Internet Of Medical Things Systems Research Articles

An Explainable Deep Learning-Enhanced IoMT Model for Effective Monitoring and Reduction of Maternal Mortality Risks

Maternal mortality (MM) is considered one of the major worldwide concerns. Despite the advances of artificial intelligence (AI) in healthcare, the lack of transparency in AI models leads to reluctance to adopt them. Employing explainable artificial intelligence (XAI) thus helps improve the transparency and effectiveness of AI-driven healthcare solutions. Accordingly, this article proposes a complete framework integrating an Internet of Medical Things (IoMT) architecture with an XAI-based deep learning model. The IoMT system continuously monitors pregnant women’s vital signs, while the XAI model analyzes the collected data to identify risk factors and generate actionable insights. Additionally, an efficient IoMT transmission model is developed to ensure reliable data transfer with the best-required system quality of service (QoS). Further analytics are performed on the data collected from different regions in a country to address high-risk cities. The experiments demonstrate the effectiveness of the proposed framework by achieving an accuracy of 80% for patients and 92.6% for regional risk prediction and providing interpretable explanations. The XAI-generated insights empower healthcare providers to make informed decisions and implement timely interventions. Furthermore, the IoMT transmission model ensures efficient and secure data transfer.

HARNESSING PRIVACY-PRESERVING FEDERATED LEARNING WITH BLOCKCHAIN FOR SECURE IoMT APPLICATIONS IN SMART HEALTHCARE SYSTEMS

The Internet of Medical Things (IoMT) refers to interconnected medical systems and devices that gather and transfer healthcare information for several medical applications. Smart healthcare leverages IoMT technology to improve patient diagnosis, monitoring, and treatment, providing efficient and personalized healthcare services. Privacy-preserving Federated Learning (PPFL) is a privacy-enhancing method that allows collaborative method training through distributed data sources while ensuring privacy protection and keeping the data decentralized. In the field of smart healthcare, PPFL enables healthcare professionals to train machine learning algorithms jointly on their corresponding datasets without sharing sensitive data, thereby maintaining confidentiality. Within this framework, anomaly detection includes detecting unusual events or patterns in healthcare data like unexpected changes or irregular vital signs in patient behaviors that can represent security breaches or potential health issues in the IoMT system. Smart healthcare systems could enhance patient care while protecting data confidentiality and individual privacy by incorporating PPFL with anomaly detection techniques. Therefore, this study develops a Privacy-preserving Federated Learning with Blockchain-based Smart Healthcare System (PPFL-BCSHS) technique in the IoMT environment. The purpose of the PPFL-BCSHS technique is to secure the IoMT devices via the detection of abnormal activities and FL concepts. Besides, BC technology can be applied for the secure transmission of medical data among the IoMT devices. The PPFL-BCSHS technique employs the FL for training the model for the identification of abnormal patterns. For anomaly detection, the PPFL-BCSHS technique follows three major processes, namely Mountain Gazelle Optimization (MGO)-based feature selection, Bidirectional Gated Recurrent Unit (BiGRU), and Sandcat Swarm Optimization (SCSO)-based hyperparameter tuning. A series of simulations were implemented to examine the performance of the PPFL-BCSHS method. The empirical analysis highlighted that the PPFL-BCSHS method obtains improved security over other approaches under various measures.

RCLNet: an effective anomaly-based intrusion detection for securing the IoMT system.

The Internet of Medical Things (IoMT) has revolutionized healthcare with remote patient monitoring and real-time diagnosis, but securing patient data remains a critical challenge due to sophisticated cyber threats and the sensitivity of medical information. Traditional machine learning methods struggle to capture the complex patterns in IoMT data, and conventional intrusion detection systems often fail to identify unknown attacks, leading to high false positive rates and compromised patient data security. To address these issues, we propose RCLNet, an effective Anomaly-based Intrusion Detection System (A-IDS) for IoMT. RCLNet employs a multi-faceted approach, including Random Forest (RF) for feature selection, the integration of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to enhance pattern recognition, and a Self-Adaptive Attention Layer Mechanism (SAALM) designed specifically for the unique challenges of IoMT. Additionally, RCLNet utilizes focal loss (FL) to manage imbalanced data distributions, a common challenge in IoMT datasets. Evaluation using the WUSTL-EHMS-2020 healthcare dataset demonstrates that RCLNet outperforms recent state-of-the-art methods, achieving a remarkable accuracy of 99.78%, highlighting its potential to significantly improve the security and confidentiality of patient data in IoMT healthcare systems.

