Architecture, Protocols, and Applications of the Internet of Medical Things (IoMT)
Abstrak
The Internet of Things (IoT) refers to the interconnected framework of web-connected objects that can collect and transfer information over a remote network without requiring any human intervention. The rapid progression in the development of IoT-based devices and their expansion towards making the medical care facility financially more savvy, proactive, and customized, has given rise to the development of the "Internet of Medical Things (IoMT)" that are assumed to function proactively in all domains of the healthcare industry. Within this framework, the IoMT-based healthcare system delivers various advantages, such as quick and unfailing treatment, enhanced communication, cost minimization, etc., through the exploitation of several new technologies. For instance, machine learning has significantly helped with the exploitation of various healthcare systems; fog computing not only minimises the cost of communication but also provides low latency; blockchain delivers its users a much better way of protecting sensitive and confidential information and data they possess. In this survey, a comprehensive elaboration of the IoMT-based healthcare systems based on modern technologies was conducted. This article describes various techniques and solutions of IoMT healthcare systems in the context of emerging technologies, and the related future trends and applications for a better understanding of how IoMT can enhance the healthcare industry now and in future.
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Huixia Ren Yi Guo Xin Jiang + 1 lainnya
2020
With the booming development of medical informatization and the ubiquitous connections in the fifth generation mobile communication technology (5G) era, the heterogeneity and explosive growth of medical data have brought huge challenges to data access, security and privacy, as well as information processing in Internet of Medical Things (IoMT). This article provides a comprehensive review of how to realize the timely processing and analysis of medical big data and the sinking of high-quality medical resources under the constraints of the existing medical environment and medical-related equipment. We mainly focus on the advantages brought by the cloud computing, edge computing and artificial intelligence technologies to the IoMT. We also explore how to rationalize the use of medical resources and the security and privacy of medical data, so that high-quality medical services can be provided to patients. Finally, we discuss the current challenges and possible future research directions in the edge-cloud computing and artificial intelligence related IoMT.
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11 Februari 2020
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Rania A. Elsayed Mohamed Hammad Mohammed S. Sayed + 4 lainnya
25 Agustus 2023
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Mohammed S. Al-kahtani Faheem Khan Whangbo Taekeun
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Daftar Referensi
5 referensiFL-PMI: Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems
Rajalakshmi Shenbaga Moorthy T. Gadekallu + 5 lainnya
1 Februari 2022
Recent technological developments, such as the Internet of Things (IoT), artificial intelligence, edge, and cloud computing, have paved the way in transforming traditional healthcare systems into smart healthcare (SHC) systems. SHC escalates healthcare management with increased efficiency, convenience, and personalization, via use of wearable devices and connectivity, to access information with rapid responses. Wearable devices are equipped with multiple sensors to identify a person’s movements. The unlabeled data acquired from these sensors are directly trained in the cloud servers, which require vast memory and high computational costs. To overcome this limitation in SHC, we propose a federated learning-based person movement identification (FL-PMI). The deep reinforcement learning (DRL) framework is leveraged in FL-PMI for auto-labeling the unlabeled data. The data are then trained using federated learning (FL), in which the edge servers allow the parameters alone to pass on the cloud, rather than passing vast amounts of sensor data. Finally, the bidirectional long short-term memory (BiLSTM) in FL-PMI classifies the data for various processes associated with the SHC. The simulation results proved the efficiency of FL-PMI, with 99.67% accuracy scores, minimized memory usage and computational costs, and reduced transmission data by 36.73%.
A Review Software Defined Networking for Internet of Things
Raad. M. Khaleefah Ihsan Amjad Abdulateef + 2 lainnya
1 Juni 2020
With the emergence of Internet of Things ( IoT), there is now increasing interest in simplifying control of wireless networks. This is a very challenging task, which includes information acquisition, information analysis, decision-making and large-scale IoT networks implementing action. Resulting in research to explore the integration of Software Defined Networking ( SDN) and IoT to make network control simpler , easier, and less strainy. SDN is a promising new paradigm shift capable of enabling a simplified and robust wireless programmable network to serve a range of physical objects and applications. This review article begins with SDN emerging. Recent significant developments in the wireless and optical domains are then highlighted with a view to integrating SDN and IoT. The SDN and IoT development issues are often explored from a security and scalability viewpoint.
The Future of Healthcare Internet of Things: A Survey of Emerging Technologies
A. Vasilakos Yazdan Ahmad Qadri + 3 lainnya
11 Februari 2020
The impact of the Internet of Things (IoT) on the advancement of the healthcare industry is immense. The ushering of the Medicine 4.0 has resulted in an increased effort to develop platforms, both at the hardware level as well as the underlying software level. This vision has led to the development of Healthcare IoT (H-IoT) systems. The basic enabling technologies include the communication systems between the sensing nodes and the processors; and the processing algorithms for generating an output from the data collected by the sensors. However, at present, these enabling technologies are also supported by several new technologies. The use of Artificial Intelligence (AI) has transformed the H-IoT systems at almost every level. The fog/edge paradigm is bringing the computing power close to the deployed network and hence mitigating many challenges in the process. While the big data allows handling an enormous amount of data. Additionally, the Software Defined Networks (SDNs) bring flexibility to the system while the blockchains are finding the most novel use cases in H-IoT systems. The Internet of Nano Things (IoNT) and Tactile Internet (TI) are driving the innovation in the H-IoT applications. This paper delves into the ways these technologies are transforming the H-IoT systems and also identifies the future course for improving the Quality of Service (QoS) using these new technologies.
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