ABSTRACT Smart cities are digitally advanced urban environments that are equipped with sensor networks to gather, share, and analyze extensive data across interconnected systems. Among various smart city applications, the intelligent transportation system represents one of the most critical and security‐sensitive domains. An intelligent transportation system relies heavily on continuous vehicular communication, a low‐latency decision‐making process, as well as real‐time traffic monitoring. Existing Internet of Things security methods encounter significant computational overhead and limited scalability, making them unfit for real‐time applications. To address these issues, this paper proposes a novel security model, named Deep Residual Stacked Bidirectional Network. The proposed system is integrated into a blockchain‐supported hybrid system to ensure security and privacy for users and systems in smart cities. This enhanced Deep‐Learning model combines the residual learning power with bidirectional long short‐term memory layers. To effectively manage deeper networks, residual connections help mitigate the vanishing gradient problem, while bidirectional long short‐term memory provides sequential dependencies in backward and forward directions. This allows the model to detect patterns in data, especially in security environments where data is highly dynamic and time‐sensitive. Four Internet of Things‐related datasets are used to evaluate the efficiency of the developed algorithm. These datasets offer various real‐world network traffic and attack scenarios that allow comprehensive performance evaluation of the proposed approach in comparison with existing methods. The test outcomes revealed that the blockchain‐supported proposed method outperforms traditional methods with an accuracy of 98.21%, specificity of 97.39%, and F1‐score of 97.46%.
With the explosive growth of online education resources, traditional education platforms that rely on centralized servers for resource distribution and storage gradually expose issues such as inefficient resource management, lack of trust in sharing, high storage costs, and a high risk of single-point failure. In response, this study designs a decentralized education resource sharing platform by integrating blockchain technology and distributed file systems. It leverages the distributed ledger, immutability, and traceability features of blockchain, along with the high data availability and low storage cost advantages of distributed file systems. Results show that the proposed education platform reaches 1,388 transactions per second when the number of nodes is 200, with a latency response time of 9.8 seconds and an average memory overhead of 268 MB. In practical performance evaluation, the platform achieves a top-10 hit rate of 97.2%, a business interruption probability as low as 4.8% under unexpected conditions, and an average resource throughput efficiency of 176 Mbps. Overall, the platform performs well in education resource sharing and demonstrates strong algorithm fault tolerance, practicality, robustness, and service stability, providing reliable technical support for global education resource sharing.
This paper explores the application of blockchain technology to manage distributed edge computing resources. The core claim is that blockchain can facilitate dynamic resource allocation and efficient utilization within edge computing environments. The proposed mechanism involves constructing a blockchain-based resource management system leveraging smart contracts to automate and optimize resource distribution. This approach addresses the challenges of centralized control, inefficient resource utilization, and security vulnerabilities commonly found in traditional edge computing models. The research investigates the potential benefits of blockchain's decentralized, transparent, and immutable ledger for enhancing edge computing resource management, ultimately leading to improved performance, scalability, and trust within distributed edge systems. The key focus is on establishing a secure and automated framework for resource sharing and access control, significantly improving the overall efficiency and reliability of edge computing deployments. ---
Nelli Yaswanth Kumar, Dr. Singothu Jhansi Rani, Setti Sarika
The rapid proliferation of Internet of Things (IoT) devices under sixth-generation (6G) networks introduces a highly dynamic, decentralized environment in which static, perimeter-based security models are no longer adequate. This paper proposes AZTM-v3 an adaptive Zero Trust framework that couples behavior-driven trust management with a Random Forest classifier to identify and isolate malicious nodes in real time. The framework is evaluated on an NS-3 simulation of a 150-node 6G IoT network subjected to Sybil, Denial-of-Service (DoS), spoofing, replay and ON-OFF attacks. Unlike prior trust-management proposals that report only qualitative or partial outcomes this work quantifies performance across five dimensions i.e detection accuracy, F1-score, false-positive rate, end-to-end latency and consensus-convergence time and benchmarks AZTM-v3 against PKI-based, centralized-trust and static-blockchain baselines. AZTM-v3 attains a 98.1% overall detection accuracy with a 1.6% false-positive rate at 150 nodes and sustains 95.4% accuracy at 200 nodes outperforming the PKI baseline by 12–18 percentage points across all tested loads. These results indicate that combining tiered trust evaluation with machine learning based classification yields a measurably more scalable and resilient security layer for 6G-enabled IoT deployments than existing static or purely cryptographic approaches.
