Search Results (320)

Wearable biosensors generate continuous physiological data in smart Internet of Disease (IoD) environments. These data can support early disease detection and remote patient monitoring. However, wearable data are often noisy, sensitive, and distributed across different devices. This paper proposes a multimodal neuro-symbolic model to overcome these limitations and incorporates it into a secure Edge–Fog–Cloud framework for anomaly detection in smart healthcare applications. The proposed system integrates the semantic analysis of clinical text using Bio-ClinicalBERT with temporal numerical data using an LSTM-based model, creating a unified neuro-symbolic artificial intelligence (AI) pipeline. Initial data processing is performed at the Edge, whereas inference is carried out at distributed Fog nodes for low-latency anomaly detection. Model training is handled in the Cloud, and privacy-preserving federated learning (FL) is supported through Homomorphic Encryption (HomEnc) to facilitate collaborative model training without sharing raw patient data. A sharded Tangle ledger is also used, with transactions broadcast by the Fog nodes and validated in the Cloud to create tamper-evident transaction logs. Furthermore, Honey Encryption (HoneyEnc) is integrated into the Fog layer to enhance security against brute-force attacks. Experimental results show that the proposed framework achieved 99.22% accuracy and a 99.31% F1-score on the held-out test set, with bootstrap 95% confidence intervals of 98.96–99.47% for accuracy and 99.08–99.53% for the F1-score. It also reduced detection latency from 185 ms in the baseline setting to approximately 50 ms in the Fog-inference setting. The blockchain layer achieved approximately 500 Transactions Per Second (TPS), while higher throughput was observed under increased transaction load and shard parallelism. Because the evaluation is based on synthetic multimodal EHR-like data and controlled simulations, the reported findings should be interpreted as proof-of-concept internal validation rather than evidence of deployment-ready clinical generalizability; external validation using real wearable biosensor data, hospital IoMT streams, or public clinical datasets such as MIMIC-III/MIMIC-IV is required before clinical deployment. These results highlight the potential of the proposed system for secure data processing and trustworthy anomaly detection in smart healthcare environments. Full article

The Internet of Medical Things (IoMT) is transforming healthcare through continuous patient monitoring, telemedicine, cloud–edge services, and Healthcare 5.0. However, the rapid growth of interconnected medical devices has expanded the healthcare cyberattack surface, making intelligent intrusion detection essential for protecting sensitive medical data and ensuring resilient clinical operations. Existing reviews examine specific aspects of AI-powered intrusion detection but rarely provide a deployment-oriented synthesis linking technical performance with operational and clinical requirements. This review critically examines Artificial Intelligence (AI)-powered Intrusion Detection Systems (IDSs) for IoMT across six analytical dimensions: detection performance, explainability, privacy preservation, computational efficiency, benchmarking practices, and cross-dataset generalization. This structured narrative review adopted the PRISMA 2020 framework to ensure transparent record identification, screening, and reporting, with evidence synthesized qualitatively rather than through quantitative meta-analysis. A total of 5127 records published between 2021 and 2026 were screened, resulting in 24 primary studies supported by 115 complementary studies. The findings show that machine learning, deep learning, hybrid AI, Explainable Artificial Intelligence (XAI), Federated Learning (FL), blockchain-assisted security, and edge intelligence have significantly advanced IoMT intrusion detection. However, despite benchmark accuracies often exceeding 95%, deployment remains constrained by dataset dependency, weak cross-dataset generalization, computational overhead, limited explainability, fragmented benchmarking, and insufficient operational validation. This review identifies deployment readiness, rather than predictive accuracy alone, as the principal challenge for next-generation healthcare cybersecurity and provides a practical framework for developing trustworthy, interoperable, privacy-preserving, and deployment-ready IoMT cybersecurity architectures supported by standardized evaluation protocols. Full article

