Blockchain technology is gaining traction in the biomedical sector due to its ability to improve trust and reduce the risk of fraud and errors in health data management. However, the large volume of biomedical datasets has slowed its adoption due to poor scalability. This challenge is especially relevant for applications that rely on blockchain's strong immutability by storing data directly on-chain. In this work, we demonstrate the potential of blockchain to create a secure and trustless environment for managing large on-chain records. Specifically, we detail an efficient, index-based approach for storing data on the Ethereum blockchain. We show that insertion and retrieval speeds remain nearly constant relative to database size, scaling linearly with the amount of data processed. Additionally, we achieve substantial efficiency gains through low-level assembly optimizations on the Ethereum Virtual Machine, highlighting the limitations of the Solidity compiler. Finally, we illustrate this approach through a practical case study, by designing and implementing a smart contract for storing and querying training certificates on the Ethereum blockchain. Our solution achieves 2x faster data insertion, 500x faster retrieval, 60% lower gas costs, and 50% lower storage usage compared to baseline methods. It won first place for track 1 of the 2022 iDASH secure genome analysis competition. We also demonstrate that this solution readily adapts to other data types, enabling efficient on-chain storage and retrieval of text, RNA-seq, or biomedical image data.
Fujiang Yuan, Xia Huang, Long Tai Zheng, Lusheng Wang · 8 authors
With the rapid development of blockchain technology, consensus algorithms have become a significant research focus. Practical Byzantine Fault Tolerance (PBFT), as a widely used consensus mechanism in consortium blockchains, has undergone numerous enhancements in recent years. However, existing review studies primarily emphasize broad comparisons of different consensus algorithms and lack an in-depth exploration of PBFT optimization strategies. The lack of such a review makes it challenging for researchers and practitioners to identify the most effective optimizations for specific application scenarios. In this paper, we review the improvement schemes of PBFT from three key directions: communication complexity optimization, dynamic node management, and incentive mechanism integration. Specifically, we explore hierarchical networking, adaptive node selection, multi-leader view switching, and a hybrid consensus model incorporating staking and penalty mechanisms. Finally, this paper presents a comparative analysis of these optimization strategies, evaluates their applicability across various scenarios, and offers insights into future research directions for consensus algorithm design.
The rapid proliferation of mobile IoT devices with inadequate security measures has elevated security to a critical concern. Researchers have proposed various systems for vulnerability detection based on conventional frameworks. However, these approaches often face challenges such as high computational costs, limited storage capacity, and slow response times. To ensure robust protection against cyberattacks, modern security solutions must continuously monitor and analyze historical data across the entire IoT network. This paper introduces a distributed security framework for IoT networks, leveraging software-defined networking (SDN), blockchain, and edge computing to efficiently detect and mitigate IoT-based attacks. In the proposed framework, SDN facilitates network-wide data monitoring and analysis, enabling effective attack detection. Blockchain technology ensures decentralized and tamper-resistant attack identification, addressing potential vulnerabilities. Meanwhile, the edge computing paradigm enables real-time attack detection at the network edge, ensuring timely alerts. An experimental evaluation of the proposed framework demonstrates its superiority over traditional approaches in terms of detection accuracy (98.7%), false positive rate (1.2%) and response time (101.1 ms), highlighting its effectiveness in securing IoT networks.
