Ran Wang, Fuqiang Ma, S.H. Tang, Zhiyuan Su · 5 authors
ABSTRACT Establishing common knowledge about environmental conditions, task objectives, and coordination rules is crucial for improving the collaborative efficiency of swarm robots. In complex scenarios, relying on a centralized facility to maintain this knowledge is impractical, necessitating a decentralized approach. Blockchain technology offers a promising solution for decentralization and can tolerate some degree of malicious or malfunctioning entities. However, widely used blockchain approaches, such as those employed in Ethereum and relying on proof‐of‐work (PoW) or proof‐of‐authority (PoA), demand significant computational resources, rendering them impractical for swarm robotics applications. This paper introduces PTEE‐BFT, a novel parallel Byzantine fault tolerance protocol leveraging the trusted platform module (TPM). PTEE‐BFT employs a Unique Sequential Identifier Generator (USIG) to ensure the monotonicity, uniqueness, and order of messages, thereby reducing the number of communication phases and replicas required. This significantly enhances the efficiency and fault tolerance of the consensus process. Additionally, PTEE‐BFT implements parallel processing strategies to substantially increase blockchain system throughput. Furthermore, we develop an algorithm that enables the robot swarm to recognize attacks from a specific type of malicious robot known as Byzantine robots. Our experimental analysis and performance evaluation demonstrate that PTEE‐BFT achieves an optimal balance among performance, scalability, and fault tolerance, outperforming practical Byzantine fault tolerance (PBFT). Results from physical robots show that our approach significantly reduces computing overhead and accelerates consensus formation compared to baseline solutions. This represents a significant advancement in blockchain consensus mechanisms for swarm robotics.
The adoption of smart home technologies has increased and that it is needed to have robust and fine-grained access control mechanism to protect on the sensitive data and stop drop privacy. In this research, we propose new framework mining blockchain technology, smart contracts that enforce granular access control in smart home environments based on biometric smart contracts. Blockchain's immutable ledger, together with the characteristics of its decentralized architecture, make blockchain a secure storage for access logs, using biometric confirmation to further identify the owner of the account and minimize the risks of the unauthorized accesses. Access policies are dynamically managed and validated by smart contracts, which allows it to make real time decision. The proposed system is experimentally evaluated and the proposed system reliably improves security with just a 96.2% success rate of enforcing access control latency less than 1.5 seconds. One of the future works will be to scale the system and incorporate adaptive learning model to adjust the access policies.
Blockchain technology has been widely explored for enhancing transparency, traceability, and security in food supply chains. However, existing blockchain implementations rely on single distributed ledgers, causing interoperability and privacy concerns. This paper introduces FoodFresh, a novel multi-chain blockchain approach that allows food supply chain stakeholders to maintain individual blockchains while ensuring interoperability via a decentralized relay hub. The system is evaluated using real-world supply chain datasets, analyzing efficiency, transaction latency, and security improvements. Results demonstrate enhanced traceability, improved data privacy, and increased scalability. Future work includes expanding cross-chain communication protocols and exploring AI integration for predictive analytics.
Bharat Bhasker, Patruni Muralidhara Rao, P. Vidhya Saraswathi, S. Gopal Krishna Patro · 8 authors
The Internet of Medical Things (IoMT) sector has advanced rapidly in recent years, and security and privacy are essential considerations in the IoMT due to the extensive scope and implementation of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have dramatically improved the functionalities and services of Healthcare 5.0, giving rise to a new domain termed Smart Healthcare. A proactive healthcare system may prevent long-term harm by recognizing issues early. This would improve patients' quality of life while alleviating their worry and healthcare expenses. The IoMT facilitates several capabilities in information technology, including intelligent and interactive healthcare. Consolidating medical information into a singular repository to train a robust ML model engenders apprehensions around privacy, ownership, and adherence to regulatory standards due to increased concentration. Federated learning (FL) addresses previous challenges using a centralized aggregate server to distribute global learning models. The local participant controls patient data, ensuring data confidentiality and security. Hence, this study proposes the Federated Blockchain-IoT Framework for Sustainable Healthcare Systems (FBCI-SHS) for a secure health monitoring system. Additionally, this paper presents the Intrusion Detection System (IDS) as a tool for healthcare network intrusion detection, allowing doctors to track patients' vitals using medical sensors and anticipate when they could become sick so they can take preventative steps. The suggested system proves that the method is well-suited for medical monitoring. In contrast, the high prediction accuracy for intrusion detection and the high efficiency in disease detection achieved by the proposed FBI-SHS healthcare 5.0 system. The proposed method achieves data privacy and security by 98.73%, intrusion detection efficiency by 97.16%, disease detection accuracy by 96.425, proactive healthcare management by 98.37%, and interoperability by 96.74%.
