Dwell time estimation at intersections for improved vehicular micro cloud operations

Abstract

Edge computing is becoming a major building block of next generation 5G/6G networks. However, infrastructure might not always be available because of slow deployment. At the same time, vehicular networks are becoming a reality now and cars are being equipped with a variety of short-range communication devices. The idea of vehicular micro clouds is to turn cars into (virtual) edge computing infrastructure. One of the challenging questions in this domain is to maintain data within and among such micro clouds. In this paper, we focus on this task and present a novel solution for such data exchange between vehicular micro clouds. For efficient operation, the dwell times of cars in such a micro cloud need to be known or accurately predicted. In an extensive study based on trace data, we investigate the distribution of dwell times of cars at intersections. We make use of this distribution as an input for designing an improved data exchange algorithm. As not all intersections are the same, adding additional variance further benefits the solution. We evaluated our algorithm in different vehicular densities, and we observed that we could maintain data 22–208% longer within the micro clouds using our new algorithm. Overall, our results show that our algorithm clearly outperforms previous solutions.