A Voice-Assisted Approach for Vehicular Data Querying from Automotive IoT-Based Databases

The automotive industry's transformative conver-gence of cutting-edge technologies, such as the Internet of Things (loT), electronic voice assistants, and custom APls, have paved the way for a remarkable array of opportunities to enhance vehicle security while revolutionizing user-car interactions. Actually, when leveraging loT sensors of multiple types and configurations, their produced, collected, and stored real-time data may offer in-depth and comprehensive insights into vehicle information, opening many possibilities when loT-generated databases are created. In this context, the primary goal of this paper is to create a new approach based on the popular Amazon Alexa voice assistant, which would allow facilitated queries of vehicular data from cloud-based databases. For that, a new Alexa service (skill) is created, as well as a custom API, allowing easy access to different types of data previously retrieved from vehicular sensors and properly stored. Doing so, the implemented skill indirectly processes data from loT sensors through the custom API, enabling users to access vital vehicular information using intuitive voice commands, remotely. A case study in a real scenario is conducted to validate and confirm the feasibility of real-time vehicle information access, showcasing the benefits of the proposed approach when combined with other solutions in a macro vehicular-centric loT ecosystem.