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Nishad, Dipesh Kumar, i Diwakar R. Tripathi. "Internet of Medical Things (IoMT): Applications and Challenges". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, nr 3 (15.12.2020): 2885–89. http://dx.doi.org/10.61841/turcomat.v11i3.14654.
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The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling remote patient monitoring, telemedicine, smart healthcare devices, and health data analytics. This paper provides a comprehensive review of the applications, benefits, challenges, and future trends of IoMT in healthcare. The applications of IoMT, including remote patient monitoring and telemedicine, have the potential to improve patient outcomes, enhance healthcare efficiency, and reduce costs. However, the implementation of IoMT is hindered by security and privacy concerns, interoperability issues, regulatory challenges, and data management complexities. Future trends of IoMT, such as the integration of artificial intelligence and machine learning, blockchain technology, IoMT ecosystem integration, and personalized medicine, promise to further enhance its capabilities and impact in healthcare. By addressing these challenges and embracing these trends, healthcare organizations can harness the full potential of IoMT to create a more connected, efficient, and patient-centric healthcare system.
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Kjwan, Alyaa Ali Hameed, i Omar Hasan Mohammad. "Enhancing Medical Data Analysis with Federated Learning in the Internet of Medical Things". April-May 2024, nr 43 (1.04.2024): 38–52. http://dx.doi.org/10.55529/ijrise.43.38.52.
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The Internet of Things refers to physical items, which are equipped with software, sensors, computing power, and other technologies, and that communicate with other electronic devices and systems over communication networks or the Internet. A collection of medical devices and software programmes known as the Internet of Medical Things (IoMT) link to healthcare networks via internet computing. Machine-to-machine communication, which is the foundation of IoMT, is made feasible by medical equipment that includes Wi-Fi. IoMT devices have the ability to analyse and store collected data by connecting to cloud services. IoMT is a different moniker for IoT in healthcare. Since data is transferred via the internet and the IoMT creates a lot of data, privacy concerns are important. The vast volume of data produced by IoMT devices calls for big data processing, and federated learning tackles privacy issues as a way to overcome these difficulties. The big data health care framework for IoMT is discussed in this article. It is built on federated learning.
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Taherdoost, Hamed. "Blockchain-Based Internet of Medical Things". Applied Sciences 13, nr 3 (18.01.2023): 1287. http://dx.doi.org/10.3390/app13031287.
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IoMT sensor nodes, Internet of Things (IoT) wearable medical equipment, healthcare facilities, patients, and insurance firms are all increasingly being included in IoMT systems. Therefore, it is difficult to create a blockchain design for such systems, since scalability is among the most important aspects of blockchain technology. This realization prompted us to comprehensively analyze blockchain-based IoMT solutions developed in English between 2017 and 2022. This review incorporates the theoretical underpinnings of a large body of work published in highly regarded academic journals over the past decade, to standardize evaluation methods and fully capture the rapidly developing blockchain space. This study categorizes blockchain-enabled applications across various industries such as information management, privacy, healthcare, business, and supply chains according to a structured, systematic evaluation, and thematic content analysis of the literature that is already identified. The gaps in the literature on the topic have also been highlighted, with a special focus on the restrictions posed by blockchain technology and the knock-on effects that such restrictions have in other fields. Based on these results, several open research questions and potential avenues for further investigation that are likely to be useful to academics and professionals alike are pinpointed.
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Gaurav, Akshat, Konstantinos Psannis i Dragan Peraković. "Security of Cloud-Based Medical Internet of Things (MIoTs)". International Journal of Software Science and Computational Intelligence 14, nr 1 (styczeń 2022): 1–16. http://dx.doi.org/10.4018/ijssci.285593.
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In this digital era expectations for medical quality have increased. As the number of patients continues to increase, conventional health care methods are having to deal with new complications. In light of these observations, researchers suggested a hybrid combination of conventional health care methods with IoT technology and develop MIoT. The goal of IoMT is to ensure that patients can respond more effectively and efficiently to their treatment. But preserving user privacy is a critical issue when it comes to collecting and handling highly sensitive personal health data. However, IoMTs have limited processing power; hence, they can only implement minimal security techniques. Consequently, throughout the health data transfer through MIoT, patient’s data is at risk of data leakage. This manuscript per the authors emphasizes the need of implementing suitable security measures to increase the IoMT's resilience to cyberattacks. Additionally, this manuscript per the authors discusses the main security and privacy issues associated with IoMT and provide an overview of existing techniques.
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Siddiq, Abdulrahman I. "A Comprehensive Review of the Internet of Medical Things in Healthcare". International Journal of Electrical and Electronic Engineering & Telecommunications 13, nr 6 (2024): 415–26. http://dx.doi.org/10.18178/ijeetc.13.6.415-426.
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The continuous and rapid development in Internet of Medical Things (IoMT) technologies has led to the expansion of their applications in the field of healthcare and to increased interest in developing their technologies for the purpose of improving their performance in light of the challenges facing their practical application. This paper aims to provide a comprehensive up-to-date review of the literature on IoMT and telemedicine. The study discusses the growing interest in the Internet of Things (IoT) in healthcare, emphasizing the IoMT. It highlights the significance of the IoMT in intelligent healthcare systems, particularly in pandemics like COVID-19, where remote monitoring becomes crucial. This study examines the utilization of the IoMT systems, emphasizing on the application of sensors in healthcare industry. Furthermore, it highlights the significance of cloud computing, communication technologies, artificial intelligence, and Blockchain in IoMT. The challenges related to accepting IoMT in practice are explored; including data security, integration of protocols, data overload, accuracy and cost. Overall, the analyzed literature confirms that IoMT-based healthcare systems demonstrate significant potential in delivering comprehensive surveillance and immediate monitoring through public catastrophes.
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Cassinadane, Ananda Vayaravel, Akshaya Sridhar, Priyanka Sekar i Sami Ranajan Sahoo. "Internet of things in medicine and dentistry". International Journal of Clinical Biochemistry and Research 9, nr 2 (15.06.2022): 98–105. http://dx.doi.org/10.18231/j.ijcbr.2022.020.
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The internet of Things (IoT) in medical arena, also known as internet of medical things (IoMT) is the collection of medical devices and application connecting healthcare Information Technology (IT) system by means of online computer networks. It enables virtually any medical devices as well as non digitalized things (like pills and beds) to connect process and communicate data via web. IoMT allows medical devices and health-care items to exchange data on the spot, online with anyone who has a genuine need for it. The aura of IoMT includes wireless communication technologies, cloud computing, wearable technologies, messaging protocols, security methods, development boards, microcontrollers, mobile/IoT operating systems, and programming languages, built upon numerous technologies including advanced sensors, IoT connectivity and artificial intelligence (AI). IoMT can improve healthcare quality and reduce costs too in hospitals and clinics. In places where distance is the limiting factor, Telemedicine plays a vital role in remote patient monitoring. Major applications include biomedical equipment remote monitoring, remote patient monitoring biosensors and radio frequency identification. IoT in Dentistry aims to streamline oral health care by enhancing oral health while reducing costs, promoting workflow, relieving dentists and dental workers of tedious and time-consuming activities, and igniting interest in personalized oral health care.
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Prasad, Vivek Kumar, Pronaya Bhattacharya, Darshil Maru, Sudeep Tanwar, Ashwin Verma, Arunendra Singh, Amod Kumar Tiwari i in. "Federated Learning for the Internet-of-Medical-Things: A Survey". Mathematics 11, nr 1 (28.12.2022): 151. http://dx.doi.org/10.3390/math11010151.
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Recently, in healthcare organizations, real-time data have been collected from connected or implantable sensors, layered protocol stacks, lightweight communication frameworks, and end devices, named the Internet-of-Medical-Things (IoMT) ecosystems. IoMT is vital in driving healthcare analytics (HA) toward extracting meaningful data-driven insights. Recently, concerns have been raised over data sharing over IoMT, and stored electronic health records (EHRs) forms due to privacy regulations. Thus, with less data, the analytics model is deemed inaccurate. Thus, a transformative shift has started in HA from centralized learning paradigms towards distributed or edge-learning paradigms. In distributed learning, federated learning (FL) allows for training on local data without explicit data-sharing requirements. However, FL suffers from a high degree of statistical heterogeneity of learning models, level of data partitions, and fragmentation, which jeopardizes its accuracy during the learning and updating process. Recent surveys of FL in healthcare have yet to discuss the challenges of massive distributed datasets, sparsification, and scalability concerns. Because of this gap, the survey highlights the potential integration of FL in IoMT, the FL aggregation policies, reference architecture, and the use of distributed learning models to support FL in IoMT ecosystems. A case study of a trusted cross-cluster-based FL, named Cross-FL, is presented, highlighting the gradient aggregation policy over remotely connected and networked hospitals. Performance analysis is conducted regarding system latency, model accuracy, and the trust of consensus mechanism. The distributed FL outperforms the centralized FL approaches by a potential margin, which makes it viable for real-IoMT prototypes. As potential outcomes, the proposed survey addresses key solutions and the potential of FL in IoMT to support distributed networked healthcare organizations.
