Follow blockchain research across journals, conferences, and preprint repositories.
8,837 results · page 9 of 369
Shilpa Mahajan, Garima Sharma, Ambika Thakur, Laxmi Upadhyay · 6 authors
No abstract is available for this record.
Jiazheng Lin, Yingying Wang
ABSTRACT The increasing complexity and interdependence of global supply chains necessitate innovative solutions to enhance collaboration while safeguarding data privacy. This paper presents a lightweight multi‐blockchain framework designed to address the challenges of privacy‐preserving collaboration in Internet of Things (IoT)‐driven supply chains. By integrating multiple blockchain networks, the proposed framework ensures data confidentiality, integrity, and traceability across various stakeholders. The system employs advanced cryptographic techniques, including zero‐knowledge proofs and differential privacy, to protect sensitive information during data exchange and processing. Additionally, the framework incorporates lightweight consensus mechanisms to accommodate the resource constraints of IoT devices. Experimental evaluations demonstrate the effectiveness of the proposed framework in improving data privacy and system scalability compared to existing solutions.
Jeyalakshmi Jeyabalan, Palavajjhala Neha Madhu Manasa, K. Vijay
No abstract is available for this record.
Ashish Revar, Khooshi Sonkar, Dhaval Deshkar, K. H. Wandra
No abstract is available for this record.
Hong Min, Yousef Ibrahim Daradkeh, Jung Taek Seo, Mohd Anjum · 5 authors
This study presents a computational modeling framework for efficient and secure computation offloading in Internet of Things (IoT)-enabled smart contract systems. The integration of IoT, edge computing, and blockchain introduces significant challenges, including limited device capacity, high verification cost, and scalability constraints. Existing blockchain verification approaches depend on computationally intensive cryptographic operations that are inefficient for resource-constrained IoT devices, resulting in increased latency, energy consumption, and transaction costs. To address these issues, this study proposes the Zero-Knowledge Fuzzy Logic Offloading and Rollup (Z-FLOR) framework, an adaptive and energy-efficient model designed to enable secure and verifiable computation in IoT-based smart contract systems. The proposed framework integrates three key components. First, a zero-knowledge proof-based verification model using the Groth16 zkSNARK module generates compact and privacy-preserving proofs that enable fast and reliable verification. Second, a Fuzzy Logic–Driven Energy-Aware Offloading module dynamically allocates computational tasks between IoT devices, edge servers, and cloud platforms based on energy availability, network delay, and device reliability. Third, an Optimistic Rollup Verification module aggregates proofs off-chain and submits them in batches to reduce gas costs and enhance scalability. Extensive simulation and experimental evaluation across diverse IoT scenarios demonstrate the effectiveness of the proposed computational framework. Results indicate that Z-FLOR achieves 99.7% verification accuracy and 98.9% proof compression efficiency, while gas cost analysis indicates gas cost reductions in the range of 80%–98%. Z-FLOR additionally achieves a 44.0% reduction in latency, 51.0% savings in gas costs, and 38.0% energy consumption compared to baseline approaches. These findings highlight the capability of the proposed approach to serve as a scalable and energy-efficient modeling solution for secure IoT smart contract execution in decentralized environments.
E. Poongothai, T. R. Saravanan, K. S. Kavitha Kumari, T. Ananth Kumar · 5 authors
No abstract is available for this record.
