Energy-Efficient Solutions in Medical IoT Devices

Purpose This study aims to examine firmware vulnerabilities in medical Internet of Things (IoT) devices, focusing on their implications for healthcare policy, management and patient safety. The research aims to provide insights into developing effective mitigation strategies and enhancing regulatory frameworks to improve device security in the rapidly advancing field of medical IoT. This study underscores the critical need for identifying and addressing vulnerabilities to safeguard patient data and maintain the integrity of healthcare systems. Design/methodology/approach The research employs a qualitative multi-case analysis of firmware vulnerabilities in medical IoT devices, including insulin pumps, pacemakers and diagnostic equipment. Cases were selected based on criteria such as device diversity, severity of vulnerabilities and their impact on patient safety, as cross-referenced with the Common Vulnerabilities Exposure database. The analysis considers factors like device age, manufacturer practices, firmware update frequency, regulatory compliance and usage context to derive insights into policy and management challenges. Findings The study identifies key factors contributing to firmware vulnerabilities in medical IoT devices and examines their implications for healthcare management and policy. It provides recommendations for manufacturers and healthcare providers to enhance device security and outlines the need for robust regulatory measures. In addition, the research highlights gaps in the healthcare technology workforce and supply chain, advocating for stronger policies and management practices to mitigate risks. Originality/value This research contributes to the literature by linking technical vulnerabilities in medical IoT devices to broader policy and management concerns. The findings underscore the necessity for enhanced regulatory frameworks, proactive management strategies and industry-wide standards to ensure the security and reliability of medical IoT devices, ultimately protecting patient safety and data integrity.

The security of Internet of Medical Things (IoMT) devices is crucial for ensuring the integrity and reliability of patients’ medical data. These devices, operating over TCP and ICMP protocols, are highly susceptible to cyberattacks. While machine learning models can detect these attacks with acceptable accuracy, their operational mechanisms remain unclear, leaving the decision-making process of the models undefined. Moreover, the accuracy and training time of machine learning models is more questionable when the datasets has large number of sparse features and class imbalances. This study introduces an interpretable feature selection technique designed to enhance intrusion detection in IoMT by reducing redundant features and improving model efficiency. Random Forest-based explainable AI model provides transparency in attack classification and better decision-making. The simulated results employing the CICIoMT2024 dataset demonstrate that the proposed method significantly improves detection performance, with the Random Forest model achieving 99% accuracy, outperforming XGBoost (98%), Decision Tree (97%), and Support Vector (98%), while ensuring explainability through SHAP-based feature analysis. Thus, the simulation outcomes reveal the key contributing factors for various cyberattacks on IoMT, facilitating enhanced security measures and real-time monitoring. The proposed approach boosts detection accuracy and interpretability, making it highly suitable for real-world IoMT security applications.

Recently, blockchain has been foreseen by industry and research community as a transformational and disruptive technology that is poised to play a major role in transforming the way we transact, trade, bank, track and register assets, and do business. Blockchain can potentially be used to provide trusted authenticity, origin, and ownership for IoT devices, in a way that is secure and decentralized without the involvement of a Trusted Third Parties (TTP) or centralized authorities and services. A TTP can be a subject to a single point of failure, and it can be disrupted, compromised or hacked. A TTP can potentially misbehave and become corrupt in the future, even if it is trustworthy now. This paper shows how Ethereum blockchain (with the powerful feature of programmability through smart contracts) can provide a trusted ownership management for medical IoT (MIoT) devices. Tracking the true ownership of MIoT devices is of a paramount importance as using counterfeited MIoT devices can be life-threatening to patients. The paper presents a general framework and solution to manage and trace back the true origin of ownership for an MIoT; whereby an MIoT device can be owned by multiple parties during its life cycle. The paper presents a detailed overview of our proposed system architecture, design, entity relationship, and interactions among all involved parties. The source code of the Ethereum smart contracts is made publicly available. Key aspects related to the algorithms, interactions, implementation and testing are discussed in the paper. Furthermore, we provide security analysis of our proposed solution in terms of satisfying the general security goals and also resiliency against popular attacks as those of DDoS, eavesdropping and replay attacks.