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Niu, Qinwang, Haoyue Li, Yu Liu, Zhibo Qin, Li-bo Zhang, Junxin Chen i Zhihan Lyu. "Toward the Internet of Medical Things: Architecture, trends and challenges". Mathematical Biosciences and Engineering 21, nr 1 (2023): 650–78. http://dx.doi.org/10.3934/mbe.2024028.
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<abstract><p>In recent years, the growing pervasiveness of wearable technology has created new opportunities for medical and emergency rescue operations to protect users' health and safety, such as cost-effective medical solutions, more convenient healthcare and quick hospital treatments, which make it easier for the Internet of Medical Things (IoMT) to evolve. The study first presents an overview of the IoMT before introducing the IoMT architecture. Later, it portrays an overview of the core technologies of the IoMT, including cloud computing, big data and artificial intelligence, and it elucidates their utilization within the healthcare system. Further, several emerging challenges, such as cost-effectiveness, security, privacy, accuracy and power consumption, are discussed, and potential solutions for these challenges are also suggested.</p></abstract>
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Alsaeed, Norah, i Farrukh Nadeem. "Authentication in the Internet of Medical Things: Taxonomy, Review, and Open Issues". Applied Sciences 12, nr 15 (26.07.2022): 7487. http://dx.doi.org/10.3390/app12157487.
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The Internet of Medical Things (IoMT) has revolutionized the world of healthcare by remotely connecting patients to healthcare providers through medical devices connected over the Internet. IoMT devices collect patients’ medical data and share them with healthcare providers, who analyze it for early control of diseases. The security of patients’ data is of prime importance in IoMT. Authentication of users and devices is the first layer of security in IoMT. However, because of diverse and resource-constrained devices, authentication in IoMT is a challenging task. Several authentication schemes for IoMT have been proposed in the literature. However, each of them has its own pros and cons. To identify, evaluate and summarize the current literature on authentication in IoMT, we conducted a systematic review of 118 articles published between 2016 and 2021. We also established a taxonomy of authentication schemes in IoMT from seven different perspectives. We observed that most of the authentication schemes use a distributed architecture and public key infrastructure. It was also observed that hybrid cryptography approaches have become popular to overcome the shortcomings of single cryptographic approaches. Authentication schemes in IoMT need to support end-to-end, cross-layer, and cross-domain authentication. Finally, we discuss some open issues and future directions.
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Alseddiqi, Mohamed, Anwar AL Mofleh, Osama Najam, Budoor AlMannaei, Leena Albalooshi, Abdulla Alheddi i Ahmed Alshaimi. "Internet of Medical Things Application in King Hamad University Hospital". Saudi Journal of Biomedical Research 8, nr 06 (23.06.2023): 74–82. http://dx.doi.org/10.36348/sjbr.2023.v08i06.001.
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Background: The Internet of Medical Things (IoMT) is a network of medical devices and applications that are connected to the internet, allowing healthcare providers to remotely monitor and manage patient health. King Hamad University Hospital (KHUH) is a tertiary care hospital in Bahrain that has implemented various IoMT applications to improve patient care. Methods: The aim of this study is to investigate the application of Internet of Medical Things (IoMT) in King Hamad University Hospital (KHUH) and its impact on patient care and hospital operations The study will be conducted at KHUH, which is a tertiary care hospital located in Bahrain. KHUH, accommodates 739 bed in all services. (In- patients including isolation rooms: 348, Out-patient clinics: 242, Other services: 149). That provides specialized medical services to patients from Bahrain and neighboring countries. Results: The survey results showed that the most commonly used IoMT applications were remote monitoring devices for vital signs, telemedicine platforms for virtual consultations, and electronic health records for patient data management. Healthcare providers reported that these applications were effective in improving patient outcomes, reducing hospital readmissions, and increasing efficiency in healthcare delivery. However, some challenges were identified during implementation, such as technical issues with connectivity and data security concerns. Conclusion: The implementation of IoMT applications in KHUH has shown promising results in improving patient care and healthcare delivery. However, ongoing efforts are needed to address the challenges faced during implementation to ensure the sustainability and scalability of these technologies. Further research is also needed to evaluate the long-term impact of IoMT on patient outcomes and healthcare costs.
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Alizadehsani, Roohallah, Mohamad Roshanzamir, Navid Hoseini Izadi, Raffaele Gravina, H. M. Dipu Kabir, Darius Nahavandi, Hamid Alinejad-Rokny i in. "Swarm Intelligence in Internet of Medical Things: A Review". Sensors 23, nr 3 (28.01.2023): 1466. http://dx.doi.org/10.3390/s23031466.
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Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.
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Abdulmohsin Hammood, Dalal, Hasliza A. Rahim, Ahmed Alkhayyat i R. Badlishah Ahmad. "Body-to-Body Cooperation in Internet of Medical Things: Toward Energy Efficiency Improvement". Future Internet 11, nr 11 (14.11.2019): 239. http://dx.doi.org/10.3390/fi11110239.
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Internet of Medical Things (IoMT) technologies provide suitability among physicians and patients because they are useful in numerous medical fields. Wireless body sensor networks (WBSNs) are one of the most crucial technologies from within the IoMT evolution of the healthcare system, whereby each patient is monitored by low-powered and lightweight sensors. When the WBSNs are integrated into IoMT networks, they are quite likely to overlap each other; thus, cooperation between WBSN sensors is possible. In this paper, we consider communication between WBSNs and beyond their communication range. Therefore, we propose inter-WBAN cooperation for the IoMT system, which is also known as inter-WBAN cooperation in an IoMT environment (IWC-IoMT). In this paper, first, a proposed architecture for the IoT health-based system is investigated. Then, a mathematical model of the outage probability for the IWC-IoMT is derived. Finally, the energy efficiency of the IWC-IoT is analysed and inspected. The simulation and numerical results show that the IWC-IoMT (cooperative IoMT) system provides superior performance compared to the non-cooperative system.
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Bhattacharjya, Aniruddha, Kamil Kozdrój, Grzegorz Bazydło i Remigiusz Wisniewski. "Trusted and Secure Blockchain-Based Architecture for Internet-of-Medical-Things". Electronics 11, nr 16 (16.08.2022): 2560. http://dx.doi.org/10.3390/electronics11162560.
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The Internet of Medical Things (IoMT) global market has grown and developed significantly in recent years, and the number of IoMT devices is increasing every year. IoMT systems are now very popular and have become part of our everyday life. However, such systems should be properly protected to preventing unauthorized access to the devices. One of the most popular security methods that additionally relies on real-time communication is Blockchain. Moreover, such a technique can be supported by the Trusted Third Party (TTP), which guarantees data immutability and transparency. The research and industrial community has predicted the proliferation of Blockchain-based IoMT (BIoMT), for providing security, privacy, and effective insurance processing. A connected environment comprises some of the unique features of the IoMT in the form of sensors and devices that capture and measure, recognize and classify, assess risk, notify, make conclusions, and take action. Distributed communication is also unique due to the combination of the fact that the Blockchain cannot be tampered with and the Peer-to-Peer (P2P) technique, especially compared to the traditional cloud-based techniques where the reliance of IoMT systems on the centralized cloud makes it somewhat vulnerable. This paper proposes a Blockchain-based technique oriented on IoMT applications with a focus on maintaining Confidentiality, Integrity, and Availability (the CIA triad) of data communication in the system. The proposed solution is oriented toward trusted and secure real-time communication. The presented method is illustrated by an example of a cloud-based hospital application. Finally, the security aspects of the proposed approach are studied and analyzed in detail.
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Zachos, Georgios, Ismael Essop, Georgios Mantas, Kyriakos Porfyrakis, José C. Ribeiro i Jonathan Rodriguez. "An Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks". Electronics 10, nr 21 (20.10.2021): 2562. http://dx.doi.org/10.3390/electronics10212562.
