Hospitals are increasingly under pressure because of the growing volume of imaging tests carried out, but also because of the sophistication of the attacks by the cybercriminal. Conventional security systems are unable to meet today's challenges to patient records and radiological data. In this research, these challenges are addressed directly by designing an advanced defence system that is specifically designed for medical imaging archiving and communication systems in radiology departments. Architected an extensive protective architecture with seven layers that are interconnected. It's a combination of cutting-edge encryption techniques capable of resisting the powerful future quantum computer, authentication processes that validate every access attempt on the fly, data patterns that are learned, suspicious activity recognized, blockchain technology that makes data impossible to tamper with, and predictive algorithms that foresee threats before they happen. Our system is proactive, identifying and neutralising threats at an early stage, instead of reacting to attacks as they happen. Real-world validation took place within five different hospital networks, covering two years, and thus subjected the framework to the real conditions of operation and to real cyber threats. The results of the system's performance were outstanding – the system had a rate of 99.9% accuracy in detecting malicious activities and a rate of 0.15% False Alarms. The overhead for security operations was just 23 milliseconds, not affecting clinical workflow. Most impressively, there was a 67% reduction in the number of attempts to break in onto the network unauthorisedly, due to the formidable defence measures that they faced.Our framework thwarted 847 real tests against it, ranging from sophisticated persistent intrusions and previously unknown software vulnerabilities to attempts by ransomware to encrypt patient information – all during testing. The system ensured complete compliance with healthcare privacy laws from various jurisdictions, aligning with the American HIPAA regulations, the European GDPR and the new quantum-security protocols. In essence, this is a paradigm shift in medical imaging security, offering healthcare institutions proactive and intelligent protection that safeguards patient privacy and institutional integrity in the face of future threats.
Wang Lei, Jasni Mohamad Zain, Nur Atiqah Sia Abdullah, Marina Yusoff · 7 authors
The proliferation of Internet of Medical Things devices within the predictive healthcare paradigm necessitates robust, privacy-centric collaborative learning frameworks to detect and mitigate rapid clinical deterioration. Traditional federated learning methodologies, while attempting to preserve patient data locality, are fundamentally constrained by multi-round gradient synchronization protocols, imposing prohibitive communication latency and remaining susceptible to false negatives under extreme non-independent and identically distributed conditions. To address these challenges, this study introduces the Feature-Augmented Analytic Federated (FaFL) Architecture, which fundamentally replaces iterative gradient synchronization with a single-round closed-form computational paradigm. By instituting a proactive feature mixing mechanism via a decoupled zero-knowledge proof global buffer, the proposed framework empowers local grassroots nodes to neutralize extreme clinical heterogeneity in a single phase. The architecture employs a closed-form analytic solution combined with a trace-weighted absolute aggregation protocol to rigorously guarantee stochastic convergence and absolute cryptographic resilience without requiring recursive parameter exchanges. Extensive empirical evaluations against existing baselines under severe Dirichlet non-independent and identically distributed conditions and Byzantine poisoning attacks demonstrate that the framework fundamentally eradicates high false-negative rates in resource-constrained clinics. Consequently, the proposed architecture robustly guarantees generalization stability, substantially outperforms existing paradigms in predictive fidelity and computational efficiency, and establishes a new operational standard for mission-critical clinical networks.
Internet of Things (IoT) technologies in the healthcare industry, also known as the Internet of Medical Things (IoMT), have proven to greatly improve patient monitoring, diagnostics, and clinical decision-making. The increasing prevalence of resource-challenged medical devices, wireless connectivity, and cloud services, however, has brought new risks around security and privacy concerns that can now directly impact patient safety and data integrity. In this paper, a thorough study of 41 peer-reviewed research papers from January 2018 through May 2025 revealed the current state of security vulnerabilities and resilience strategies in healthcare IoT systems. It provides a comprehensive analysis of security threats at the device, network, and application levels such as unauthorized access, malware and ransomware, data breaches, and denial-of-service attacks delivered in a systematic manner. This contrasts with existing surveys, which consider single security mechanisms and improve upon various multi-layered security means such as AI-enabled anomaly detection, blockchain-based authentication and auditability, low-compute cryptographic techniques, and privacy-preserving methods such as federated learning. The outcomes also show that although emerging technologies add a great deal of security and trust capabilities, issues on scalability, interoperability, deployment, and regulations are not yet fully addressed. This review highlights important knowledge gaps and offers structured knowledge and future directions for research to address the design of secure, resilient, and practically deployable IoMT architectures for real-world healthcare environments.