A DATA DRIVEN APPROACH THROUGH IOMT BASED PATIENT HEALTHCARE MONITORING SYSTEM

The current circumstances underscore the pressing need to accelerate the adoption of Internet of Things platforms in realm of the medical field to improve the health of mankind. Given the present challenges, I propose the development of an Advancing healthcare monitoring system built on the foundation of Internet of Medical Things (IOMT). Advancing patients healthcare IOMT system serves as a communication conduit between patients and doctors, facilitated by the IoMT platform. This platform utilizes various medical sensors connected to a server through technologies such as WiFi and Bluetooth. The data collected and stored in the server is then leveraged to analyse patients' health conditions, guiding subsequent treatment protocols. In this proposed system, the microcontroller serves as an intermediary interface between the sensors and the server. This cutting-edge technology enables remote patient monitoring, eliminating the need for physical presence. Within the prototype, patients' summaries and information is supplied using an online platform. Through the adoption of Advancing Healthcare through Data-Driven IOMT Patient Monitoring, lives can be safeguarded from critical conditions, underscoring the vital importance of embracing such advanced technologies in healthcare.

Transforming Public Health Data Management in IoMT Networks Based on Innovative Offloading Scheme

Healthcare platform monitoring IoT-oriented technologies constitute the idea of Internet of Medical Things (IoMT) in public health and medical services. The amount and quality of created data have a substantial influence on data management and privacy compute offloading solutions are unable to keep up with the growing needs of the health industry, especially when fast and dependable communication was needed. The study suggests a unique approach called Mutated Barnacles Mating Optimization (MBMO) for assisting the data management issues in IoMT systems. The suggested MBMO framework successfully addresses problems that are common in the medical industry by using a data offloading technique. To overcome the issues of discrete tasks and resource allocation, guarantees the needs of dependable and efficient communication. We implemented Java software. The evaluation of the performance step encompasses several measures, such as energy consumption (J/ms) and Time delay (ms)model to assess the efficiency of the suggested forecasting algorithm. We performed an assessment of comparison with other established approaches results indicate that the suggested model produces superior results for assisting the data management issues in IoMT systems.

Advanced hybrid malware identification framework for the Internet of Medical Things, driven by deep learning

AbstractThe Internet of Things (IoT) effortlessly enables communication between items on the World Wide Web and other systems. This extensive use of IoTs has created new services and automation in numerous industries, enhancing the standard of living, especially in healthcare. Internet of Medical Things (IoMT) adoption has been beneficial during pandemic conditions by enabling remote patient monitoring and therapy. Nevertheless, the excessive use of IoMT has raised security concerns as it can compromise critical data. This breach in security can result in an inaccurate diagnosis or violate privacy. This research presents a novel approach to hybrid deep learning‐based detection of malware solutions for the IoT. This study uses RNN‐Bi‐LSTM to detect and extract significant features related to an already existing dataset. The proposed model exhibits a detection accuracy of 98.38% when evaluated using these existing datasets. Statistical tests like Mathew co‐relation and Log Loss also indicated reliability of proposed framework. The distinguished feature of our framework is its ability to combine complex deep learning models for IoMT security, which is of economic and scientific importance. It certainly offers a reliable solution for healthcare applications that rely on real‐time functionality and dependency on IoMT systems.

