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  • Open Access

    ARTICLE

    DNBP-CCA: A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

    Abdulwadood Alawadhi1,*, Mohd. Hasbullah Omar1, Abdullah Almogahed2, Noradila Nordin3, Salman A. Alqahtani4, Atif M. Alamri5

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2851-2878, 2024, DOI:10.32604/cmc.2024.050154

    Abstract The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use of Body Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, including contention during finite backoff periods, association delays, and traffic channel access through clear channel assessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions, and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet delivery ratio, packet drop rate, and packet delay.… More >

  • Open Access

    ARTICLE

    Machine Learning-Enabled Communication Approach for the Internet of Medical Things

    Rahim Khan1,3, Abdullah Ghani1, Samia Allaoua Chelloug2,*, Mohammed Amin4, Aamir Saeed5, Jason Teo1

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1569-1584, 2023, DOI:10.32604/cmc.2023.039859

    Abstract The Internet of Medical Things (IoMT) is mainly concerned with the efficient utilisation of wearable devices in the healthcare domain to manage various processes automatically, whereas machine learning approaches enable these smart systems to make informed decisions. Generally, broadcasting is used for the transmission of frames, whereas congestion, energy efficiency, and excessive load are among the common issues associated with existing approaches. In this paper, a machine learning-enabled shortest path identification scheme is presented to ensure reliable transmission of frames, especially with the minimum possible communication overheads in the IoMT network. For this purpose, the… More >

  • Open Access

    ARTICLE

    Priority Detector and Classifier Techniques Based on ML for the IoMT

    Rayan A. Alsemmeari1,*, Mohamed Yehia Dahab2, Badraddin Alturki1, Abdulaziz A. Alsulami3

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1853-1870, 2023, DOI:10.32604/cmc.2023.038589

    Abstract Emerging telemedicine trends, such as the Internet of Medical Things (IoMT), facilitate regular and efficient interactions between medical devices and computing devices. The importance of IoMT comes from the need to continuously monitor patients’ health conditions in real-time during normal daily activities, which is realized with the help of various wearable devices and sensors. One major health problem is workplace stress, which can lead to cardiovascular disease or psychiatric disorders. Therefore, real-time monitoring of employees’ stress in the workplace is essential. Stress levels and the source of stress could be detected early in the fog… More >

  • Open Access

    ARTICLE

    Intelligent Intrusion Detection System for the Internet of Medical Things Based on Data-Driven Techniques

    Okba Taouali1,*, Sawcen Bacha2, Khaoula Ben Abdellafou1, Ahamed Aljuhani1, Kamel Zidi3, Rehab Alanazi1, Mohamed Faouzi Harkat4

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1593-1609, 2023, DOI:10.32604/csse.2023.039984

    Abstract Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’ information and provide a proper diagnosis as needed, resulting in the Internet of Medical Things (IoMT). However, obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant challenge. However, due to the computational resources being limited, an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms. Therefore, designing and developing a lightweight detection mechanism is crucial. To address the aforementioned challenges, a new lightweight IDS approach is developed to… More >

  • Open Access

    ARTICLE

    Securing Healthcare Data in IoMT Network Using Enhanced Chaos Based Substitution and Diffusion

    Musheer Ahmad1, Reem Ibrahim Alkanhel2,*, Naglaa F. Soliman2, Abeer D. Algarni2, Fathi E. Abd El-Samie3, Walid El-Shafai3,4

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2361-2380, 2023, DOI:10.32604/csse.2023.038439

    Abstract Patient privacy and data protection have been crucial concerns in E-healthcare systems for many years. In modern-day applications, patient data usually holds clinical imagery, records, and other medical details. Lately, the Internet of Medical Things (IoMT), equipped with cloud computing, has come out to be a beneficial paradigm in the healthcare field. However, the openness of networks and systems leads to security threats and illegal access. Therefore, reliable, fast, and robust security methods need to be developed to ensure the safe exchange of healthcare data generated from various image sensing and other IoMT-driven devices in… More >

  • Open Access

    REVIEW

    A Systematic Review on the Internet of Medical Things: Techniques, Open Issues, and Future Directions

