Recently, Edge-Cloud Collaborative (ECC) has emerged as an efficient and promising technique to empower various computation-intensive applications in Digital Twin Network (DTN). The integration of ECC and DTN serves to bridge the gap between data analysis and physical states. In ECC, a reliable and optimal task offloading scheme is required to maximize resource utilization and provide satisfying services to End Users (EU). However, existing offloading schemes still face significant challenges, such as the instability and complexity of network topologies, the intricacies of massive data, and the lack of trust among EU. In this paper, we propose anenhancinGedge-clOud collaboraTion wiTh blockchain-assistEd digital twin intelligence offloadiNgscheme (GOTTEN) which transmits large-scale tasks generated by DTs to Edge Station (ES) or Cloud Station (CS) in dynamic DTN scenarios. We first formulate this resource allocation and task offloading problem and provide an appropriate initial solution which guarantees that tasks generated by DTs can be accurately mapped to physical entities, while optimizing block allocation and reducing the decision space of task offloading. Then, we employ the Lagrange Multiplier based Distributed Island model-enhanced Genetic Algorithm (LM-DIGA) to transform our formulated problem into a convex form and achieve an optimal resource allocation under a specific scheme. Additionally, our proposed architecture also leverages blockchain verification mechanisms to enhance system stability, strengthening privacy protection for DT data as well. Finally, extensive simulation results demonstrate that, compared with seven baselines, our proposed scheme achieves a 10 percent the total system delay and privacy overhead with regard to other schemes in ECC.
Seung Eel Oh, Jong‐Hoon Kim, Ji-Young Kim, Jae Hwan Ahn
The complexity of contemporary supply chains and the rise in foodborne illness cases have made ensuring food safety and traceability a top responsibility on a worldwide scale. Traditional traceability systems are prone to data tampering, fragmentation, and limited compatibility. Public blockchains have scalability, latency, and privacy problems that limit their use in real-time food safety systems, despite the fact that blockchain provides a secure data structure. Using Hyperledger Fabric, GS1 EPCIS standards, and Internet of Things-enabled environmental sensors, this paper suggests a private blockchain-based food safety monitoring system. To guarantee fault-tolerant, high-throughput processing in a permissioned blockchain setting, a Raft consensus mechanism was used. Hyperledger Caliper was used to benchmark the system once it was deployed with four nodes. According to experimental data, transaction throughput peaked at 230.2 TPS and averaged 207.4 ± 10.2 TPS. As the network grew from two to four nodes, latency increased somewhat from 259.3 ± 9.5 ms to 278.7 ± 9.1 ms, while block finalization time stayed below 3.184 ± 0.113 s. Over 114,925 documented transactions, data integrity was confirmed to be flawless. These results demonstrate that private blockchain technology can provide effective, scalable, and impenetrable food traceability, boosting openness and confidence throughout food networks.
The rapid expansion of 5G networks and edge computing has amplified security challenges in Internet of Things (IoT) environments, including unauthorized access, data tampering, and DDoS attacks. This paper introduces EdgeChainGuard, a hybrid blockchain-based authentication framework designed to secure 5G-enabled IoT systems through decentralized identity management, smart contract-based access control, and AI-driven anomaly detection. By combining permissioned and permissionless blockchain layers with Layer-2 scaling solutions and adaptive consensus mechanisms, the framework enhances both security and scalability while maintaining computational efficiency. Using synthetic datasets that simulate real-world adversarial behaviour, our evaluation shows an average authentication latency of 172.50 s and a 50% reduction in gas fees compared to traditional Ethereum-based implementations. The results demonstrate that EdgeChainGuard effectively enforces tamper-resistant authentication, reduces unauthorized access, and adapts to dynamic network conditions. Future research will focus on integrating zero-knowledge proofs (ZKPs) for privacy preservation, federated learning for decentralized AI retraining, and lightweight anomaly detection models to enable secure, low-latency authentication in resource-constrained IoT deployments.