Stakeholders throughout the food supply chain-from farmers to consumers-can gain safe, unchangeable views of the origin, movement, and processing of food through the distributed and immutable ledger of blockchain technology. Traceability and accountability are thereby made possible by this transparency, ensuring food items meet safety and quality guidelines and allowing consumers to make informed decisions. Our proposal for blockchain-based agri-food traceability follows the Bitcoin SHA-256 hash mechanism to provide a safe and impregnable way of monitoring agricultural products across the supply chain. Data is collected, annotated, and given a SHA-256 hash before getting written across decentralized blockchain networks. As the blockchain is fully visible and unchangeable, data integrity is assured so that diverse stakeholders can track the provenance of any product. In addition, the automated and easy use of smart contracts creates further accessibility for participants and clients, thereby ensuring trust and transparency in the agri-food system.
The rapid evolution of telemedicine has enhanced healthcare accessibility, yet significant challenges persist, particularly in data security, patient engagement, latency, and scalability. Existing telemedicine solutions rely on centralized architectures, making Electronic Health Records (EHRs) susceptible to data breaches and unauthorized access. This research proposes a novel system which integrates the metaverse and blockchain into telemedicine which can be a transformative approach to solve problems in remote healthcare. By combining immersive virtual environments with decentralized data management, the proposed solution described in this paper aims to give users more ways to interact with each other, enhanced data security, increased efficiency, and higher scalability. The Metaverse serves as the foundation for the implementation of 3D consultation rooms, virtual training spaces, and individual care. Blockchain offers safe, transparent, and immutable data exchange that will create patient-empowered medical records for them. Real-time devices and analysis of real-time physiological data from wearables, sensors, Internet of Things (IoT) devices, and Artificial Intelligence (AI) analytics complete the system. The proposed solution extensively uses Virtual Reality (VR)/Augmented Reality (AR) devices, IoT sensors, Ethereum, and the Unity 3D platform, among others. Assessments indicate that system receives a significantly high level of satisfaction from its users, better secured data, increased automation of processes, and compliance with global standards such as General Data Protection Regulation (GDPR). Compliance with such global standards is achieved through smart contract-based access management, smart contract-based consent management, and immutable audit trails in the blockchain. Moreover, this research demonstrates that incorporating high-tech tools like AI and VR into telemedicine is currently feasible. This paves the way for the creation of even more secure and user-friendly telemedicine platforms that employ neural networks. This research sets a foundation for next-generation telemedicine ecosystems.
S M Mostaq Hossain, Amani Altarawneh, Maanak Gupta
As blockchain technologies are increasingly adopted in enterprise and research domains, the need for secure, scalable, and performance-transparent node infrastructure has become critical. While self-hosted Ethereum nodes offer operational control, they often lack elasticity and require complex maintenance. This paper presents a hybrid, service-oriented architecture for deploying and monitoring Ethereum full nodes using Amazon Managed Blockchain (AMB), integrated with EC2-based observability, IAM-enforced security policies, and reproducible automation via the AWS Cloud Development Kit. Our architecture supports end-to-end observability through custom EC2 scripts leveraging Web3.py and JSON-RPC, collecting over 1,000 real-time data points-including gas utilization, transaction inclusion latency, and mempool dynamics. These metrics are visualized and monitored through AWS CloudWatch, enabling service-level performance tracking and anomaly detection. This cloud-native framework restores low-level observability lost in managed environments while maintaining the operational simplicity of managed services. By bridging the simplicity of AMB with the transparency required for protocol research and enterprise monitoring, this work delivers one of the first reproducible, performance-instrumented Ethereum deployments on AMB. The proposed hybrid architecture enables secure, observable, and reproducible Ethereum node operations in cloud environments, suitable for both research and production use.
