An Edge-AI enabled Autonomous Connected Ambulance Route Resource Recommendation Protocol (ACA-R3) for eHealth in Smart Cities

• Published in: IEEE internet of things journal Vol. 10; no. 13; p. 1
• Main Authors: Ahmed, Syed Thouheed, Basha, Syed Muzamil, Ramachandran, Manikandan, Daneshmand, Mahmoud, Gandomi, Amir H.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Ambulance services; Automation; Autonomous Ambulances; Biomedical monitoring; Computer networks; Data exchange; Decision making; Dynamic Routing; Ecosystems; Edge computing; intelligent vehicular system; Internet of Things; Monitoring; Resource management; Route Recommendation; Routing protocols; Traffic congestion; Transportation; Unmanned vehicles
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2023.3243235

The Autonomous Connected Ambulance (ACA) has been an unprecedented necessity in the demand-supply management sector of the healthcare sector. However, the traditional prototypes designed for such an un-manned vehicle do not match the demands of the advanced communication technologies incorporated in today's sophisticated distributed networks. As a result, in the current era of edge computing strengthened by many AI-enabled algorithms, there is an urgent need to design a Route Resource Recommendation Protocol for ACA under Edge-AI. Designing such a protocol requires addressing the major challenges to optimize the routes for ACA and, thereby, enhance the services of emergency eHealth centers through a governing telehealth monitoring administrator. Therefore, in this article, a dedicated and novel "ACA-Route Resource Recommendation (R3)" protocol is proposed to address the issues of connectivity and resource management in ACA to optimize the routes for ACA. The ACA-R3 protocol abides by the operational standards of both the eHealth protocol and governing protocol. The primary objective of the current research work was to reduce the handover time and simplify the patient-information exchange during the time of demanded emergencies. The proposed ACA-R3 protocol can enhance the collaborative distributive resource management for reliable decision-making from the data generated by the GPS tracking unit in ACA and Edge-AI. The experimental results obtained from three different cases of traffic congestion are evaluated, validated, and reported in this article.