Open access
Blockchain Technology Applications and Security
IoT and Edge/Fog Computing
Advanced Steganography and Watermarking Techniques
The deployment of Internet of Things (IoT) devices and edge computing has grown exponentially and has reinvented the world of data processing and making it possible to deliver low-latency applications in real-time settings. Notwithstanding, with this shift towards the use of distributed systems, we are faced with new challenges of ensuring there is effective management of energy consumption. The main aim of the proposed study was to design, deploy, and test a new decentralized edge computing framework that combines blockchain technology and artificial intelligence to achieve optimized energy efficiency. To be more precise, we intended to create AI models that are able to recognize and forecast energy usage patterns at the edge in real-time. The system of 250 edge devices on a network in this study simulated the environment of the smart infrastructure, which portrays a medium-sized U.S. urban grid. All of these devices were able to record important performance and system data on an ongoing basis over more than 30 days at a resolution of 10 seconds, and provide more than 60 million data points. Prominent variables that are recorded are CPU usage (%)/memory load (MB) and energy level (Watts), which is a reflection of the device in terms of operation strain and efficiency. So that edge workloads can be classified according to their energy consumption rates and usage trends to facilitate energy-efficient scheduling. Three supervised machine learning models were chosen: Logistic Regression, Random Forest Classifier, and Support Vector Classifier (SVC). The preprocessed dataset was divided into 80:20 train and test sets to ensure that there was no data leakage, and all three models were trained on the datasets and evaluated on the test set. Based on the measurement, Random Forest had the most accurate predictions, meaning that it tended to slightly outdo the other models in this comparison. The next two models, notably logistic Regression and SVM, respectively, had the lowest accuracy of the three models. The encountered blockchain mechanism, i.e., lightweight transaction ledgers including Hyperledger Sawtooth, offered informative transparency and traceability of energy behavior in edge networks. Introducing blockchain-based green edge computing is about to change the energy management approach in smart cities and intelligent energy grids in the U.S. The introduction of IoT-powered networks in metropolitan areas such as New York City, San Francisco, and Chicago, including traffic sensors and adaptive lighting, autonomous transportation, and Wi-Fi hotspots, has also meant that the energy requirements of distributed edge networks are being placed at a serious burden. Green edge computing with blockchain has an important role in defense and the safety of the population by assuring safe, energy-saving decision-making in the field. The DOD (U.S Department of Defense) mainly depends on mobile and distributed sensor networks to perform surveillance of the theaters of operation, environmental tracking, and real-time information. The findings of the current research add value to the potential of AI-powered methods in increasing energy efficiency in edge computing solutions, especially when combined with blockchain frameworks.
The integration of blockchain technology with the Internet of Things (IoT) introduces significant scalability, energy efficiency, and security challenges, particularly when using traditional consensus mechanisms like Proof of Work (PoW). IoT networks generate vast amounts of data while operating under resource constraints, necessitating the development of consensus algorithms that balance energy efficiency, transaction throughput, and security. Addressing these challenges is critical for the sustainable adoption of blockchain in IoT ecosystems. This research aims to enhance blockchain scalability and performance in IoT environments through the development of the Enhanced Efficient Proof of Stake (EePoS) consensus algorithm. The objective is to provide a framework that optimizes validator selection, minimizes energy consumption, and ensures robust security against common blockchain threats. The proposed method employs a multi-layered architecture, selective validation, and a behavior-aware penalty-reward system to ensure efficient consensus. Key security metrics, including Probability of Successful Attack (PSA) and Forking Rate (FR), were evaluated to demonstrate the algorithm’s resilience. EePoS reduces PSA by dynamically adjusting validator selection based on stake, behavior, and transaction load while decreasing FR through cluster-based voting and hierarchical aggregation. Experimental results demonstrated 20% lower PSA, 30% reduced FR, and 8% faster consensus time compared to ePoS. Throughput improved to 296 TPS while reducing CPU and memory utilization, ensuring robust performance for resource-constrained IoT networks. The novelty of this work lies in the tailored enhancements to the PoS framework, specifically designed for IoT constraints, making EePoS a scalable, energy-efficient, and secure solution for IoT blockchain integration.
Yuri Bespalov, Lyudmila Kovalchuk, Hanna Nelasa, Roman Oliynykov
Abstract Decentralized consensus protocols have a variety of parameters to be set during their deployment for practical applications in blockchains. The analysis given in most research papers proves the security state of the blockchain, at the same time usually providing a range of acceptable values, thus allowing further tuning of the protocol parameters. In this paper, we investigate Ouroboros Praos, the proof-of-stake consensus protocol deployed in Cardano and other blockchains. In contrast to its predecessor, Praos allows multiple honest slot leaders that lead to fork creation and resolution, consequently decreasing the block rate per time unit. In our analysis of dependence on protocol parameters such as active slot coefficient and p2p network block propagation time, we obtain new theoretical results and explicit formulas for the expectation of the length of the longest chain created during the Praos epoch, the length of the longest unintentional fork created by honest slot leaders, the efficiency of block generation procedure (the ratio of blocks included in the final longest chain vs the total number of created blocks), and other characteristics of the blockchain throughput. We study these parameters as stochastic characteristics of the block generation process. The model is described in terms of the two-parametric family ξ ij of independent Bernoulli random variables which generate deformation of the binomial distribution by a positive integer parameter—the delay (deterministic or random). An essential part of our paper is a study of this deformation in terms of denumerable Markov chains and generating functions.