Marco Scarpa, Mohammad Sadeghzadeh, Saeed Javanmardi, Bahareh Pahlevanzadeh · 5 authors
Blockchain provides secure and decentralized data storage. Normal blockchains permanently store data. Mutable blockchains allow users to change data, but this reduces tamper resistance. This paper tests both methods in PaB-PIF, a hybrid architecture for IoT-Fog networks. Our design uses an immutable mainchain in the cloud layer and mutable sidechains in the fog layer. We analyze throughput, latency, and tamper resistance using math models and simulations. Results show that blockchain greatly improves network security. Without blockchain, the network has zero tamper resistance. The mutable blockchain in the fog layer has a tamper resistance of 0.58. The immutable blockchain in the cloud layer reaches 0.99. However, this extra security increases latency and reduces throughput. The mutable blockchain has lower latency, so it is a good fit for the fog layer. The immutable blockchain provides maximum security, which is best for the cloud layer. This trade-off works well for IoT systems like the Internet of Vehicles, where data integrity and legal rules are essential. We also compare PaB-PIF with an IoT-Fog network that has no blockchain.
The rapid expansion of fourth industrial revolution (4IR) technologies has intensified the expectation that artificial intelligence (AI), blockchain, the Internet of Things (IoT), big data analytics, and automation can accelerate the process of achieving the United Naitons Sustainable Development Goals (SDGs), particularly in developing nations. Whether these technologies live up to their expectation, however, depends not only on technological capability but also on the legal, regulatory, and institutional environment in which they operate. However, the governance of 4IR technologies has gained far less scholarly attention than their technological potential. The present study examines how legal frameworks, policy instruments, and governance arrangements influence the contribution of 4IR technologies to sustainable development in developing countries. Following the PRISMA 2020 guidelines, literature published between 2015 and 2025 was identified through searches of Web of Science, Scopus, Google Scholar, Pub Med, and arXiv. From 721 retrieved records, 50 peer reviewed studies met the eligibility criteria and were synthesised using a narrative approach. The analysis reveals three consistent patterns. First, legal authority is fragmented within and across jurisdictions. Second, policy commitments frequently outstrip implementation capacity, a performativity in which governments announce SDG ambitions without building the institutional means to deliver them. Third, governance is constrained by limited expertise, weak enforcement, and poor coordination between agencies. The review also identifies three important gaps in the literature: a predominant focus on artificial intelligence at the expense of other technologies, limited empirical testing of the links between fourth industrial revolution technologies and SDG outcomes, and minimal attention to how rules are enforced in practice. These finding suggest that the prime difficulty of harnessing 4IR technologies for sustainable development in developing nations are institutional rather than technological. Therefore, it is not only about advancing technological innovation but also strengthening regulatory coherence, governance capacity, and the effective implementation of legal frameworks to achieve SDGs in developing countries.
Aasem N. Alyahya, Muidh Awadh Algahtani, Amani Ibraheem, Naglaa F. Soliman · 8 authors
Industry 4.0 is evolving rapidly, 6G networks are emerging, and this has led to a dramatic increase in ultra-latency-critical, computationally demanding jobs in Industrial Internet of Things (IIoT) environments such as real-time digital twins, collaborative robots, and augmented reality-guided assembly. However, the conventional D2D-assisted mobile edge computing (MEC) systems suffer from the severe performance degradation due to the harsh factory propagation environment, severe security threats on the open D2D links, strict industrial data privacy requirements, selfish resource sharing behaviour, and frequent service migration due to device mobility. In this research, we propose a holistic secure task co-offloading system that integrates Reconfigurable Intelligent Surfaces (RIS), permissioned blockchain with smart contracts, and federated learning, into an integrated D2D-MEC architecture for the IIoT. A federated secure multi-armed bandit algorithm enables privacy-preserving decentralized decision-making without revealing sensitive industrial data. Blockchain records off-load transactions through immutable ledgers and enables smart-contract-based incentive enforcement. RIS renders unreliable wireless channels dynamic with minimum energy overhead. In scenarios with hostile and imprecise information, the collaborative design can lower the long-term cost of the system, including task latency, energy consumption, migration overhead, and blockchain transaction fees. The proposed framework, compared to state-of-the-art baselines, reduces the average job completion latency by 29.6%, energy consumption by 36.8%, and migration cost by 41.2%, as demonstrated by extensive trace-driven simulations in actual 6G-IIoT manufacturing scenarios. The experimental results also show the robust performance under the simulated willingness manipulation and poisoned federated updates. The permissioned blockchain architecture provides the architectural security against the Sybil attack and other trust-related threats.