Recently, extensive research into healthcare services and their technological advancements is being carried out globally. The Internet of Things (IoT) has proven to be a predominantly potential candidate in these advancements. IoT supports infrastructure development in connecting a wide range of medical devices, sensors, and health practitioners in order to ensure the best medical treatment for patients in both cities and remote places. This has enabled the IoT to provide major support in medical application, giving rise to assisted healthcare facilities. The IoT ecosystem has a complicated architecture, with several components interacting and an evolving variety of applications for users. This is a comprehensive and interdependent system that involves real-time data analysis, interconnections of device, data transfers, and analysis. The IoT creates a connected ecosystem developed from several intelligent systems that integrates respondents with machines and allows for statistics decision making. A network of interconnected devices that perceive vital data related to medical conditions of patients in real time is referred to as the Internet of Medical Things (IoMT), a healthcare application of IoT technology. Sensors used for sensing real-time data related to monitoring temperature, blood pressure levels, heartbeat levels, specialized signal converters, and communication interfaces can be added to existing medical facilities to turn them into IoMT devices. IoMT can be used to create wearable devices, home-care and personal-care medical devices, and mobile healthcare apps, along with creating communication with medical specialists in a given area. The IoMT system’s challenges and ecosystem interface requirements are also a matter of concern considering the complexity of the systems. This study offers a comprehensive evaluation of the various domains of use of IoMT, as well as its future scope and challenges. Recent advances in sensor technology, networks, cloud storage and processing, mobility, and big data analytics have made it possible to create affordable smart medical devices and a connected healthcare environment.

Abstract: The Internet of Medical Things (IOMT) provides solutions to healthcare problems through online computers. IOMT devices are linked with cloud platforms such as Amazon web services that store and analyze data. It helps to reduce the symptoms temporarily at the starting stage of the disease. This is also known as IoT (internet of things) health-care. It also includes remote patient monitoring and tracking the patient details and patient medication orders, the location of the hospital where the patient is admitted, the patient’s wearable m-health (mobile health) devices, all this information sent to caregivers. It helps to track vital and heart performance, monitor glucose, other body systems, activity, sleeping levels. healthcare aims to create an IoT is playing a central role in healthcare industry to improve the living conditions of individuals through suitable technological solutions. Clearly such advanced systems work by processing complex data continuously produced across a variety of different scenarios, such as physical and environmental signals. Never the less, data coming from all of these sensors can easily saturate the capacity of communication networks, which makes it necessary to design proper transmission process capable of preserving the reliability of the network at cost of the lower possible leak of information. Cardiovascular Disease is a disorder of the heart and blood vessels. which includes hypertension, coronary artery disease, congenital artery disease, heart attack and heart failure, peripheral vascular disease, vascular heart disease. CVD affects a large percentage of the population. Cardiovascular Disease is an overall term of condition that will influence the heart or veins and expansion in blood clumps. For the most part, the conditions that influence the heart like Heart assault or an unexpected blockage to the heart blood or oxygen supply the manifestations that are to find CVD resemble wickedness, weakness, or cold sweats commonly the danger of CVD increments as the age that beginnings about the age of 45 where 1 out of each 100 men foster indications of coronary illness going on like this the 55 above matured individuals the danger will be multiplied around 2.1 every 100 men. The danger of CVD may be expanded by smoking, Consumption of liquor or BP by decreasing the liquor, Tobacco, or eating new products of the soil we can forestall this CVD by doing medicine to diminish the low thickness of cholesterol and it further develops bloodstream by utilizing treatment like heart restoration can be effectively treated cardiovascular illness. Iomt gadgets are wearable they assist with realizing how the heart performing. Elderly individuals neglect to take their prescription it assists them with requiring some investment they took the drug. We collected this information by reading some journals and from different websites. CVD is a type of heart or any blood vessel disease. It is mainly caused because of high BP. we can prevent this disease by avoiding smoking and alcohol. It gives information about how to prevent the disease at the starting stage. CVD are among the most common serious illness affecting human health’s may be prevented or mitigated by early diagnosis and this may reduce mortality rates.

The rapid development of Internet of Things (IoT) technology has significantly impacted various market sectors. According to Li et al (2024), an estimated 75 billion devices will be on the market in 2025. The healthcare industry is a target to improve patient care and ease healthcare provider burdens. Chronic respiratory disease is likely to benefit from their inclusion, with 545 million people worldwide recorded to suffer in patients using these devices can track their dosage, while healthcare providers can improve medication administration and monitor respiratory health (Soriano et al, 2020). The growing prevalence of IoT devices, including intelligent inhalers like the Propeller Health System Smart Inhaler, Breather Fit, and Lookee O2 Ring, underscores the increasing importance of network connectivity and software development. While IoT medical devices offer numerous benefits, they also come with security vulnerabilities that can expose patient data to cyber-attacks. It’s crucial to prioritize security measures in developing and deploying IoT medical devices, especially in personalized health monitoring systems for individuals with respiratory conditions. Addressing the security gaps and vulnerabilities in IoT devices is essential to ensure patient data’s safety and privacy. Efforts are underway to assess the security risks associated with intelligent inhalers and respiratory medical devices by understanding usability behaviour and technological elements to identify and address vulnerabilities effectively. This work analyses usability behaviour and technical vulnerabilities, emphasizing the confidentiality of information gained from Smart Inhalers. It then extrapolates to interrogate potential vulnerabilities with Implantable Medical Devices (IMDs). Our work explores the tensions in device development through the intersection of IoT technology and respiratory health, particularly in the context of intelligent inhalers and other breathing medical devices, calling for integrating robust security measures into the development and deployment of IoT devices to safeguard patient data and ensure the secure functioning of these critical healthcare technologies.

