ONE Summit 2022

This talk focuses on the usage of a simple design and open source tools for the provisioning of real-time and accurate vehicular data that play a crucial role in the data provisioning performance in vertical edge applications. A simple mathematical model is designed to enhance vehicular data accuracy. This model involves the integration of a popular estimation technique with road information. For this, the Kalman filter was utilized as an estimation technique, which makes the use of road information through OpenStreetMap. This talk mainly focuses on the modeling, developing, and testing of the proposed system, which overall proves to be useful as a promising solution in the form of a vertical edge application for the provisioning of real-time and accurate vehicular data. In addition, the above technique is aligned with the design provided in the ETSI MEC standards, which enables a promising infrastructure for multiple edges responsible to provide the respective vehicular data. Finally, this study and work is shown to be a part of the Akraino community's blueprint "MEC-based Stable Topology Prediction for Vehicular Networks", and has been added to release 6.