IoMT landscape: Navigating current challenges and pioneering future research trends

Alturki, Badraddin, Abu Al-Haija, Qasem, Alsemmeari, Rayan A., Alsulami, Abdulaziz A., Alqahtani, Ali, Alghamdi, Bandar M., Bakhsh, Sheikh Tahir and Shaikh, Riaz Ahmed (2024) IoMT landscape: Navigating current challenges and pioneering future research trends. Discover Applied Sciences, 7. ISSN 3004-9261

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Abstract

Technological advancement drives the growth of the Internet of Things (IoT) applications in many fields, such as smart homes, smart cities, smart grids, and healthcare. IoT in healthcare is called the Internet of Medical Things (IoMT), which provides remote patient treatment using information and communications technology. This new telemedicine technology simplifies the regular and effective communication between medical and computing devices. Critical motivations for adopting the IoMT are reduced cost, increased quality of life, and timely medical intervention. IoMT is significant because it enables continuous, real-time patient monitoring during routine everyday activities using a variety of wearables and sensors. With big data, IoMT technology makes excellent use of Machine Learning (ML) to support disease detection and health condition prediction, alerting patients and healthcare providers. Many research studies have been conducted to explore several aspects of IoMT and its applications in the real world. However, it is challenging to comprehend all the techniques and solutions proposed by the research community. Therefore, this survey sheds light on some crucial aspects of IoMT technology and explores the potential research gaps and directions the research community could tackle. The survey examines and discusses the characteristics of IoMT standards, protocols, and types. It then delves into the layers of IoMT and distinguishes them into fog and edge. The studies published under each type were explored, and the limitations of these works were highlighted. The research gaps and directions on IoMT approaches and technology were also highlighted. With such findings and research directions, further research endeavors could be carried out to address the issues and existing limitations in the IoMT.

Item Type:	Article
Additional Information:	Data availability statement: No datasets were generated or analysed during the current study.
Faculty \ School:	Faculty of Science > School of Computing Sciences
Related URLs:	https://link.springer.com/10.1007/s42452...
Depositing User:	LivePure Connector
Date Deposited:	21 Feb 2025 12:30
Last Modified:	21 Feb 2025 20:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/98554
DOI:	10.1007/s42452-024-06351-w

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