Security in resource-constrained IoT deployments remains a persistent challenge: devices used in industrial control, smart healthcare, and transportation must authenticate quickly, consume minimal energy, and resist physical attacks — yet existing protocols rarely address all three requirements at once. To the best of current knowledge, no prior protocol jointly optimises security, energy, and latency within a single formally verified framework. This paper presents OPAQUE-IoT, an Optimization-driven PUF-Blockchain AKA Protocol for constrained IoT networks. The framework integrates PUF-based hardware identity verification, a permissioned blockchain for decentralized trust management, and the Adaptive Security-Energy Trade-off Optimizer (ASETO), which jointly minimizes authentication latency and energy consumption under formal security constraints. Convergence of ASETO is proven under Lipschitz-continuous objective functions. Formal security analysis under the Real-or-Random (RoR) model with explicit Random Oracle and ECDH hardness assumptions demonstrates resistance to replay, impersonation, man-in-the-middle, PUF modeling, insider, and side-channel attacks, with a security advantage bound of approximately 2^(-68). Simulation results across heterogeneous IoT topologies ( N = 50 to 5000 devices) show 31.8% lower energy consumption, 30.2% reduced authentication latency, and 41.1% higher throughput compared to the best-performing blockchain-capable baseline, with O(log N) Merkle-indexed blockchain query complexity and O(T_max·N·P) per-epoch optimiser complexity.
Abstract The integration of Internet of Things (IoT) and blockchain technologies enables secure, decentralized data management for real-time applications. However, limitations in processor, memory, and energy resources restrict the direct processing of large datasets. Notably, Garbage Collector (GC) mechanisms in high-level languages increase variance in P99 queue latencies, while expanding data volumes can result in system crashes due to Out-of-Memory (OOM) errors. This study introduces a hybrid Rust-Python Simplified Payment Verification (SPV) native hashing engine deployed on resource-constrained edge devices (Raspberry Pi Zero W) and high-capacity gateways (Raspberry Pi 5). Laboratory evaluations demonstrate that the hybrid system achieves a verification capacity of 34,000 Merkle nodes per second, representing a 4.35-fold speed improvement over pure Python on the Pi Zero W. Additionally, the system reduces GC-induced latency fluctuations and offers up to 75% potential energy savings, as indicated by theoretical modeling based on active processor cycle analyses. Bottleneck analyses on Raspberry Pi 5 indicate that Foreign Function Interface (FFI)-related data transfer costs limit parallel processing benefits for low-volume datasets. In contrast, the scalability of the hybrid architecture is evident with datasets containing 1.5 million records. The memory-mapped streaming architecture minimizes OOM risks and achieves a cache miss rate of 0.34%. Memory safety was assessed using the MIRI tool.
Haitham A. Mahmoud, Ahmed Soliman, Mohammed El-Meligy, Azhar Imran · 5 authors
Abstract Modern digital ecosystems rely mostly on blockchain technology, such as decentralized and immutable ledger systems. This technology avails guarantees of secure transaction and data administration in keeping with the privacy of consumers. Thus, the blockchain systems often suffer in resource-constrained environments to experience considerable computational overhead along with low scalability and issues in handling real-time data. To overcome these restrictions, this research incorporates federated learning, decentralized storage using IPFS, and lightweight cryptographic methods to deliver secure, scalable, and real-time analytics in the IoT system. This research has proposed a novel framework based on blockchain, privacy-preserving techniques, and predictive maintenance models to address some of the security, scalability, and reliability challenges observed in IoT ecosystems. The framework guarantees secure data management, efficient real-time analytics, and robust anomaly detection by using the most advanced technologies such as federated learning, decentralized storage, and lightweight cryptographic methods. The suggested technique exceeds traditional methods by means of accuracy and error reduction with the astonishingly low FPV value of 0.005954% and FNR value of 0.000274% while giving extraordinary performance metrics that reach 99.88% accuracy, 99.89% precision, 99.97% recall, and 99.93% F1-score. This solution establishes secure, scalable, and tamper-proof infrastructure for all the applications from industrial automation, healthcare to vehicular networks, hence enabling smart and sustainable IoT governance for these applications.