Smart cities (SCs) use the Internet of Things (IoT) to collect and process data to communicate with their infrastructure and assets in real time. A great deal of techniques, such as encryption protocols, Random Forest-based AI-driven threat detection, and blockchain architectures, have been developed to address cybersecurity challenges in smart cities (SCs). These techniques, however, have limitations such as their scalability, high computational expenses, and energy inefficiency. Therefore, in this study, to overcome these challenges, we propose a blockchain-based infrastructure called BlockSafeNet. This uses artificial intelligence, big data, and blockchain to enhance cybersecurity in SCs. The effectiveness of the proposed BlockSafeNet framework was evaluated using responsiveness, computational time, encryption quality score, detection rate, false positive rate, latency, throughput, and energy consumption as the primary cybersecurity performance metrics. These metrics were selected to assess communication efficiency, threat detection capability, privacy preservation, scalability, and overall security performance within smart-city IoT environments. To ensure secure data transactions, robust threat detection, and efficient communication. The system’s high calculation speed and detection rate show potential for managing sensitive maternal health data collected by IoT devices. The platform also shows how IoT may be used by healthcare services to monitor public health in real time, allowing hospitals, emergency services, and public health agencies to securely share data. This aids in resource optimization, improving service delivery, and preserving data privacy and trust in SCs. Data was obtained from the UCI Machine Learning Repository on Kaggle to validate the developed framework. By evaluating the effectiveness of BlockSafeNet in tackling cybersecurity challenges, we establish its practical relevance and usability in SCs. The proposed BlockSafeNet framework achieved a responsiveness of 24 s, an encryption quality score of 0.89, computational time of 85 s, and a detection rate of 91%, demonstrating significant improvements in secure IoT communication, privacy preservation, and AI-driven cyber threat detection within smart city infrastructures. shows that SC IoT security has significantly improved through the adoption of new data protection methods and better measures of security, providing a positive impact on the SC ecosystem. Full article

Federated learning (FL) and edge computing are transformative technologies that enhance privacy, efficiency, and real-time intelligence for distributed machine learning across diverse edge device networks. FL enables devices to collaboratively train models locally, so sensitive data remains on each device, ensuring privacy and regulatory compliance (like GDPR and HIPAA). Edge computing complements FL by bringing data processing closer to sources such as IoT sensors and smartphones, which reduces latency, bandwidth use, and dependence on cloud servers. This architecture is vital for smart cities, healthcare, industry, and autonomous systems, supporting real-time decision-making. The review details challenges such as resource heterogeneity, communication constraints, security risks, and management complexity, while highlighting opportunities for scalable orchestration, decentralized architectures, and blockchain integration. Together, FL and edge computing create a robust paradigm for scalable, privacy-aware distributed intelligence across multiple domains. Full article

This paper presents a systematic literature review examining how blockchain technologies can enhance the security and performance of multi-cloud systems. Multi-cloud architectures offer resilience, scalability, and flexibility; however, they also pose complex security challenges related to APIs, service-level agreements (SLAs), orchestration, and authentication. The promise of blockchain technology to improve the security and transparency of numerous applications, including cloud storage systems, has attracted considerable attention in recent years. Much research has focused on decentralized storage in cloud environments, spanning supply chains, FinTech, healthcare, and education. Still, the integration of blockchain with the cloud and its potential to enhance security and performance warrant an in-depth study. Using the PRISMA methodology, a structured search was conducted across six major scientific databases, including IEEE, ACM Digital Library, ScienceDirect, Scopus, Web of Science, and IJIMAI. Twenty-four primary papers published between 2019 and 2025 were selected for analysis after clear inclusion and exclusion criteria were applied. This review examines the security dimensions in multi-cloud environments—architectural vulnerabilities, API security, authentication, orchestration and automation vulnerabilities, SLAs, and cybersecurity compliance issues—in relation to blockchain technology. Based on the identified gaps, we propose a SOC-LLM-augmented security architecture that integrates blockchain-based evidence integrity, statistical anomaly detection, machine learning, large language models, and autonomous AI agents to enable intelligent attack detection and response. The proposed framework introduces specialized agents for detection, correlation, threat intelligence retrieval, blockchain evidence validation, explanation generation, and response planning. The analysis shows that integrating SOC-LLM capabilities with blockchain can move multi-cloud security from passive auditability toward proactive, explainable, and human-in-the-loop cyber defense. Finally, this paper discusses open challenges, including LLM hallucination, data scarcity, real-time scalability, evaluation standardization, and trustworthy deployment in critical multi-cloud infrastructures. The study’s conclusion highlights research gaps and suggests future lines of inquiry concerning scalable blockchain architectures and the incorporation of AI for proactive cloud security monitoring. Full article