A. Baranidharan, P. Velmurugadass, A. Athiraja Atheeswaran
The design and development of a Wireless Body Area Network (WBAN) with improved security measures for data access within a cloud network is presented as a novel solution in thispaper. The primary goal is to provide a safe environment for exchanging and gaining access to patient medical records using cutting-edge hybrid blockchain technology. The suggested method addresses the issue of storage constraints while integrating components of blockchain technology to enable effective data storage and retrieval. A sequential aggregate signature approach is presented to streamline the storage procedure, which not only eases storage restrictions but also improves overall computing effectiveness. The investigation also seeks to accelerate the authentication procedure in distributed ledgers, enabling speedy and secure access to patient records. This approach aims to revolutionize the exchange and management of medical data within a WBAN by lowering computational complexity and putting in place strong confidentiality protections, ultimately helping to develop healthcare technology.
Mallellu Sai Prashanth, Seetha Srujana, V. Maheswari, Rajanikanth Aluvalu · 6 authors
Smart healthcare technologies are of paramount importance due to their potential to revolutionize healthcare delivery and improve patient outcomes. They enable the collection and analysis of real-time health data, allowing for early detection and prevention of diseases. Smart technologies facilitate remote patient monitoring, enhancing access to healthcare in underserved areas. This chapter presents an advanced and efficient Solidity-backed blockchain solution for smart healthcare technologies. The proposed system leverages the power of blockchain technology, specifically utilizing the Solidity programming language, to address critical challenges in healthcare data management, security, and transactional efficiency. The research introduces two key components: the HealthCoin token contract and the HealthRecord contract. The HealthCoin contract implements a token economy for healthcare transactions, allowing secure and transparent exchange of value within the healthcare ecosystem. It incorporates innovative features such as token transfers, approvals, and allowances, ensuring seamless interoperability between participants. The HealthRecord contract addresses the critical need for secure and accessible storage of healthcare records on the blockchain. It introduces a novel approach to record management, utilizing a timestamped structure coupled with a private mapping, allowing individuals to store and retrieve their health records efficiently. The novelty of the proposed solution lies in several key aspects. Firstly, the implementation demonstrates the effective utilization of Solidity, a widely adopted programming language for Ethereum smart contracts, to develop robust and scalable blockchain ventures in the healthcare domain. Secondly, the HealthCoin token contract offers a unique value proposition by providing a dedicated token economy designed specifically for healthcare transactions.
The integration of Internet of Things (IoT) devices in healthcare has enhanced medical efficiency but poses challenges such as data privacy risks and internal abuse. Traditional IoT data access frameworks suffer from centralization, limited scalability, and static permission controls. To address these issues, we propose MedAccessX, a blockchain-based access control framework combining attribute-based access control (ABAC) and role-based access control (RBAC). MedAccessX utilizes four types of smart contracts: a user management contract (UMC) for managing user operations, a medical data management contract (MDMC) for handling data, a policy contract (PC) for managing access rights, and an access control contract (ACC) for enforcing permissions and facilitating data sharing. Our evaluation, conducted on a private Ethereum blockchain network with multiple nodes, assesses security, deployment cost, gas consumption, throughput, and response time. Comparative analysis demonstrates that MedAccessX achieves lower deployment costs and higher throughput, outperforming existing solutions.