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Over the past few years, the healthcare sector is being transformed due to the rise of the Internet of Things (IoT) and the introduction of the Internet of Medical Things (IoMT) technology, whose purpose is the improvement of the patient’s quality of life. Nevertheless, the heterogenous and resource-constrained characteristics of IoMT networks make them vulnerable to a wide range of threats. Thus, novel security mechanisms, such as accurate and efficient anomaly-based intrusion detection systems (AIDSs), considering the inherent limitations of the IoMT networks, need to be developed before IoMT networks reach their full potential in the market. Towards this direction, in this paper, we propose an efficient and effective anomaly-based intrusion detection system (AIDS) for IoMT networks. The proposed AIDS aims to leverage host-based and network-based techniques to reliably collect log files from the IoMT devices and the gateway, as well as traffic from the IoMT edge network, while taking into consideration the computational cost. The proposed AIDS is to rely on machine learning (ML) techniques, considering the computation overhead, in order to detect abnormalities in the collected data and thus identify malicious incidents in the IoMT network. A set of six popular ML algorithms was tested and evaluated for anomaly detection in the proposed AIDS, and the evaluation results showed which of them are the most suitable.
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Ahmad, Ehsan, Brian Larson i Abdulbasid Banga. "Trusted Composition of Internet of Medical Things over Imperfect Networks". Future Internet 16, nr 7 (28.06.2024): 230. http://dx.doi.org/10.3390/fi16070230.
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The Internet of Medical Things (IoMT) represents a specialized domain within the Internet of Things, focusing on medical devices that require regulatory approval to ensure patient safety. Trusted composition of IoMT systems aims to ensure high assurance of the entire composed system, despite potential variability in the assurance levels of individual components. Achieving this trustworthiness in IoMT systems, especially when using less-assured, commercial, off-the-shelf networks like Ethernet and WiFi, presents a significant challenge. To address this challenge, this paper advocates a systematic approach that leverages the Architecture Analysis & Design Language (AADL) along with Behavior Language for Embedded Systems with Software (BLESS) specification and implementation. This approach aims to provide high assurance on critical components through formal verification, while using less-assured components in a manner that maintains overall system determinism and reliability. A clinical case study involving an automated opioid infusion monitoring IoMT system is presented to illustrate the application of the proposed approach. Through this case study, the effectiveness of the systemic approach in achieving trusted composition of heterogeneous medical devices over less-assured networks is demonstrated.
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Joby, P. P. "A Review on Data Securing Techniques using Internet of Medical Things". September 2021 3, nr 3 (27.09.2021): 150–63. http://dx.doi.org/10.36548/jucct.2021.3.001.
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At present, the traditional healthcare system is completely replaced by the revolutionary technique, the Internet of Medical Things (IoMT). Internet of Medical Things is the IoT hub that comprises of medical devices and applications which are interconnected through online computer networks. The basic principle of IoMT is machine-to-machine communication that takes place online. The major goal of IoMT is to reduce frequent or unwanted visits to the hospitals which makes it comfortable and is also highly preferred by the older people. Another advantage of this methodology is that the interpreted or collected data is stored in cloud modules unlike amazon and Mhealth, making it accessible remotely. Although there are countless advantages in IoMT, the critical factor lies in data security or encryption. A surplus number of threat related to devices, connectivity, and cloud might occur under unforeseen or threatening circumstances which makes the person in the situation helpless. Yet, with the help of data security techniques designed especially for Internet of Medical Things, it is possible to address these challenges. In this paper, a review on data securing techniques for the internet of medical things is made along with a discussion on related concepts.
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Zhao, Zhuo, Chingfang Hsu, Lein Harn, Qing Yang i Lulu Ke. "Lightweight Privacy-Preserving Data Sharing Scheme for Internet of Medical Things". Wireless Communications and Mobile Computing 2021 (12.09.2021): 1–13. http://dx.doi.org/10.1155/2021/8402138.
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Internet of Medical Things (IoMT) is a kind of Internet of Things (IoT) that includes patients and medical sensors. Patients can share real-time medical data collected in IoMT with medical professionals. This enables medical professionals to provide patients with efficient medical services. Due to the high efficiency of cloud computing, patients prefer to share gathering medical information using cloud servers. However, sharing medical data on the cloud server will cause security issues, because these data involve the privacy of patients. Although recently many researchers have designed data sharing schemes in medical domain for security purpose, most of them cannot guarantee the anonymity of patients and provide access control for shared health data, and further, they are not lightweight enough for IoMT. Due to these security and efficiency issues, a novel lightweight privacy-preserving data sharing scheme is constructed in this paper for IoMT. This scheme can achieve the anonymity of patients and access control of shared medical data. At the same time, it satisfies all described security features. In addition, this scheme can achieve lightweight computations by using elliptic curve cryptography (ECC), XOR operations, and hash function. Furthermore, performance evaluation demonstrates that the proposed scheme takes less computation cost through comparison with similar solutions. Therefore, it is fairly an attractive solution for efficient and secure data sharing in IoMT.
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Rafique, Wajid, Maqbool Khan, Salabat Khan i Juma Said Ally. "SecureMed: A Blockchain-Based Privacy-Preserving Framework for Internet of Medical Things". Wireless Communications and Mobile Computing 2023 (21.04.2023): 1–14. http://dx.doi.org/10.1155/2023/2558469.
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The Internet of Medical Things (IoMT) connects a huge amount of smart sensors with the Internet for healthcare service provisioning. IoMT’s privacy-preserving becomes a challenge considering the life-saving data collected and transferred through IoMT. Traditional privacy protection techniques use centralized management strategies, which lead to a single point of failure, lack of trust, state modification, information disclosure, and identity theft. Edge computing enables local computation of IoMT data, which reduces traffic to the cloud and also helps in accomplishing latency-sensitive healthcare applications and services. This paper proposes a novel framework (i.e., SecureMed) that uses blockchain-based distributed authentication implemented at the edge cloudlets to enforce privacy protection. In SecureMed, IoMT devices interact with edge cloudlets using smart contracts. It uses trusted edge nodes to implement an authentication algorithm that uses public/private key matching to authenticate IoMT. Experimental evaluation performed using the Pythereum blockchain shows that SecureMed outperforms the traditional blockchain scheme based on latency, bandwidth consumption, deployment time, scalability, and accuracy. Therefore, it can be used to protect the edge-enabled IoMT from privacy attacks and to ensure end-to-end healthcare service provisioning.
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Jeong, Yoon-Su, i Seung-Soo Shin. "Internet of Medical Things-Based Multiple Data Processing Techniques Optimized for Healthcare Environments". Journal of Computational and Theoretical Nanoscience 18, nr 5 (1.05.2021): 1506–12. http://dx.doi.org/10.1166/jctn.2021.9580.
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As IT technology has recently been combined with portable device (smartphones, tablets, etc.), demands for state-of-the-art infrastructure are growing. Among them, there is a need for IoMT technology that combines big data and IoT technologies into healthcare. In this paper, we propose a distributed management technique that can efficiently manage the health care (monitoring and chronic disease management, etc.) information of remote patients by applying various artificial intelligence-related technologies (big data analysis, blockchain, deep learning, etc.) to IoMT technology. The proposed technique facilitates the collection and management of data by equating the basic management structure of the data with the existing patient model of the hospital. In particular, since the information collected from the IoMT sensor is composed of multiple blockchains, the task weight of the endpoints is minimized. Also, the proposed technique improved the accuracy of the IoMT information by using the stratified cumulative probability values of the IoMT sensor information. Proposed technique have improved the efficiency of data collection processing by 14.7% over conventional techniques by automating the linkage between IoMT equipment on the central server for all actions of the IoMT device.
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Alsadhan, Afnan, Areej Alhogail i Hessah Alsalamah. "Blockchain-Based Privacy Preservation for the Internet of Medical Things: A Literature Review". Electronics 13, nr 19 (28.09.2024): 3832. http://dx.doi.org/10.3390/electronics13193832.
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The Internet of Medical Things (IoMT) is a rapidly expanding network comprising medical devices, sensors, and software that collect and exchange patient health data. Today, the IoMT has the potential to revolutionize healthcare by offering more personalized care to patients and improving the efficiency of healthcare delivery. However, the IoMT also introduces significant privacy concerns, particularly regarding data privacy. IoMT devices often collect and store large amounts of data about patients’ health. These data could be used to track patients’ movements, monitor their health habits, and even predict their future health risks. This extensive data collection and surveillance could be a major invasion of patient privacy. Thus, privacy-preserving research in an IoMT context is an important area of research that aims to mitigate these privacy issues. This review paper comprehensively applies the PRISMA methodology to analyze, review, classify, and compare current approaches of preserving patient data privacy within IoMT blockchain-based healthcare environments.