Blockchain is one important building blocks of the Internet of the future, called Web3. The Blockchain technology supports a wide range of applications, spanning from Smart Cities and automotive industries, from agriculture to energy. The healthcare sector, in particular, has experienced a profound impact from blockchain-based technologies, paving the way for the development of true digital healthcare systems. By enabling secure and immutable data storage, and facilitating the sharing of this information among all nodes possessing a local copy of the distributed ledger, blockchain plays a vital role in the analysis of healthcare data. This paper provides a comprehensive survey of the main blockchain platforms utilized in the digital healthcare, integrated with a comparative analysis. In addition, the implementation of Innovative permissioned Blockchain for eHealth (IBEH) is presented and discussed in detail. IBEH addresses key challenges in digital health data management, including secure and controlled access to sensitive health information, ensuring data integrity and traceability, and secure sharing between different healthcare institutions and organizations. This is made possible by decoupling the application and blockchain layers and by a flexible, customizable, and easily deployable infrastructure. IBEH integrates the application-oriented and embedded layer with that of a blockchain network built with the MultiChain platform, which uses smart contracts with permissions, REST APIs, and RPC calls. The main features and its associated smart contracts within the healthcare domain are discussed. Finally, the analysis of performance is provided.
Sunday Yunisa, A. Ajah Ifeyinwa, Eturpa Salami Emmanuel
Internet of Medical Things (IoMT) devices, due to their resource constraints, require specialized security solutions that can operate efficiently without compromising performance and maintaining data confidentiality and integrity while minimizing computational overhead. This review examines various IoMT-based security frameworks designed to secure healthcare records in the cloud, emphasizing their effectiveness, challenges, and best practices. The study was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. 100 studies were identified published between 2020to 2025 and 50 papersthat were relevant to the study was carefully selected through a structured screening process. The papers were obtained from major academic databases such as PubMed, Scopus, IEEE Xplore, SpringerLink, Wiley, and Google Scholar. A systematic review protocol was developed before the literature search to establish clear criteria for inclusion and exclusion, ensuring transparency and reproducibility. The review showed that despite the progress made in safeguarding IoMT cloud-based health records, numerous prevailing frameworks predominantly emphasize either encryption or blockchain technology in a singular context, thereby neglecting to exploit the synergistic advantages inherent in the integration of both methodologies. Also, the encryption techniques currently employed for the protection of records within IoMT cloud environments frequently fail to achieve the essential equilibrium between security and operational performance which is characterized by limited resources. The study recommends the formulation of a framework that integrates several encryption schemes and blockchain technology to address the prevailing security problems.
Homomorphic-encryption blockchain frameworks for IoT sensor aggregation generally rely on classical cryptographic hardness assumptions and seldom account for network topology in liveness and performance analysis. This work introduces Phi-PHE-BC, a topology-aware homomorphic blockchain architecture for secure and privacy-preserving IoT sensor data aggregation. The framework combines threshold Paillier decryption with graph-parameterized security and performance analysis, linking protocol behavior to the validator graph. On-chain Paillier ciphertexts support homomorphic aggregation while providing IND-CPA confidentiality under the Decisional Composite Residuosity assumption, and authentication signatures provide EUF-CMA transaction integrity. Threshold partial-decryption shares are protected by a noise-flooding wrapper that provides information-theoretic privacy under the configured statistical-hiding condition. Under partial synchrony and Byzantine fault-tolerance assumptions, liveness requires validator connectivity kappa(Gv) >= f+1. We derive topology-dependent throughput bounds for tree, star, mesh, and scale-free networks, together with a per-block communication-cost model. A game-theoretic analysis shows that honest validator participation is a dominant strategy under the stated utility model, yielding an all-honest Nash equilibrium. Experiments on Hyperledger Fabric 2.5 show lower end-to-end latency than the selected traditional PHE-blockchain baseline while maintaining controllable threshold-decryption overhead. Results across topology scaling, validator sensitivity, threshold decryption, and Byzantine-load experiments indicate that Phi-PHE-BC is a practical architecture for secure, privacy-preserving, and topology-aware IoT sensor aggregation.
