Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Suhana, Syed; Prathap, Boppuru Rudra; Narang, Kavish; Anto, Ivin Title A name given to the resource IoT-Based Emergency Vehicle Detection Using YOLOv8 Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Journal of Automation, Mobile Robotics and Intelligent Systems;Volume;19;Issue;2;pp.79-88 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14313/jamris-2025-018" target="_blank" rel="noreferrer noopener">https://doi.org/10.14313/jamris-2025-018</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105009935330?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105009935330?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Suhana S., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), India; Prathap B.R., Department of Computer Science and Engineering, M. S. Ramaiah University of Applied Sciences, India; Narang K., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), India; Anto I., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), India Description An account of the resource The rapid response of emergency services plays a critical role in saving lives and minimizing the impact of emergencies. However, identifying and locating emergency vehicles in real-Time can be challenging, especially in congested urban areas. This paper focuses on the emergency vehicle identification using the You Only Look Once version 8 (YOLOv8) algorithm and is focused on Internet of Things (IoT). The goal of this research is to develop a real-Time and precise emergency vehicle detection system using You Only Look Once version 8 (YOLOv8) algorithm, trained and tested with a dataset from a camera placed on a busy road, to enhance emergency service response times. The findings demonstrate the suggested system's ability to recognize emergency vehicles at a speed of 31 frames per second and with a 95% accuracy rate. Modern object identification algorithms include the You Only Look Once version 8 (YOLOv8) algorithm, which has shown promising results in various applications. The proposed system is built on a Raspberry Pi, which acts as an edge device and processes the video stream in realtime. The system consists of an Internet of Things (IoT) device with a camera that captures the live video stream, which is then fed into the algorithm for object detection. Once an emergency vehicle is detected, the system sends an email notification to the nearby emergency services, like a police station, using Simple Mail Transfer Protocol (SMTP), who can then take appropriate action. The results of this investigation show that the Internet of Things and You Only Look Once version 8 (YOLOv8) algorithms have great promise for creating effective and dependable emergency vehicle detection systems. The proposed system possesses the capacity to save lives and improve the effectiveness of emergency response by speeding up response times for emergency services. The suggested solution is also inexpensive, simple to implement, and adaptable to existing infrastructure. Through the development of intelligent transportation systems, emergency services can operate more safely and effectively. More sophisticated machine learning algorithms may be incorporated into the proposed system, and further sensors can be added to utilize alternative methods beyond camera-based detection to identify emergency vehicles. Overall, this research shows the potential of Internet of Things (IoT) and machine learning in creating creative emergency services solutions. 2025 Syed Suhana et al.Published by Sciendo. Subject The topic of the resource email notifications; emergency vehicles; Internet of Things (IoT); Pi Camera; Raspberry Pi; simple mail transfer protocol (SMTP); You Only Look Once (YOLOv8) Publisher An entity responsible for making the resource available Sciendo Relation A related resource ISSN: 18978649; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online