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Srivastava, Jyoti, Sidheswar Routray, Sultan Ahmad i Mohammad Maqbool Waris. "Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress". Computational Intelligence and Neuroscience 2022 (16.07.2022): 1–17. http://dx.doi.org/10.1155/2022/7218113.
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Internet of Medical Thing (IoMT) is the most emerging era of the Internet of Thing (IoT), which is exponentially gaining researchers’ attention with every passing day because of its wide applicability in Smart Healthcare systems (SHS). Because of the current pandemic situation, it is highly risky for an individual to visit the doctor for every small problem. Hence, using IoMT devices, we can easily monitor our day-to-day health records, and thereby initial precautions can be taken on our own. IoMT is playing a crucial role within the healthcare industry to increase the accuracy, reliability, and productivity of electronic devices. This research work provides an overview of IoMT with emphasis on various enabling techniques used in smart healthcare systems (SHS), such as radio frequency identification (RFID), artificial intelligence (AI), and blockchain. We are providing a comparative analysis of various IoMT architectures proposed by several researchers. Also, we have defined various health domains of IoMT, including the analysis of different sensors with their application environment, merits, and demerits. In addition, we have figured out key protocol design challenges, which are to be considered during the implementation of an IoMT network-based smart healthcare system. Considering these challenges, we prepared a comparative study for different data collection techniques that can be used to maintain the accuracy of collected data. In addition, this research work also provides a comprehensive study for maintaining the energy efficiency of an AI-based IoMT framework based on various parameters, such as the amount of energy consumed, packet delivery ratio, battery lifetime, quality of service, power drain, network throughput, delay, and transmission rate. Finally, we have provided different correlation equations for finding the accuracy and efficiency within the IoMT-based healthcare system using artificial intelligence. We have compared different data collection algorithms graphically based on their accuracy and error rate. Similarly, different energy efficiency algorithms are also graphically compared based on their energy consumption and packet loss percentage. We have analyzed our references used in this study, which are graphically represented based on their distribution of publication year and publication avenue.
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Rajawat, Anand Singh, S. B. Goyal, Pradeep Bedi, Tony Jan, Md Whaiduzzaman i Mukesh Prasad. "Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)". Future Internet 15, nr 8 (15.08.2023): 271. http://dx.doi.org/10.3390/fi15080271.
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Internet of Medical Things (IoMT) is an ecosystem composed of connected electronic items such as small sensors/actuators and other cyber-physical devices (CPDs) in medical services. When these devices are linked together, they can support patients through medical monitoring, analysis, and reporting in more autonomous and intelligent ways. The IoMT devices; however, often do not have sufficient computing resources onboard for service and security assurance while the medical services handle large quantities of sensitive and private health-related data. This leads to several research problems on how to improve security in IoMT systems. This paper focuses on quantum machine learning to assess security vulnerabilities in IoMT systems. This paper provides a comprehensive review of both traditional and quantum machine learning techniques in IoMT vulnerability assessment. This paper also proposes an innovative fused semi-supervised learning model, which is compared to the state-of-the-art traditional and quantum machine learning in an extensive experiment. The experiment shows the competitive performance of the proposed model against the state-of-the-art models and also highlights the usefulness of quantum machine learning in IoMT security assessments and its future applications.
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Huang, Xucheng, i Shah Nazir. "Evaluating Security of Internet of Medical Things Using the Analytic Network Process Method". Security and Communication Networks 2020 (1.09.2020): 1–14. http://dx.doi.org/10.1155/2020/8829595.
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Internet of Medical Things (IoMT) plays an important role in healthcare. Different devices such as smart sensors, wearable devices, handheld, and many other devices are connected in a network in the form of Internet of Things (IoT) for the smooth running of communication in healthcare. Security of these devices in healthcare is important due to its nature of functionality and efficiency. An efficient and robust security system is in dire need to cope with the attacks, threats, and vulnerability. The security evaluation of IoMT is an issue since couple of years. Therefore, the aim of the proposed study is to evaluate the security of IoMT by using the analytic network (ANP) process. The proposed approach is applied using ISO/IEC 27002 (ISO 27002) standard and some other important features from the literature. The results of the proposed research demonstrate the effective IoMT components which can further be used as secure IoMT.
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Stitini, Oumaima, Fathia Ouakasse, Said Rakrak, Soulaimane Kaloun i Omar Bencharef. "Combining IoMT and XAI for Enhanced Triage Optimization: An MQTT Broker Approach with Contextual Recommendations for Improved Patient Priority Management in Healthcare". International Journal of Online and Biomedical Engineering (iJOE) 20, nr 07 (6.05.2024): 145–62. http://dx.doi.org/10.3991/ijoe.v20i07.47483.
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The widespread adoption of the Internet of Things has significantly enhanced our daily lives across various dimensions. E-health has significantly benefited from advancements in the Internet of Things (IoT), particularly with the emergence of the Internet of Medical Things (IoMT). A sophisticated wireless sensor network produces a huge amount of data, requiring robust cloud-based hardware for precise processing and categorization. The IoMT allows for the extensive gathering of medical data from incoming hospital patients, enabling real-time monitoring of vital signs and health statuses. Nevertheless, effectively prioritizing patients in emergencies is challenging due to the importance and complicatedness of the data. To tackle this issue, an innovative solution involves integrating Explainable Artificial Intelligence into the IoMT ecosystem. By incorporating Explainable AI, the system enhances explainability, fostering trust and reliability in patient prioritization. This provides healthcare providers a more reliable prioritization mechanism that aligns with established medical guidelines. The study explores IoMT devices for collecting medical data from incoming patients, focusing on the MQTT protocol for lightweight devices, aiming to guide patients to the right department and prioritize emergency management through IoMT data analysis.
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R, Sindhuja, Kapse Arvind S. i Kapse Avinash S. "A Survey of Internet of Medical Things (IoMT) Applications, Architectures and Challenges in Smart Healthcare Systems". ITM Web of Conferences 56 (2023): 05013. http://dx.doi.org/10.1051/itmconf/20235605013.
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Internet of Medical Things (IoMT) or Healthcare IoT is a technological under IoT catering to the healthcare sector. It refers to the interconnection of medical devices, sensors, applications and systems to the Internet. IoMT enables the collection, transmission and analysis of patient’s data in real-time, allowing for remote monitoring and early detection of health issues. IoMT systems present a promising opportunity for prevention, prediction, and monitoring of emerging infectious diseases such as COVID-19. This paper provides a survey of IoMT devices, applications, benefits, challenges, and its impact on the healthcare industry.
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Rajput, Adil, Samara Ahmed i Lora Kasher. "Patients’ Mental Health Data and Internet of Medical Things (IoMT) Safety: Analyzing Raspberry pi vulnerabilities". Rawal Medical Journal 49, nr 1 (2024): 1. http://dx.doi.org/10.5455/rmj.20240506101607.
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Objective: The goal of this study us to evaluate the safety of patient’s data in an Internet of Medical Things (IoMT) ecosystem. The study focuses on three main objectives: To assess the vulnerability of IoMT devices and systems to cyber threats and data breaches. Specifically, we look at the vulnerability of the raspberry pi servers that are considered the essential building block of an IOT/IOMT environment; to pinpoint vulnerability areas of IoMT ecosystem; and to identify and propose mitigation strategies for the key risks associated with the IoMT ecosystem. Methodology The study employed setting up a representative IOMT model where a raspberry pi server controls the IOMT devices. Specifically, we address the following: 1. Vulnerability Assessment: An empirical analysis of a range of IoMT devices and systems was performed to identify specific vulnerabilities. This involved penetration testing and security assessments in controlled environments. 2. Identify the mitigation strategies to gauge the effectiveness. 3. Test the effectiveness of the mitigation strategies. Results: The study highlights several critical vulnerabilities within the IoMT ecosystem such as weak encryption methods, lack of proper monitoring, effective authentication mechanisms, and the use of outdated software. The study showed that the underlying raspberry pi server are vulnerable to common cyberattack vectors. However, such vulnerabilities can be countered by effective mitigation techniques allowing to capitalize on the IoMT ecosystem. Conclusion: The study confirms the need for ongoing monitoring and improvement in the IoMT ecosystem. Given the continuous advances in the technology field, the healthcare practitioners and administrators need to put a string governance process in place to ensure the continuous monitoring of potential vulnerabilities and in turn providing an effective remedy.