The metaverse is emerging as a persistent and immersive digital environment that combines extended reality, cloud edge computing, artificial intelligence, big data analytics, digital twins, blockchain, and future wireless connectivity. However, real time metaverse services require ultra low latency, high data rates, context aware intelligence, scalable cloud infrastructure, and trustworthy data governance. This paper presents a PRISMA informed systematic review of AI driven cloud and 6G enabled metaverse research with special attention to big data analytics, security, privacy, and digital trust. A structured search strategy was designed across major scholarly databases and citation snowballing sources, and 45 studies were selected for qualitative synthesis. The review classifies the literature into six themes: AI and real time analytics, cloud edge device orchestration, 6G connectivity, metaverse security, privacy preserving mechanisms, and digital trust governance. The findings show that 6G and edge intelligence can support immersive metaverse services through sub millisecond interaction, distributed rendering, semantic communication, integrated sensing, and adaptive resource allocation. At the same time, the literature reveals open challenges involving identity management, biometric privacy, adversarial AI, cross platform interoperability, data provenance, and user trust. The paper concludes with a conference oriented research agenda for trustworthy AI cloud 6G metaverse systems.
The concept of Cloud-Edge computing has proven to be an efficient paradigm for dealing with latency-sensitive applications through closer access to computational services. However, the selection of the right Edge service provider is difficult because of the dynamic nature of resource availability, quality of service, workload variability, provider reliability, and requirements for different applications. Current approaches to Edge service allocation concentrate on cost minimization or scheduling but do not offer sufficient assistance in transparent decision-making, provider verification, adaptive Edge service allocation, monitoring, and intelligent payment management. Such approach leads to the inefficiency of the allocation process, interruptions in the execution of tasks, and increased human intervention. The paper proposes the Explainable Agent AI-Based Intelligent Edge Service Allocation Framework for Cloud-Edge Computing. The Explainable Agent AI-Based Intelligent Edge Service Allocation involves the implementation of an autonomous Explainable Agent AI in the cloud that is responsible for the provider registration, provider verification, intelligent Edge service allocation, data-aware classification of services, reliability assessment, explainable decision-making, smart contract creation, escrow payment management, and continuous monitoring of services. The Agent AI conducts a preliminary assessment of the live status of the registered providers and performs evaluations for budget compatibility, computation, storage, bandwidth, reliability, scalability, workload, past performance history of the services, service rating, connectivity reliability, and QoS to determine the best available provider of Edge service. Throughout the process, the framework keeps an eye on the quality of the services and reassigns the rest of the workload to a new provider in case of any drop in the service quality or even failure. Escrow based smart contracts ensure that the payment is done only after the successful completion of the task, which ensures fair reward and prevention of financial loss. Experimental analysis shows enhancements in the efficiency of Edge service allocation, resource utilization, service reliability, service continuity, quality of data transmission, and operational performance.
T G Keshavamurthy, S. Guruprasad, K. N. Hareesh, M. V. Chidananda Murthy · 6 authors
In recent years, smart IoT systems pose significant challenges regarding security, scalability, and intelligent decision-making for IoT data, particularly amid advances in quantum computing attacks. Traditional cryptographic and machine learning techniques seem insufficient for real-time IoT systems where data is dynamic and large-scale and security requirements are strict. In this paper, a novel Post-Quantum Probabilistic Hidden-State Deep Learning (PQP_HS_DL) framework is presented that comprises of effective probabilistic hidden state modelling, lattice-based post-quantum cryptography and blockchain technology to process IoT data securely and efficiently. In the proposed PQP_HS_DL framework, a probabilistic hidden state model is applied to learn temporal dynamics and uncertainty in IoT data streams to provide enhanced prediction and reliable anomaly detection capabilities. A lattice-based cryptographic scheme ensures quantum-resistant security, and blockchain provides data integrity, transparency, and decentralized trusted authority management. The system is further enhanced by edge computing to alleviate latency and realize real-time processing performance. The experimental evaluation of the proposed framework is carried out under 100 IoT nodes to evaluate its performance. The results of the PQP_HS_DL provide a high classification accuracy (97.6%), and higher precision, recall, and F1-score compared to the existing techniques. The latency (72 ms) is lower, the throughput (285 transactions per second) is higher, and energy consumption (0.91) is also effective in the PQP_HS_DL framework for real-time applications of IoT. Security analysis shows that the entropy (0.98) is very high, and the attack probability (0.01) is very low. The framework uses lattice-based post-quantum cryptographic mechanisms, which are effective against any classical attacker as well as against existing quantum cryptanalytic methods, based on standard computational assumptions. Scalability analysis demonstrates that the proposed framework, evaluated with run on IoT networks with over 1000 nodes, exhibits significant performance.