Sensors, wearables, implanted devices, and cloud platforms provide real-time monitoring, diagnosis, and clinical decision-making with the Internet of Medical Things (IoMT). Growth of IoMT infrastructure affects data privacy, cybersecurity, interoperability, and trust. IoMT systems, non-standard communication protocols, device capabilities, and medical network incursions are covered. We suggest data fusion, encryption, and decentralised trust enforcers for patient data. A decentralised, unchangeable, and secure blockchain. Blockchain-based solutions protect IoMT data flow, eliminate single-point-of-failure, and secure distributed medical device trust evaluation. Distributed ledger data integrity, authentication, and trust score storage improve active blockchain trust models. AI, data fusion, cloud/fog computing, smart hospitals, and blockchain secure IoMT. This chapter covers IoMT system design, cybersecurity, MULC frameworks, and blockchain-based health innovations. We discuss open research, regulation, and resilient, scalable, and dependable IoMT ecosystems.
Gifar Arif Haryadi, Allwinnaldo, Muhammad Rasyid Redha Ansori, Jae-Min Lee · 5 authors
PureRx is an innovative blockchain-based prescription management system designed to overcome the limitations of traditional paper prescriptions, including medication errors, fraud, and inefficiencies in tracking patient records. By leveraging Non-Fungible Tokens (NFTs) and batch minting, PureRx enables efficient, secure, and patient-centric control of prescription data. Patients maintain ownership of their medical records, while healthcare providers and pharmacies benefit from transparent and immutable prescription workflows. Experimental evaluation shows that PureRx improves operational efficiency by 37.18% in initial cycles, increasing to 51% in subsequent cycles compared to existing systems, while reducing costs by 46.76% in high-volume scenarios. Security analysis confirmed that no critical vulnerabilities were found and that all prescription issuance and claiming events are tamper-resistant and auditable on-chain. By combining scalability, transparency, and patient empowerment, PureRx demonstrates the potential of blockchain to transform prescription management in modern healthcare systems.
Rana Hassam Ahmed, Muhammad Sarfraz Khan, Amirmohammad Delshadi, Naseer Ahmad · 5 authors
Internet of Medical Things (IoMT) provides the possibility to conduct continuous monitoring of health, perform intelligent diagnostics, and make a clinical decision based on data. Nonetheless, there are security, privacy, scalability, latency, and energy issues with large-scale deployment. Although Federated learning (FL) provides less exposure to data, and blockchain provides trust, current solutions that combine both blockchain and FL have high consensus overhead, fixed privacy, and adversarial resilience. To handle them, we present an Edge-Intelligent Hierarchical Blockchain-IoMT framework that integrates Hierarchical FL (HFL), Adaptive Differential Privacy (ADP), Lightweight Homomorphic Encryption (LHE), Zero-Knowledge Proof (ZKP) authentication, and an Energy-Aware PoS with Edge Learning (PoS-EL) consensus. Hierarchical aggregation minimizes bottlenecks in communication. ADP minimizes security vs utility. ZKP achieves authentication and PoS-EL minimizes energy consumption. Experiments on real-world data demonstrate 99.21% accuracy of detecting anomalies, 34% decreased latency, 41% decreased energy usage, 52 percent lower blockchain overhead and 97 percent resistance to adversarial attacks, which justifies the framework in real-time, mission-critical IoMT systems.