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Elsayeh, Muhammad, Kadry Ali Ezzat, Hany El-Nashar i Lamia Nabil Omran. "CYBERSECURITY ARCHITECTURE FOR THE INTERNET OF MEDICAL THINGS AND CONNECTED DEVICES USING BLOCKCHAIN". Biomedical Engineering: Applications, Basis and Communications 33, nr 02 (16.03.2021): 2150013. http://dx.doi.org/10.4015/s1016237221500137.
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The internet of medical things (IoMT) has a great role in improving the health around the world. IoMT is having a great impact in our life in which the clinical data of the patient is observed and checked and then can be transferred to the third party for using in the future such as the cloud. IoMT is a huge data system with a continuous developing rate, which implies that we should keep a lot of data secure. We propose a combined security architecture that fuses the standard architecture and new blockchain technology. Blockchain is a temper digital ledger which gives peer-to-peer communication and provides communication between non-trust individuals. Using standard in-depth strategy and blockchain, we are able to develop a method to collect vital signs data from IoMT and connected devices and use blockchain to store and retrieve the collected data in a secure and decentralized fashion within a closed system, suitable for healthcare providers such as private clinics, hospitals, and healthcare organizations were sharing data with each other is required. Right now initially examine the innovation behind Blockchain then propose IoMT-based security architecture utilizing Blockchain to guarantee the security of information transmission between associated nodes. Experimental analysis shows that the proposed scheme presents a non-significant overhead; yet it brings major advantages to meet the standard security and privacy requirements in IoMT.
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Sadhu, Pintu Kumar, Venkata P. Yanambaka, Ahmed Abdelgawad i Kumar Yelamarthi. "Prospect of Internet of Medical Things: A Review on Security Requirements and Solutions". Sensors 22, nr 15 (24.07.2022): 5517. http://dx.doi.org/10.3390/s22155517.
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With the widespread and increasing use of Internet-of-Things (IoT) devices in all aspects of daily life, a hopeful future for people, data, and processes is emerging. Extensive spans allow for an integrated life cycle to be created from home to enterprise. The Internet of Medical things (IoMT) forms a flourishing surface that incorporates the sensitive information of human life being sent to doctors or hospitals. These open an enormous space for hackers to utilize flaws of the IoMT network to make a profit. This creates a demand for standardizing regulations and a secure system. Though many authorities are making standards, there are some lacking in the system which makes the product vulnerable. Although many established mechanisms are present for the IoT network, there are a number of obstacles preventing its general implementation in the IoMT network. One of the adoption challenges is the IoMT devices itself, because many IoMT networks consist of battery-powered devices with constrained processing capability. A general overview of the different security integrations with IoT applications has been presented in several papers. Therefore, this paper aims to provide an overview of the IoMT ecosystem, regulations, challenges of standards, security mechanisms using cryptographic solutions, physical unclonable functions (PUF)-based solutions, blockchain, and named data networking (NDN) as well, with pros and cons.
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Safdar, Zanab, Kalsoom Safdar, Ruqia Safdar Bajwa, Shafiq Hussain i Ahmad Karim. "Internet of Medical Things (IoMT) for Covid-19 Epidemic Affected People". South Asian Journal of Social Sciences and Humanities 2, nr 5 (2021): 65–84. http://dx.doi.org/10.48165/sajssh.2021.2505.
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i. Purpose of the study: Due to the rapid increase of COVID-19 cases, it has become challenging for the entire world to identify and treat infected patients at existing hospitals. In order to stop the spread of COVID-19, diseased persons need to be isolated for treatment. Hence, there is an enormous necessity to identify, monitor, and isolate patients to analyze their current situation and improve diagnostic accuracy to prevent more spread and deaths. ii. Methodology: Providentially, the recent advances in Information and communications technology (ICT) like the Internet of Medical Things (IoMT) bring us opportunities to win the battle against the COVID-19 crisis. The exploratory research distributes practical assistance to the researcher for the rudimentary work. In this study, the exploratory research method was executed to explore the existing literature intensively and recognize the COVID-19 affected patients’ issues or challenges. iii. Main Findings: A new approach of IoMT-based E-Health has been designed and proposed for affected patient’s treatment in real-time. IoMT based E-Health model venture a prodigious promise to treat isolated patients where it applies existing technologies to increase quality control and access to patient healthcare centers in this COVID-19 pandemic effectively. iv. Applications of this study: IoMT-based E-Health involves significant components to track, identify, monitor, manage, store, and analyze patient information for the ongoing COVID-19 pandemic. With the help of the proposed approach, existing hospitals and healthcare centers can manage many infected patients, suggest treatment, and respond quickly according to their emergency alerts. v. Novelty/Originality of this study: This study aims to identify current health systems’ challenges and design a specialized model for the IoMT based E-Health systems by focusing mainly on the challenges that surfaced during COVID-19.
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Sundar, R., Amit Gangopadhyay, T. Raghavendra Gupta, P. L. Srinivasa Murthy, Sreenivasulu Gogula, M. N. Sharath i Kireet Muppavaram. "Heart health prediction and classification: An IoMT and AI collaborative model". MATEC Web of Conferences 392 (2024): 01142. http://dx.doi.org/10.1051/matecconf/202439201142.
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Internet of Things (IoT) technology has been used in medical care as the Internet of Medical Things (IoMT) to gather sensor data for diagnosing and predicting cardiac disease. IoMT allows users to access real-time tracking information and manually estimate the person's health using Machine Learning (ML) algorithms. The primary goal of the study proposal is to categorize data and forecast heart illness using health information and medical imagery. The suggested IoMT-based Heart Health Prediction and Classification (IoMT-HHPC) model is a medical data categorization and forecasting framework in two phases. If the first stage's outcome effectively predicts heart disease, the second step is image classification. Data collected from medical equipment attached to the person's body were initially categorized. Echocardiography (ECG) images were analyzed to forecast cardiac problems. This article used many ML techniques to forecast cardiac disease. An IoMT-HHPC model with ANN achieved an accuracy of 99.02%, surpassing the performance of other ML algorithms.
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Sarkar, Mekhla, Tsong-Hai Lee i Prasan Kumar Sahoo. "Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence". Electronics 13, nr 12 (13.06.2024): 2309. http://dx.doi.org/10.3390/electronics13122309.
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Ambient Intelligence (AMI) represents a significant advancement in information technology that is perceptive, adaptable, and finely attuned to human needs. It holds immense promise across diverse domains, with particular relevance to healthcare. The integration of Artificial Intelligence (AI) with the Internet of Medical Things (IoMT) to create an AMI environment in medical contexts further enriches this concept within healthcare. This survey provides invaluable insights for both researchers and practitioners in the healthcare sector by reviewing the incorporation of AMI techniques in the IoMT. This analysis encompasses essential infrastructure, including smart environments and spectrum for both wearable and non-wearable medical devices to realize the AMI vision in healthcare settings. Furthermore, this survey provides a comprehensive overview of cutting-edge AI methodologies employed in crafting IoMT systems tailored for healthcare applications and sheds light on existing research issues, with the aim of guiding and inspiring further advancements in this dynamic field.
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Arora, Pallavi, Baljeet Kaur i Marcio Andrey Teixeira. "CYBERSECURITY IN IIOT AND IOMT NETWORKS USING MACHINE LEARNING ALGORITHMS - A SURVEY". ICTACT Journal on Communication Technology 12, nr 4 (1.12.2021): 2577–81. http://dx.doi.org/10.21917/ijct.2021.0381.
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Rapid advancements in micro-computing, mini-hardware manufacturing, and machine-to-machine (M2M) communications have allowed for innovative Internet of Things (IoT) solutions to redefine numerous networking applications. With the emergence of IoT branches such as the Internet of Medical Things (IoMT) and the Industrial Internet of Things (IIoT), healthcare and industrial systems have been changed by IoT. This paper presents an overview of the technologies that are being used to secure IoMT as well as IIoT frameworks seen within the research articles.
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Hemmati, Atefeh, i Amir Masoud Rahmani. "Internet of Medical Things in the COVID-19 Era: A Systematic Literature Review". Sustainability 14, nr 19 (4.10.2022): 12637. http://dx.doi.org/10.3390/su141912637.