Open access
Blockchain Technology Applications and Security
IoT and Edge/Fog Computing
Advanced Steganography and Watermarking Techniques
The collection and application of health care data are crucial for advancing research and improving healthcare. However, privacy and security concerns, particularly with sensitive data, pose significant challenges. Traditional identity-based verification systems, which rely on centralized servers, struggle in medical contexts due to regional data management complexities and the vulnerabilities of centralized models. In this paper, we propose MedZKChain, a privacy-preserving health care device verification system designed to address these challenges. By combining blockchain technology with zero-knowledge proofs, MedZKChain enables decentralized device attribute verification while ensuring data integrity and privacy. The system provides a solution for managing the access of medical records in different regions. MedZKChain leverages decentralized storage to reduce blockchain burden and uses zero-knowledge proofs to allow for secure verification and access authorization without revealing sensitive data. Experimental results demonstrate that, when authorized querying 1,500 patient data records, the proof size in MedZKChain remains less than 100 KB, the proving time is less than 3 seconds, and the verification time is below 0.8 seconds. These results highlight the system efficiency, scalability, and its effectiveness in enabling decentralized, verifiable.
A. Althaf Ali, M. A. Gunavathie, V. Srinivasan, M. Aruna · 6 authors
The integration of smart city applications with healthcare has revolutionized patient monitoring and medical data management. However, ensuring the privacy and security of Electronic Health Records (EHR) remains a critical challenge, especially in IoT-based environments with resource-constrained devices. This paper proposes a novel Blockchain-Enabled Federated Learning (BFL) framework to enhance privacy preservation in EHR processing. The proposed framework leverages zero-knowledge proofs (ZKP) for authentication and homomorphic encryption for secure computation, ensuring robust data security without exposing raw patient data. Federated Learning (FL) enables decentralized model training across IoT devices, reducing privacy risks while maintaining data utility. Additionally, blockchain technology enhances the integrity and transparency of EHR transactions by creating a tamper-proof ledger. The performance of the proposed BFL framework is evaluated based on data utility, model accuracy, execution time, and scalability across varying sizes of EHR datasets. Results demonstrate improved privacy preservation, reduced computational overhead, and enhanced model efficiency, making it a promising approach for secure and privacy-aware IoT-based smart healthcare systems.
In the philanthropy sector, new technology is required to address issues like lack of transparency, trust, and accountability, as well as extended processing times and expenses. So, the present research examines the application of blockchain (BC) technology to enhance the donation tracking Platform, which delivers data immutability, and transaction transparency, as well as peer-to-peer transactions, as well as it is all the rage in today's technological period. BC enables the tracking of every donation, by permitting the donors to track the usage of their funds. The three primary roles of the present research are nongovernmental organization, Donors, as well as scholar beneficiaries. Corporate social responsibility (CSR) funds of industries are considered as donations, which causes educational scholarship for the initial prototype building. Subsequently, this prototype is scalable to every feasible field of educational scholarship as well as other methods of philanthropy. Thus, a Crypto-Go-Charity is proposed to solve the problems of the supply chain by utilizing the Ethereum BC and delivering a more secure and immutable ecosystem for scholarships. Solidity language is utilized here to write smart contracts and InterPlanetary File System for document storage. The performance is demonstrated by calculating the evaluation metrics as the privacy ratio and computational time for 200 users with the number of verifications at 300, as well as a key size of 50 kb, is 97.52% and 89.28 s, respectively. Likewise, the detection rate and memory usage of the system acquired the value of 98% and 81.46 MB for 200 users with the number of verifications at 300 as well as a key size of 50 kb.