Abstract Rural health systems are networks, which are geographically disseminated and resource limited, in which inefficient inter-hospital coordination has a strong influence on patient outcomes, operational stability and surgical resilience. Regardless of the development of smart hospital technologies, such as 5G-enabled communication opportunities, the integration of digital coordination centers, and telemedicine, the current frameworks are more focused on streamlining intra-hospital processes instead of the inter-hospital distribution of resources. This structural disintegration leads to slow shifts, poor use of bed space, inaccessibility of specialists, and poor responsiveness to surges. This paper suggests Smart Inter-Hospital Representation Network (SIHCN) to be a rural hospital ecosystem distributed systems architecture. The framework combines a granted blockchain based resource registry, real-time capacity monitoring strategies, specialist allocation registries, and adaptive routing logic into a coordination infrastructure. The proposed architecture will be able to guarantee decentralized system control against centralized command models, fault tolerance, and scalable interoperability among autonomous hospital nodes. The paper introduces a conceptual systems model that specifies the network topology, operational data flow, distributed resource synchronization and performance evaluation metrics. The simulation modeling is based on a scenario simulation that assesses the system performance when under routine and emergency surge conditions, showing that the transfer latency, resource balancing, and coordination efficiency is improved. The results make distributed ledger-based coordination a potential engineering technique in enhancing the resilience of rural health networks. This study also addresses the Healthcare Systems Engineering field by re-conceptualizing rural hospital coordination as a distributed resource optimization problem and suggesting an architecture-layer solution that can be applied to low-density, high-variability healthcare settings.
R. Yuvarani, R Mahaveerakannan, T. Tamilvizhi, L Kartheesan
The integration of blockchain into 6G-enabled Internet of Medical Things (IoMT) networks promises secure and decentralized communication but introduces challenges related to energy efficiency, latency, and authentication overhead. Existing clustering and security schemes fail to balance these aspects effectively in heterogeneous networks. This paper proposes a novel energy-aware cluster head (CH) selection framework using Artificial Democratic Cuckoo Glowworm Remora Optimization (ADCGRO), integrated with a lightweight blockchain layer for secure authentication and data integrity. The system optimizes task allocation across advanced, intermediate, and normal IoMT devices to minimize energy depletion while meeting ultra-reliable low-latency communication (URLLC) requirements. Simulation results demonstrate that the proposed approach enhances network lifetime by 27%, reduces average latency by 35%, and achieves 99% authentication accuracy, surpassing baseline protocols such as LEACH and HEED. These results highlight the effectiveness of combining ADCGRO-based optimization with blockchain to enhance performance and security in 6G wireless networks.
T Gomathi, S. Maflin Shaby, Saroo Raj R B, Prathap Kumar K
Body Area Network (BAN) systems ensure that physically integrated wearable and implantable biomedical devices are networked to make health monitoring possible. However, one of the major difficult issues that researchers and practicing health professionals have continued to face is protecting identified sensitive health information while at the same time addressing the consumer's right to privacy. ZHIs can offer significant benefits to assist BAN systems improve data security and privacy by enabling one party to prove the possession of certain information without revealing it to another party. This study focuses on the BAN into which ZKPs are incorporated to enhance the security of authentication, access, and data sharing among the stakeholders that include the healthcare givers and patients. Through the use of ZKPs, the proposed approach ensures that only the right people can prove the authenticity of the health data without revealing the data hence reducing on the risk of data leakage and other related issues. COVERY is specifically designed to have a low computational overhead for BAN devices by utilizing the ZKP technique. In this case, following a discussion of the proposed scheme, the simulation and real-world mode of the scheme are carried out and analyzed to determine its effectiveness and feasibility. The BAN systems enhanced through the integration of ZKP are found to enhance the data privacy of a network, decrease the attack angles and also ensure faster transfer of data securely in a healthcare network. This paper discusses how ZKPs can be adopted as a revolutionary solution for achieving privacy-preserving solutions in healthcare.
Munir Hussain, Amjad Mehmood, Muhammad Altaf Khan, Jaime Lloret · 5 authors
The recent developments in telecommunication technologies and monitoring devices have brought many changes in modern electronic healthcare systems (EHSs) by improving quality and decreasing healthcare expenses. Despite the benefits, they have privacy and security issues because the communication between patients and service providers takes place generally over public channels. Several user authentication protocols using distributed ledger technology (DLT) have recently been proposed to address these issues in EHSs. However, many are still vulnerable to a single point of failure (SPoF), privacy, and security attacks. Besides, they suffered from high communication and computational costs. Therefore, in this paper, we proposed a user authentication protocol using DLT to avoid these issues. A Burrows-Abadi-Needham (BAN) logic proof method has been used to check the security of the proposed protocol and ensure it achieves the desired security goals. In addition, an informal security analysis has been conducted to verify its important security requirements. A formal security analysis has been performed via the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool and Real-or-Random (ROR) model for further security strength. The results demonstrate that the proposed user authentication protocol is SAFE against all types of Man-in-the-Middle (MitM) attacks, impersonation, replay, and forgery attacks . Finally, performance analysis has been performed and results show that it achieves better performance by consuming 29.63 % and 13.21 % less communication and computational overheads as compared to existing related user authentication protocols. The security and performance analysis make it a more appropriate choice for the EHSs.
