Abstract Smart agriculture promises to alleviate the burden of climate risks on crop production by leveraging machine learning tasks. These tasks act as a decision support instrument for making well-informed choices by stakeholders in the agricultural value chain. Currently, predictive models in smart agriculture demand a centralized collection of diverse data, fragmented across multiple information systems leading to a single point of failure. The application of the Federated Learning (FL) technique restricts the movement of raw data and trains the model at the data source. However, the FL approach does not ensure trust factors like privacy, authentication, data provenance, transparency and traceability. To address this, a decentralized federated learning framework built on blockchain can be a potential solution. In this study, we introduce a blockchain-based framework called AgriFLChain for trusted federated learning in the context of smart agriculture. We focus on crop yield prediction as an illustrative use case, initially discussing centralized deep learning models (ResNet-16, ResNet-28, CNN-DNN, and CNN-LSTM). We then detail the authentication and data provenance mechanisms for federated learning participants, utilizing blockchain-based Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). We implement these models using vanilla federated learning and Differential Privacy (DP) federated learning approaches, achieving transparency and traceability through smart contracts by recording metadata of model updates into the blockchain. Finally, detailed evaluation demonstrates that AgriFLChain achieves comparable efficiency to centralized models while maintaining scalability in blockchain transactions for higher data volumes.
This research introduces FoReChain (Fog-based Re-Encryption Chain), a blockchain-enabled proxy re-encryption (PRE) framework designed for secure, low-latency data sharing in fog-based IoT networks. The framework addresses key challenges related to data security, privacy, and performance in distributed environments, where traditional models face issues like high latency, limited scalability, and inefficient key management. FoReChain integrates blockchain with ECC-based proxy re-encryption to secure data without exposing original content. A delegated Practical Byzantine Fault Tolerance (d-PBFT) consensus mechanism ensures efficient transaction validation. The framework processes data at fog nodes, reducing delays commonly found in cloud-dependent models. Key management relies on time-based key updates stored immutably on the blockchain, while zero-knowledge proofs support secure, anonymous data sharing. The study evaluates FoReChain against FE-PRE and PREA models using metrics such as latency, throughput, scalability, blockchain consensus time, and adaptive policy effectiveness. Results show lower latency, higher throughput, and better adaptability in FoReChain, especially under heavy network conditions like smart healthcare and industrial IoT setups. FoReChain demonstrates secure data sharing, efficient resource utilization, and reliable key management in dynamic IoT environments. It offers consistent performance under varying loads, with improved scalability and data integrity maintained through decentralized validation.
Mohammad Yaser Mofatteh, Ujjwal Khadka, Omid Fatahi Valilai
Energy management can be designed from different perspectives including production, distribution, and consumption. Focusing on consumption perspective, manufacturing systems can be enhanced by enabling smart machines as agents which operate with their own knowledge representation models in a shopfloor. These agents can benefit from industry 4.0 enablers like IoT including sensors, controllers, and actuators. This paper focuses on how these agents can interoperate with each other and exchange knowledge to optimize energy consumption. Since different knowledge models may not be capable of interacting with other ones based on their different provider semantics. This paper explores the application of blockchain technology for secure, decentralized storage and sharing knowledge models in smart energy systems. The research introduces EnerChain as a blockchain-integrated and a decentralized application (DApp) system prototype that employs smart contracts for access management and conflict resolution. It also incorporates the InterPlanetary File System (IPFS) for efficient off-chain storage, addressing scalability concerns. The feasibility and practicality of this approach are demonstrated through the development of EnerChain. The findings highlight the significant potential of blockchain technology in facilitating efficient knowledge model management for smart shopfloors. Additionally, an operational scenario has been evaluated as a case study for the proposed conceptual model to illustrate how it can solve energy conflicts in a smart environment. An impact analysis at the end of this research shows that EnerChain can make annual 27.5 TWh reduction in residential energy consumption which yields to annual 7.8 million tonnes reduction in CO 2 emissions and annual âŹ8.25 billion financial benefits.
