Autonomous transportation in emergency healthcare services: Framework, challenges, and future work

Khalid, Muhammad, Awais, Muhammad ORCID: https://orcid.org/0000-0001-6421-9245, Singh, Nishant, Khan, Suleman, Raza, Mohsin, Malik, Qasim Badar and Imran, Muhammad (2021) Autonomous transportation in emergency healthcare services: Framework, challenges, and future work. IEEE Internet of Things Magazine, 4 (1). pp. 28-33. ISSN 2576-3180

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Abstract

In pandemics like Covid-19, the use of autonomy and machine learning technologies are of high importance. The Internet of Things (IoT)-enabled autonomous transportation system (ATS) envisions a fundamental change in the traditional transportation system. It aims to provide intelligent and automated transport of passengers, goods, and services with minimal human interference. While ATS targets a broad spectrum of transportation (cars, trains, planes, etc.), the focus of this article is limited to the use of vehicles and road infrastructure to support healthcare and related services. This article offers an IoT-based ATS framework for emergency healthcare services using autonomous vehicles (AVs) and deep reinforcement learning (DRL). The DRL-enabled framework identifies emergency situations smartly and helps AVs make faster decisions on providing emergency health aid and transportation services to patients. Using ATS and DRL for healthcare mobility services will also contribute toward minimizing energy consumption and environmental pollution. This article also discusses current challenges and future works in using ATS for healthcare services.

Item Type:	Article
Uncontrolled Keywords:	software,information systems,hardware and architecture,computer networks and communications,computer science applications,sdg 7 - affordable and clean energy,sdg 9 - industry, innovation, and infrastructure,sdg 3 - good health and well-being,sdg 12 - responsible consumption and production ,/dk/atira/pure/subjectarea/asjc/1700/1712
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	25 Nov 2023 03:18
Last Modified:	10 Dec 2024 01:42
URI:	https://ueaeprints.uea.ac.uk/id/eprint/93756
DOI:	10.1109/iotm.0011.2000076

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