A. Baranidharan, P. Velmurugadass, A. Athiraja Atheeswaran
The design and development of a Wireless Body Area Network (WBAN) with improved security measures for data access within a cloud network is presented as a novel solution in thispaper. The primary goal is to provide a safe environment for exchanging and gaining access to patient medical records using cutting-edge hybrid blockchain technology. The suggested method addresses the issue of storage constraints while integrating components of blockchain technology to enable effective data storage and retrieval. A sequential aggregate signature approach is presented to streamline the storage procedure, which not only eases storage restrictions but also improves overall computing effectiveness. The investigation also seeks to accelerate the authentication procedure in distributed ledgers, enabling speedy and secure access to patient records. This approach aims to revolutionize the exchange and management of medical data within a WBAN by lowering computational complexity and putting in place strong confidentiality protections, ultimately helping to develop healthcare technology.
Body area networks (BANs) frequently generate sensitive healthcare data from sensors and other devices. Security and privacy breaches in BAN systems can compromise information affecting patients’ physical health, emotional state, and financial well-being. The lack of well-defined security perimeters and qualified personnel to administer security in such dynamic environments requires an authorization framework for protecting patient data, where access depends on the users’ credentials, location, and time. Toward this end, this work aims to define a secure system architecture to incorporate fine-grained information access management. It also leverages a spatiotemporal attribute-based access control (STABAC) model to make it possible to enforce location and time factors with BAN policies and required attributes to make access decisions. The BAN policies have various dynamic constraints that may conflict with each other or introduce inconsistencies. Therefore, this work proposes a formal verification framework using timed colored Petri nets to ensure such errors are not introduced. The blockchain network is utilized to maintain policy integrity, where STABAC verifies policy integrity from the network through smart contract services before making access decisions. Finally, the policy and attribute management framework ensures that STABAC maintains a verified set of policies and attributes for authorizing uninterrupted care and services.
The evolution toward sixth-generation (6G) wireless communication networks introduces unparalleled opportunities, alongside complex security and privacy challenges. This paper presents a broad and flexible roadmap for securing 6G networks, exploring the diverse range of threats posed by advanced technologies such as Distributed Ledger Technology (DLT), quantum computing, and AI/ML. Rather than focusing on a singular problem or solution, the paper emphasizes the need for adaptable frameworks capable of addressing the multifaceted and evolving security landscape of 6G. Key areas of focus include quantum-safe cryptography, AI-based threat detection, and privacy-preserving technologies like homomorphic encryption and federated learning. The paper also underscores the critical role of standardization efforts by key organizations such as ETSI, ITU, and 3GPP in shaping secure, resilient, and trustworthy 6G networks. By maintaining a general perspective, this roadmap offers a foundation for future research and collaboration, guiding the development of context-specific security solutions as 6G technology advances.
Inas Al Khatib, Abdulrahim Shamayleh, Malick Ndiaye
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.
Andreas Baumgartner, Nada Akkari, Sudip Barua, Thomas Bauschert
In this work, we propose a network hierarchy to enable low-power IoT services applying the directed-acyclic graph (DAG) based Distributed Ledger Technology (DLT) IOT $\Lambda$ Streams on top of the low-power wide-area network (LPWAN) protocol LoRaWAN. For LPWAN technologies, the challenges for applying distributed ledger application layer protocols are the small payload sizes and duty cycle regulations with open frequency spectrum protocols and the transport protocol limitations in massive-machine-type mobile communication protocol derivations (e.g. NB-IoT). For low-power embedded end devices, additional challenges may arise in terms of local computation limitations, which can be a limiting factor in performing necessary DL protocol-related functions at the device level (e.g. hashing, encryption) to maximize DL-related technology benefits end-to-end. In this paper, we show the concept of a scalable, feeless, low-power communication solution for reliable and secure data transmission and processing in sensor networks employing the DL technology IOT $\Lambda$ based Streams protocol together with the LPWAN protocol LoRaWAN and demonstrate our design for the use case of a smart metering application.