Zero Trust Architecture (ZTA) offers a critical security framework for AI-powered cloud systems, replacing traditional perimeter-based defenses with the principle of "never trust, always verify." As organizations deploy increasingly sophisticated AI workloads in distributed cloud environments, they face unique and acute security challenges including model poisoning, adversarial attacks, and extraction attempts targeting valuable intellectual property. ZTA addresses these challenges through continuous authentication, least privilege access, micro-segmentation, and ongoing monitoring specifically calibrated for AI systems. Implementation requires balancing security with performance considerations, managing complexity, addressing skill gaps, and overcoming technical debt in legacy systems. Emerging approaches including AI-powered security tools, zero-knowledge proofs, hardware-based security measures, and standardized frameworks for autonomous systems are shaping the future of AI security in cloud environments, enabling organizations to realize the benefits of AI innovation while maintaining robust protection.
The paper presents a novel decentralized training framework for Ethical Artificial Intelligence (EAI) that leverages blockchain and IPFS technologies. The system addresses significant issues with the reliability, transparency, and ability to handle large amounts of data by including local nodes for data collection and local models generation. And global nodes for data authentication and global models generation. The framework's ability to enhance the development of ethical AI in several fields is emphasized by its design considerations and potential applications, including Healthcare, Finance, Internet of Things (IoT), Cyber Physical Systems (CPS), and Supply Chain Management (SCM).
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
The development of 6G should enable users in remote and harsh areas to enjoy computation-intensive services including metaverse entertainment, intelligent transportation, and immersive communications. Low Earth Orbit (LEO) satellite constellations widely constructed in recent years have been recognized as an efficient solution to complement the terrestrial infrastructure with seamless coverage and decreasing expenses for both communication and computation services. However, the widely studied Federated Reinforcement Learning (FRL) based task offloading strategies neglect the potential trust concerns like malicious satellites and buffer pollution, while 6G service providers may rent the LEO satellites belonging to different companies to minimize the expense. To address these issues, blockchain has been considered in the Zero Trust (ZT) scenario, with the group consensus mechanism through the smart contract. Moreover, we propose a Constrained Correction Voting Mechanism (CCVM) to give punishing correction to the aggregation weight of malicious voting satellites. Furthermore, a Cold Start Reputation Aggregation (CSRA) scheme is adopted to first severely degrade and then gradually recover the weight of Federated Learning (FL) sub-models trained by malicious satellites. Thus, the Blockchain-enabled Cold Start Aggregation FRL (BCSA-FRL) scheme is proposed to make effective and secure offloading decisions in the ZT LEO satellite Networks. The numerical results illustrate the advantages of our proposal.
The Internet of Medical Things (IoMT) is transforming healthcare by seamlessly connecting medical devices, wearables, and sensors to enable personalized, real-time health monitoring and treatment for consumers. As IoMT continues to advance, ensuring the security and privacy of transmitted data has become a critical concern. Blockchain technology has emerged as a promising solution to enhance privacy and security, particularly in sensitive areas such as medical data within the Internet of Things. By integrating blockchain with IoT, secure transmission of medical data can be achieved, paving the way for improved healthcare services, enhanced consumer privacy, and accelerated medical advancements. In this paper, we propose EHRGuard: Enhancing Privacy and Security of Electronic Health Records through Blockchain Technology. EHRGuard is a novel system that leverages blockchain technology to address key challenges in the management of Electronic Health Records (EHRs), with a focus on improving privacy, security, and interoperability in healthcare data systems for consumers. The framework utilizes the Internet of Medical Things (IoMT) to collect real-time health data from consumers through sensors and integrates blockchain technology to ensure data anonymity, security, and integrity. By combining IoMT and blockchain, EHRGuard enables the seamless and secure gathering of real-time health data, ensuring that sensitive information is protected from unauthorized access and tampering. Experimental results demonstrate that the proposed system outperforms traditional healthcare systems in terms of service quality and consumer data monitoring. This innovative approach not only enhances the security and privacy of EHRs but also fosters trust and efficiency in healthcare systems, ultimately benefiting consumers and advancing medical research and treatment.