As artificial intelligence (AI) becomes integral to microservices deployed across multi-cloud environments, ensuring secure and scalable observability is critical. Traditional centralized observability methods often fail to address the privacy, compliance, and performance challenges inherent to distributed AI systems. This paper presents a federated learning–based framework for AI observability that preserves data privacy and scalability across heterogeneous cloud platforms. The proposed framework decentralizes telemetry collection and analysis by integrating local observability agents with secure federated aggregation, while maintaining interoperability with modern DevOps pipelines. We evaluate the architecture through case studies in retail, healthcare, and finance sectors, demonstrating improvements in anomaly detection, regulatory compliance, and operational efficiency. Additionally, the paper examines ethical considerations such as data privacy, fairness, and transparency, and outlines future directions including edge observability, privacy-enhanced computation, and automated governance. This research provides a foundational strategy for building trustworthy and efficient observability systems tailored to AI-powered microservices within complex multi-cloud ecosystems. Traditional observability methods struggle with privacy and performance in AI-powered multi-cloud microservices. We propose a federated learning–based framework that enables decentralized telemetry monitoring while ensuring compliance and scalability. Our evaluation across healthcare, finance, and retail shows improvements in anomaly detection latency (25%), fraud detection accuracy (18%), and GDPR/HIPAA alignment. This work lays the groundwork for trustworthy and efficient AI observability in complex cloud-native ecosystems.
Nothile C. Masango, Jeffrey O. Agushaka, Mercy C. Amaefule, Olutosin Taiwo · 9 authors
Abstract Data security in mobile environments has become a critical concern, driven by the growing demand for mobile services and the proliferation of data-intensive applications such as online gaming, virtual reality, and augmented reality. These applications generate massive amounts of data, challenging the storage, computational capacity, and battery life of mobile devices. Cloud environments offer a solution through task offloading, but centralized architectures introduce latency and potential vulnerabilities. Edge computing-based cloudlet networks have emerged as a promising alternative, providing localized resources to enhance service quality. However, their proximity to users increases susceptibility to security threats, posing barriers to widespread adoption. This paper presents a novel approach to addressing these challenges by integrating blockchain technology with cloudlet networks, bolstered by an agent-layer concept. The proposed architecture features an agent between mobile devices and cloudlets, utilizing a unique "proof of trust" consensus mechanism. This mechanism evaluates trust and experience based on the number of coins held by nodes, selecting miners for message verification using an elliptic curve cryptography scheme. In cases of dispute, a third miner resolves conflicts, with incorrect verifications resulting in penalties that deter malicious behavior. Experimental results demonstrate that this solution significantly enhances security, mitigates latency, and improves network performance compared to existing methods. These findings highlight the potential of blockchain-integrated cloudlet networks to revolutionize mobile data processing, offering robust security and reliable interactions between mobile devices and cloudlets.
IoT-enabled smart agriculture enhances farming with real-time data insights but faces security and scalability challenges. This study integrates IOTA’s distributed ledger technology into smart irrigation systems, leveraging its Tangle architecture for fee-less, lightweight, and scalable transactions. The system ensures tamper-proof data integrity and real-time decision-making, crucial for precision agriculture. Experimental results show efficient data transmission (336 bytes) with processing times of 0.7–1.3 seconds and rapid data retrieval (1×10⁻⁶– 4×10⁻⁶ seconds). A comparative analysis highlights IOTA’s superiority over traditional blockchain systems, demonstrating its robustness, scalability, and efficiency for secure, real-time IoT-based smart farming.
Abstract With the rapid growth of hyperconnected devices and decentralized data architectures, safeguarding Internet of Things (IoT) transactions is becoming increasingly challenging. Blockchain presents a promising solution, yet its effectiveness depends on the underlying consensus algorithm. Conventional mechanisms, such as Proof of Work and Proof of Stake, are often impractical for resource-constrained IoT environments. To address these limitations, this work introduces a fair and lightweight hybrid consensus algorithm tailored for IoT. The proposed approach minimizes resource demands on the nodes while providing a fair and secure agreement process. Specifically, it utilizes a distributed lottery mechanism to ensure fair block proposals without requiring dedicated hardware. In addition, to enhance trust and establish finality, a reputation-based voting mechanism is incorporated. Finally, we experimentally validated the key features of the proposed consensus algorithm.
