Real-Time Data-Intensive Telematics Functionalities at the Extreme Edge of the Network: Experience with the PrEstoCloud Project

In recent years, use of different sensors connected to vehicles is dramatically increasing in order to enhance transportation efficiency. The current Big Data technologies are predominantly used to store large amount of telematics data especially in the cloud, and they are only able to perform simple querying for the purpose of reporting. While all the data is stored in the cloud-centric datacenters, these telematics systems are not capable of exploiting other functionalities offered by advanced real-time analytics such as run-time anomaly detection. In this paper, we propose an advanced telematics system orchestrated upon an edge computing framework in the context of the PrEstoCloud (Proactive Cloud Resources Management at the Edge for Efficient Real-Time Big Data Processing) project. This telematics system is a real-time data-intensive application running at the extreme edge of the network for drivers' behavior profiling and triggering run-time alerts. Such functionalities may be useful in order to notify stakeholders for example drivers and logistic centers on situations where a possible accident may occur or attention is required