A traffic data clustering framework based on fog computing for VANETs

Abstract

Vehicular Ad-hoc Networks (VANETs) are based on vehicle to infrastructure communications in which the vehicles periodically broadcast information to update a Road Side Unit (RSU). The traffic data is forwarded from all RSUs to a cloud or a central server for global analysis and detection of congestion levels on the roads. However, communication costs may considerably increase when a large amount of data is transmitted to such cloud-like service providers. In this paper, we propose a data clustering framework to perform traffic information reduction at the edge of vehicular networks by exploiting fog computing. The proposed data clustering framework defines two methods for the reduction of the traffic data stream: (i) Baseline method, which is an ordinary traffic congestion detection approach, and (ii) two adapted clustering methods for a data stream; namely, the Ordering Points to Identify the Clustering Structure (OPTICS) and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The results have shown that the proposed traffic data framework using clustering methods is accurate even when the vehicular traffic condition is highly congested, potentially reducing the communication costs and bringing significant results for the development of VANETs.