Cloud computing is popular in modern businesses because it is scalable, versatile, and cost-effective. Due to the spread and complexity of cloud infrastructures, fault tolerance is crucial. This research focuses on improved cloud failure tolerance using Distributed Ledger Technology (DLT). Decentralised and immutable DLT offers verifiable and transparent transaction history, data consistency, and decentralised recovery, promising to improve cloud infrastructure stability and robustness. This study examines how DLT improves cloud-based detection, recovery, and fault tolerance. Using blockchain and cloud services to create a fault-tolerant system is novel. Comparison of fault tolerance solutions in existing cloud settings with DLT integration is the plan. Data was obtained during failure testing simulations using genuine cloud infrastructure platforms and blockchain networks. Recovery time, defect detection, energy utilisation, and uptime were measured. Graphviz was used to create flowcharts, and Matplotlib was utilised for graphs and plots. Test results show improved fault tolerance due to lower fault impact and faster recovery from DLT. DLT can make cloud systems more fault-tolerant and resilient, setting the pace for distributed computing innovation, the research concludes.
Mohd Anjum, Naoufel Kraïem, Hong Min, Ashit Kumar Dutta · 6 authors
The healthcare industry, aided by technology, leverages the Internet of Things (IoT) paradigm to offer patient/user-related services that are ubiquitous and personalized. The authorized repository stores ubiquitous data for which access-level securities are granted. These security measures ensure that only authorized entities can access patient/user health information, preventing unauthorized entries and data downloads. However, recent sophisticated security and privacy attacks such as data breaches, data integrity issues, and data collusion have raised concerns in the healthcare industry. As healthcare data grows, conventional solutions often fail due to scalability concerns, causing inefficiencies and delays. This is especially true for multi-key authentication. Dependence on conventional access control systems leads to security flaws and authorization errors caused by static user behaviour models. This article introduces an Opportunistic Access Control Scheme (OACS) for leveraging access-level security. This approach is a defendable access control scheme in which the user permissions are based on their requirement and data. After accessing the healthcare record, a centralized IoT security augmentation and assessment is provided. The blockchain records determine and revoke the access grant based on previous access and delegation sequences. This scheme analyses the possible delegation methods for providing precise users with interrupt-free healthcare record access. The blockchain recommendations are analyzed using a trained learning paradigm to provide further access and denials. The proposed method reduces false rates by 11.74%, increases access rates by 13.1%, speeds up access and processing by 12.36% and 13.23%, respectively, and reduces failure rates by 9.94%. The OACS decreases false rates by 10.64%, processing time by 15.62%, and failure rates by 10.95%.
The Space-Air-Ground Integrated Network (SAGIN) is a pivotal direction for the advancement of the sixth generation mobile communication systems (6G), and blockchain technology has been recognized as a potential solution for secure spectrum sharing within SAGIN. However, the implementation of wireless blockchain networks encounters significant challenges, particularly limited throughput and scalability. These challenges are primarily due to the limitations of existing consensus protocols, which were designed for general distributed systems, struggling to adapt to highly dynamic SAGIN scenarios. An efficient and secure spectrum sharing framework can be established by leveraging delegated proof of stake (DPoS) and practical byzantine fault tolerance (PBFT). Accordingly, we propose SatBFT, a scalable consensus protocol that emphasizes the inclusion of satellites and employs a DPoS-PBFT mechanism tailored to SAGIN. The protocol is carefully designed to accommodate the various links in SAGIN and to manage coexisting interference. By adopting a multi-layer architecture, SatBFT introduces a comprehensive consensus framework that incorporates radio environment sensing, dynamic behavior evaluation and efficient block generation. Simulations based on the proposed security model, latency model, and spectrum utilization model confirm that SatBFT significantly enhances the overall performance of blockchain in SAGIN, achieving an optimal balance between efficiency, scalability, and security.