Dorothy Gatwiri Bundi, Stephen Mutua, Simon Karume
This study of the literature delves into the complex area of medical systems interoperability, focusing on mitigating variables that impact security and data transfer at the structural and semantic levels. In the era of digital healthcare, the secure sharing of medical data is crucial, and this study looks at how Distributed Ledger Technologies (DLTs) can play a major role in addressing these challenges. Complex interoperability issues that come from differences in communication protocols, data formats, established data structures, data models, and data meaning and codification methodologies face the healthcare industry. These problems typically impede the seamless transmission of electronic medical records between healthcare systems. Because of their decentralized structure and cryptographic foundation, DLTs offer a workable solution to these issues. By critically evaluating previous research and case studies, DLTs may be able to lessen these interoperability issues, as this literature review illustrates. Since DLTs provide an immutable and secure platform for the transmission of medical data, guaranteeing data integrity and confidentiality, they are a natural fit for the sensitive nature of healthcare data. Their importance in creating safe communication protocols, enhancing the meaning of data, and defining models and formats for data is emphasized in this review. A comprehensive architecture for DLT interoperability in healthcare is also recommended by the research. This framework encourages the development of DLT integration, shared data models, standardized data formats, and governance and policy. By implementing this strategy and strengthening secure medical data sharing, healthcare organizations and governments may increase the efficiency, precision, and speed of healthcare delivery. The crucial role that DLTs play in removing the structural and semantic barriers to safe medical systems interoperability is highlighted in the conclusion of this literature review. By adopting DLTs, the healthcare sector may usher in a new era of standardized, safe, and efficient medical data transmission, which will ultimately benefit both patients and healthcare providers. This study shows how distributed ledger technologies (DLTs) have the potential to revolutionize the healthcare industry by enabling the secure and meaningful exchange of medical data between different systems, thereby improving patient care and healthcare outcomes.
The integration of the Internet of Things (IoT) with blockchain technology is significantly transforming smart health monitoring systems. IoT enables the seamless collection of health data, while blockchain's decentralized ledger ensures the integrity and security of this data, mitigating potential breaches. This symbiosis addresses current healthcare challenges by enhancing security, transparency, and efficiency, fostering a patient-focused and reliable monitoring system. With the rise of the Medical Internet of Things (MIoT), personalized and cost-effective healthcare solutions are more accessible, thanks to technologies like Wireless Body Area Networks (WBAN), which improve data quality, and Machine Learning (ML), which effectively processes large datasets. Fog Computing has been instrumental in ensuring efficient data communication with reduced latency, and advancements in Software-Defined Networking (SDN) and Network Function Virtualization (NFV) offer simpler, adaptable networks for healthcare. However, increased data volume raises privacy and security concerns, propelling a shift towards blockchain for enhanced data protection and transparency. Innovations like blockchain-based federated learning aim to safeguard privacy without compromising model accuracy, and smart contracts on platforms like Hyperledger Fabric provide secure patient history logs and immediate access to medical records. Tools such as the Libelium e-Health toolkit further aim to revolutionize monitoring, diagnosis, and treatment processes.
Wireless body area networks (WBAN) are essential components of intelligent healthcare monitoring techniques. Especially, when the number and datatype inWBANincreases. InWBAN, secure multidimensional data aggregation received a lot of attention. However, the related schemes consume more computational and communication overhead to encrypt/decrypt the multidimensional health reports. In this paper, a blockchain-assisted scalable and secure multidimensional data aggregation scheme is introduced for fog-basedWBAN. The multidimensional health data are efficiently generated, encrypted, and decrypted by using the Paillier cryptosystem. Further, the batch verification method is used to achieve efficient authentication. The proposed system offers significant security attributes with less computation and communication overhead in comparison with competing systems. Further, it supports statistical analyses such as summation and variance to analyze the received health report.