Blockchain is a fast-changing field that is highly useful in such areas as finance, supply chain management, voting systems, and healthcare. As a consequence, software developers are increasingly creating Blockchain-Based Applications (BBAs) and Smart Contracts (SCs). However, the development of BBAs has been associated with various problems, especially in the process of updating and debugging such systems with a high degree of reliability. This is due to the immutability of deployed SCs. In this paper, we conduct an in-depth analysis of 61 published BBA articles between 2017 and 2025 to identify some causes of these challenges. Our results indicate that there is inadequate adaptation of the Software Development Life Cycle (SDLC) for BBAs. In particular, few BBA projects—only 32% of the reviewed projects—address the analysis phase, and only 29% deal with the design phase, frequently ignoring formal modeling methods. Based on these observations, we propose a new, context-adaptive methodology that facilitates BBA developers passing through the requirements, analysis, design, and implementation processes. Formal modeling techniques—such as Use Case Maps (UCMs), Finite State Machines (FSMs), and extended Unified Modeling Language (UML) class and sequence diagrams—are used within the methodology to document BBA structural and behavioral features and maintain complete traceability between requirements and implementation. In order to overcome the blockchain-specific drawbacks of traditional UML, we present formal stereotype extensions of UML class diagrams, where a four-compartment structure is introduced to differentiate state variables, functions, events, and access modifiers on SCs. We also provide analogous extensions to UML sequence diagrams using differentiated arrow notations to distinguish between function calls and event emissions to support accurate modeling of decentralized transaction flows. These extensions are described with a rationale and are formally defined and justified by mapping rules. Our methodology is justified by two case studies that prove its applicability in different fields of blockchain. The initial case study thus designs and executes a system of a halal chicken meat supply chain on Ethereum, showing the complete traceability of requirements that are based on UCM-based requirements and FSM-generated algorithms to implement SCs. The second case study applies the methodology to a decentralized Electronic Health Record (EHR) management system, and it shows coverage and completeness modeling. The methodology was evaluated through two case studies using a structured questionnaire and quantitative metrics, including traceability accuracy, reduction-in-error indicators, SC defect and gas-analysis results, modeling overhead measurements, and static security analysis with Slither. It is also evaluated based on a group of seven literature-based qualitative evaluation criteria that include workflow expressiveness, reusability, technical concept coverage, intelligibility, completeness, tool support, and blockchain limitation modeling. Full article

The quick transition of the healthcare industry to digital during the era of the Internet of Medical Things and Artificial Intelligence has ignited the demand for frameworks for data sharing while retaining safety and patient privacy. Centralized learning models place potentially sensitive patient data at risk of leakage, regulatory violation, and cyber-attacks which undermine receptivity and responsible ownership of big medical data. Federated learning is a novel paradigm that allows patients from various healthcare entities to train machine learning models while maintaining the ability to leverage their data without sharing their direct data. This study proposes a systematic literature review of approaches of privacy-preserving federated learning frameworks in healthcare applications. Following PRISMA guidelines, searches were conducted across Web of Science, Scopus, IEEE Xplore, ScienceDirect, PubMed, and ACM Digital Library with predefined query strings, explicit inclusion/exclusion criteria, and quality appraisal procedures. A total of 80 peer-reviewed studies, published from January 2015 to December 2025, were included in this systematic review, which examined cryptographic, architectural and algorithmic methods including differential privacy, homomorphic encryption, and Secure Multi-Party Computation, along with integrations using blockchain to enhance trust and confidence in distributed healthcare systems. The findings indicate a gradual shift towards hybrid privacy-preserving federated learning architectures which combined multiple security mechanisms to improve trust, confidentiality and robustness. Although significant progress has been achieved, the real-world deployment of such systems is heavily affected due to the challenges in communication efficiency, non-IID data distribution, adversarial attacks, and regulatory requirements. This research highlights future research directions for scalable, explainable and interoperable federated architectures that strike an optimal balance of privacy, utility and system performance for next-gen health intelligence. Trial registration: PROSPERO (CRD420261401073). Full article