N. A. Natraj, Krishnapillai Vijayakumar, Sundaravadivazhagan Balasubaramanian, V. Kiruthika
Healthcare uses many facts to organize scientific investigations, collect scientific data, handle health insurance claims, and monitor patients daily. In practice, blockchain initiatives have focused on developing distributed ledgers for virtual currency, but this generation's enthusiasm has spread to the scientific industry. Programmable blockchains may solve some of the healthcare industry's biggest problems, such as a loss of powerful clinical report documentation, fragmented fitness statistics, and communication gaps. Blockchain is ready to transform healthcare, despite the lack of study. Blockchain's decentralised principles can improve patient data privacy and accessibility, which might weaken the present healthcare hierarchy and build a new one where patients pay for their own treatment. This study analyzes existing literature to identify healthcare stakeholders' main challenges and examine blockchain generation's potential solutions. Many issues and limits with this era should also be considered.
Abstract Applying Distributed Ledger Technologies to securely manage intercommunicated data between IoT applications has recently been adopted on an enormous scale. They enable data integrity, privacy, and robustness to public, open, permission-less P2P networks. Voting-based consensus algorithms proved high efficiency even with limited computing and less power IoT devices. Moreover, they can identify legitimate information and isolate malicious attackers through repetitive voting queries to adjacent peers asking their opinions about the validity of each transaction. Several lightweight validation models are introduced to enrich IoT networks with better performance and higher security. Nevertheless, the current algorithms struggle to find adequate parameters that balance network security and operability, in addition to balancing fairness in distributed environments. This paper introduces an Autonomous Lightweight Ledger Constructor to resolve common defects and threats. Based on Reinforcement Learning, it can dynamically construct a valid distributed ledger in limited-computing systems under several adversarial conditions. The validity of transactions in this approach is calculated based on their cumulative weights and the issuerâs reputation, which are inferred subjectively by a lightweight Bayesian-like function. A new simulator is developed to evaluate ALLC performance and security. The experimental results demonstrate reasonable performance and high resistance against known compromises targeting Distributed Ledger Technologies.
Judith Nkechinyere Njoku, Cosmas Ifeanyi Nwakanma, JaeâMin Lee, DongâSeong Kim
This study presents a digital twin framework for predicting the state of health (SoH) in battery management systems (BMS). This framework integrates a single particle model with electrolytes (SPME) and a long-short-term memory (LSTM) network to model battery behaviour based on a NASA battery dataset. To ensure the security of battery data, data is recorded on the Ethereum blockchain and queried when needed for secure prediction. To ensure the interpretability of the predictions, an explainable AI (XAI) approach, SHAP, is employed. Experimentation shows the viability of the proposed framework in accurately predicting the SoH of physical batteries.
Ensuring scalability in cryptocurrency systems is significant in guaranteeing real-world utility along with the remarkable increment of cryptographic currency. As an alternative in solving scalability issue, payment channel allows users to deliver extensive offline transactions without uploading massive transaction details to the blockchain, such that increasing efficiency can be achieved. However, the implementation of payment channel still encounters privacy concerns when considering the publicly available transaction amounts and the potentials in mining associations between transaction parties. In this paper, we propose a novel payment channel scheme, entitledCommitment-basedAnonymousPayment ChannEl (CAPE), to facilitate unlimited off-chain bidirectional payments while guaranteeing participantsâ privacy. The proposed scheme adopts zero-knowledge proof (zk-SNARKs) and verifiable timed (VTD) commitments to ensure the anonymity of the relationship between on-chain and off-chain transactions, privacy of transaction amounts, and security of balances. We comprehensively formalize security definitions and present rigorous proofs for each security attribute. Experiment results further demonstrate the practical viability of CAPE.