khaled Gadouh, Hend Koubaa, Manel Boujelben
Resource allocation in blockchain networks is an urgent issue due to changes in transaction load, networkcongestion, and the computational challenges associated with smart contract execution. Suboptimal resource utilization leads to high operational costs and reduced network performance. In this context, this paper presents a new hybrid algorithm for resource management in blockchain networks based on the integration of Q-learning reinforcement learning and the Ship Rescue Optimization (SRO) algorithm. The SRO algorithm is used to optimize the hyperparameters and the initial Q-learning policy, enabling the learning process to converge more effectively to an optimal solution and make better resource allocation decisions. We formulate the resource allocation problem as a Markov Decision Process (MDP), in which the agent learns optimal scaling policies for CPU, memory, and bandwidth resources. Comprehensive testing on an implemented blockchain network with 100 nodes and 245,782 transactions across 1,000 blocks shows significant improvements, including an average reduction of 38.76 % in CPU usage, 35.99% in RAM usage, and 38.51% in transaction latency, along with a 58.21% increase in throughput compared to baseline methods. All improvements are statistically significant (p 3.0) according to the statistical analysis. The ablation study shows that each component plays a significant role in the overall system performance. In particular, the proposed hybrid algorithm provides an additional 13.88% performance improvement compared to Q-learning alone and . Furthermore, the suggested framework outperforms existing methods, such as Deep Q-Networks, Genetic Algorithms, and Particle Swarm Optimization, establishing a new benchmark for blockchain resource allocation.
Preethi, Mohammed Mujeer Ulla, R. Sapna, Dr Raghavendra M Devadas · 6 authors
No abstract is available for this record.
Archana Kurde, Sushil Kumar Singh
The swift expansion of IoT devices in smart cities demands decentralized and open systems of attentive exchange of assets in automotive supply chains. Nevertheless, the majority of the available blockchain-based solutions are focused on traceability and ignore scalability, conditional payment automation, and real-time IoT verification. To overcome those pitfalls, the research proposes a Blockchain-based framework implemented on Hyperledger Fabric, incorporating Non-Fungible Tokens, and escrow-based smart contracts, to facilitate verifiable, automated vehicle transactions. The payment is conditionally released, and the vehicle is represented as a discrete NFT that undergoes authenticated release under Fabric Certificate Authority with escrow verification. Sub-millisecond latency (0.0003 s), constant throughput, and minimal computational cost experimentally verify the effectiveness of the framework in terms of its efficiency, privacy, and scalability in the efficient and autonomous exchange of assets in next-generation smart cities.
Chaimae El Filali, Imad Bourian, Khalid Chougdali
Authentication is becoming essential due to the expansion of the Internet of Things (IoT) applications in smart cities, supply chain, and healthcare. In the healthcare sector, hospitals use centralized server-based systems to manage user information and patient medical records. However, this approach may lead to scalability, interoperability, security and privacy challenges. To address these issues, this paper presents a blockchain-based multi-factor authentication (MFA) framework for IoT healthcare systems. The framework uses the Ethereum blockchain and smart contracts to improve authentication security and minimize unauthorized access risk. It also uses the InterPlanetary File System (IPFS) to securely and efficiently store sensitive medical data. Performance and security are evaluated to show the effectiveness, reliability, and feasibility of the proposed system.
Tingting Zhao, Shiya Feng, Daohua Pan, Qi Wang
Healthcare systems increasingly rely on digital twin technology to create virtual representations of patients, medical devices, and clinical workflows. However, secure data sharing across medical digital twin edge networks faces critical privacy and security challenges when handling sensitive patient information and treatment protocols. This paper presents a medical digital twin blockchain sharding (MDTBS) framework that leverages blockchain sharding technology to address security and privacy concerns in healthcare data sharing while maintaining the real-time responsiveness required for clinical operations. The framework incorporates a novel dual-layer architecture combining local medical data sharing chains using directed acyclic graph consensus for intra-hospital communications with global medical data sharing chains employing delegated proof of stake consensus for inter-hospital collaboration. Considering the dynamic characteristics of medical environments and mapping errors between physical healthcare systems and their digital twins, we formulate an adaptive resource allocation model that jointly optimizes medical cluster head selection, hospital base station consensus access, and spectrum and computation resource allocation to maximize blockchain sharding transaction throughput. A medical digital twin edge network-empowered two-layer proximal policy optimization algorithm solves the complex optimization problems while adapting to time-varying medical workflows and equipment configurations. Simulation experiments demonstrate that the framework achieves superior performance across all evaluation metrics compared to baseline methods, including 15-25% improvements in transaction throughput with statistical significance (p-value less than 0.001), sub-three-second emergency response times, and 85%+ privacy preservation efficiency scores.