Kassim Kalinaki, Owais Ahmed Malik, Gusti Ahmad Fanshuri Alfarisy, Jalia Nassanga
The fusion of blockchain technologies and non-fungible tokens (NFTs) into smart city ecosystems presents new security challenges, impeding the widespread adoption of NFT in urban settings. Accordingly, this study comprehensively reviews the cybersecurity aspects surrounding NFTs within smart city environments. Firstly, a discussion of the various applications of NFTs in smart cities is provided. This is followed by an exploration of the unique cybersecurity vulnerabilities emanating from implementing NFTs in smart city ecosystems, including data privacy issues, smart contract vulnerabilities, token theft, and the potential for market manipulation, etc. Moreover, various countermeasures and best practices to negate NFTs' cybersecurity concerns and vulnerabilities have been detailed. Finally, emerging trends in NFT security are equally also analyzed. This review study provide urban planners, policymakers, technologists, students, and researchers with a refined understanding of the cybersecurity concerns of NFT in smart cities.
Maintaining integrity and traceability throughout the pharmaceutical cold chain logistics is critical to preserving the efficacy of temperature-sensitive products. Traditional tracking systems lack transparency and accurate monitoring, increasing risks of counterfeiting and adulteration that harm patient health. This paper proposes a blockchain-based cold storage management system that uses smart contracts and IoT sensors to securely monitor real-time temperature and quality parameters for pharmaceutical products. Optimized smart contracts automate processes and enforce predefined conditions, ensuring accountability and reducing transaction cost. Our approach leverages IPFS decentralized storage for transaction data, generating unique SHA-256 cryptographic hashes stored on the blockchain to optimize security and reduce gas costs. Transactions are validated through proof-of-stake consensus. The system provides a secure and transparent solution for pharmaceutical cold storage management while enhancing patient safety and contributing significantly to the medical sector.
Ting Ye, Yang Wang, Enrico Zio, Jie Man · 6 authors
Digital twin technology can offer support to ship intelligence by the elaboration of massive data. However, false data may hinder the expected functions and even lead to serious navigation accidents. Therefore, credibility of data is a crucial issue that demands urgent attention. To this end, we propose an information credibility determination scheme suitable for the digital twin framework. This scheme combines the subjective logic model with the Dempster-Shafer theory. Firstly, based on the historical reputation values and interaction records of broadcasting ships, this scheme generates an initial subjective logic model via a smart contract. Subsequently, the model undergoes dynamic updates by taking into account the viewpoints provided by other verified ships. Then, the Dempster-Shafer theory is employed to fuse viewpoints and obtain the final information credibility evaluation results. In addition, a Delegated Proof of Stake consensus algorithm is designed___combined with an evolutionary game mechanism. The aim is to incentivize witness vote ships to select witness ships with high reputation values____to obtain the right to create blocks, thus enhancing overall security and reliability. Simulation results show that the proposed scheme can effectively identify false information, resist the attack of malicious ships, and significantly improve robustness and credibility.
The Smart Mobility vision calls for dynamic resource and service discovery to cope with the intrinsic topology volatility of Internet of Things (IoT) platforms without sacrificing the required business continuity and service flexibility. For an extended automation of collaboration within and across enterprise boundaries, trust management is equally important, granting security, reliability and scalability at the same time. To tackle the above challenges, this paper proposes the integration of a semantic-based service management layer in an IoT infrastructure grounded on the Hyperledger Sawtooth blockchain. Every service in the outlined framework is annotated with reference to a domain ontology, so that smart contracts can exploit knowledge representation and non-standard reasoning for service registration, discovery, outcomes explanation and service selection. A case study on power management of Plug-in Electric Vehicles (PEVs) is proposed to clarify the benefits of the proposal. Early performance evaluation results support the feasibility and sustainability of the approach.
