Vehicle-to-Vehicle (V2V) communication technology, presents a promising solution to overcome these challenges. In this regard, this article proposes a parallel processing-based computational task offloading mechanism aimed at improving response time in intelligent transportation applications. The proposed mechanism is a weighted decision-making algorithm based on opportunistic V2V communication, which, by dividing tasks and executing them in parallel on a selected vehicle, can significantly decreases response time in the transport network. As a result, it markedly reduces delays caused by task offloading. Experimental results show that the proposal mechanism reduces the average wait time compared to simple and priority algorithms by 43.96% and 36.04%. Therefore, a significant improvement in the response time of the transport system is achieved.