Blockchain technology and IoT-edge framework for sharing healthcare services

An end-to-end four tier remote healthcare monitoring framework using edge-cloud computing and redactable blockchain

The Medical Internet of Things (MIoTs) encompasses compact, energy-efficient wireless sensor devices designed to monitor patients' body outcomes. Healthcare networks provide constant data monitoring, enabling patients to live independently. Despite advancements in MIoTs, critical issues persist that can affect the Quality of Service (QoS) in the network. The wearable IoT module collects data and stores it on cloud servers, making it vulnerable to privacy breaches and attacks by unauthorized users. To address these challenges, we propose an end-to-end secure remote healthcare framework called the Four Tier Remote Healthcare Monitoring Framework (FTRHMF). This framework comprises multiple entities, including Wireless Body Sensors (WBS), Distributed Gateway (DGW), Distributed Edge Server (DES), Blockchain Server (BS), and Cloud Server (CS). The framework operates in four tiers. In the first tier, WBS and DGW are authenticated to the BS using secret credentials, ensuring privacy and security for all entities. In the second tier, authenticated WBS transmit data to the DGW via a two-level Hybridized Metaheuristic Secure Federated Clustered Routing Protocol (HyMSFCRP), which leverages Mountaineering Team-Based Optimization (MTBO) and Sea Horse Optimization (SHO) algorithms. In the third tier, sensor reports are prioritized and analyzed using Multi-Agent Deep Reinforcement Learning (MA-DRL), with the results fed into the Hybrid-Transformer Deep Learning (HTDL) model. This model combines Lite Convolutional Neural Network and Swin Transformer networks to detect patient outcomes accurately. Finally, in the fourth tier, patients' outcomes are securely stored in a cloud-assisted redactable blockchain layer, allowing modifications without compromising the integrity of the original data. This research enhance the network lifetime by 18.3 %, reduce the transmission delays by 15.6 %, ensures classification accuracy of 7.4 %, with PSNR of 46.12 dB, SSIM of 0.8894, and MAE of 22.51 when compared to the existing works.

Opportunistic access control scheme for enhancing IoT-enabled healthcare security using blockchain and machine learning

The healthcare industry, aided by technology, leverages the Internet of Things (IoT) paradigm to offer patient/user-related services that are ubiquitous and personalized. The authorized repository stores ubiquitous data for which access-level securities are granted. These security measures ensure that only authorized entities can access patient/user health information, preventing unauthorized entries and data downloads. However, recent sophisticated security and privacy attacks such as data breaches, data integrity issues, and data collusion have raised concerns in the healthcare industry. As healthcare data grows, conventional solutions often fail due to scalability concerns, causing inefficiencies and delays. This is especially true for multi-key authentication. Dependence on conventional access control systems leads to security flaws and authorization errors caused by static user behaviour models. This article introduces an Opportunistic Access Control Scheme (OACS) for leveraging access-level security. This approach is a defendable access control scheme in which the user permissions are based on their requirement and data. After accessing the healthcare record, a centralized IoT security augmentation and assessment is provided. The blockchain records determine and revoke the access grant based on previous access and delegation sequences. This scheme analyses the possible delegation methods for providing precise users with interrupt-free healthcare record access. The blockchain recommendations are analyzed using a trained learning paradigm to provide further access and denials. The proposed method reduces false rates by 11.74%, increases access rates by 13.1%, speeds up access and processing by 12.36% and 13.23%, respectively, and reduces failure rates by 9.94%. The OACS decreases false rates by 10.64%, processing time by 15.62%, and failure rates by 10.95%.

Blockchain in Health Information Systems: A Systematic Review

(1) Background: With the increasing digitalization of healthcare systems, data security and privacy have become crucial issues. In parallel, blockchain technology has gradually proven to be an innovative solution to address this challenge, as its ability to provide an immutable and secure record of transactions offers significant promise for healthcare information management. This systematic review aims to explore the applications of blockchain in health information systems, highlighting its advantages and challenges. (2) Methods: The publications chosen to compose this review were collected from six databases, resulting in the initial identification of 4864 studies. Of these, 73 were selected for in-depth analysis. (3) Results: The main results show that blockchain has been used mainly in electronic health records (63%). Furthermore, it was used in the Internet of Medical Things (8.2%) and for data sharing during the COVID-19 pandemic (6.8%). As advantages, greater security, privacy, and data integrity were identified, while the challenges point to the need for standardization and regulatory issues. (4) Conclusions: Despite the difficulties encountered, blockchain has significant potential to improve healthcare data management. However, more research and continued collaboration between those involved are needed to maximize its benefits.