Digital product passports (DPPs) will become a reality for several regulated products in Europe. The topic is still in its infancy but will significantly impact product information and the infrastructure required by manufacturing in increasingly agile and circular supply chains. This paper presents the results of a two-year design science research to develop an end-to-end blockchain-based DPP prototype instantiated in the textile industry. On the one hand, the upstream supply chain involves physical product transformations from the early stages of production, requiring a robust traceability architecture. On the other hand, multiple events occur during downstream phases that need to be easily accessible by different stakeholders. Our results confirm the suitability of blockchain-based DPPs and define the information flow within the product life cycle. This paper advances the literature on sustainable product identification, ensuring tamper-proof and transparent information in the supply chain. Although aiming at the end consumers, the DPP will majorly impact production, requiring proper industry planning. For practitioners, this paper provides one of the first DPP instantiations in different segments of the textile supply chain, highlighting the requirements that the industry should be aware of for the coming years. Eleven design principles for blockchain-based DPPs are proposed.
These challenges were further amplified in recent years, causing recruitment processes to be more transparent in aspects like education, work experience, and professional verification. Traditional methods of validating credentials - contacting institutions or employers directly - can be time-consuming, mistake-prone and susceptible to fraud. Blockchain technology offers a solution that can assist in the digitization of credential verification, enabling employers to rapidly and securely verify a candidate's credentials. This paper explores blockchain use in talent acquisition and verification of credentials, highlighting its game-changing potential for the hiring process. Blockchain is a distributed and immutable digital ledger in which data cannot be altered once it has been entered, providing a high degree of trust and transparency. Blockchain technology enables educational institutions, employers and other credentialing bodies to issue verified digital credentials that are securely stored and can be easily presented to employers. One of the emerging trends is Verified CVs verified on blockchain based solutions, candidates can represent their validated qualifications on blockchain based solutions and employers can have instant access for any information regarding educational qualifications, certifications, experience regarding employments and skills related information. The blockchain based approach in the proposed credential verification process is because all the session information or credentials would be stored in a secured block so that respective academic institutions or bodies and their employers can verify using the cryptographic signatures as mentioned earlier. Each credential is identifiable and stored on the blockchain, which means that the record cannot be tampered with and can be verified from any interested party with valid access rights. As a result, they can also save time as smart contracts allow these individuals to execute the agreed-upon terms, provided certain conditions are satisfied, and avoid the need for intermediaries to obtain validated information. The key benefit of verification on blockchain is time and money. Verifying the authenticity of an employee goes through traditional checks like background checks and manual verification processes that take days or weeks which means a delay in hiring and increased costs of operation.
Future generations of wireless networks at high-frequency spectrum suffer from limited coverage and Non-Line- of-Sight signal blockage, challenging emerging applications, such as smart industries and intelligent automation systems. Collaborative and cooperative communications with smart relays via Non-Orthogonal Multiple Access (NOMA) could be a breakthrough solution to this challenge. This paper presents a blockchain-integrated framework for NOMA wireless communication systems that incentivizes cooperation among users serving as relays. By leveraging Ethereum-based smart contracts, we introduce a Service Verification Contract featuring a Proof of Quality of Experience (PQoE) mechanism. The contract uses trust scores, weighted verifications, and dynamic validation thresholds to ensure honest behavior and deter malicious activities. The simulation results show that honest participants gradually increase their trust scores and require fewer verifications, while malicious verifiers lose influence over repeated rounds. Our findings indicate that combining trust-based incentives with a decentralized ledger can effectively promote reliable data-relaying services and streamline payment processes in collaborative and smart wireless networking systems.
