An IOT-Based Vehicle Tracking System with User Validation and Accident Alert Protocol

Balaji, S.; Gumber, Abhinav; Santhakumar, R.; M, Rajesh Kumar; Tiwari, Agastya; Debnath, Himayan

doi:10.1007/978-981-15-0184-5_61

Cited by 3 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A smart alert technique is implemented for establishing an intelligent vehicle that enables automatic avoidance of accidents made by the drowsy driver using Raspberry pi [36]. IoT enabled Arduino and Node MCU-based vehicle tracking is implemented for alerting incidents of the accident to the prescribed users that preset during installation of the system and also authentication of the user is achieved using their fingerprint [37]. From the literature review, it is concluded that the system with multiple features like helmet authorization and authentication of the user is very limited.…”

Section: Review Of Literaturementioning

confidence: 99%

WPAN and IoT Enabled Automation to Authenticate Ignition of Vehicle in Perspective of Smart Cities

Gehlot

Singh

Kuchhal

et al. 2021

Sensors

View full text Add to dashboard Cite

Currently, two-wheelers are the most popular mode of transportation, driven by the majority the people. Research by the World Health Organization (WHO) identifies that most two-wheeler deaths are caused due to not wearing a helmet. However, the advancement in sensors and wireless communication technology empowers one to monitor physical things such as helmets through wireless technology. Motivated by these aspects, this article proposes a wireless personal network and an Internet of Things assisted system for automating the ignition of two-wheelers with authorization and authentication through the helmet. The authentication and authorization are realized with the assistance of a helmet node and a two-wheeler node based on 2.4 GHz RF communication. The helmet node is embedded with three flex sensors utilized to experiment with different age groups and under different temperature conditions. The statistical data collected during the experiment are utilized to identify the appropriate threshold value through a t-test hypothesis for igniting the two-wheelers. The threshold value obtained after the t-test is logged in the helmet node for initiating the communication with the two-wheeler node. The pairing of the helmet node along with the RFID key is achieved through 2.4 GHZ RF communication. During real-time implementation, the helmet node updates the status to the server and LABVIEW data logger, after wearing the helmet. Along with the customization of hardware, a LABVIEW data logger is designed to visualize the data on the server side.

show abstract

Section: Review Of Literaturementioning

confidence: 99%