The Internet of Medical Things (IoMT) has revolutionized healthcare by bringing real-time monitoring and data-driven treatments. Nevertheless, the security of communication between IoMT devices and servers remains a huge problem because of the inherent sensitivity of the health data and susceptibility to cyber threats. Current security solutions, including simple password-based authentication and standard Public Key Infrastructure (PKI) approaches, typically do not achieve an appropriate balance between security and low computational overhead, resulting in the possibility of performance bottlenecks and increased vulnerability to attacks. To overcome these limitations, we present an intelligent two-phase dual authentication framework that improves the security of sensor-to-server communication in IoMT environments. During the registration phase, our framework is based on Elliptic Curve Diffie-Hellman (ECDH) for rapid key exchange, and during real-time communication, our framework uses the Advanced Encryption Standard Galois Counter Mode (AES-GCM) to encrypt data securely. The efficiency of the proposed framework was rigorously tested through simulations that evaluated encryption-decryption time, computational cost, latency, and packet delivery ratio. The security resilience was also evaluated against man-in-the-middle, replay, and brute force attacks. The results show that encryption/decryption time is reduced by over 45%, overall computational cost by 45.38%, and latency by 28.42% over existing approaches. Furthermore, the framework achieved a high packet delivery ratio and strong defense against cyber threats for maintaining the confidentiality and integrity of the medical data across IoMT networks. However, the dual authentication approach doesn’t affect the functionality of medical IoT devices while enhancing IoMT security, which makes it an ideal integration option for existing healthcare systems.

The traditional healthcare systems are confronted with new issues as the number of patients keeps rising. The design of the Internet of Medical Things (IoMT) addresses this problem along with improving the healthcare system’s accuracy, dependability, productivity, and efficacy. IoMT devices are subjected to network interruption and denial of service assaults as a result of these attacks. IoMT may be viewed as an improvement and expenditure in order to respond to patient requirements more effectively and efficiently. An enormous number of medical devices are introduced into the healthcare system, which makes it a challenging task to identify traditional methods of attack. Deep learning is an approach to design models of optimal security based on actual device data. Using the N-BaIoT dataset, botnet attacks are our main focus against various IoMT devices that come in a variety of shapes and sizes and deep learning models that are based on it are constructed, for each gadget type. The findings of the experiment demonstrate that incorporating features decreases training time and model complexity while increasing bot detection rates. Our model delivers greater precision and accuracy when compared to other neural network approaches. There is a need to handle security in order to assure user confidence, encourage wide acceptance, and realize the benefits of IoMT. These security issues highlight an increasing trend of cyber-assaults on system infrastructure, as well as system inherent weaknesses inside the environment.

The energy efficiency paradigm is a major bottleneck for the development of wireless sensor networks (WSNs) and Internet of Things (IoT) architectures and technologies. This edited book presents comprehensive coverage of energy harvesting sources and techniques that can be used for WSN and IoT systems

The rapid integration of the Internet of Medical Things (IoMT) is transforming healthcare through real-time monitoring, AI-driven diagnostics, and remote treatment. However, the growing reliance on IoMT devices, such as robotic surgical systems, life-support equipment, and wearable health monitors, has expanded the attack surface, exposing healthcare systems to cybersecurity risks like data breaches, device manipulation, and potentially life-threatening disruptions. While 6G networks offer significant benefits for healthcare, such as ultra-low latency, extensive connectivity, and AI-native capabilities, as highlighted in the ITU 6G (IMT-2030) framework, they are expected to introduce new and potentially more severe security challenges. These advancements put critical medical systems at greater risk, highlighting the need for more robust security measures. This study leverages AI techniques to systematically identify security vulnerabilities within 6G-enabled healthcare environments. Additionally, the proposed approach strengthens AI-driven security through use of multiple XAI techniques cross-validated against each other. Drawing on the insights provided by XAI, we tailor our mitigation strategies to the ITU-defined 6G usage scenarios, with a focus on their applicability to medical IoT networks. We propose that these strategies will effectively address potential vulnerabilities and enhance the security of medical systems leveraging IoT and 6G networks.