An Ensemble Model Health Care Monitoring System

Internet of things (IoT) is utilized to enhance conventional health care systems in several ways, including patient's disease monitoring. The data gathered by IoT devices is very beneficial to medical facilities and patients. The data needs to be secured against unauthorized modifications because of security and privacy concerns. Conversely, a variety of procedures are offered by block chain technology to safeguard data against modifications. Block chain-based IoT-based health care monitoring is thus a fascinating technical advancement that may aid in easing security and privacy problems associated withthe collection of data during patient monitoring. In this work, we present an ensemble classification-based monitoring system with a block-chain as the foundation for an IoT health care model. Initially, data generation is done by considering the diseases including chronic obstructive pulmonary disease (COPD), lung cancer, and heart disease. The IoT health care data is then preprocessed using enhanced scalar normalization. The preprocessed data was used to extract features such as mutual information (MI), statistical features, adjusted entropy, and raw features. The total classified result is obtained by averaging deep maxout, improved deep convolutional network (IDCNN), and deep belief network (DBN) ensemble classification. Finally, decision-making is done by doctors to suggest treatment based on the classified results from the ensemble classifier. The ensemble model scored the greatest accuracy (95.56%) with accurate disease classification at a learning percentage of 60% compared to traditional classifiers such as neural network (NN) (89.08%), long short term memory (LSTM) (80.63%), deep belief network (DBN) (79.78%) and GT based BSS algorithm (89.08%).

The Hyperledger fabric as a Blockchain framework preserves the security of electronic health records

The Hyperledger Fabric (HF) framework is widely studied for securing electronic health records (EHRs) in the healthcare sector. Despite the various cross-domain blockchain technology (BCT) applications, little is known about the role of the HF framework in healthcare. The purpose of the systematic literature review (SLR) is to review the existing literature on the HF framework and its applications in healthcare. This SLR includes literature published between January 2015 and March 2023 in the ACM digital library, IEEE Xplore, SCOPUS, Springer, PubMed, and Google Scholar databases. Following the inclusion and exclusion criteria, a total of 57 articles emerged as eligible for this SLR. The HF framework was found to be useful in securing health records coming from the Internet of Medical Things (IoMT) and many other devices. The main causes behind using the HF framework were identified as privacy and security, integrity, traceability, and availability of health records. Additionally, storage issues with transactional data over the blockchain are reduced by the use of the HF framework. This SLR also highlights potential future research trends to ensure the high-level security of health records.

Tripartite evolutionary game study on coordination information security in prescription circulation

To further reform the medical and health care system, regulating multi-level treatment and rationalizing the use of medicine, and securing prescription circulation information, this study explores the evolutionary behavior of three players in terms of information security collaboration under the prescription circulation policy, analyzes the evolutionary paths, and examines the influence of key parameters on evolutionary outcomes by constructing a tripartite evolutionary game model consisting of hospitals, retail pharmacies, and healthcare service platforms. The study shows the following: (1) When the information security costs of prescription circulation increase, the willingness of hospitals to promote information collaboration weakens, the probability of control and regulation by healthcare platforms will be enhanced, and the incentive for retail pharmacies to undertake prescription circulation increases and then decreases. (2) The increased profitability of prescription drug sales can cause a decrease in the likelihood of both parties working together to promote information security. Increasing the collaborative space between hospitals and retail pharmacies is conducive to improving information security in the circulation of prescriptions. (3) A bi-directional constraint relationship exists between the circulation and control subjects. The shorter the technology spillover time from the healthcare service platform is, the higher the probability that hospitals and retail pharmacies will maintain the security of prescription information. (4) In the early stages of prescription circulation, the external regulatory action of the healthcare service platform is essential to improve the coordination of information security. Finally, combined with the tripartite evolutionary game model and simulation analysis results, it offers countermeasures and suggestions for the government to realize the prescription circulation information security collaboration.

