IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

IoT-Based Smart Accident Detection and Alert System

IoT-Based Smart Accident Detection and Alert System
View Sample PDF
Author(s): C. V. Suresh Babu (Hindustan Institute of Technolgy and Science, India), Akshayah N. S. (Hindustan Institute of Technology and Science, India), Maclin Vinola P. (Hindustan Institute of Technology and Science, India)and R. Janapriyan (Hindustan Institute of Technology and Science, India)
Copyright: 2023
Pages: 16
Source title: Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT
Source Author(s)/Editor(s): P. Swarnalatha (Department of Information Security, School of Computer Science and Engineering, Vellore Institute of Technology, India)and S. Prabu (Department Banking Technology, Pondicherry University, India)
DOI: 10.4018/978-1-6684-8098-4.ch019

Purchase

View IoT-Based Smart Accident Detection and Alert System on the publisher's website for pricing and purchasing information.

Abstract

The smart accident detection and alert system using IoT is a technical solution that detects accidents and alerts authorities and emergency services. The system mainly relies on sensors, GPS, and Arduino UNO to detect and collect information about the location and severity of the accident. The system then transmits this information in real time to the appropriate authorities using algorithms and protocols, enabling them to respond quickly and effectively, therefore increasing the possibility of saving lives and benefiting road users, emergency services, and transportation authorities in case of accidents.

Related Content

Fatima Ahmed Mohamed Abdalla, Noor Asiah Rashid. © 2026. 32 pages.
Fatima Ahmed Mohamed Abdalla, Noor Asiah Rashid. © 2026. 32 pages.
Azana Hafizah Mohd Aman, Wan Muhd Hazwan Azamuddin, Maznifah Salam, Zainab S. Attarbashi. © 2026. 32 pages.
Azana Hafizah Mohd Aman, Wan Muhd Hazwan Azamuddin, Maznifah Salam, Zainab S. Attarbashi. © 2026. 36 pages.
Salaheldin Mohamed Ibrahim Edam. © 2026. 42 pages.
Rubi Kadyan, Sunita Rani, Vinod Kr. Saroha. © 2026. 46 pages.
Mamoon M. Saeed, Zeinab E. Ahmed, Rania A. Mokhtar, Rashid A. Saeed. © 2026. 34 pages.
Body Bottom