    Apurva Sonavane1, Aditya Khamparia2,*, Deepak Gupta3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1525-1550, 2023, DOI:10.32604/cmes.2023.028203

    Abstract IoT usage in healthcare is one of the fastest growing domains all over the world which applies to every age group. Internet of Medical Things (IoMT) bridges the gap between the medical and IoT field where medical devices communicate with each other through a wireless communication network. Advancement in IoMT makes human lives easy and better. This paper provides a comprehensive detailed literature survey to investigate different IoMT-driven applications, methodologies, and techniques to ensure the sustainability of IoMT-driven systems. The limitations of existing IoMT frameworks are also analyzed concerning their applicability in real-time driven systems More >

  • Open Access

    ARTICLE

    Efficient Group Blind Signature for Medical Data Anonymous Authentication in Blockchain-Enabled IoMT

    Chaoyang Li*, Bohao Jiang, Yanbu Guo, Xiangjun Xin

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 591-606, 2023, DOI:10.32604/cmc.2023.038129

    Abstract Blockchain technology promotes the development of the Internet of medical things (IoMT) from the centralized form to distributed trust mode as blockchain-based Internet of medical things (BIoMT). Although blockchain improves the cross-institution data sharing ability, there still exist the problems of authentication difficulty and privacy leakage. This paper first describes the architecture of the BIoMT system and designs an anonymous authentication model for medical data sharing. This BIoMT system is divided into four layers: perceptual, network, platform, and application. The model integrates an anonymous authentication scheme to guarantee secure data sharing in the network ledger.… More >

  • Open Access

    ARTICLE

    Energy and Latency Optimization in Edge-Fog-Cloud Computing for the Internet of Medical Things

    Hatem A. Alharbi1, Barzan A. Yosuf2, Mohammad Aldossary3,*, Jaber Almutairi4

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1299-1319, 2023, DOI:10.32604/csse.2023.039367

    Abstract In this paper, the Internet of Medical Things (IoMT) is identified as a promising solution, which integrates with the cloud computing environment to provide remote health monitoring solutions and improve the quality of service (QoS) in the healthcare sector. However, problems with the present architectural models such as those related to energy consumption, service latency, execution cost, and resource usage, remain a major concern for adopting IoMT applications. To address these problems, this work presents a four-tier IoMT-edge-fog-cloud architecture along with an optimization model formulated using Mixed Integer Linear Programming (MILP), with the objective of… More >

  • Open Access

    ARTICLE

    BIoMT: A Blockchain-Enabled Healthcare Architecture for Information Security in the Internet of Medical Things

    Sahar Badri1, Sana Ullah Jan2,*, Daniyal Alghazzawi1, Sahar Aldhaheri1, Nikolaos Pitropakis2

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3667-3684, 2023, DOI:10.32604/csse.2023.037531

    Abstract Rapid technological advancement has enabled modern healthcare systems to provide more sophisticated and real-time services on the Internet of Medical Things (IoMT). The existing cloud-based, centralized IoMT architectures are vulnerable to multiple security and privacy problems. The blockchain-enabled IoMT is an emerging paradigm that can ensure the security and trustworthiness of medical data sharing in the IoMT networks. This article presents a private and easily expandable blockchain-based framework for the IoMT. The proposed framework contains several participants, including private blockchain, hospital management systems, cloud service providers, doctors, and patients. Data security is ensured by incorporating More >

  • Open Access

    ARTICLE

    Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

    Kamalrulnizam Bin Abu Bakar1, Fatima Tul Zuhra2,*, Babangida Isyaku1,3, Fuad A. Ghaleb1

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 785-798, 2023, DOI:10.32604/cmc.2023.034540

    Abstract The Internet of Medical Things (IoMT) emerges with the vision of the Wireless Body Sensor Network (WBSN) to improve the health monitoring systems and has an enormous impact on the healthcare system for recognizing the levels of risk/severity factors (premature diagnosis, treatment, and supervision of chronic disease i.e., cancer) via wearable/electronic health sensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy plays a very significant role in the detection of gastric cancer. Convolutional Neural Network (CNN) has been widely used to diagnose gastric cancer based on various feature extraction models, consequently, limiting the identification and categorization… More >

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