This article suggests that blockchain and AI might overcome the key issues healthcare institutions have while handling IoMT data. This paper describes a three-step method: optimizing data, transferring data securely, and applying AI to make judgments. Filtering and compressing IoMT data make it simpler to store and transmit. Safe communication using blockchain technology ensures data privacy and security. AI-based decision-making gives healthcare practitioners real-time data insights using machine learning models. The recommended solution outperforms others in data integrity, security, scalability, fault tolerance, real-time processing, and power economy. With 99.99% data correctness and 100% security, the approach ensures IoMT data processing is trustworthy and secure. A 9/10 score indicates scalability, making it a suitable alternative for major healthcare networks. It utilizes less energy and reduces network traffic, making it ideal for resource-constrained situations. Blockchain and AI provide secure, adaptable, and effective IoMT data management, healthcare decision-making, and patient outcomes, according to the research.
In an era where securing Internet of Things (IoT) devices within Metaverse environments is increasingly critical, existing frameworks often lack robust, quantum-resistant protection suitable for resource-constrained devices. This study aims to develop a comprehensive quantum-resistant security framework designed for IoT-enabled Metaverse applications. Our multilayered architecture incorporates Ideal Coset Lattice Cryptography (ICLC) and a Hypercomplex Multivariate Encryption Scheme (HMES) across the Device, Network, and Metaverse layers. ICLC provides lightweight, quantum-resistant encryption for devices with limited computational resources, while HMES enhances security through complex algebraic structures resistant to quantum attacks. We implement a Zero-Knowledge Proof Authentication mechanism over Hypercomplex Algebras (ZKPHA) to authenticate devices without exposing private keys. An edge computing strategy that employs convex optimization minimizes latency and computational load, ensuring scalability and efficiency. Simulations over a 260-minute period compared our framework with six state-of-the-art methods under various conditions. The results show that our framework reduces the rate of successful cyberattacks on encrypted data to 0.15%, achieves encryption and decryption times of 2.2 milliseconds per operation, and maintains 98.5% system availability during attacks.
Preethi Preethi, Mohammed Mujeer Ulla, R. Sapna, Raghavendra M Devadas
Over the last few years, the conceptualization of Smart Home has received acceptance. The extensive issues regarding a smart home include offloading computational tasks, data security aspects, privacy issues, authentication of Internet of Things (IoT) devices, and so on. Presently, existing smart home automation addresses either of these issues, nevertheless, Smart Home automation that also necessitates decision-making for offloading computational tasks with improved QoS (i.e., latency and throughput) and systematic features apart from being reliable and safe is a definite necessity. To address these gaps in this, work a QoS-improved method called, Blockchain-modeled Swarm Optimized Lyapunov Smart Contract Deep Reinforced Tasks Offloading (BSOLSC-DRTO) in smart home is proposed. The BSOLSC-DRTO method is split into two sections, namely, Offloading Computational Tasks based on the Particle Swarm Optimized Lyapunov model and Temporal Difference Deep Reinforced Secured Offloading. First to solve the offloading issue and therefore improve the QoS, we developed a Particle Swarm Optimized Lyapunov model using a Lyapunov optimization function. This optimization problem aims to minimize latency and improve throughput considerably. Second, to boost the offloading security, we propose a trustworthy access control using the Temporal Difference Deep Reinforced Secured Offloading model that can safeguard devices against illegal offloading. Then to handle the computation management for addressing the offloading decisions in the queue temporal difference function is applied, therefore improving the smart contract accuracy and precision involved in offloading computational tasks. Evaluation results from experiments and numerical simulations exhibit the notable advantages of the proposed BSOLSC-DRTO method over existing methods.•Develop a Particle Swarm Optimized Lyapunov model to minimize latency and significantly improve throughput.•Proposed a Temporal Difference Deep Reinforced Secured Offloading model for trustworthy access control, protecting devices against illegal offloading
Federated learning (FL) has emerged as a leading methodology for facilitating collaborative edge learning (EL) across Artificial Intelligence of Things (AIoT) devices, enabling efficient model training and bolstering privacy protection. Nevertheless, current EL methods that depend on trusted servers engender apprehensions concerning potential data leakage and misuse. Moreover, the untrusted AIoT environment increases security threats in EL collaboration. In addressing these challenges, we introduce an innovative swarm reputation (SR)-based decentralized autonomous organization (DAO) autonomous FL framework, SRFL. Within SRFL, we utilize DAO nodes as autonomous units for processing local services, effectively diminishing the communication overhead attributed to frequent interactions, the SR-based DAO committee oversees the FL process and ensures model consistency. SRFL seamlessly integrates FL with the distributed consensus process and introduces an SR-based consensus mechanism to enhance the collaboration process’s trustworthiness. SR utilizes a hierarchical reward and punishment mechanism, designed to equitably reward honest participants and hammer penalize those undermining the system’s stability. Through extensive experimentation with SRFL, employing different models and datasets, we have substantiated its superior performance in efficiency and robustness.