Ensuring data integrity is crucial for IoT-based healthcare and emotion care services, which utilize Fog computing to bring resources and services closer to the network edge. This proximity, however, increases the risks of data tampering, loss, and unauthorized access. To mitigate these risks, Distributed Ledger Technology (DLT) platforms such as Hash graph, Big chain-DB, IOTA (Internet of Things Application) and Blockchain are being investigated for their potential to enhance data integrity within Fog computing environments. This study presents a framework designed to ensure data integrity in IoT-based healthcare and emotion care services by leveraging IOTA technology. IOTA, which employs a directed a-cyclic graph (DAG) structure known as the Tangle, provides a secure, decentralised and tamper-resistant method for data storage and sharing. Unlike traditional blockchain, IOTAâs consensus mechanism operates without miners, offering improved scalability and efficiency suitable for IoT environments. Our proposed framework exploits IOTAâs capabilities to deliver a robust solution for maintaining data integrity in Fog computing contexts. The evaluation results demonstrate the frameworkâs feasibility and effectiveness in enhancing data integrity for IoT-based healthcare and emotion care services. Although IOTA significantly improves data integrity by complicating unauthorized data alterations, it is essential to acknowledge that complete immutability is influenced by various factors, such as consensus mechanisms and the number of network participants, similar to the limitations observed in other DLTs. ⢠Integrating Fog Computing with Distributed Ledger Technology (DLT) utilizing IOTA. ⢠Leveraging the âImmutable Data Tangleâ structure to safeguard data against unauthorized modifications and tampering. ⢠Fortifying Resilience against Security Threats using DLT (IOTA). ⢠Provides insights into the effectiveness of hybrid cryptanalytic attacks and the role of DLT (IOTA) integration in countering them. ⢠Practical implementations are meticulously presented, accompanied by real-world case studies.
Introduction The insurance industry has evolved into a global multi-billion-dollar sector, with health insurance gaining prominence due to escalating healthcare costs. This rapid expansion brings heightened risks, including data breaches, fraud, and difficulties in safeguarding sensitive policyholder information. Indonesiaâs National Health Insurance (NHI)âone of the largest national insurance programs worldwideâcovers over 200 million citizens, aiming to provide universal healthcare. However, this extensive coverage raises substantial concerns about data privacy and traceability, particularly during the claim process, as policyholders currently have limited control over and insight into how their data is accessed and used. Methods To address these challenges, we propose a blockchain-based model designed to enhance policyholdersâ private control over data access and improve traceability throughout the NHI claim process. Our approach employs three complementary architecturesâfunctional, logical, and physicalâto guide system implementation. The functional architecture is illustrated via a use case diagram that outlines the roles and actions of each participant. The logical architecture employs Business Process Model and Notation (BPMN) diagrams to depict the revised process flow and data movement, while also incorporating a layered design concept. The physical data architecture provides a class diagram detailing data structures and actor relationships. A proof-of-concept prototype was developed to demonstrate the core functionalities of the new system. Results By integrating blockchain technology, our prototype ensures authorized access, bolsters data privacy, and maintains data integrity in the NHI claim workflow. The systemâs layered design and use of smart contracts guarantee transparent, tamper-proof record-keeping, while parallelized processes in the logical architecture streamline claims handling. Initial tests of the prototype confirm the feasibility and robustness of the proposed solution, illustrating how blockchain can facilitate traceability and preserve confidentiality. Discussion The blockchain-based design addresses pressing concerns surrounding data security and accountability in large-scale health insurance systems. It allows policyholders to monitor and control their personal information, reducing the likelihood of unauthorized use. Furthermore, the transparent and immutable ledger enables stakeholders to verify data provenance and transactions, enhancing trust. Future work will focus on scalability, regulatory compliance, and integration with existing healthcare IT infrastructures to fully realize the benefits of blockchain in national health insurance programs.