This paper presents a blockchain-based health information self-management platform that leverages smart contracts and Non-Fungible Tokens (NFTs) technologies to ensure data integrity and secure access control. Existing health information systems face challenges related to data security, patient privacy, and interoperability. Our proposed platform addresses these issues through a decentralized architecture that utilizes NFTs for ownership management, InterPlanetary File System (IPFS) for distributed data storage, and smart contracts for access control. Experimental results verified that the system operated as intended, with complete tampering detection and accurate access control during ownership transfers, in line with the security guarantees inherent in blockchain technology. Although the distributed architecture results in longer response times (7836.4 ms), this is a trade-off to achieve cryptographically guaranteed security. Overall, the proposed system enhances data transparency and trust in the management of health information.
Elvir Akhmetshin, E. Saranya, Badabagni Adilakshumma, Suryaprakash Nalluri
Academic credentials are becoming more vulnerable by fraud, falsification, and inefficiencies in existing verification processes. This research uses Python and Docker to create a safe, lightweight, and flexible blockchain-based academic credential verification prototype. The approach uses a hybrid blockchain with personal validation nodes for institutional activities and public nodes for external verification. Cryptographic key-based credential signature, SHA-256 data hashing, QR code embedding for credential search and Byzantine Fault Tolerance (BFT) consensus for multi-node validation constitute vital developments. The prototype’s block replication speeds of 0.02 seconds and low resource utilisation (about 200 KB of RAM per transaction) make it appropriate for resource-constrained academic situations. Degrees are issued through a signed PDF certificate with a blockchain-verifiable QR code. Users may verify records using a public query interface employing the hash alone. This architecture has lower latency, simpler implementation, better modularity and better data privacy than Blockcerts & Ethereum-based registries. This study further supports academic institutions, professional licensing, legal documents and supply chain certification with a realistic and scalable basis for safe digital credentials.
Carlos Beis-Penedo, Rebeca P. Díaz-Redondo, Ana Fernandez-Vilas, Manuel Fernández‐Veiga · 5 authors
Collaborative machine learning in sensitive domains demands scalable, privacy-aware and access-controlled solutions for enterprise-grade deployment. Conventional federated learning (FL) relies on a central server, introducing single points of failure and privacy risks, while split learning (SL) partitions models for privacy but scales poorly because of sequential training. We present HLF-FSL, a decentralized architecture that combines federated split learning (FSL) with the permissioned blockchain Hyperledger Fabric (HLF). Chaincode orchestrates split-model execution and peer-to-peer aggregation without a central coordinator, leveraging HLF’s transient fields and Private Data Collections (PDCs) to keep raw data and model activations off-chain and access-controlled. On CIFAR-10, MNIST and ImageNet-Mini, HLF-FSL matches the accuracy of a standard server-coordinated FSL baseline while reducing per-epoch training time versus Ethereum-based baselines. Performance and scalability tests quantify the Fabric coordination overhead via a component-level breakdown of SDK-facing latencies and communication volumes; empirically, this overhead increases wall-clock epoch time while preserving the same accuracy-vs-epoch behavior as a FedSplit Learning baseline.