The digital sharing of healthcare data necessitates a careful balance between the need for verifiability and the protection of patient privacy. In many real-world scenarios, particularly in employer and third-party verification processes, excessive clinical information is disclosed beyond what is strictly required. This practice introduces significant privacy risks and conflicts with data minimization principles. To address this problem, this study proposes a blockchain-based, privacy-preserving system architecture that enables health report verification without revealing diagnosis information. The proposed system is built upon a dual-layer architecture that structurally separates clinical data from verification processes. In the clinical data layer, health reports are encrypted on the client side and stored in off-chain environments, while only reference data and access control information are recorded on the blockchain. The system further integrates revocation mechanisms, role-based access control, and auditability through a modular smart contract design. In conclusion, this study introduces a modular, privacy-oriented, and practically applicable solution for secure healthcare data verification. By eliminating the need for clinical data disclosure during verification, the proposed architecture offers a novel design perspective and contributes both conceptually and technically to the development of blockchain-based healthcare information systems. Full article

The growing demand for real-time data synchronisation has increased the importance of supervisory control systems in industrial automation, smart grids, healthcare monitoring, and environmental applications. Low-power wireless sensor networks (LPWSNs) have emerged as key enablers of scalable and energy-efficient monitoring. However, achieving reliable synchronisation remains challenging due to latency, energy constraints, scalability limitations, security vulnerabilities, and data integrity concerns. This review examines the role of time synchronisation in supervisory control systems and evaluates how LPWSNs support real-time monitoring and decision-making. Established synchronisation protocols, including Reference Broadcast Synchronisation (RBS), the Flooding Time Synchronisation Protocol (FTSP), and the Timing-Sync Protocol for Sensor Network (TPSN), are analysed in terms of accuracy, energy efficiency, and scalability. Key optimisation strategies, such as clock drift compensation, data aggregation and compression, and edge computing, are also discussed. Recent advances, including artificial intelligence and machine learning (AI/ML)-based predictive synchronisation, blockchain, software-defined networking (SDN), and 5G-enabled LPWSNs, are reviewed across industrial, energy, healthcare, and agricultural applications. The review critically evaluates their benefits and trade-offs and identifies remaining challenges related to cybersecurity, energy efficiency, and large-scale deployment. Finally, future research directions are outlined to support robust, scalable, and efficient real-time synchronisation in LPWSNs. Full article