The growing adoption of digital medical health care systems makes it necessary to build efficient, secure, and interoperable medical information exchange services. Nevertheless, existing traditional healthcare systems are centralized, inefficient in communication, vulnerable in terms of the integrity of data, and lack transparency. In this study, a novel blockchain-based secure framework is proposed with the integration of Ethereum smart contracts, CBOR compression, AES-256 GCM encryption, and SHA-256 validation. A multispecialty hospital dataset including patients’ information, laboratory information, prescriptions, and billing details is used in testing. A study obtained a compression rate of 7.22, validation speed of 0.0039 ms, encryption in 0.36 ms, average API latency of 98.47 ms, and throughput capacity of 52.9 TPS with a blockchain-based proposed system. Security analysis proved that this system provides security in terms of encryption, tamper resistance, access control, and immutability. The study also contributes a new model of communication within the health sector, which is both lightweight and secure, and increases blockchain performance and security.
The rapid growth of urbanization in India has significantly increased the demand for efficient urban infrastructure, intelligent public services, and sustainable resource management. Cities are facing numerous challenges, including traffic congestion, rising energy consumption, water scarcity, environmental pollution, inefficient waste management, and increasing pressure on healthcare and public safety systems. Conventional urban management techniques are often inadequate for handling these complex and interconnected challenges because they rely heavily on manual monitoring and reactive decision-making. The Internet of Things (IoT), combined with smart electronic systems, has emerged as a transformative technology capable of addressing these issues by enabling real-time monitoring, automation, and intelligent decision-making. IoT-based smart electronics integrate sensors, embedded processors, wireless communication technologies, cloud computing, artificial intelligence, and data analytics to create interconnected systems that continuously collect, process, and exchange information. These technologies enable city administrators to monitor infrastructure, optimize resource utilization, improve service delivery, and enhance the quality of life for citizens. In India, the Smart Cities Mission has accelerated the adoption of IoT-enabled technologies across various sectors, including transportation, energy management, water distribution, environmental monitoring, healthcare, public safety, and digital governance. Smart electronics have enabled intelligent traffic control systems, smart street lighting, smart electricity meters, connected surveillance systems, and automated waste management solutions, thereby improving operational efficiency and reducing environmental impact. Despite significant progress, several challenges remain, including cybersecurity threats, interoperability issues, data privacy concerns, high deployment costs, and the need for standardized communication protocols. This paper presents a comprehensive discussion on the role of IoT-based smart electronics in building smart cities in India. It examines the technological architecture, key applications, implementation challenges, and future opportunities associated with IoT-driven urban development. The paper concludes that the integration of IoT with emerging technologies such as artificial intelligence, edge computing, fifth-generation (5G) communication, blockchain, and digital twin technologies will play a crucial role in achieving sustainable, resilient, and citizen-centric smart cities in India.
In recent years the growth of cloud computing, Internet of Things (IoT), artificial intelligence (AI) and edge intelligence has been increasing, and with it the need for portable, scalable and secure computing infrastructures that can process vast amounts of data that is dispersed, and has very low latency. Traditional cloud infrastructures are typically based on central server deployments which can be costly to deploy, immobile, have potentially greater communication latency, and waste resources in dynamic workload environments. In this paper, we introduced an Intelligent Portable Edge – Cloud Computing Architecture (IPECA) that combines the portable computing hardware, AI-based workload prediction, adaptive resource optimization, container-based virtualization and secure edge-cloud collaboration into a single computing architecture. In conventional architectures, there is no intelligent resource orchestration mechanism, which can provide flexible allocation of computational resources according to the property of workload, thermal status, energy consumption, network availability and so on. The architecture also features an adaptive security layer leveraging multiple layers of authentication, secure communication protocols, blockchain for integrity verification and on-the-fly system health monitoring to enhance cyber resilience. Simulations are conducted with varying workloads to gauge the effectiveness of the proposed architecture, and compared to traditional cloud and edge-cloud architectures with the metrics of latency, throughput, CPU utilization, response time, energy consumption, thermal efficiency, and resource utilization. Experiments demonstrate significant energy savings, scalability, responsiveness of the system and efficiency of computations using secure distributed processing. The suggested architecture is viable for the coming intelligent cloud infrastructures that are essential for smart city, industrial IoT, digital healthcare, education and enterprise computing.