The Internet of Vehicles (IoV) enhances road safety through real-time vehicle-to-vehicle (V2V) communication of traffic messages. However, V2V wireless connectivity poses security and privacy threats, as malicious adversaries can eavesdrop and modify V2V messages or compromise vehicle identity privacy. Existing authenticated key agreement (AKA) schemes attempt to address these threats but suffer from security flaws, computational inefficiency, high communication overhead, single points of failure, and trust deficits, making them unsuitable for resource-constrained and delay-sensitive IoV applications. To address the above challenges, we propose a blockchain-powered AKA scheme with a reputation-incentive mechanism (BAKARI) for V2V communication. BAKARI employs Schnorr signatures and lightweight elliptic curve cryptographic operations to improve computational efficiency, and minimizes communication overhead by completing the AKA phase with only two messages. BAKARI leverages blockchain ledger and smart contracts to maintain vehicle authentication information and V2V messages. Additionally, it incorporates a reputation-incentive model, where trustworthy vehicles are rewarded while malicious ones are penalized. A rigorous security analysis, including formal proof under the random or real model, informal analysis, and ProVerif verification, demonstrates BAKARI’s resilience against security and privacy threats. Performance evaluation shows that BAKARI balances computational efficiency, communication overhead, and security better than the benchmark schemes. Finally, simulations on Hyperledger Fabric and Veins frameworks validate BAKARI’s practicality in real-world IoV environments.
Cloud computing has become a critical component of modern IT infrastructure, offering businesses scalability, flexibility, and cost efficiency. Unoptimized cloud migration strategies can lead to significant financial waste due to inefficient resource allocation, redundant workloads, and unpredictable cloud expenses. Traditional methods often rely on static provisioning and manual decision-making, leading to suboptimal cloud resource utilization. This research introduces an AI-driven framework for intelligent cloud planning and migration aimed at reducing cloud costs while maintaining high performance and compliance standards. The proposed framework leverages machine learning (ML), deep learning (DL), and reinforcement learning (RL) techniques to automate workload distribution, real-time scaling, and dynamic cost optimization. It integrates Predictive Analytics Engine: Uses AI models (Long Short-Term Memory LSTMs, CNNs, and Transformers) to analyze historical workload data and forecast future resource demands. Optimization Algorithm: Implements AI-driven cost minimization functions, optimizing resource allocation while maintaining Quality of Service (QoS). Automated Migration Engine: Reduces manual intervention by executing AI-based cloud workload transfers efficiently. Security and Compliance Module: Uses explainable AI (XAI) and federated learning to maintain cloud security, privacy, and regulatory compliance. A proof of concept (PoC) is developed and evaluated across multiple cloud platforms (AWS, Azure, Google Cloud) with real-world datasets. Experimental results indicate that the AI-driven framework achieves: Cost savings of up to 42% compared to traditional cloud migration strategies. Resource utilization improvement by 53%, ensuring minimal wastage. Reduction in system downtime by 75%, leading to higher reliability. Reduction in manual intervention by 85%, automating resource scaling and load balancing. The research paper also presents real-world case studies across finance, healthcare, e-commerce, and manufacturing sectors, demonstrating the tangible impact of AI-based cloud optimization. This research explores future advancements in cloud computing, including Quantum AI for cloud workload acceleration, Blockchain for transparent cloud cost auditing, and Decentralized AI governance for multi-cloud management. This study contributes to the growing field of AI-driven cloud cost optimization, providing a roadmap for enterprises, cloud architects, and AI researchers to achieve cost-efficient, high-performance, and automated cloud management.