In the absence of a permanent infrastructure, the networks are utilized for temporary events, military operations, and disaster recovery. A Blockchain-Enabled Intelligent Vehicle Communication Systems: Trust Bit Rewards and Clustering for Autonomous Vehicles (BIVC-CA), an algorithm designed to ensure secure communication between devices and optimize efficient routing in intelligent vehicle systems, is also used to provide quick emergency response. Using blockchain, each vehicle is given its own Bit Trust ID to track its past data and calculate its trust levels. This system ensures transparency and trust between vehicles through cryptographic processes such as key generation, secure message encryption, and message verification, which improve overall communication security. It also includes an Emergency Vehicle Communication System (EUC) that uses OBU devices and sensors to detect collisions and send GSM-based rescue messages, greatly improving road safety and the efficiency of emergency operations. The road-based clustering model optimizes route selection considering traffic conditions and distances and organizes vehicles into clusters to ensure effective communication and coordination. Using the path distance algorithm, clustering is achieved by computing the similarity of routes and grouping accordingly for efficient route management with minimal network congestion. In addition, the model introduces a comprehensive network architecture that integrates vehicle cloud technology and blockchain technology, enabling intelligent vehicles to make quick decisions based on real-time data. With these integrated components, the BIVC-CA model provides a reliable framework for secure data transmission, intelligent routing, and rapid emergency response. This framework will ultimately improve the efficiency of intelligent vehicle systems and contribute to the development of advanced vehicle networks and smarter transportation solutions. We calculated the results with routing expenses, packet delivery ratio, end-to-end delay, throughput, and packet loss ratio using this our BIVC-CA model has achieved less routing expenses, and delay.
The rapid growth of IoT has increased the demand for large-scale data processing. However, traditional centralized methods struggle with real-time requirements and data security . This paper introduces VCD-TSNet, a novel real-time IoT data processing framework that combines blockchain and edge computing . By integrating deep learning models like VGG, ConvLSTM , and DNN , VCD-TSNet effectively performs spatial feature extraction, temporal modeling , and decision-making, while using blockchain to ensure data integrity and privacy. Experimental results demonstrate that VCD-TSNet outperforms baseline models in classification accuracy , prediction precision, and real-time performance. For instance, on the BoT-IoT dataset, the classification accuracy reaches 97.5%, throughput increases to 920 TPS, and response time stays below 85 ms. This study validates the modelâs effectiveness and highlights its potential in large-scale IoT environments, offering efficient, secure solutions for real-time data processing. It also provides insights for future improvements in frameworks that combine edge computing with blockchain.
Web3 is the next-generation internet, utilizing blockchain technology to power decentralized applications and give users greater control. However, the scalability limitations of blockchain create performance bottlenecks that hinder Web3âs overall processing capabilities. Among current scalability solutions, multi-chain architecture has been considered a promising approach with high flexibility. However, current multi-chain architecture lacks portability to existing blockchains and relies on relayers to solve timing issues in the interoperability process. The lack of portability makes it challenging for existing blockchains to adopt the current multi-chain architecture, significantly impeding multi-chain promotion. Moreover, relying on relayers to address timing issues leads to low efficiency and potential reliability risks. This paper introduces Zunesha, a multi-chain architecture that designs a smart-contractbased multi-chain toolkit (STACK) to provide a portable multi-chain architecture. Additionally, it introduces the Dynasty-Based Consensus Node Set Verification (DB-CNSV) protocol as a foundational safety mechanism to eliminate relayers in the interoperability process and address timing issues. Our evaluation shows that Zunesha significantly enhances the overall performance of the blockchain. As the number of subchains increases, the throughput grows almost linearly. Furthermore, the performance of inter-chain transactions surpasses that of the current mainstream multi-chain architecture, Cosmos.
Damilare Peter Oyinloye, Je Sen Teh, Mohd Najwadi Yusoff
Blockchain technology has been adopted in various sectors including health, energy and agriculture. It functions as a distributed ledger maintained by multiple nodes, relying on consensus protocols to verify transactions and reach agreement. However, conventional protocols like Proof of Work (PoW) and Proof of Stake (PoS) face challenges with scalability and energy efficiency. This paper introduces Proof of Collaborative Contribution (PoCC), a new consensus protocol aimed at encouraging positive network contributions and fostering collaboration. It utilizes a simplified hashing mechanism secured by trusted execution environments (TEEs). As a proof of concept, PoCC is applied to a solar energy generation system, where prosumers are incentivized to offset their energy consumption and feed excess energy back into the grid. Using real-world energy data from AusGrid, our simulations demonstrate that PoCC ensures equitable block leader selection. We also show that PoCC scales well with the number of nodes, consumes significantly less energy than PoW, and is resistant to Sybil and 51% attacks. We also briefly explore other potential applications of PoCC in systems that aim to reward node contributions.