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In recent years, the medical industry has rapidly modernized, incorporating technology to aid in accelerating and simplifying procedures for better accuracy. This technology is becoming more interconnected to create a larger network known as the Internet of Medical Things (IoMT) that can combat the pandemic’s spread. In other words, IoMT emphasizes health applications while maintaining the core concept of the Internet of Things (IoT). The further spread of Coronavirus Disease-2019 (COVID-19) can be halted by employing it. Consequently, this paper uses the Systematic Literature Review (SLR) methodology to evaluate recently published articles in the IoMT domain during the COVID-19 era. Between 2019 and 2022, we analyzed 41 studies. An analysis of the evaluation criteria reveals that the delay factor comprises 38% of the evaluation criteria, the highest percentage because a low-delay IoMT device has a quick response time between the time a request is made and the time a response is received. Moreover, the performance factor accounts for 22%, the accuracy factor accounts for 28%, the security factor for 6%, and the cost factor for 6%. Finally, we concentrate on open issues and future research challenges in IoMT during the COVID-19 era.
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Alkatheiri, Mohammed Saeed, i Ahmed S. Alghamdi. "Blockchain-Assisted Cybersecurity for the Internet of Medical Things in the Healthcare Industry". Electronics 12, nr 8 (11.04.2023): 1801. http://dx.doi.org/10.3390/electronics12081801.
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The Internet of Medical Things (IoMT) plays an important role in strengthening sustainable healthcare systems. IoMT significantly influences our healthcare because it facilitates monitoring and checking patient medical information before transferring the data to a cloud network for future use. The IoMT is a big-data platform which is growing rapidly, so it is critical to maintain all data safely and securely. In this study, Blockchain-Assisted Cybersecurity (BCCS) for the IoMT in the healthcare industry is proposed. Blockchain is a decentralized digital ledger that allows end-to-end communication and provides interaction between untrustworthy persons. BCCS uses a conventional in-depth approach and blockchain to create a procedure for collecting medical information from the IoMT and integrated devices. The proposed system utilizes blockchain to record and extract the accumulated information in a secure and distributed manner within a closed environment suitable for healthcare professionals, such as nursing homes, hospitals, and the healthcare industry where data exchange is needed. The experimental outcomes show that the proposed system has a high security rate of 99.8% and the lowest latency rate of 4.3% compared to traditional approaches. In all, the reliability of the proposed system gives the highest rate of 99.4%.
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Chaganti, Rajasekhar, Azrour Mourade, Vinayakumar Ravi, Naga Vemprala, Amit Dua i Bharat Bhushan. "A Particle Swarm Optimization and Deep Learning Approach for Intrusion Detection System in Internet of Medical Things". Sustainability 14, nr 19 (8.10.2022): 12828. http://dx.doi.org/10.3390/su141912828.
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Integrating the internet of things (IoT) in medical applications has significantly improved healthcare operations and patient treatment activities. Real-time patient monitoring and remote diagnostics allow the physician to serve more patients and save human lives using internet of medical things (IoMT) technology. However, IoMT devices are prone to cyber attacks, and security and privacy have been a concern. The IoMT devices operate on low computing and low memory, and implementing security technology on IoMT devices is not feasible. In this article, we propose particle swarm optimization deep neural network (PSO-DNN) for implementing an effective and accurate intrusion detection system in IoMT. Our approach outperforms the state of the art with an accuracy of 96% to detect network intrusions using the combined network traffic and patient’s sensing dataset. We also present an extensive analysis of using various Machine Learning(ML) and Deep Learning (DL) techniques for network intrusion detection in IoMT and confirm that DL models perform slightly better than ML models.
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Bughio, Kulsoom S., David M. Cook i Syed Afaq A. Shah. "Developing a Novel Ontology for Cybersecurity in Internet of Medical Things-Enabled Remote Patient Monitoring". Sensors 24, nr 9 (27.04.2024): 2804. http://dx.doi.org/10.3390/s24092804.
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IoT has seen remarkable growth, particularly in healthcare, leading to the rise of IoMT. IoMT integrates medical devices for real-time data analysis and transmission but faces challenges in data security and interoperability. This research identifies a significant gap in the existing literature regarding a comprehensive ontology for vulnerabilities in medical IoT devices. This paper proposes a fundamental domain ontology named MIoT (Medical Internet of Things) ontology, focusing on cybersecurity in IoMT (Internet of Medical Things), particularly in remote patient monitoring settings. This research will refer to similar-looking acronyms, IoMT and MIoT ontology. It is important to distinguish between the two. IoMT is a collection of various medical devices and their applications within the research domain. On the other hand, MIoT ontology refers to the proposed ontology that defines various concepts, roles, and individuals. MIoT ontology utilizes the knowledge engineering methodology outlined in Ontology Development 101, along with the structured life cycle, and establishes semantic interoperability among medical devices to secure IoMT assets from vulnerabilities and cyberattacks. By defining key concepts and relationships, it becomes easier to understand and analyze the complex network of information within the IoMT. The MIoT ontology captures essential key terms and security-related entities for future extensions. A conceptual model is derived from the MIoT ontology and validated through a case study. Furthermore, this paper outlines a roadmap for future research, highlighting potential impacts on security automation in healthcare applications.
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Hameed, Shilan S., Wan Haslina Hassan, Liza Abdul Latiff i Fahad Ghabban. "A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches". PeerJ Computer Science 7 (23.03.2021): e414. http://dx.doi.org/10.7717/peerj-cs.414.
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Background The Internet of Medical Things (IoMTs) is gradually replacing the traditional healthcare system. However, little attention has been paid to their security requirements in the development of the IoMT devices and systems. One of the main reasons can be the difficulty of tuning conventional security solutions to the IoMT system. Machine Learning (ML) has been successfully employed in the attack detection and mitigation process. Advanced ML technique can also be a promising approach to address the existing and anticipated IoMT security and privacy issues. However, because of the existing challenges of IoMT system, it is imperative to know how these techniques can be effectively utilized to meet the security and privacy requirements without affecting the IoMT systems quality, services, and device’s lifespan. Methodology This article is devoted to perform a Systematic Literature Review (SLR) on the security and privacy issues of IoMT and their solutions by ML techniques. The recent research papers disseminated between 2010 and 2020 are selected from multiple databases and a standardized SLR method is conducted. A total of 153 papers were reviewed and a critical analysis was conducted on the selected papers. Furthermore, this review study attempts to highlight the limitation of the current methods and aims to find possible solutions to them. Thus, a detailed analysis was carried out on the selected papers through focusing on their methods, advantages, limitations, the utilized tools, and data. Results It was observed that ML techniques have been significantly deployed for device and network layer security. Most of the current studies improved traditional metrics while ignored performance complexity metrics in their evaluations. Their studies environments and utilized data barely represent IoMT system. Therefore, conventional ML techniques may fail if metrics such as resource complexity and power usage are not considered.
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Cano, Maria-Dolores, i Antonio Cañavate-Sanchez. "Preserving Data Privacy in the Internet of Medical Things Using Dual Signature ECDSA". Security and Communication Networks 2020 (10.06.2020): 1–9. http://dx.doi.org/10.1155/2020/4960964.
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The disclosure of personal and private information is one of the main challenges of the Internet of Medical Things (IoMT). Most IoMT-based services, applications, and platforms follow a common architecture where wearables or other medical devices capture data that are forwarded to the cloud. In this scenario, edge computing brings new opportunities to enhance the operation of IoMT. However, despite the benefits, the inherent characteristics of edge computing require countermeasures to address the security and privacy issues that IoMT gives rise to. The restrictions of IoT devices in terms of battery, memory, hardware resources, or computing capabilities have led to a common agreement for the use of elliptic curve cryptography (ECC) with hardware or software implementations. As an example, the elliptic curve digital signature algorithm (ECDSA) is widely used by IoT devices to compute digital signatures. On the other hand, it is well known that dual signature has been an effective method to provide consumer privacy in classic e-commerce services. This article joins both approaches. It presents a novel solution to enhanced security and the preservation of data privacy in communications between IoMT devices and the cloud via edge computing devices. While data source anonymity is achieved from the cloud perspective, integrity and origin authentication of the collected data is also provided. In addition, computational requirements and complexity are kept to a minimum.
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Wang, Kuan, Mingxuan Song, Genqing Bian, Bilin Shao i Kaiqi Huang. "A Lightweight Identity-Based Network Coding Scheme for Internet of Medical Things". Electronics 13, nr 7 (31.03.2024): 1316. http://dx.doi.org/10.3390/electronics13071316.