A Two-Phase Blockchain-Enabled Framework for Securing Internet of Medical Things Systems

The healthcare industry has witnessed a transformative impact due to recent advancements in sensing technology, coupled with the Internet of Medical Things (IoMTs)-based healthcare systems. Remote monitoring and informed decision-making have become possible by leveraging an integrated platform for efficient data analysis and processing, thereby optimizing data management in healthcare. However, this data is collected, processed, and transmitted across an interconnected network of devices, which introduces notable security risks and escalates the potential for vulnerabilities throughout the entire data processing pipeline. Traditional security approaches rely on computational complexity and face challenges in adequately securing sensitive healthcare data against evolving threats, thus necessitating robust solutions that ensure trust, enhance security, and maintain data confidentiality and integrity. To address these challenges, this paper introduces a two-phase framework that integrates blockchain technology with IoMT to enhance trust computation, resulting in a secure cluster that supports the quality-of-service (QoS) for sensitive data. The first phase utilizes the decentralized interplanetary file system and hashing functions to create a smart contract for device registration, establishing a resilient storage platform that encrypts data, improves fault tolerance, and facilitates data access. In the second phase, communication overhead is optimized by considering power levels, communication ranges, and computing capabilities alongside the smart contract. The smart contract evaluates the trust index and QoS of each node to facilitate device clustering based on processing capabilities. We implemented the proposed framework using OMNeT++ simulator and C++ programming language and evaluated against the cutting-edge IoMT security approaches in terms of attack detection, energy consumption, packet delivery ratio, throughput, and latency. The qualitative results demonstrated that the proposed framework enhanced attack detection by 6.00%, 18.00%, 20.00%, and 27.00%, reduced energy consumption by 6.91%, 8.19%, 12.07%, and 17.94%, improved packet delivery ratio by 3.00%, 6.00%, 9.00%, and 10.00%, increased throughput by 7.00%, 8.00%, 11.00%, and 13.00%, and decreased latency by 4.90%, 8.81%, 11.54%, and 20.63%, against state-of-the-art methods and was supported by statistical analysis.

An RFE/Ridge-ML/DL based anomaly intrusion detection approach for securing IoMT system

Smart healthcare is one of the promising areas of the Internet of Things (IoT), particularly in the case of the Covid-19 pandemic. Real-time patient monitoring and remote diagnostics facilitate better medical services to preserve human lives using Internet of Medical Things (IoMT) technology. Regardless of the numerous benefits, IoMT devices are susceptible to sophisticated cyber-attacks at a breakneck pace, which lead to tampering with healthcare data and threaten patients' lives. In a similar context, the 2022 ransomware cyber-attack on Versailles André-Mignot Hospital compromised the healthcare system and disclosed tremendous amounts of patient information. Towards this direction, most researchers have solely developed either machine learning or Deep learning algorithms to identify network traffic anomalies. Motivated by the above challenges, an effort has been made in this paper to design a Recursive Feature Elimination (RFE) integrated with machine learning paradigms and a Ridge regression merged into deep learning models for implementing accurate anomaly intrusion detection based on the real-time dataset WUSTL-EHMS. Among the paradigms used, the proposed approach confirms that the RFE-based Decision Tree (DT) outperforms state-of-the-art techniques with a training accuracy of 99 % and a testing accuracy of 97.85 % while maintaining a reduction of FAR to 0.03. In a nutshell, it has been proven that the suggested framework can be deployed to build anomaly intrusion detection, reinforcing IoMT against widespread cyber-attacks and safeguarding the integrity of advanced healthcare systems.

BFLIDS: Blockchain-Driven Federated Learning for Intrusion Detection in IoMT Networks.

The Internet of Medical Things (IoMT) has significantly advanced healthcare, but it has also brought about critical security challenges. Traditional security solutions struggle to keep pace with the dynamic and interconnected nature of IoMT systems. Machine learning (ML)-based Intrusion Detection Systems (IDS) have been increasingly adopted to counter cyberattacks, but centralized ML approaches pose privacy risks due to the single points of failure (SPoFs). Federated Learning (FL) emerges as a promising solution, enabling model updates directly on end devices without sharing private data with a central server. This study introduces the BFLIDS, a Blockchain-empowered Federated Learning-based IDS designed to enhance security and intrusion detection in IoMT networks. Our approach leverages blockchain to secure transaction records, FL to maintain data privacy by training models locally, IPFS for decentralized storage, and MongoDB for efficient data management. Ethereum smart contracts (SCs) oversee and secure all interactions and transactions within the system. We modified the FedAvg algorithm with the Kullback-Leibler divergence estimation and adaptive weight calculation to boost model accuracy and robustness against adversarial attacks. For classification, we implemented an Adaptive Max Pooling-based Convolutional Neural Network (CNN) and a modified Bidirectional Long Short-Term Memory (BiLSTM) with attention and residual connections on Edge-IIoTSet and TON-IoT datasets. We achieved accuracies of 97.43% (for CNNs and Edge-IIoTSet), 96.02% (for BiLSTM and Edge-IIoTSet), 98.21% (for CNNs and TON-IoT), and 97.42% (for BiLSTM and TON-IoT) in FL scenarios, which are competitive with centralized methods. The proposed BFLIDS effectively detects intrusions, enhancing the security and privacy of IoMT networks.