The Internet of Things (IoT) is considered an evolving technology, consisting of different types of connected devices that have sensors, specialized hardware, and software. However, considerable challenges related to user data privacy, security, and device integrity are presented by this swift expansion, with digital identity being identified as a significant concern in the online landscape. Established by the World Wide Web Consortium (W3C) [2], Decentralized Identifiers (DIDs) represent a standardized format for defining identity. A peer-to-peer model for managing identity and credentials through self-sovereign identity is facilitated by DIDs, in conjunction with blockchain technology and public-key cryptography. The development of verifiable credentials is enabled by the combination of DIDs and blockchain technology, allowing for the verification of the identities of transacting parties through distributed identity registration and the identification of cryptographic keys necessary for validating electronic authentication during transactions. This integrated framework is regarded as both reliable and trusted. Device registration, verification, and credential revocation are enabled by it. In addition, security is ensured, the privacy of participating nodes is respected, and data integrity is upheld to address several existing challenges, such as data scalability and confidentiality. Trusted management and transaction of IoT data are ensured by the presented IoT data platform. Blockchain, decentralized identifiers, and public key cryptography are integrated by the authors to develop this platform. Self-sovereign identity is granted to each participating device, and dynamic changes in components like consensus algorithms and databases are supported by key features. Different IoT communication protocols are evaluated within a decentralized blockchain infrastructure for identity verification by the authors, focusing on interactions between IoT devices and networked servers through wireless connections. The Hyper ledger framework is used to implement blockchain. Consequently, subsequent optimization solutions are proposed to expand the platform's application range and improve the efficiency of IoT data management.
For developing and less-developed countries, the e-governance system is highly helpful. The public mostly uses the e-governance system; thus, security is highly required. In prevailing studies, the security enhancement of the e-governance cloud application was concentrated. However, a security shortage issue was presented. To solve this issue, this study proposes ETCC-centric secure data storage in e-governance cloud applications. Primarily, the user registers into the server by utilizing their username and password. Next, only the location-matched users are permitted to access the server. Then, the public and private keys are generated by the key generation. Subsequently, by utilizing user behavior extraction, important behavior selection by BMFKO and prediction by ANOVA-based PA-ChatGPT, the trust is analyzed. The predicted abnormal behavior user is blocked. Next, the normal behavior user is permitted to upload and download the data. The data is securely stored by the proposed ETCC technique. The Beta Delegated Proof of Stake (BDPoS) technique considers blockchain for enhancing security. Authorized data user downloads the data and decrypts it with their private key. Based on performance measures, the proposed techniques are analogized with the prevailing techniques in experimental analysis. The security level attained by the proposed technique is 99.2%.
This article presents a novel framework for implementing decentralized identity management in microservices architecture using blockchain technology. The proposed solution addresses the inherent challenges of traditional centralized identity management systems by leveraging distributed ledger technology and smart contracts to create a secure, transparent, and user-centric authentication mechanism. The framework incorporates comprehensive privacy controls and consent management features while ensuring compliance with regulatory requirements across various industries. Through extensive evaluation across multiple use cases in healthcare, financial services, and government sectors, the results demonstrate enhanced security, improved scalability, and better user privacy control compared to conventional approaches. The article suggests that blockchain-based identity management can significantly reduce the risk of security breaches while providing a more robust and flexible authentication mechanism for modern distributed systems. This article contributes to the growing body of knowledge in distributed systems security and provides practical insights for organizations looking to implement decentralized identity management solutions.