D C Saputro, Noor A Setiawan, Azkario Rizky Pratama, Avinanta Tarigan
The increasing adoption of blockchain technology in education has introduced alternative approaches to identity verification beyond centralized credential systems. This study proposes and implements a decentralized authentication mechanism for Moodle LMS using ERC-721 non-fungible tokens (NFTs) verified through MetaMask. Developed as a proof-of-concept following a design science methodology, the system links on-chain identity tokens to Moodle accounts without storing usernames or passwords. The architecture integrates Ethereum smart contracts, nonce-based digital signature verification, and Moodle’s Role-Based Access Control (RBAC) framework. Functional testing confirms that access is granted exclusively to users possessing valid ERC-721 tokens and verified wallet signatures. Replay attack simulations demonstrate effective resistance through nonce validation, while ensuring that no sensitive credential data is exposed during the authentication process, in contrast to default Moodle login mechanisms. Performance evaluation using Apache JMeter indicates stable operation under moderate loads. Although scalability and token management limitations remain, the results demonstrate the technical feasibility and enhanced security advantages of NFT-based authentication for learning management systems.
T Krishna, Vadlakonda Sai Vishal, J. Vamsinath
ABSTRACT Efficient disaster response requires scalable, transparent and trustworthy resource management systems. However, centralized approaches frequently suffer from coordination delays, data tampering risks, limited transparency and single points of failure, reducing reliability during large‐scale crises. This study presents a decentralised blockchain‐based framework that integrates smart contracts, decentralised multi‐source oracles for Internet of Things (IoT)‐enabled field reporting, role‐based access control and adaptive urgency scoring to improve allocation prioritisation and trust calibration. The architecture follows a structured three‐tier design. The edge layer supports real‐time sensing and secure data offloading through the Inter‐Planetary File System (IPFS). The blockchain logic layer enforces operational policies, dynamic prioritisation, and reputation scoring using modular, gas‐efficient smart contracts. An integration/API layer ensures secure interoperability among emergency agencies and stakeholders. A hybrid blockchain model combines Ethereum Proof‐of‐Stake (PoS) for public transparency with a permissioned consortium chain for controlled governance. Natural Language Processing (NLP) derives urgency scores from textual disaster reports, while a dynamic supply‐demand aware algorithm adapts resource allocation in real time. Multi‐signature governance and reputation mechanisms further enhance accountability. Experimental evaluation on a simulated testnet demonstrates throughput up to 1000 Transactions Per Second (TPS), alongside measurable improvements in fairness, auditability and allocation efficiency.
Richa Golash, Shahnawaz Ahmad, Bhawana, Naween Kumar
No abstract is available for this record.
Hexiao LI, Jaafar Gaber, Salah Laghrouche
No abstract is available for this record.
P. Manju Bala, S. Usharani, A. Balachandar, A. Olukayode
No abstract is available for this record.
Odnala Srinivas, Mallu Shiva Rama Krishna, G. Bhavani, Pullela Gokul Krishna · 7 authors
The exponential growth of IoT devices in smart city infrastructures generates vast edge data, demanding secure, low latency, energy-efficient processing. Conventional cloud-centric models face bandwidth bottlenecks, latency overhead, and single-point vulnerabilities, necessitating decentralized management. This research introduces BQAREM: Blockchain-Secured Quantum Adaptive Resource Management for Edge Machine Learning, integrating blockchain security, quantum optimization, reinforcement learning. The framework employs timestamped identity verification, multi parameter trust assessment, and a weighted Proof-of-Stake consensus for secure coordination. Quantum adaptive scheduling and smart contracts ensure efficient, tamper-proof resource allocation, achieving superior latency, energy efficiency, SLA compliance. Comprehensive performance evaluation demonstrates that BQAREM significantly enhances reliability, scalability, intelligent resource orchestration across heterogeneous edge environments. Testing in various smart city scenarios including Smart Grid Control, Traffic Management, Healthcare Monitoring, Surveillance Systems, and Emergency Response demonstrates high accuracy (> 95%), reduced latency (< 50 ms), and efficiency improvements exceeding 80%, with balanced energy use. BQAREM uniquely unifies blockchain-backed trust like timestamped identity + weighted PoS, quantum-adaptive risk-sensitive RL, and multi-resource orchestration with a new Robust Performance Index (RPI) for secure, low-latency, energy-aware Edge-ML scheduling.
