Architecture, Protocols, and Applications of the Internet of Medical Things (IoMT)

Askar, Naeem Ali; Habbal, Adib; Mohammed, Alaa Hamid; Sajat, Mohd Samsu; Yusupov, Ziyodulla Yusupov Ziyodulla; Kodirov, D

doi:10.12720/jcm.17.11.900-918

Cited by 20 publications

(4 citation statements)

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“…Askar et al 26 discussed about the protocols, and applications of the IoMT networks for healthcare applications. The practice of using IoMT technology to remotely supervise hospitals is known as telemedicine.…”

Section: Related Workmentioning

confidence: 99%

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

Nandagopal,

Seerangan,

Govindaraju

et al. 2024

Sci Rep

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In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial and has made it possible to effectively control disease using networks of interconnected sensors worn by individuals. The purpose of this work is to develop an AI-IoMT framework for identifying several of chronic diseases form the patients’ medical record. For that, the Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new AI-IoMT framework, has been developed for rapid diagnosis of chronic diseases like heart disease, diabetes, and stroke. Then, a Deep Auto-Encoder Model (DAEM) is used in the proposed framework to formulate the imputed and preprocessed data by determining the fields of characteristics or information that are lacking. To speed up classification training and testing, the Golden Flower Search (GFS) approach is then utilized to choose the best features from the imputed data. In addition, the cutting-edge Collaborative Bias Integrated GAN (ColBGaN) model has been created for precisely recognizing and classifying the types of chronic diseases from the medical records of patients. The loss function is optimally estimated during classification using the Water Drop Optimization (WDO) technique, reducing the classifier’s error rate. Using some of the well-known benchmarking datasets and performance measures, the proposed DACL’s effectiveness and efficiency in identifying diseases is evaluated and compared.

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Section: Related Workmentioning

confidence: 99%

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

Nandagopal,

Seerangan,

Govindaraju

et al. 2024

Sci Rep

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“…A specialist can monitor the data the perception layer collects in real time over the Internet. There are multiple communication protocols (i.e., Radio Frequency Identification (RFID), Bluetooth Low Energy (BLE), Wireless Sensor Networks (WSNs), Zigbee, and IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN)) used for collecting and transmitting data from IoT nodes [57], [58], [59], [60]. This layer is susceptible to multiple attacks affecting the infrastructure in various ways, as depicted in Figure 4.…”

Section: ) the Perception Layermentioning

confidence: 99%

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Khatun,

Memon,

Eising

et al. 2023

IEEE Access

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The Healthcare Internet-of-Things (H-IoT), commonly known as Digital Healthcare, is a datadriven infrastructure that highly relies on smart sensing devices (i.e., blood pressure monitors, temperature sensors, etc.) for faster response time, treatments, and diagnosis. However, with the evolving cyber threat landscape, IoT devices have become more vulnerable to the broader risk surface (e.g., risks associated with generative AI, 5G-IoT, etc.), which, if exploited, may lead to data breaches, unauthorized access, and lack of command and control and potential harm. This paper reviews the fundamentals of healthcare IoT, its privacy, and data security challenges associated with machine learning and H-IoT devices. The paper further emphasizes the importance of monitoring healthcare IoT layers such as perception, network, cloud, and application. Detecting and responding to anomalies involves various cyber-attacks and protocols such as Wi-Fi 6, Narrowband Internet of Things (NB-IoT), Bluetooth, ZigBee, LoRa, and 5G New Radio (5G NR). A robust authentication mechanism based on machine learning and deep learning techniques is required to protect and mitigate H-IoT devices from increasing cybersecurity vulnerabilities. Hence, in this review paper, security and privacy challenges and risk mitigation strategies for building resilience in H-IoT are explored and reported.

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“…The Internet of Things (IoT) is the most promising technology in recently developed applications, including those in business, healthcare, agriculture, energy management, security, and other areas [9], [10]. IoT uses involve gathering, enabling, and sharing anonymous data from industrial equipment, vehicles, smart homes, and other smart devices.…”

Section: Introductionmentioning

confidence: 99%

BSDCE-IoV: Blockchain-Based Secure Data Collection and Exchange Scheme for IoV in 5G Environment

Karim¹,

Habbal²,

Chaudhry

et al. 2023

IEEE Access

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The Internet of Vehicles (IoV) is a network that connects vehicles and their environment: inbuilt devices, pedestrians, and infrastructure through the Internet using heterogeneous access technologies. During communication between vehicles, roadside units, and control rooms, data confidentiality and privacy are critical issues that require effective measures. Several works have been proposed for securing IoV environments based on vehicles-to-infrastructure authentication; However, some schemes have security vulnerabilities, while others have shown efficiency issues. Due to its decentralization, stability, and transaction tracking capabilities, Blockchain as an emerging technology presents a potential solution for IoV security. This article provides an in-depth examination of the benefits of blockchain for a 5G-based IoV environment.In particular, we propose and evaluate a novel blockchain-based secure data exchange (BSDCE-IoV) scheme based on Elliptic Curve Cryptography algorithm. Our solution is designed to eliminate several potential attacks that pose a threat to the IoV environment. Deep examination using the Real-or-Random oracle model and Scyther tool, in addition to the informal security analysis, validates the scheme regarding security and privacy. The Multi-precision Integer and Rational Arithmetic Cryptographic Library (MIRACL) assesses the computational and communication overhead. Computational and communicative overheads were also evaluated using the Multi-precision Integer and Rational Arithmetic Cryptographic Library (MIRACL). BSDCE-IoV shows higher performance in terms of security, functionality, and time delay than a number of recent selective work in IoV security.

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Architecture, Protocols, and Applications of the Internet of Medical Things (IoMT)

Cited by 20 publications

References 0 publications

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

BSDCE-IoV: Blockchain-Based Secure Data Collection and Exchange Scheme for IoV in 5G Environment

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