As the acceptance of Internet of Things (IoT) systems quickens, guaranteeing their sustainability and reliability poses an important challenge. Faults in IoT systems can result in resource inefficiency, high energy consumption, reduced security, and operational downtime, obstructing sustainability goals. Thus, blockchain (BC) technology, known for its decentralized and distributed characteristics, can offer significant solutions in IoT networks. BC technology provides several benefits, such as traceability, immutability, confidentiality, tamper proofing, data integrity, and privacy, without utilizing a third party. Recently, several consensus algorithms, including ripple, proof of stake (PoS), proof of work (PoW), and practical Byzantine fault tolerance (PBFT), have been developed to enhance BC efficiency. Combining fault detection algorithms and BC technology can result in a more reliable and secure IoT environment. Thus, this study presents a sustainable BC-Driven Edge Verification with a Consensus Approach-enabled Optimal Deep Learning (BCEVCA-ODL) approach for fault recognition in sustainable IoT environments. The proposed BCEVCA-ODL technique incorporates the merits of the BC, IoT, and DL techniques to enhance IoT networksâ security, trustworthiness, and efficacy. IoT devices have a substantial level of decentralized decision-making capacity in BC technology to achieve a consensus on the accomplishment of intrablock transactions. A stacked sparse autoencoder (SSAE) model is employed to detect faults in IoT networks. Lastly, the Piranha Foraging Optimization Algorithm (PFOA) approach is used for optimum hyperparameter tuning of the SSAE approach, which assists in enhancing the fault recognition rate. A wide range of simulations was accomplished to highlight the efficacy of the BCEVCA-ODL technique. The BCEVCA-ODL technique achieved a superior FDA value of 100% at a fault probability of 0.00, outperforming the other evaluated methods. The proposed work highlights the significance of embedding sustainability into IoT systems, underlining how advanced fault detection can provide environmental and operational benefits. The experimental outcomes pave the way for greener IoT technologies that support global sustainability initiatives.
Nabil A. Ismail, Shaimaa Abu Khadra, Gamal Attiya, Salah Eldin S. E. Abdulrahman
Abstract Blockchain technology offers a robust framework for integration with the Internet of Things (IoT), enhancing interoperability, security, privacy, and scalability in modern technological ecosystems. However, traditional cryptographic protocols used in blockchain systems are increasingly vulnerable to quantum attacks due to advancements in quantum computing. In response, the National Institute of Standards and Technology (NIST) has prioritized research in post-quantum cryptography, presenting challenges and opportunities for developing blockchain-based applications tailored to IoT devices. Among the post-quantum cryptographic schemes evaluated in NIST's third standardization round, the Supersingular Isogeny Key Encapsulation (SIKE) protocol stands out for its relatively small public and private key sizes. Despite this advantage, SIKE faces challenges related to high latency, necessitating efficient implementations to make it viable for real-world applications. This research focuses on optimizing the cryptographic foundations of blockchain networks to securely and efficiently integrate resource-constrained IoT ecosystems. By enhancing the SIKE protocol, which exhibits strong resistance to brute-force and whitewashing attacks, the study achieves significant performance improvements. Our FPGA-based implementation on the VIRTEX-6 XC6VLX760 demonstrates reduced latency, achieving a key generation time of 24 ms, encapsulation time of 72 ms, and decapsulation time of 73 ms for SIKEp434. These results highlight the feasibility of deploying SIKE-optimized blockchain networks in IoT environments with stringent resource constraints.