K. V. Panduranga Rao, S. K. Yakoob, C Dastagiraiah, T. Veeranna
No abstract is available for this record.
Mohammad Fardad, Elham Mohammadzadeh Mianji, Gabriel‐Miro Muntean, Irina Tal
No abstract is available for this record.
Yibei Li, Jingyi Yang, Yiwei Lai, Mingzhe Liu
No abstract is available for this record.
Qianqian Pan, Jun Wu
In the coming 6G era, Internet of Consumer Electronics (ICE) is promising in academia and daily life. To improve the intelligence and reliability of ICE devices with limited resources, a cloud-edge-end collaborative intelligent architecture is designed. However, the current collaborative intelligent ICE system faces multiple security threats, e.g., illegal access to the intelligent data/model resources and poisoning/backdoor attacks during collaborative intelligent model training. The security of the intelligent collaborative ICE is still an open issue. To solve this problem, we propose a zero-knowledge proof (ZKP)-driven zero trust method to protect the security of the intelligent collaborative ICE. First, a zero-trust intelligent collaborative ICE security framework is established for the confidentiality and availability protection of data/model resources. Second, we propose a zero-trust multi-factor access control mechanism for mutual authentication and trust evaluation-based resource access authorization. Third, the ZKP-driven collaborative intelligent ICE mechanism is designed. In this mechanism, the selective differential privacy-based data preservation scheme and the zero-knowledge collaborative intelligent model training scheme are devised. Finally, experimental results demonstrate the effectiveness and efficiency of the proposed secure intelligent collaborative ICE.
Amrendra Singh Yadav, Mihir Bhatt, Sameer Yadav, Sanjeev Kumar Dwivedi · 5 authors
The rapid evolution of the Internet of Vehicles (IoV) necessitates secure, scalable, and low-latency route navigation mechanisms that can operate in highly dynamic vehicular environments. Emerging paradigms such as Vehicular Digital Twins (VDTs) further enhance IoV ecosystems by enabling real-time virtual representations of physical vehicles, facilitating predictive analytics, intelligent decision-making, and context-aware routing. However, conventional VANET-based approaches suffer from centralized trust dependencies, high computational overhead, and limited adaptability to real-time traffic conditions. This paper proposes BFRN-IoV, a blockchain- and fog-enabled route navigation framework that integrates lightweight ECC-HMAC-based mutual authentication, RSU-assisted fog routing, and global route validation via a Geo-Location Provider (GLP), while leveraging VDTs for enhanced situational awareness and dynamic route optimization. The framework ensures key security properties-including confidentiality, integrity, pseudonymity, unlinkability, and non-repudiation-using ECDH-derived session keys, HKDF-based key expansion, and HMAC verification, while preserving privacy through pseudonym-based identity management. A permissioned blockchain provides immutable and auditable logging of routing interactions without exposing vehicle identities. Simulation results using SUMO and implementation via Web3 demonstrate significant improvements in routing accuracy, along with reduced communication and computational overhead compared to existing approaches. Formal verification using the Scyther tool confirms robustness against replay, impersonation, and man-in-the-middle attacks. The proposed framework delivers a unified, secure, and efficient solution for real-time IoV route navigation, further strengthened by the integration of VDTs in next-generation intelligent transportation systems.
Kumud Sachdeva, Ayush Mahanta
No abstract is available for this record.
Baptiste Beltzer, Emmanuel Conchon, Sylvain Giroux
No abstract is available for this record.