With the integrating development of Internet of Things (IoT) and edge computing, data sharing among various IoT devices has become the trend for extensive applications. However, data sharing in IoT environments is challenged by limited terminal resources and distributed data storage, which places higher demands on security and effectiveness. Even though existing searchable encryption technologies provide feasible solutions, there remain challenges in terms of trustworthy retrieval and execution efficiency. To address these issues, this paper proposes an efficient supply-demand-aligned and trustworthy multi-keyword (ESTM) search scheme in edge-assisted IoT environments, where encrypted documents are stored in edge servers. Furthermore, blockchain-based smart contracts are employed so that search results are consensus on the Fabric ledger and data users can verify whether the returned encrypted documents are reliable using encrypted hashes. To achieve the supply-demand-aligned requirement, the RoBERTa (Robustly optimized BERT approach) model is introduced for text classification and data users can judge which edge server stores data best suits their demands. Meanwhile, coordinate (COO) format is adopted into index vectors and search vectors, which can decrease the time required for constructing an index tree to about 2.7% and the time required for generating trapdoors to about 4.6%. Finally, we conducted an in-depth security analysis and performance comparison with existing works, results show that the proposed scheme is effective and feasible.
The process of exchanging healthcare data introduces stringent requirements regarding users’ privacy. Federated learning (FL) is a novel model-sharing technique that aims to give additional privacy guarantees during machine learning process. Blockchain, as a form of distributed ledger technology, possesses the characteristic of trustworthiness; however, it is deficient in terms of computational capacity with a high-latency network due to its laborious consensus protocols. In this paper we present a distributed healthcare FL-based secure model sharing architecture to ensure healthcare data privacy and scalability. The solution relies on state channels technique to reduce on-chain transactions, contrast architecture latency, and reduce bandwidth consumption, alleviating the burden on the blockchain. State channels can be utilized to efficiently execute the tasks of federated learning models sharing and to solve the scalability problem.
Internet of Thing is a promising technology for creating smart home systems. Devices are being added gradually in the smart home's environments, causes the significant challenges into security, scalability, compatibility, Interoperability, etc. Traditional centralized authentication methods are not able to keep the dynamic and diverse nature of these smart environment. To address these challenges, we proposed an adaptive mutual authentication scheme with Zero Knowledge Proof and machine learning integrated within blockchain based key management and storage system. The proposed approach used Elliptic Curve Cryptography technique for key generation, a consortium blockchain for storing keys and device metadata, and a hybrid encryption scheme adaptively choosing between AES-GCM and ChaCha20-Poly1305 based on IoT device capabilities. Machine learning model is integrated to predicts the optimal cryptographic parameters, and also to ensure both security and resource efficiency. The proposed mutual authentication scheme provides a secure and scalable model for smart home systems.
M. Sukanya, R. Balasubramaniyan, V. Samuthira Pandi, Thaer Ahmad Abu-Saleem · 6 authors
The combination of Artificial Intelligence (AI) and blockchain has great potential to solve centralized challenges, including but not limited to scalability and energy effectiveness, as demonstrated by this paper. Traditional consensus algorithms including PoW and PoS have been under severe criticism for being slow and tremendously energy consuming. With increasing size and complexity of blockchain networks, the demand for improved consensus mechanisms that can operation at reduced computation costs, and associated energy consumption, has become critical. In a recent research, a Mechanism integrated Artificial Intelligence (AI) Techniques which is designed to improve the Blockchain performance especially in scalability and energy efficiency is introduced. The suggested architecture embeds machine learning algorithms to dynamically tune consensus decisions according to network status, transaction influx, and computing power. Using AI, it can anticipate network congestion and adapt block size, mining difficulty, and transaction priority accordingly for higher throughput and lower energy consumption. In addition, AI-powered anomaly detection algorithms can detect potential security threats or fraudulent behaviors and increase the overall trustworthiness of the block chain. This adaptive method enables blockchain networks to rapidly scale without sacrificing security, a necessity as the technology expands to include new applications across industries and in applications from financial transactions to supply chain management. Research also investigates the possibility of hybrid consensus models that mix traditional consensus models with AI-enabled models in order to design more efficient and secure frameworks. These hybrid designs combine AI with PoW, PoS or Byzantine Fault Tolerance (BFT) to better compromise among decentralization, scalability, and energy consumption. The AI elements permit real-time decision-making for the network, and can respond rapidly to changing conditions, leading to enhancement of overall system performance. By experimentations and simulations, this paper proves AI-enabled consensus mechanisms can make traditional systems inferior to them in the energy consumption and transaction speed.