A Novel Edge-Based Trust Management System for the Smart City Environment Using Eigenvector Analysis

The proposed Edge-based Trust Management System (E-TMS) uses an Eigenvector-based approach for eliminating the security threats present in the Internet of Things (IoT) enabled smart city environment. In most existing trust management systems, the trust aggregation process completely depends on the direct trust ratings obtained from both legitimate and malicious neighboring IoT devices. E-TMS possesses an edge-assisted two-level trust computation approach for ensuring the malicious free trust evaluation of IoT devices. The E-TMS aims at removing the false contribution on aggregated trust data. It utilizes the properties of the Eigenvector for identifying compromised IoT devices. The Eigenvector Analysis also helps to avoid false detection. The analysis involves a comparison of all the contributed trust data about every single connected device. A spectral matrix will be generated corresponding to the contributions and the received trust will be scaled based on the obtained spectral values. The absolute sum of obtained values will contain only true contributions. The accurate identification of false data will remove the effect of malicious contributions from the final trust value of a connected IoT device. Since the final trust value calculated by the edge node contains only the trustworthy data, the prediction about the malicious nodes will be accurate. Eventually, the performance of E-TMS has been validated. Throughput and network resilience are higher than the existing system.

A Review of Blockchain Technology Applications in Ambient Assisted Living

The adoption of remote assisted care was accelerated by the COVID-19 pandemic. This type of system acquires data from various sensors, runs analytics to understand people’s activities, behavior, and living problems, and disseminates information with healthcare stakeholders to support timely follow-up and intervention. Blockchain technology may offer good technical solutions for tackling Internet of Things monitoring, data management, interventions, and privacy concerns in ambient assisted living applications. Even though the integration of blockchain technology with assisted care is still at the beginning, it has the potential to change the health and care processes through a secure transfer of patient data, better integration of care services, or by increasing coordination and awareness across the continuum of care. The motivation of this paper is to systematically review and organize these elements according to the main problems addressed. To the best of our knowledge, there are no studies conducted that address the solutions for integrating blockchain technology with ambient assisted living systems. To conduct the review, we have followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology with clear criteria for including and excluding papers, allowing the reader to effortlessly gain insights into the current state-of-the-art research in the field. The results highlight the advantages and open issues that would require increased attention from the research community in the coming years. As for directions for further research, we have identified data sharing and integration of care paths with blockchain, storage, and transactional costs, personalization of data disclosure paths, interoperability with legacy care systems, legal issues, and digital rights management.

QoS-Ledger: Smart Contracts and Metaheuristic for Secure Quality-of-Service and Cost-Efficient Scheduling of Medical-Data Processing

Quality-of-service (QoS) is the term used to evaluate the overall performance of a service. In healthcare applications, efficient computation of QoS is one of the mandatory requirements during the processing of medical records through smart measurement methods. Medical services often involve the transmission of demanding information. Thus, there are stringent requirements for secure, intelligent, public-network quality-of-service. This paper contributes to three different aspects. First, we propose a novel metaheuristic approach for medical cost-efficient task schedules, where an intelligent scheduler manages the tasks, such as the rate of service schedule, and lists items utilized by users during the data processing and computation through the fog node. Second, the QoS efficient-computation algorithm, which effectively monitors performance according to the indicator (parameter) with the analysis mechanism of quality-of-experience (QoE), has been developed. Third, a framework of blockchain-distributed technology-enabled QoS (QoS-ledger) computation in healthcare applications is proposed in a permissionless public peer-to-peer (P2P) network, which stores medical processed information in a distributed ledger. We have designed and deployed smart contracts for secure medical-data transmission and processing in serverless peering networks and handled overall node-protected interactions and preserved logs in a blockchain distributed ledger. The simulation result shows that QoS is computed on the blockchain public network with transmission power = average of −10 to −17 dBm, jitter = 34 ms, delay = average of 87 to 95 ms, throughput = 185 bytes, duty cycle = 8%, route of delivery and response back variable. Thus, the proposed QoS-ledger is a potential candidate for the computation of quality-of-service that is not limited to e-healthcare distributed applications.