Anwar Ali Sathio, Muhammad Malook Rind, Shafique Ahmed Awan
The integration of Internet of Things (IoT), Fog Computing, and Green Blockchain technologies is transforming digital systems by making them more efficient, scalable, and sustainable. IoT generates vast data that requires real-time processing, but this creates challenges in latency, bandwidth, and resource management. Fog Computing addresses these by decentralizing data processing to the network's edge, reducing latency and improving performance for time-sensitive applications in smart cities, healthcare, and automation. Despite advancements, traditional systems' energy consumption is a concern as IoT devices grow. Green Blockchain optimizes energy usage by employing energy-efficient consensus algorithms like Proof of Stake (PoS), reducing the computational load of Proof of Work (PoW). This chapter explores how IoT, Fog Computing, and Green Blockchain together enhance scalability, security, and energy efficiency, offering sustainable solutions for smart environments and their convergence enables decentralized, low-latency, and energy-efficient systems for future industries.
The rise of Internet of Things (IoT) devices, fog computing, and blockchain technologies has reshaped modern distributed systems, but energy consumption poses a critical challenge. This chapter explores energy patterns in IoT, fog, and blockchain ecosystems, emphasizing the importance of efficiency. It discusses the interplay between these systems, energy usage in IoT devices, network protocols, cloud and edge computing impacts, energy needs in fog computing, and challenges in distributed fog nodes. It also examines the energy implications of blockchain consensus mechanisms like proof-of-work and proof-of-stake, sustainable protocols, and energy-efficient strategies such as machine learning. Real-world examples highlight successful energy-efficient deployments in smart cities and green energy systems. The chapter concludes by stressing the need for energy-efficient practices in designing and implementing these technologies for a sustainable digital future.
Echezona Uzoma, Joy Onma Enyejo, Toyosi Motilola Olola
The integration of distributed ledger technologies (DLTs) into multi-cloud environments presents a transformative approach to addressing data integrity and transactional security challenges in modern digital infrastructures. This review comprehensively examines the intersection of multi-cloud computing and distributed ledger systems, highlighting their potential to provide decentralized, tamper-proof, and transparent data management solutions across diverse cloud platforms. The paper explores key architectural frameworks, consensus mechanisms, interoperability protocols, and cryptographic models that enable seamless integration while ensuring scalability, reliability, and enhanced security. Furthermore, it analyzes current use cases, such as supply chain management, financial services, and healthcare, where multi-cloud DLT integration mitigates risks of single points of failure, data breaches, and unauthorized access. By identifying emerging trends, technological limitations, and research gaps, this review offers valuable insights into optimizing multi- cloud DLT deployments for robust data integrity and secure transactional processes. The study underscores the growing importance of cross-cloud blockchain interoperability and regulatory compliance in advancing secure and resilient multi- cloud ecosystems.
In recent years, the concept of the metaverse has gained considerable momentum as virtual environments become more immersive and sophisticated. As this technology advances, it can potentially affect various aspects of our lives, including how we produce, consume, and trade energy. Peer-to-peer (P2P) energy trading, facilitated by digital twins (DTs) and blockchain technology, could revolutionize the energy industry and promote sustainable energy use in the metaverse. In this context, we introduce a novel framework, called MetaPower, that leverages the power of DTs and blockchain technology to enable secure and decentralized energy trading in the metaverse. The use of DTs allows for creating a virtual representation of the physical energy assets, including generators, storage devices, and virtual power plants; a DT can be considered a building block of the metaverse. The framework uses blockchain technology to provide a secure and decentralized platform for energy trading. Our proposed framework, MetaPower, is implemented and deployed on the official Ethereum network. MetaPower shows promising performance results in terms of scalability, security, usability, flexibility, cost-effectiveness, and fairness.