As healthcare systems increasingly adopt fog computing to improve responsiveness and real-time data processing at the edge, significant security challenges emerge due to the decentralized architecture. The traditional perimeter-based security models are inadequate for addressing the dynamic and distributed nature of fog networks, leaving them vulnerable to unauthorized access, data tampering, and latency issues. Therefore, this paper proposes a novel security framework that integrates blockchain (BC) and software-defined network (SDN) technologies, underpinned by zero-trust (ZT) principles, to address these challenges in latency-sensitive healthcare environments. The proposed framework enhances security by combining BCâs immutable transaction logs for data integrity and traceability with SDNâs dynamic network reconfiguration for real-time access control and anomaly detection. The integration of BC and SDN supports continuous authentication and monitoring using cryptographic protocols (SHA-256A and RSA-2048) to secure data transmission. Additionally, tasks are dynamically allocated to fog nodes based on a multi-metric scheduling mechanism that considers fog node capacity, proximity, and compliance with predefined security protocols. The framework was evaluated using iFogSim, simulating a healthcare environment with 50 IoT devices, 10 fog nodes, and varying workloads (100â1000 tasks/min). The key evaluation performance metrics include intrusion detection rate (IDR), data integrity (DI), task completion rate (TCR), average task response time (ART), and average block time. The implementation results demonstrate satisfactory improvements compared to existing models: a 40% increase in IDR, a 30% enhancement in DI, a 15.29% rise in TCR, and a 39.66% reduction in ART. Moreover, the baseline IDR (85%) and DI (70%) were drawn from ZT-1, while TCR (85%) and ART (300 ms) were measured using ZT-2 as benchmarks. These findings illustrate the feasibility of integrating BC, SDN, and ZT principles to mitigate threats such as unauthorized access, data tampering, and delays in latency-sensitive tasks.
M. Natarajan, A. Bharathi, C. Sai Varun, Shitharth Selvarajan
Nowadays, most of the medical records are maintained in a digital format known as Electronic Health Records Sharing (EHRS) framework. Patients have individual login credentials for accessing these medical records. In the BCT, the information about the owner of the block and its dependency over other blocks is maintained in itself. Moreover, each block is linked with its nearby blocks, leading to a network controlled by patients responsible for storing and sharing the information. In healthcare, BCT can help with mobile health apps, monitoring equipment, sharing and keeping of clinical trial data, electronic medical records, and insurance information storage. This study proposes a secure Patient Login Credential System (PLCS) for EHRS. The proposed scheme has been included for block encryption with the symmetric and asymmetric cryptography algorithms with respect to the hospital server and patients. Additionally, the Quantum Secure Trust Protocol (QSTP) is integrated to enhance trust and security between the patient-side and hospital-side, maintaining data integrity and confidentiality. Similarly, the Tune Swarm Optimization (TSO) algorithm is utilized to optimize performance metrics. The security analysis for the proposed scheme has been evaluated with basic security assumptions for information systems like, availability, access control, maintaining forward secrecy, and maintaining data integrity. The proposed scheme demonstrated enhanced security and performance, with IDEA achieving encryption in 58 ms and decryption in 278 ms for a 512-bit block, offering the best performance in terms of encryption speed.
As IoT continues to expand, the security of connected devices remains a critical concern, particularly in the face of DDoS attacks. This study introduces a novel approach that leverages blockchain technology through smart contracts integrated with an advanced attack detection mechanism. Central to this approach is the Enhanced Residual Gated Recurrent Unit (ERGRU) architecture, designed to effectively identify and mitigate DDoS attacks within IoT networks. The Adaptive Coati Optimization Algorithm (ACOA) was used to adjust the hyperparameters of the ERGRU model, such as the learning rate and the number of GRU neurons, to further improve detection accuracy. In addition, the proposed framework uses a one-way compression function to generate secure hashes for input data, utilizing the Merkle-DamgĂĽrd cryptography technique to ensure data integrity and confidentiality. The proposed solution was tested through a rigorous process using a DDoS dataset. Performance was assessed by focusing on metrics such as processing time, data integrity rate, and confidentially rate. The results demonstrate the effectiveness of the proposed smart contract-based framework in providing a durable and efficient protection mechanism against DDoS attacks in IoT environments.