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Network coding is a potent technique extensively utilized in decentralized Internet of Things (IoT) systems, including the Internet of Medical Things (IoMT). Nevertheless, the inherent packet-mixing characteristics of network coding expose data transmission to pollution attacks, potentially compromising the integrity of original files. The homomorphic signature scheme serves as a robust cryptographic tool that can bolster network coding’s resilience against such attacks. However, current schemes are computationally intensive for signature verification, making them impractical for IoMT environments. In this study, we propose a lightweight identity-based network coding scheme (IBNS) that minimizes computational overhead during the signing and verification processes. This scheme has been demonstrated to be secure against adaptive chosen-message attacks and is well-suited for IoMT applications. Furthermore, we assess the performance of our IBNS through both theoretical and experimental analyses. Simulation outcomes confirm that our scheme outperforms previous ones in terms of practicality and efficiency.
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Marchang, Jims, Jade McDonald, Solan Keishing, Kavyan Zoughalian, Raymond Mawanda, Corentin Delhon-Bugard, Nicolas Bouillet i Ben Sanders. "Secure-by-Design Real-Time Internet of Medical Things Architecture: e-Health Population Monitoring (RTPM)". Telecom 5, nr 3 (10.07.2024): 609–31. http://dx.doi.org/10.3390/telecom5030031.
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The healthcare sector has undergone a profound transformation, owing to the influential role played by Internet of Medical Things (IoMT) technology. However, there are substantial concerns over these devices’ security and privacy-preserving mechanisms. The current literature on IoMT tends to focus on specific security features, rather than wholistic security concerning Confidentiality, Integrity, and Availability (CIA Triad), and the solutions are generally simulated and not tested in a real-world network. The proposed innovative solution is known as Secure-by-Design Real-Time IoMT Architecture for e-Health Population Monitoring (RTPM) and it can manage keys at both ends (IoMT device and IoMT server) to maintain high privacy standards and trust during the monitoring process and enable the IoMT devices to run safely and independently even if the server is compromised. However, the session keys are controlled by the trusted IoMT server to lighten the IoMT devices’ overheads, and the session keys are securely exchanged between the client system and the monitoring server. The proposed RTPM focuses on addressing the major security requirements for an IoMT system, i.e., the CIA Triad, and conducts device authentication, protects from Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks, and prevents non-repudiation attacks in real time. A self-healing solution during the network failure of live e-health monitoring is also incorporated in RTPM. The robustness and stress of the system are tested with different data types and by capturing live network traffic. The system’s performance is analysed using different security algorithms with different key sizes of RSA (1024 to 8192 bits), AES (128 to 256 bits), and SHA (256 bits) to support a resource-constraint-powered system when integrating with resource-demanding secure parameters and features. In the future, other security features like intrusion detection and prevention and the user’s experience and trust level of such a system will be tested.
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Zhou, Jerry, Vincent Ho i Bahman Javadi. "New Internet of Medical Things for Home-Based Treatment of Anorectal Disorders". Sensors 22, nr 2 (14.01.2022): 625. http://dx.doi.org/10.3390/s22020625.
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Home-based healthcare provides a viable and cost-effective method of delivery for resource- and labour-intensive therapies, such as rehabilitation therapies, including anorectal biofeedback. However, existing systems for home anorectal biofeedback are not able to monitor patient compliance or assess the quality of exercises performed, and as a result have yet to see wide spread clinical adoption. In this paper, we propose a new Internet of Medical Things (IoMT) system to provide home-based biofeedback therapy, facilitating remote monitoring by the physician. We discuss our user-centric design process and the proposed architecture, including a new sensing probe, mobile app, and cloud-based web application. A case study involving biofeedback training exercises was performed. Data from the IoMT was compared against the clinical standard, high-definition anorectal manometry. We demonstrated the feasibility of our proposed IoMT in providing anorectal pressure profiles equivalent to clinical manometry and its application for home-based anorectal biofeedback therapy.
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Norouzi, Monire, Zeynep Gürkaş-Aydın, Özgür Can Turna, Mehmet Yavuz Yağci, Muhammed Ali Aydin i Alireza Souri. "A Hybrid Genetic Algorithm-Based Random Forest Model for Intrusion Detection Approach in Internet of Medical Things". Applied Sciences 13, nr 20 (10.10.2023): 11145. http://dx.doi.org/10.3390/app132011145.
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The Internet of Medical Things (IoMT) is a bio-network of associated medical devices, which is slowly improving the healthcare industry by focusing its abilities on enhancing personal healthcare benefits with medical data. Moreover, the IoMT tries to deliver sufficient and more suitable medical services at a low cost. With the rapid growth of technology, medical instruments that are widely used anywhere are likely to increase security issues and create safe data transmission issues through resource limitations and available connectivity. Moreover, the patients probably face the risk of different forms of physical harm because of IoMT device attacks. In this paper, we present a secure environment for IoMT devices against cyber-attacks for patient medical data using a new IoMT framework with a hybrid genetic algorithm-based random forest (GA-RF) model. The proposed algorithm achieved better results in terms of accuracy (99.999%), precision, and recall (100%, respectively) to detect cyber-attacks based on two NSL-KDD and UNSW_2018_IoT_Botnet data sets than the other machine learning algorithms.
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Mukhopadhyay, Manishka, Subhrajyoti Banerjee i Chitrangada Das Mukhopadhyay. "Internet of Medical Things and the Evolution of Healthcare 4.0: Exploring Recent Trends". Journal of Electronics, Electromedical Engineering, and Medical Informatics 6, nr 2 (14.04.2024): 182–95. http://dx.doi.org/10.35882/jeeemi.v6i2.402.
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Enhanced patient care and remote health monitoring have always been important issues. Internet of Medical Things (IoMT) is a subsection of Healthcare 4.0 that uses recent technologies like mobile computing, medical sensors, and cloud computing to track patients' medical information in real-time. These data are stored in a cloud computing framework that may be accessed and analyzed by healthcare experts. IoMT and Healthcare 4.0 have immense potential for revolutionizing patient care and diagnostics, despite facing numerous complex challenges. This paper thoroughly analyzes technical, structural, and regulatory obstacles encountered by the healthcare sector. Challenges in IoMT implementation include cost considerations, network stress, interoperability issues, ethical limitations, policy intricacies, security concerns, and vulnerabilities jeopardizing patient privacy. However, amidst these challenges, the study highlights the prospective long-term benefits, including diminished medical costs and enhanced patient care. In this study, we have portrayed a comprehensive exploration of the field of IoMT and different related technologies from more than 100 papers to represent the transformation and growth in this decade. We have illustrated some of the significant findings of applications and innovations in the domain of IoMT. This paper delves into IoMT's application in dementia detection and care, improved data management, fortified cybersecurity measures, and modernizing existing healthcare systems. The study also offers valuable insights into potential mitigation strategies, offered by ongoing research and innovation to address emerging trends and challenges, propelling the trajectory of Healthcare 4.0 towards an optimized and transformative future for patient well-being. Hence future research needs to integrate more prudent technologies addressing challenges including security, privacy, interoperability, and implementation costs.
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Gautam, Kalpna, Vikram Puri, Jolanda G Tromp, Chung Van Le i Nhu Gia Nguyen. "Internet of Things and Healthcare Technologies: A Valuable Synergy from Design to Implementation". International Journal of Machine Learning and Networked Collaborative Engineering 2, nr 3 (30.09.2018): 128–42. http://dx.doi.org/10.30991/ijmlnce.2018v02i03.005.
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Internet of Things (IoT) promises to be a reliable technology for the future. Healthcare is one of the fields which are rapidly developing new solutions. The synergy between IoT and healthcare promises to be very beneficial for human healthcare and evolved into a new field of research and development: the Internet of Medical Things (IoMT). This paper presents a review on various enabling IoMT technologies based on the latest publications and technology available in the marketplace. This article also analyzes the various software platforms available in the field of IoMT and the current challenges faced by the industry
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Mabrouk, Alhassan, Abdelghani Dahou, Mohamed Abd Elaziz, Rebeca P. Díaz Redondo i Mohammed Kayed. "Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things". Computational Intelligence and Neuroscience 2022 (13.07.2022): 1–22. http://dx.doi.org/10.1155/2022/9112634.