The consensus protocol is essential in practically every blockchain application. Most of these existing blockchain consensus protocols need massive computationalcapabilities, substantial energy consumption, and dependency on monetary stakes. These shortcomings in the mainstream consensus approach lead to their unsuitability for low-resource applications like IoT. As a result of this work, a lightweight consensus process referred as Delegated Proof of Accessibility(DPoAC) is implemented and evaluated. DPoAC makes use of Shamir secret sharing, Proof of Stake (PoS) with random selection, and the Inter-PlanetaryFile System (IPFS). The DPoAC operation is composed of four modules: secret generation and distribution, retrieval of secret shares, block creation andverification, and block rewards and penalty. A detailed description of DPoAC has been provided and implemented in JavaScript and experimental resultsdemonstrate that our solution meets the necessary performance and security requirements for a lightweight scalable protocol for IoT systems.
The Internet of Things (IoT) is a disruptive technology that underpins Industry 5.0 by integrating various service technologies to enable intelligent connectivity among smart objects. These technologies enhance the convergence of Information Technology (IT), Operational Technology (OT), Core Technology (CT), and Data Technology (DT) networks, improving automation and decision-making capabilities. While cloud computing has become a mainstream technology across multiple domains, it struggles to efficiently manage the massive volume of OT data generated by IoT devices due to high latency, data transfer costs, limited resilience, and insufficient context awareness. Fog computing has emerged as a viable solution, extending cloud capabilities to the edge through a distributed peer-to-peer (P2P) network, enabling decentralized data processing and management. However, IoT networks still face critical challenges, including connectivity, heterogeneity, scalability, interoperability, security, and real-time decision-making constraints. Security is a key challenge in IoT implementations, including secure data communication, IoT edge and fog device identity, end-to-end authentication, and secure storage. This paper presents an efficient blockchain-based framework that creates a secure end-to-end communication cooperative flow IoT network. The framework utilizes a hybrid blockchain network that collaborates to offer a collaborative flow of end-to-end secure communication from end devices to cloud storage. The fog servers will maintain a private blockchain as a next-generation public key infrastructure to identify and authenticate the IoT's edge devices. The consortium blockchain will be maintained in the cloud and integrated with the permission blockchain system. This system ensures secure cloud storage, authorization, efficient key exchange, and remote protection (encryption) of all sensitive information. To improve the synchronization and block generation, reduce overhead, and ensure scalable IoT network operation, we proposed the threshold signature-based Proof of Stake and Validation (PoSV) consensus. Additionally, lightweight authentication protects resource-constrained IoT nodes using an aggregate signature, ensuring security and performance in real-time scenarios. The proposed system is implemented, and its performance is evaluated using key metrics such as cryptographic processing overhead, consensus efficiency, block acceptance time, and transaction delay. The findings show that threshold signature-based Proof of Stake and Validation (PoSV) consensus, reduces the computational burden of individual signature verification, which results in an optimized transaction latency of 80-150 ms, compared to the previous 100-200 ms without Non-PoSV. Additionally, aggregating multiple signatures from different authentication events reduces signing time by 1.98 ms compared to the individual signature time of 2.72 ms and the overhead of verifying multiple individual transactions is 2.87 ms is significantly reduced to1.46 ms along with authentication delay ranges between 95-180 ms. Hence, the proposed framework improves over existing approaches regarding linear computing complexity, increased cryptographic methods, and a more efficient consensus process.
Open access
Blockchain Technology Applications and Security
IoT and Edge/Fog Computing
Advanced Steganography and Watermarking Techniques