Abstract In the current digital era, the storage of electronic health records on centralized platforms presents significant integrity, privacy and security challenges. Further, access to this stored healthcare data should be quick and efficient, especially during emergencies. Blockchain and edge computing brought a great revolution in managing healthcare data by ensuring security, immutability, and decentralized data sharing with reduced latency. But, the integration of edge computing with the blockchain networks is still a gap to achieve ideal healthcare goals of data security with real-time data processing. The contribution of this work is two-fold. First, a novel deep reinforcement learning based medical data offloading scheme is proposed for offloading healthcare data to the nearby edge servers from the end users. The learning policy uses the proximal policy optimization algorithm for making the optimal offloading decision and minimizes the overall delay and energy consumption of healthcare devices and edge servers. Second, we proposed a secure, scalable, and consent-based data sharing scheme among multiple stakeholders such as patients, hospitals, doctors, healthcare research institutes etc. The EHR sharing scheme uses the AES and RSA algorithms for encryption, which ensures only authorized and consent-based access to the sensitive data stored in IPFS. The performance of the proposed offloading scheme is evaluated in terms of delay and energy consumption whereas data sharing scheme is evaluated in terms of latency and throughput using Hyperledger Besu and Hyperledger Caliper platforms. The experimental study exhibits that the proposed approach is both feasible and scalable, making it suitable for integration into the e-healthcare systems.
The growing use of blockchain in e-commerce has produced hybrid environments in which private enterprise ledgers and public blockchain networks operate side by side. Consequently, efficient and secure interoperability between these networks has become increasingly important. This study presents a cross-chain interoperability framework that links a permissioned Hyperledger Fabric network with a public Ethereum network. The framework provides attestations of selected business events rather than moving assets. An interoperability smart contract on Fabric emits cross-chain events; an off-chain validator enforces uniqueness and replay protection; and a public verification contract on Ethereum records an immutable, publicly verifiable attestation of each event. The framework uses a two-of-three validator threshold to attest events, so safety holds as long as no more than one of the three validators is compromised. The prototype was evaluated by processing 21,000 events across sequential, concurrent, and peak-load workloads. On the local network, message validation averaged approximately 12 ms per event, and the interoperability layer added less than 200 ms of overhead per attestation. Sustained throughput ranged from 13.2 to 14.2 attestations per second, while the validator used approximately 16% mean CPU and less than 194 MiB of memory, with no sustained memory growth during the full experiment. On the Ethereum Sepolia public testnet, 55 transactions were confirmed with a 100% success rate and a mean confirmation time of 10,676.62 ms. Gas consumption stayed stable at about 51,743 gas per verification on the local network and about 189,092 gas on Sepolia, and the mean public testnet transaction cost was 0.000692 Sepolia ETH. Five adversarial tests were conducted, covering replay, forgery, malicious relayers, concurrent replay, and denial-of-service attacks. All five tests passed, including the rejection of 500 concurrent replay attempts with zero double registrations. The results show that the framework provides efficient, verifiable, and replay-resistant cross-chain interoperability suited to hybrid e-commerce ledgers.