Blockchain-assisted off-chain storage for IoT must simultaneously manage low-latency tiered data placement, trusted and dynamic on-chain mapping, migration consistency, and failure recovery—four concerns that existing designs address in isolation. Tiered storage systems optimize placement without modeling the scalable coordination cost of keeping object–location bindings trustworthy, while blockchain-metadata studies assume static storage topologies with no dynamic tier migration. This paper presents a scalable and trusted metadata-coordinated tiered off-chain storage framework, which bridges traditional trust systems (e.g., legacy authentication) with blockchain networks powered by Proof of Capacity (PoC) consensus. In this framework, adaptive heat-driven placement, dynamic on-chain mapping evolution with batched commitment, migration-aware redirect control, and rollback-safe recovery operate as a single coordinated workflow, with the five-stage write–verify–commit–redirect–retire pipeline acting as a lightweight coordination protocol that maintains ordered and atomic state transitions under message loss, out-of-order delivery, and single-node failures. The distinctive contribution lies in the framework’s coupled control: every placement decision propagates through a verifiable metadata path that can be audited and, when necessary, rolled back. Simulation across multiple workload patterns shows that the proposed method reduces average access latency by 28% and raises the hot-tier hit ratio from 0.19 to 0.65 relative to a dynamic baseline without trusted mapping coordination under the simulated registry write cost. To achieve high-throughput mapping operations, batched on-chain commitment cuts metadata transactions by 50× at the cost of a tunable mapping freshness delay. The framework scales from 1 k to 50 k managed objects, effectively managing tens of millions of bytes of data (10+ MB scale) without disproportionate overhead growth; beyond this scale, hot-tier capacity rather than coordination becomes the dominant bottleneck, and smarter predictive placement becomes the natural next lever. All tested fault types achieve 100% rollback success with sub-millisecond local data plane interruption; audit-visible recovery depends on the assumed chain finality delay and, for heavily regulated IoT domains, such as finance and healthcare, should be treated as the operationally binding recovery time objective. These results, together with extended evaluations—including asymmetric write latency stress, coordination ablation, tail latency analysis, and benefit–complexity assessment—provide quantitative evidence that scalable, dynamic mapping coordination can be integrated into tiered off-chain data management at an acceptable and measurable operational cost under the simulated configuration. Full article

Modern smart cities increasingly depend on resilient and intelligent energy infrastructures to maintain critical urban services during large-scale disturbances and multi-fault conditions. Conventional restoration approaches are often limited by centralized operation, delayed response, and inadequate coordination of distributed energy resources (DERs) under emergency conditions. To address these challenges, this paper proposes a Federated AI-Driven Urban Energy Resilience Framework for Smart City Critical Infrastructure Restoration using Virtual Power Plant (VPP) coordination, blockchain-enabled peer-to-peer (P2P) energy trading, and intelligent distributed energy management. The proposed framework is validated on the IEEE 118-bus radial distribution system under severe dual-fault outage conditions, representing urban disaster-induced infrastructure interruptions. Critical urban service zones, including healthcare support systems, emergency loads, smart residential sectors, and EV charging corridors, are considered during the restoration process. The Seagull Optimization Algorithm (SOA) is employed to optimize DER dispatch and improve restoration performance under operational constraints. A progressive restoration strategy comprising conventional outage conditions, VPP-assisted restoration, blockchain-enabled decentralized energy trading, and AI-driven coordinated restoration is analyzed. Simulation results demonstrate that the proposed framework significantly enhances urban energy resilience by increasing load restoration from 55.05% to 94.20%, reducing Energy Not Supplied (ENS), improving voltage stability, and lowering interruption-related economic losses. The minimum bus voltage improves to 0.965 p.u. under the proposed coordinated restoration strategy. The results show that coordinated VPP operation and blockchain-based energy sharing can support reliable restoration of critical urban infrastructure during major outage conditions. The results indicate that integrating AI-assisted VPP coordination with secure decentralized energy trading can effectively support smart city critical infrastructure continuity during extreme outage conditions. The proposed framework provides a scalable and resilient solution for future intelligent urban energy systems and disaster-resilient smart city applications. Full article

The increasing pace of Internet of Things (IoT) and Industrial Internet of Things (IIoT) applications has exacerbated the security challenges in resource-constrained environments, where traditional cryptographic protocols incur prohibitively high computational and energy costs. These constraints are also worsened by the advent of quantum computing, which poses a long-term security risk to popular crypto-key cryptographic-based efforts. To overcome these difficulties, this paper proposes an Energy-Efficient Cryptographic Protocol Framework (EECPF) that provides mutual optimization between energy consumption, security level, and communication latency to achieve sustainable IoT security. The presented framework proposes an adaptive encryption selection mechanism that dynamically chooses cryptographic algorithms depending on device capabilities, network conditions, and threat levels derived from intrusion detection outputs. EECPF combines privacy-preserving federated learning for distributed intrusion detection with collaborative threat intelligence sharing, eliminating centralized data sharing. In addition, lattice-based post-quantum cryptography primitives are added and combined with lightweight blockchain-enforced identity management to ensure long-term authentication resilience. The models on which the framework is based are mathematically based, modeling the consumption of energy, the robustness of security, and latency, providing principled multi-objective optimization under resource constraints. The publicly available Edge-IIoTset dataset was subjected to extensive experimental assessment under realistic IIoT and IoT attack scenarios. Experiments show that EECPF can reach an intrusion detection rate of 94.7%, while reducing energy consumption by 47.3% and latency by 23.8% compared with other commonly used lightweight cryptographic methods. These were continually noticed across different heterogeneous devices and deployment environments. In general, EECPF offers an energy-aware, quantum-resilient, and scalable security solution that can be used for next-generation IoT systems, such as smart healthcare, industrial automation, and smart city infrastructures. Full article

