Over the Horizon of Connected Vehicles: Advancing Security, Reliability, and Cost-Efficiency through Vehicular Edge Computing

Abstract

The rapid technological advancements have significantly impacted numerous industries, including the automotive sector. Amid the pursuit of creating a better vehicle, Connected Vehicles (CV) emerged as a candidate technology that promises safety and enhances the overall user experience. The initial trials of realizing the CV technology involved the usage of Cloud Computing, but the idea was short-lived due to high latency and bandwidth requirement issues. This deadlock scenario was then handled by bringing the computation resources closer to the user through Mobile Edge Computing (MEC). MEC involves localized computation units that are relatively less powerful than the cloud but can handle the CV requirements. MEC technology was further enhanced by considering the CVs’ dynamic network topology, which was then called Vehicular Edge Computing (VEC). Realizing CV technology through the VEC approach involves a central cloud server with multiple edge servers handling the requirements of the CVs. A general request from a CV can involve data delivery or task offloading requirements via edge servers. Although VEC technology offers a nearly assured solution to realize CV technology, it is often faced with challenges, such as mobility, resource allocation, security, affordability, computation delay, scalability, power consumption, caching, etc. Considering a few such critical challenges, this work aims to bridge the gap in realizing CV technology through two contributions. The first work proposes a framework that incorporates MAC protocol constraints in the data delivery optimization framework to ensure practicality and reliability in data transmission. This work presents a data-frame collision-free optimization framework by adopting a time-slot-based MAC layer strategy that uses slot assignment to ensure collision-free data delivery for multiple vehicles across various transmission channels at each edge in different test conditions. The second contribution incorporates security constraints along with the price incurred for task offloading from vehicles to edges. This work presents a price optimization framework that minimizes the overall price for realizing the network, making it affordable while considering various task-specific security requirements. Further, both works consider various CV-specific constraints, such as vehicle flow, edge resources, and overlaps, thereby ensuring practicality.