Secure-by-Design Real-Time Internet of Medical Things Architecture: e-Health Population Monitoring (RTPM)

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.

Active Reconfigurable Intelligent Surface Enhanced Internet of Medical Things.

The incredible potentiality of reconfigurable intelligent surface (RIS) in addressing power supply and obstacle environment of Internet of Medical Things (IoMT) has been capturing our interest. Considering the nettlesome "double-fading" effect introduced by passive RIS, we investigate an active RIS-enhanced IoMT system in this article, where the wireless power transfer (WPT) from power station (PS) to IoMT devices and the wireless information transfer (WIT) from IoMT devices to the access point (AP) are both implemented with the assistance of active RIS. Aiming to maximize the sum throughput of the considered IoMT system, a joint design of time schedules and reflecting coefficient matrices of the active RIS is proposed. Trapped by the non-convex and obstinate optimization problem, we explore the semi-definite programming (SDP) relaxation and successive convex approximation (SCA) techniques based on alternating optimization (AO) algorithm. Simulation results verify our solution approach to the intractable optimization problem and showcase the boosted spectrum and energy efficiency of the active RIS-enhanced IoMT system.

Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence

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.

Edge Server Deployment for Health Monitoring With Reinforcement Learning in Internet of Medical Things

The Internet of Medical Things (IoMT) has recently gained a lot of interest in the health care industry. IoMT enables real-time and omnipresent monitoring of a patient's health status, resulting in massive amounts of medical data being generated. The centralized massive data processing places enormous strain on the typical cloud computing, rendering it incapable of supporting a variety of real-time health care applications. Therefore, edge computing that moves application programs and data processing from central infrastructure to the edge nodes has attracted wide attention. However, adopting existing edge server (ES) deployment strategies for IoMT is not suitable due to the decentralized and high real-time service requirements of IoMT systems. In particular, traditional ES deployment strategies in IoMT system confront major load imbalance across ESs, latency issues, and energy consumption concerns. To address these challenges, a deployment strategy of ESs based on the state-action-reward-state-action (SARSA) learning, named ESL, is designed. Specifically, ESs are quantified by evaluating the silhouette coefficient (SC) and the sum of squared errors. Then, through fuzzy C-means (FCM) algorithm, the preliminary division of health monitoring units (HMUs) and the initial locations of ESs are obtained. Finally, SARSA learning is adopted to determine the deployment of ESs. Furthermore, extensive experiments and analyses confirm that ESL achieves the core objective of optimizing load balancing among ESs while also optimizing request-response latency and request processing energy consumption.

Federated Fusion of Magnified Histopathological Images for Breast Tumor Classification in the Internet of Medical Things.

Breast tumor detection and classification on the Internet of Medical Things (IoMT) can be automated with the potential of Artificial Intelligence (AI). Deep learning models rely on large datasets, however, challenges arise when dealing with sensitive medical data. Restrictions on sharing these medical data result in limited publicly available datasets thereby impacting the performance of the deep learning models. To address this issue, we propose an approach that combines different magnification factors of histopathological images using a residual network and information fusion in Federated Learning (FL). FL is employed to preserve the privacy of patient data, while enabling the creation of a global model. Using the BreakHis dataset, we compare the performance of FL with centralized learning (CL). We also performed visualizations for explainable AI. The final models obtained become available for deployment on internal IoMT systems in healthcare institutions for timely diagnosis and treatment. Our results demonstrate that the proposed approach outperforms existing works in the literature on multiple metrics.