The increasing volume of digital health data generated through Electronic Health Records (EHRs), emergency care systems, and real-time monitoring technologies has intensified the need for secure cross-institutional healthcare analytics. However, privacy concerns, regulatory restrictions, institutional mistrust, and risks associated with centralized data aggregation continue to limit large-scale healthcare data sharing. This paper presents OPAL4Health, a governance-oriented and privacy-preserving distributed healthcare analytics framework grounded in the MIT Open Algorithms (OPAL) paradigm. The framework integrates federated analytics, blockchain-based auditability, explainable artificial intelligence (XAI), and institutional governance mechanisms within a unified computation-to-data healthcare ecosystem. Unlike conventional federated healthcare systems that primarily focus on decentralized computation alone, OPAL4Health emphasizes governance, transparency, auditability, and policy-aligned distributed analytics while preserving institutional data sovereignty. The privacy protections supported by OPAL4Health are primarily architecture-based and governance-oriented, relying on local institutional data retention, controlled query execution, and blockchain-auditable analytical workflows rather than formally provable cryptographic privacy guarantees. The framework was evaluated through a real-world urgent care pilot across seven hospitals in Riyadh, Saudi Arabia, using 184 anonymized patient cases collected between May 2015 and September 2016. Analytical findings identified a median onset-to-arrival delay of 285 min (95% Confidence Interval (CI): 270–302), low ambulance utilization (18.5%), and hospital bypass behavior in 42% of cases. Peak Emergency Department (ED) congestion periods were also identified. Scenario-based modeling projected potential long-term healthcare savings of approximately $602 million over 15 years through improved Emergency Medical Services (EMS) allocation and reduced disability-adjusted life years (DALYs). The findings demonstrate the feasibility of governance-oriented, privacy-preserving distributed healthcare analytics within OPAL4Health while generating actionable operational and policy-relevant insights without centralizing sensitive patient-level records. The proposed framework provides a transferable model for secure, transparent, and accountable digital health collaboration across healthcare ecosystems. Full article

Modern cybersecurity challenges span multiple layers, from human behavior and identity management to network communication and device security. This paper proposes a unified multi-layered security framework that integrates human-centric, identity-centric, and communication-centric defenses into a coherent architecture. Drawing on insights from diverse domains (industrial control systems, IoT, healthcare, blockchain, and quantum communications), we identify common defense-in-depth principles and interdependencies across layers. The study highlights the persistent gaps in current research, which often focuses on isolated layers or domain-specific models, and addresses these gaps by synthesizing a cross-domain framework. We develop a mixed-method methodology to compare and integrate multi-layer security mechanisms, and we implement a proof-of-concept risk assessment engine to evaluate the framework’s effectiveness. Preliminary results from this implementation demonstrate that combining layers yields significantly improved detection performance and resilience compared to single-layer baselines. The framework’s contributions include a comprehensive literature-driven model, an operational validation in a simulated environment, and guidelines for deploying multi-layer defenses in complex, interconnected infrastructures. Empirical findings confirm that an integrated multi-layer approach can adapt to varied threat scenarios and reduce vulnerabilities, underscoring the value of coordinated controls across technical and human factors. The proposed framework lays a foundation for future work on scalable, cross-layer cybersecurity architectures that better protect contemporary cyber–physical systems. Full article