The Internet of Medical Things (IoMT) is a collection of medical equipment and software that can help patients get better care. The purpose of this study is to improve the security of data collected through remote health monitoring of patients utilizing Constrained Application Protocol (CoAP). Asymmetric cryptography techniques may be used to assure the security of such sensor networks. For communication between different IoMT devices and a remote server, the safe CoAP is compatible with the Datagram Transport Layer Security (DTLS) protocol for creating a secure session using existing algorithms such as Lightweight Establishment of Secure Session. The DTLS layer of CoAP, in contrast, has shortcomings in key control, session establishment, and multicast message exchange. As a consequence, for IoMT communication, the creation of an efficient protocol for safe CoAP session establishment is needed. Thus, to solve the existing problems related to key management and multicast security in CoAP, we have proposed an efficient and secure communication technique to establish a secure session key between IoMT devices and distant servers using lightweight Energy-Efficient and Secure CoAP Elliptic Curve Cryptography (E2SCEC2). The advantage of using E2SCEC2 over other identification methods such as Rivest-Shamir-Adleman (RSA) is its compact key size, which allows it to use a smaller key size. This article also compares these algorithms on parameters such as time spent generating keys, signature generation, and verification of E2SCEC2 and RSA algorithms, as well as energy consumption and radio duty cycle, to see if they are compatible in constrained environments.

The Internet of Medical Things (IoMT) has a bright future with the development of smart mobile devices. Information technology is also leading changes in the healthcare industry. IoMT devices can detect patient signs and provide treatment guidance and even instant diagnoses through technologies, such as artificial intelligence (AI) and wireless communication. However, conventional centralized machine learning approaches are often difficult to apply within IoMT devices because of the difficulty of large-scale collection of patient data and the potential risk of privacy breaches. Therefore, we propose a blockchain-based two-stage federated learning approach that allows IoMT devices to train a global model collaboratively without gathering the data to a central server. Specifically, to address the problem of poor training performance on non-independent identically distributed (non-IID) data, we design a blockchain-based data-sharing scheme that can significantly improve the model’s accuracy without threatening user privacy. We also design a client selection mechanism to further improve the system’s efficiency. Finally, we validate the feasibility and effectiveness of our system through simulation experiments on three popular datasets (i.e., MNIST, Fashion-MNIST, and CIFAR-10).

Applying the extended technology acceptance model (TAM2), this research study examined the doctors' perceptions about acceptance of internet-of-things medical devices (IoT-MDs) technology to track and monitor the health of anemic pregnant women in remote areas. The authors used structural equation modeling on a survey data of doctors (N=153), the path analysis of which reiterated certain key considerations of TAM2. The results showed that perceived ease of use has a significantly high impact on behavioral intentions of the doctor. Result demonstrability had an indirect impact on the doctor's intention to use IoT-MDs if mediated through perceived usefulness. This paper discussed research implications before stating the limitations and future directions.

The proliferation of Internet of Things (IoT) devices has significantly expanded the attack surface for ransomware, with the healthcare sector-particularly Internet of Medical Things (IoMT) devices-emerging as a critical target. Traditional cybersecurity solutions often fail to address the unique vulnerabilities and operational constraints of eHealth IoT environments, where ransomware attacks can result in severe consequences, including patient safety risks and operational disruptions. This paper introduces a novel hybrid detection framework that combines Long Short-Term Memory (LSTM) networks with Autoencoders to identify ransomware activity in IoMT devices. By leveraging the sequential data analysis capabilities of LSTMs and the anomaly detection strengths of Autoencoders, the proposed model effectively detects subtle and sophisticated ransomware behaviors while maintaining computational efficiency suitable for real-world deployment. The approach incorporates adaptive thresholding to accommodate dynamic IoMT environments and employs continuous learning to adapt to evolving ransomware tactics. Experimental results demonstrate that the hybrid model outperforms conventional detection methods in both accuracy and sensitivity, achieving robust real-time detection with minimal false positives. These findings lay the groundwork for future research into resilient, AI-driven security solutions for industrial and medical IoT applications

Coronavirus disease (COVID-19) is a viral infection caused by SARS-CoV-2. The modalities such as computed tomography (CT) have been successfully utilized for the early stage diagnosis of COVID-19 infected patients. Recently, many researchers have utilized deep learning models for the automated screening of COVID-19 suspected cases. An ensemble deep learning and Internet of Things (IoT) based framework is proposed for screening of COVID-19 suspected cases. Three well-known pretrained deep learning models are ensembled. The medical IoT devices are utilized to collect the CT scans, and automated diagnoses are performed on IoT servers. The proposed framework is compared with thirteen competitive models over a four-class dataset. Experimental results reveal that the proposed ensembled deep learning model yielded 98.98% accuracy. Moreover, the model outperforms all competitive models in terms of other performance metrics achieving 98.56% precision, 98.58% recall, 98.75% F-score, and 98.57% AUC. Therefore, the proposed framework can improve the acceleration of COVID-19 diagnosis.