Secure cross‐chain transactions for medical data sharing in blockchain‐based Internet of Medical Things

SummaryBlockchain technology has greatly promoted the development of the Internet of Medical Things (IoMT) from a centralized form to a distributed form. However, with many independent blockchain‐based IoMT (BIoMT) systems being established by different institutions, these heterogeneous blockchain systems have created a new “data islands” problem. This paper proposes a multi‐chain fusion (MCF) multi‐party governance model based on relay chain technology. The MCF model helps establish cross‐chain interaction and communication among the multiple heterogeneous blockchain systems in different medical institutions through the cross‐chain gateway. Meanwhile, by utilizing the inter‐blockchain transfer protocol (IBTP), the MCF model takes responsibility for legitimacy verification and reliable routing of cross‐chain transactions, thus addressing the “data islands problem” inherent in traditional blockchain‐based medical systems. Then, an elliptic curve signature scheme is adopted to protect user privacy in cross‐chain transactions, which helps establish transaction verification for the MCF model in BIoMT systems. Finally, several experiments are carried out to prove the feasibility of the cross‐chain scheme and the security of the elliptic curve signature scheme, and the performance index of the cross‐chain system is evaluated. The performance results show that the MCF model can provide secure and efficient cross‐chain services for sharing medical data among heterogeneous BIoMT systems, thus addressing the “data islands problem” inherent in traditional blockchain‐based medical systems.

Internet of Medical Things (IoMT) optimization for healthcare: A deep learning-based interference avoidance model

The Internet of Medical Things (IoMT) is a significant component of the broader Internet of Things (IoT), IoMT is responsible for monitoring, collecting, and transmitting critical medical data to central medical computer centers. IoMT faces significant challenges, including energy efficiency, interference from other devices in the spectrum, latency, and security and privacy concerns. As a result, in hospital settings, the development of a precise and dependable IoMT system is crucial. Consequently, this investigation offered an innovative, accurate, and dependable IoMT method. The suggested model intends to reduce or eliminate interference caused by other transmitting devices sharing the same IoMT spectrum within hospitals or medical institutions. It also presents an interference-avoidance distributed deep learning model for IoMT to medical receptionists. This model uses data from the Lagrange optimization technique to determine the ideal distance between interfering devices and medical receptions. As a result, the required system signal-to-interference-plus-noise (SINRth) is met while maintaining the highest energy efficiency (EE) and system achievable data rate (R). The proposed analytical model and deep learning model demonstrate the efficacy of the proposed approach by achieving the required system signal-to-interference-plus-noise (SINRth) with the best energy efficiency (EE) and system achievable data rate (R) while suppressing interference to any medical receptions.

Security and privacy challenges, issues, and enhancing techniques for Internet of Medical Things: A systematic review

AbstractThe Internet of Things (IoT) is a rapidly expanding network of interconnected things that use embedded sensors to gather and share data in real‐time. IoT technologies have given rise to many networking applications in our everyday life such as smart homes, smart cities, smart transport, and so forth. Smart healthcare is one such application that has been revolutionized by the IoT, introducing a new branch of IoT known as the Internet of Medical Things (IoMT). IoMT encompasses an entire ecosystem consisting of smart wearable, implantable sensing equipment's or devices, transmitters that are critical for monitoring the patients remotely and continuing the real‐time and has opened the door to new innovative smart healthcare approaches while improving patient care outcomes. IoMT wearable and embedded sensing devices are commonly utilized in smart healthcare to capture medical data and transmit the medical data in a communication network stored in the cloud. The large volume of data generated and transmitted by these IoMT devices is rising at an exponential rate, resulting in an increase in security and privacy vulnerabilities of healthcare data. To ensure the Confidentiality and integrity of the IoMT devices and the sensitive medical data, there should be proper security and privacy measures such as access control, passwords, multifactor authentication, and encryption of data generated, transmitted, or processed in the IoMT framework. In this paper, we identified the internet of things and its applications in smart healthcare systems. Additionally, the paper focuses on the architecture of IoMT, and several challenges, including the IoMT security and privacy requirements, and attack taxonomy. Furthermore, the paper thoroughly investigates both cryptographic and non‐cryptographic based security and privacy‐enhancing techniques for IoMT or healthcare systems with particular emphasis on advancements in key areas such as Homomorphic Encryption, Differential Privacy, and Federated Learning.