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The Internet of Medical Things (IoMT) has dramatically benefited medical professionals that patients and physicians can access from all regions. Although the automatic detection and prediction of diseases such as melanoma and leukemia is still being investigated and studied in IoMT, existing approaches are not able to achieve a high degree of efficiency. Thus, with a new approach that provides better results, patients would access the adequate treatments earlier and the death rate would be reduced. Therefore, this paper introduces an IoMT proposal for medical images’ classification that may be used anywhere, i.e., it is an ubiquitous approach. It was designed in two stages: first, we employ a transfer learning (TL)-based method for feature extraction, which is carried out using MobileNetV3; second, we use the chaos game optimization (CGO) for feature selection, with the aim of excluding unnecessary features and improving the performance, which is key in IoMT. Our methodology was evaluated using ISIC-2016, PH2, and Blood-Cell datasets. The experimental results indicated that the proposed approach obtained an accuracy of 88.39% on ISIC-2016, 97.52% on PH2, and 88.79% on Blood-cell datsets. Moreover, our approach had successful performances for the metrics employed compared to other existing methods.
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Al Khatib, Inas, Abdulrahim Shamayleh i Malick Ndiaye. "Healthcare and the Internet of Medical Things: Applications, Trends, Key Challenges, and Proposed Resolutions". Informatics 11, nr 3 (16.07.2024): 47. http://dx.doi.org/10.3390/informatics11030047.
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In recent years, the Internet of medical things (IoMT) has become a significant technological advancement in the healthcare sector. This systematic review aims to identify and summarize the various applications, key challenges, and proposed technical solutions within this domain, based on a comprehensive analysis of the existing literature. This review highlights diverse applications of the IoMT, including mobile health (mHealth) applications, remote biomarker detection, hybrid RFID-IoT solutions for scrub distribution in operating rooms, IoT-based disease prediction using machine learning, and the efficient sharing of personal health records through searchable symmetric encryption, blockchain, and IPFS. Other notable applications include remote healthcare management systems, non-invasive real-time blood glucose measurement devices, distributed ledger technology (DLT) platforms, ultra-wideband (UWB) radar systems, IoT-based pulse oximeters, accident and emergency informatics (A&EI), and integrated wearable smart patches. The key challenges identified include privacy protection, sustainable power sources, sensor intelligence, human adaptation to sensors, data speed, device reliability, and storage efficiency. The proposed mitigations encompass network control, cryptography, edge-fog computing, and blockchain, alongside rigorous risk planning. The review also identifies trends and advancements in the IoMT architecture, remote monitoring innovations, the integration of machine learning and AI, and enhanced security measures. This review makes several novel contributions compared to the existing literature, including (1) a comprehensive categorization of IoMT applications, extending beyond the traditional use cases to include emerging technologies such as UWB radar systems and DLT platforms; (2) an in-depth analysis of the integration of machine learning and AI in IoMT, highlighting innovative approaches in disease prediction and remote monitoring; (3) a detailed examination of privacy and security measures, proposing advanced cryptographic solutions and blockchain implementations to enhance data protection; and (4) the identification of future research directions, providing a roadmap for addressing current limitations and advancing the scientific understanding of IoMT in healthcare. By addressing current limitations and suggesting future research directions, this work aims to advance scientific understanding of the IoMT in healthcare.
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Abbas, Sidra, Gabriel Avelino Sampedro, Mideth Abisado, Ahmad Almadhor, Iqra Yousaf i Seng-Phil Hong. "Harris-Hawk-Optimization-Based Deep Recurrent Neural Network for Securing the Internet of Medical Things". Electronics 12, nr 12 (9.06.2023): 2612. http://dx.doi.org/10.3390/electronics12122612.
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The healthcare industry has recently shown much interest in the Internet of Things (IoT). The Internet of Medical Things (IoMT) is a component of the IoTs in which medical appliances transmit information to communicate critical information. The growth of the IoMT has been facilitated by the inclusion of medical equipment in the IoT. These developments enable the healthcare sector to interact with and care for its patients effectively. Every technology that relies on the IoT can have a serious security challenge. Critical IoT connectivity data may be exposed, changed, or even made unavailable to authenticated users in the case of such attacks. Consequently, protecting IoT/IoMT systems from cyber-attacks has become essential. Thus, this paper proposes a machine-learning- and a deep-learning-based approach to creating an effective model in the IoMT system to classify and predict unforeseen cyber-attacks/threats. First, the dataset is preprocessed efficiently, and the Harris Hawk Optimization (HHO) algorithm is employed to select the optimized feature. Finally, machine learning and deep learning algorithms are applied to detect cyber-attack in IoMT. Results reveal that the proposed approach achieved an accuracy of 99.85%, outperforming other techniques and existing studies.
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Hermawan, Dadang, Ni Made Dewi Kansa Putri i Lucky Kartanto. "Cyber Physical System Based Smart Healthcare System with Federated Deep Learning Architectures with Data Analytics". International Journal of Communication Networks and Information Security (IJCNIS) 14, nr 2 (30.09.2022): 222–33. http://dx.doi.org/10.17762/ijcnis.v14i2.5513.
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Data shared between hospitals and patients using mobile and wearable Internet of Medical Things (IoMT) devices raises privacy concerns due to the methods used in training. the development of the Internet of Medical Things (IoMT) and related technologies and the most current advances in these areas The Internet of Medical Things and other recent technological advancements have transformed the traditional healthcare system into a smart one. improvement in computing power and the spread of information have transformed the healthcare system into a high-tech, data-driven operation. On the other hand, mobile and wearable IoMT devices present privacy concerns regarding the data transmitted between hospitals and end users because of the way in which artificial intelligence is trained (AI-centralized). In terms of machine learning (AI-centralized). Devices connected to the IoMT network transmit highly confidential information that could be intercepted by adversaries. Due to the portability of electronic health record data for clinical research made possible by medical cyber-physical systems, the rate at which new scientific discoveries can be made has increased. While AI helps improve medical informatics, the current methods of centralised data training and insecure data storage management risk exposing private medical information to unapproved foreign organisations. New avenues for protecting users' privacy in IoMT without requiring access to their data have been opened by the federated learning (FL) distributive AI paradigm. FL safeguards user privacy by concealing all but gradients during training. DeepFed is a novel Federated Deep Learning approach presented in this research for the purpose of detecting cyber threats to intelligent healthcare CPSs.
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Hamed, Taherdoost. "Blockchain-based internet of medical things (IoMT) for healthcare management". i-manager’s Journal on Cloud Computing 9, nr 2 (2022): 21. http://dx.doi.org/10.26634/jcc.9.2.19153.
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When it comes to the use of the Internet of Things (IoT), the healthcare sector is set to become the next frontier of the digital revolution thanks to the Internet of Medical Things (IoMT). Due to their weight, importance, and sensitivity, these files must be protected in the strictest manner. Now that blockchain is becoming more widespread, scientists are concentrating on how to use blockchain tactics within healthcare management to improve data security. Nevertheless, owing to the differing needs of these two technologies, such integration is exceptionally complex and demanding. In order to help users, take full control of their data, this study provides an overview of the current state of blockchain platforms for the IoMT by focusing on the difficulties presented by combination systems. This article will examine blockchain's use in healthcare IoT, including supply chain transparency, health data arrangement, smart contracts, and IoT security for remote monitoring. The final portions focus on challenges and potential developments in the future.
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Villegas-Ch, William, Joselin García-Ortiz i Isabel Urbina-Camacho. "Framework for a Secure and Sustainable Internet of Medical Things, Requirements, Design Challenges, and Future Trends". Applied Sciences 13, nr 11 (30.05.2023): 6634. http://dx.doi.org/10.3390/app13116634.
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The framework presented in this article provides a guide for designing secure and sustainable internet of medical things (IoMT) solutions. The main objective is to address the challenges related to safety and sustainability in the medical field. The critical conditions driving these challenges are identified, and future trends in the field of IoMT are discussed. To assess the effectiveness of the proposed framework, a case study was carried out in a private medical clinic. In this study, an IoMT system was implemented to monitor patients’ vital signs, even when they were not in the clinic. The positive results demonstrated that the implemented IoMT system met the established security and sustainability requirements. The main statistical findings of the case study include the real-time monitoring of the vital signs of the patients, which improved the quality of care and allowed for the early detection of possible complications. In addition, medical devices such as the blood pressure monitor, pulse oximeter, and electrocardiograph were selected, proving safe, durable, and energy and maintenance efficient. These results were consistent with previous research that had shown the benefits of IoMT in remote monitoring, the early detection of health problems, and improved medical decision-making.