This publication presents a novel cryptographic commit scheme named DFTWS which is used to enable deterministic, fair, and transparent winner selection in an open source Proof-of-Useful-Work blockchain for High Energy Physics (HEP) called gophy. In gophy, instead of spamming hashing operations to mine blocks, miners are running computationally expensive Monte Carlo simulations to support a real-world HEP experiment with necessary data required to conduct the experiment. To preserve the usefulness property, block problems are defined over time by a Root Authority which is coordinated by a representative of a real-world HEP experiment. In order to be able to provide a transparent mechanism that allows for fair block winner selection from a list of eligible miners that solved a block problem, DFTWS is employed to achieve consensus between nodes. A strength of this approach is that every node is able to verify the fairness of the winner selection process. This publication provides an in-depth description and theoretical fairness analysis of DFTWS, a practical evaluation of its performance under real-world conditions and considerations of potential bottlenecks that can potentially occur as the node network scales. It also discusses a deployment strategy for using DFTWS on top of existing blockchain infrastructure like the Ethereum network. Additionally, theoretical performance aspects of DFTWS are compared with various state-of-the-art cryptographic commitment schemes and Zero-Knowledge Proof systems.
As Industry 5.0 emerges, the convergence of advanced technologies like the Internet of Things (IoT) and blockchain is vital in shaping the future of industrial automation. Industry 5.0 emphasizes the collaborative relationship between humans and machines, requiring robust, decentralized systems to ensure security, accountability, and trust in interconnected ecosystems. Currently, IoT data processing is cloud-centric, which introduces challenges like fragmented data silos, limiting the potential for seamless and secure real-time analytics. Blockchain technology offers a solution by providing a decentralized and transparent ledger that can enhance data integrity and security across IoT applications. This study investigates the integration of blockchain with the IoT in the context of Industry 5.0, highlighting the potential for improved data management, security, and human-machine collaboration. By conducting a comprehensive analysis of IoT application designs and blockchain platforms, we evaluate existing literature to uncover the challenges, benefits, and limitations of this integration. Our research contributes by proposing a framework for selecting optimal blockchain platforms for IoT applications in Industry 5.0, providing actionable recommendations for enhanced data trust and resilience. Future research directions are also outlined to address the evolving demands of this technological convergence, ensuring that IoT ecosystems are secure, scalable, and human-centered in the era of Industry 5.0.
• Security challenges in RFID, edge computing and blockchain enabled supply chains are analysed, focusing on authentication. • A lightweight mutual authentication protocol is proposed for RFID-based supply chains in edge computing and blockchain systems. • The protocol uses XOR, bitwise rotation, and hash functions to ensure security with minimal computational overhead. • Formal security analysis is performed using the Real-Or-Random (ROR) model and validation with the AVISPA tool. • The protocol is adaptable for various supply chains and customizable to meet different IIoT security needs. This paper addresses security challenges, especially in the authentication mechanism of Industrial Internet of Things (IIoT)-enabled supply chain systems by proposing an enhanced Radio Frequency Identification (RFID) authentication protocol. The current system faces significant security risks due to increased connectivity and data exchange within supply chain networks. The proposed protocol integrates edge computing and blockchain to ensure secure, efficient mutual authentication between RFID tags and supply chain nodes. By utilizing the real-time processing capabilities of edge computing and the decentralization and immutability of blockchain, the protocol enhances the security of data transmitted in the system. The proposed protocol utilizes lightweight cryptographic functions optimized for resource-constrained edge devices, ensuring secure authentication and data transmission without compromising scalability or efficiency. Permissioned blockchain technology further strengthens trust and transparency in the supply chain by providing a decentralized, tamper-resistant ledger. The protocol employs cryptographic techniques such as a cryptographically secure one-way hash function, random number generation function, and circular shift operations to ensure data integrity and confidentiality, achieving mutual authentication, forward secrecy, and resistance to cryptographic attacks. Formal security analysis of the proposed authentication protocol is performed using the Real-Or-Random (ROR) model. The results demonstrate that the protocol offers superior trade-offs in term of security, computational cost, and communication efficiency compared to existing authentication protocols in this field. Simulation of the protocol is performed using Automated Validation of Internet Security Protocols and Applications (AVISPA) tools. Its lightweight design makes it suitable for real-world application in resource-constrained IIoT environments.