Network security framework for Internet of medical things applications: A survey

Abstract Limited device resources and an ever-changing cybersecurity landscape compound the challenges faced by the network protection infrastructure for Internet of medical things (IoMT) applications, which include different device ecosystems, privacy concerns, and problems with interoperability. Protecting private medical information in IoMT apps is challenging; a comprehensive strategy that provides user education, standard protocols, and robust security mechanisms is necessary to overcome these obstacles. With the advancement of IoMT, the network of clinical systems, gadgets, and sensors is integrated with the Internet of things (IoT) to enable intelligent healthcare solutions. However, the sensitive data sharing and the substantial connections in the IoMT systems raise security and privacy concerns in the network. Therefore, network security is critical in IoMT applications due to data breaches, vulnerabilities, and distributed denial of service attacks on medical data. This study reviews the network security techniques implemented in the existing studies for IoMT applications using machine learning and blockchain technology. This study presents an overview of IoMT healthcare applications by highlighting the security challenges encountered and the necessity of adopting advanced techniques to deal with complex threats. The research is mainly about how deep reinforcement learning (DRL), commonly used for intrusion detection, access control, and anomaly detection, works over time and how it can be used in IoMT applications. With the notion of providing robust security in IoMT applications, this study appraises the benefits of blockchain technology, such as data integrity, accountability, and confidentiality. Besides, this study addresses the limitations and challenges of various security techniques that IoMT systems employ. This work assesses the findings, research gaps, and future advancements for enhancing network security in IoMT applications. With an extensive analysis of existing research, this survey guides researchers, medical practitioners, and decision-makers to scale up the DRL and blockchain in IoMT systems more efficiently in the future.

Attribute-based key management for patient-centric and trusted data access in blockchain-enabled IoMT

Internet of Medical Things (IoMT) provides real-time data collection and access services to medical entities by connecting their devices to Internet. However, within such a complex architecture, achieving patient-centric data access still presents trustworthiness challenges of transmission security and usage reliability. Ciphertext-policy attribute-based encryption (CP-ABE) and blockchain are both the promising primitives that support patients with secure access control over their data. Nevertheless, both have their unique key management issues. In this paper, we propose a novel patient-centric and trusted data access (PCT-DA) system in blockchain-enabled IoMT. Firstly, we develop an online/offline encryption and verifiable outsourced decryption CP-ABE scheme to relieve the computation pressure on patients and users. Secondly, we design a flexible attribute-based key management mechanism to overcome the security drawbacks of encryption and attribute keys, such as single point of failure, key escrow and privacy leakage. Finally, we build a trusted integrity audit contract to guarantee the incentive fairness between patients and users in access services. Moreover, theoretical analyses prove that the PCT-DA system not only achieves confidentiality, integrity, traceability and forward/backward security, but also guarantees static security under random oracles. Simulation experiments indicate that our proposal reduces the time cost by approximately 14.10 seconds over state-of-the-art and is more practical in blockchain-enabled IoMT system.

Collaboration of Vague Theory and Mathematical Techniques for Optimizing Healthcare by Recommending Optimal Blockchain Supplier

The Internet of Medical Things (IoMTs) has the potential to revolutionize healthcare delivery by connecting medical devices and applications to healthcare IT systems via the internet. This connectivity enables the collection, transmission, and analysis of patient data, facilitating remote healthcare delivery and enhancing patient outcomes. However, the security and privacy of the data transmitted and stored within IoMT systems remain critical concerns. Blockchain technology offers a promising solution to address these challenges in IoMTs applications. Blockchain provides a decentralized and immutable ledger where transactions, in this case, patient data, are securely recorded and cannot be altered retroactively. This ensures the integrity and confidentiality of sensitive medical information while eliminating the need for a central authority to manage data exchange. This study proposes a comprehensive fuzzy decision-making framework for selecting the best blockchain provider in the IoMT field. The framework comprises two main steps: Determining the relative importance weights of the criteria utilizing the Stepwise Weight Assessment Ratio Analysis (SWARA) method. Determining the most suitable supplier for blockchain services providers employing the Ranking Alternatives with the Fuzzy ROOT Assessment Method (RAM) technique. A comparison between Fuzzy Multi-Criteria Decision-Making (MCDM) methods is employed to ensure the robustness and validity of the proposed framework.