Muhammad Farooq Shaikh, S. Hamza Hassan, Jawwad Shamsi, Alessia Maccaro · 5 authors
Background and objective The integration of blockchain and digital twin (DT) technologies is increasingly recognised as a promising approach for improving healthcare data integrity, interoperability, privacy, and clinical decision support. While digital twins enable dynamic patient modelling and predictive healthcare applications, blockchain provides secure data governance through decentralised trust, auditability, and access control. However, existing research remains fragmented, with limited synthesis of the architectural integration, regulatory readiness, ethical governance, and interoperability of blockchain-enabled healthcare digital twin systems. This systematic scoping review addresses these gaps by providing a comprehensive architectural and compliance-oriented analysis of the current evidence. Methods A systematic scoping review was conducted following PRISMA 2020 guidelines using Scopus, PubMed, and Web of Science. From 148 identified records, 55 eligible studies published between 2020 and 2025 were included after duplicate removal and eligibility screening. Data were extracted on digital twin functionality, blockchain architecture, healthcare application domains, consensus mechanisms, privacy-preserving strategies, and regulatory and ethical alignment. Structured Python-based visual mapping and comparative analyses were performed to identify architectural, governance, and compliance patterns across the literature. Results The findings demonstrate that blockchain is predominantly employed to provide access control, audit logging, data integrity, consent management, and secure data provenance within healthcare digital twin ecosystems. Patient-level and EHR-centred digital twins represented the most mature application areas, whereas cross-domain and infrastructure-level frameworks dominated early architectural exploration. The review identifies recurring compliance-oriented architectural patterns while revealing substantial gaps in clinically validated deployments, interoperability with established healthcare standards, decentralised governance models, and formal implementation of GDPR- and HIPAA-compliant engineering practices. Comparative heatmap analyses further highlight the uneven maturity of ethical governance and regulatory integration across blockchain functionalities. Conclusion This review provides the first comprehensive compliance-oriented architectural synthesis of blockchain-enabled healthcare digital twin systems by integrating technical architecture, regulatory readiness, ethical governance, and privacy-preserving design patterns within a unified analytical framework. The proposed architectural mapping identifies critical research gaps in interoperability, governance engineering, consensus optimisation, and real-world clinical validation, providing a foundation for the development of trustworthy, GDPR/HIPAA-aligned, FHIR-compatible, and clinically interoperable healthcare digital twin ecosystems.
The use of a wireless sensor network is increasingly supporting e-governance functions such as municipal utility monitoring, environmental monitoring, grievance-based field reporting, and smart public service delivery. Most wireless sensor network architectures rely on a gateway or database. However, this introduces vulnerabilities to data integrity, node accountability, and auditability. This study examines transparency through a blockchain-enabled WSN architecture for e-governance. The study applies a reproducible Python-based Monte Carlo simulation with a fixed random seed, five node densities, three architectural scenarios, and 450 observations. The scenarios that are compared in this work are a normal WSN, a centralized secure WSN, and a permissioned blockchain-enabled WSN with smart-contract-based identity registration, hash-linked data records, trust scoring, and tamper verification. Descriptive statistics, one-way ANOVA, Welch t-tests, Pearson correlation, and multiple linear regression analysis. The blockchain-assisted WSN, as evidenced by the simulation findings of our project, produced the highest mean data integrity score, tampering detection rate, trust score, malicious node detection rate, and packet delivery ratio. The architecture also improved the composite service efficiency index relative to the conventional baseline, even though it introduced higher latency, transaction confirmation time, and energy consumption. The research indicates that the permissioned blockchain can enhance public-sector WSN transparency with edge aggregation and lightweight cryptographic operations along with carefully tuned endorsement rules. The methods presented in this study allow for scrutiny of secure WSN designs tailored for e-governance.
Secure and transparent attendance management has become increasingly important in educational institutions as conventional attendance systems often face challenges such as proxy attendance, unauthorized record modification, and limited traceability. Most existing solutions rely on centralized databases, making them susceptible to data tampering, accidental loss, and single-point failures. This paper presents a Blockchain-Based Attendance Management System that leverages blockchain technology to provide a decentralized and immutable mechanism for recording and verifying attendance information. The proposed framework integrates a React.js-based user interface with a Node.js and Express.js backend, while Firebase Authentication and Firestore manage user authentication and application data. Attendance records are securely stored through Ethereum smart contracts executed on the Ganache blockchain network, with transaction hashes linked to Firebase for efficient retrieval and verification. This hybrid architecture combines the scalability of cloud-based data management with the integrity and transparency of blockchain technology. Once attendance is recorded, the information cannot be altered without detection, ensuring reliable auditability and improved trust among students, faculty members, and administrators. The implemented system demonstrates secure attendance recording, fast verification, and efficient transaction management while reducing the possibility of record manipulation. The proposed solution offers a practical, scalable, and cost-effective approach for modern attendance management and provides a strong foundation for future enhancements such as biometric authentication, QR code-based attendance, and cloud-enabled blockchain deployment.