An empirical investigation of performance challenges within context-aware content sharing for vehicular ad hoc networks

Connected vehicles is a leading use-case within the Industrial Internet of Things (IIoT), which is aimed at automating a range of driving tasks such as navigation, accident avoidance, content sharing, and auto-driving. Such systems leverage vehicular ad hoc networks (VANETs) and include vehicle to vehicle and vehicle to roadside infrastructure communication along with remote systems such as traffic alerts and weather reports. However, the device endpoints in such networks are typically resource-constrained and, therefore, leverage edge computing, wireless communications, and data analytics to improve the overall driving experience, influencing factors such as safety, reliability, comfort, response, and economic efficiency. Our focus in this article is to identify and highlight open challenges to achieve a secure and efficient convergence between the constrained IoT devices and the high-performance capabilities offered by the clouds. Therein, we present a context-aware content-sharing scenario for VANETs and identify specific requirements for its achievement. We also conduct a comparative study of simulation software for edge computing paradigm to identify their strengths and weaknesses, especially within the context of VANETs. We use FogNetSim++ to simulate diverse settings within VANETs with respect to latency and data rate highlighting challenges and opportunities for future research.