The classic design of the Practical Byzantine Fault Tolerance (PBFT) protocol relies on a centralized primary node, which not only creates a performance bottleneck but also introduces severe data censorship risks, threatening the data integrity and security of Edge Computing networks. To address this challenge, this paper proposes DC-PBFT (Decoupled PBFT), a censorship-resistant consensus protocol for Edge-Internet of Things (Edge-IoT) environments. The core innovation of DC-PBFT lies in the decoupling of the Proposer and Primary roles, supplemented by Verifiable Random Function (VRF)-based dynamic role rotation, which fundamentally eliminates the arbitrary power of a single node. Building on this, the protocol introduces a parallel group consensus mechanism: an elected Consensus Committee (CC) composed of Active Edge Nodes leads the consensus, while an independent Replica Network (RN) performs parallel validation. When a disagreement arises, the protocol triggers a global disagreement arbitration process involving all nodes to guarantee final consistency and attribute fault. To ensure long-term incentive compatibility, we also designed a hybrid election mechanism combining Proof-of-Stake and dynamic reputation, along with corresponding economic incentives and a tiered penalty system. Theoretical analysis proves that DC-PBFT satisfies Consistency and Liveness, and achieves strong censorship resistance guarantees. Simulation results demonstrate that DC-PBFT’s scalability significantly outperforms PBFT and RepChain; its reputation mechanism effectively improves long-term performance under sustained Byzantine attacks; and, compared to asynchronous censorship-resistant protocols like HoneyBadgerBFT, DC-PBFT achieves censorship resistance with over 45% lower transaction confirmation latency.
The continuous digitization of the modern farming sector demands secure, intelligent, privacy-preserving, and scalable infrastructures for real-time data analysis. However, existing smart farming systems face significant challenges, including cyberthreats, data authenticity issues, and the need for reliable decision support. This article proposes a secure Digital Twin (DT) architecture for smart agriculture recommendation systems, integrated with Blockchain and Federated Gated Recurrent Units (FGRU). At the perception layer, IoT sensors monitor soil, crop, and environmental data, which is gathered by a Request Control Authority (RCA) and transmitted to local models. To ensure privacy, a GRU-based Federated Learning (FL) approach is employed to detect cyberattacks—such as Sybil, Man-in-the-Middle (MITM), DDoS, and Replay attacks—without exposing raw decentralized data. Furthermore, a Blockchain-assisted Zero-Knowledge Proof-based Authority (ZKPA) mechanism is integrated to ensure data authenticity. The validated farming data is stored at the architecture’s final layer, enabling a Physical Twin to monitor real-time processes and generate precise recommendations. The architecture was evaluated using a paddy field dataset (26 features, 10,081 samples). Experimental results show that the proposed federated GRU model achieves perfect detection performance for all considered attacks, while the ZKPA-based authentication mechanism achieves a 98–99% authentication success rate with sub-10 ms verification time and only 15–25% additional computational overhead, which is better than existing works.
Blockchain-Enhanced AI: Securing Data Pipelines in Hybrid Cloud Environments: The data is vulnerable to tampering and breaches, which would completely undermine the accuracy of AI-driven insights, when running data pipelines in hybrid cloud setups.Blockchain Enhanced AI is a framework that combines the unchangeable records of blockchain technology with AI to check the accuracy of real-time data in multi-cloud systems. This can be done with the help of smart contracts that automatically flag anomalies, using sophisticated machine learning models such as LSTM networks. We can virtually eliminate the possibility of man-in-the-middle attacks by hashing the data blocks and validating them via consensus algorithms. In software engineering, the process streamlines the DevOps pipeline and reduces the risk of breaches by 30% on simulations in AWS and Azure. At the heart of the framework lies a robust core architecture and also includes privacy-preserving zero-knowledge proofs, and we’ve put this system to the test with empirical results in prototypes processing enormous petabyte-scale datasets. Attendees will be able to learn about the real-world implementation of this technology, challenges to scaling, and the ethical concerns around decentralized AI governance, and in doing so will help in developing more secure cloud-native applications.
Wahida Mansouri, Abdulrahman M. Qahtani, M. M. Kamruzzaman, Anandhavalli Muniasamy · 6 authors
The evolution of 6G networks brings unprecedented connectivity and processing capabilities to Internet of Things environments, yet it also raises more challenges in data security, intrusion detection, and computational efficiency. In this paper, a complete end-to-end framework with the inclusion of Artificial Intelligence, blockchain technology, and novel encryption techniques is proposed to address these challenges in 6G-IoT networks. Then, the AI-Powered Cybersecurity Events Dataset is utilized first, wherein network traffic is normalized through Min-Max normalization to normalize heterogeneous features to a uniform scale. A Gated Recurrent Unit-based neural network is then trained on this normalized data to detect real-time intrusions by learning complex temporal dependencies. Upon detecting anomalies, a blockchain layer is called to execute smart contracts that apply automatic security measures, e.g., quarantining affected nodes. For efficient processing, the architecture accommodates GRU-assisted task offloading optimization on a multi-factor delay, energy, and network load optimization model. Blockchain smart contracts manage load balancing and delegation verification autonomously without any central authority. Homomorphic encryption and proxy re-encryption also ensure data confidentiality in edge computing and secure multi-party computation. All encryption and task offloading operations are permanently stored onto the blockchain, ensuring system-wide transparency and auditability. Federated learning support is built-in to enable privacy-preserving decentralized AI training from distributed edge nodes. The entire framework is implemented in Python using TensorFlow/Keras for the GRU model and web3.py for blockchain-related interactions. Experimental measurements demonstrated the efficiency and stability of the system by exhibiting a percentage of accuracy of 98.9% in intrusion detection, justifying the applicability for future 6G-IoT secure and scalable operations. The experiments utilize the AI-Powered Cybersecurity Events Dataset containing approximately 1.5 million network flow records with class distribution of 52% benign traffic, 21% DoS, 15% probing, and 12% privilege escalation events.
Mobile Crowd Sensing (MCS) systems enable large-scale data collection from heterogeneous IoT and mobile devices but face critical challenges related to data reliability, participant trust, and decentralized validation. Existing blockchain-based MCS frameworks often rely on energy-intensive or static consensus mechanisms and lack adaptive intelligence for detecting malicious contributors, limiting their real-world scalability. This paper proposes an intelligent, decentralized trust management framework that integrates a Delegated Proof-of-Stake (DPoS) blockchain with a Dilated RNN–BiGRU deep learning model. The blockchain ensures tamper-proof transaction validation and trust-based consensus, while the deep network dynamically predicts node reliability using temporal behavior patterns. The integration creates a feedback loop where learned trust scores influence validator selection in real time. The proposed hybrid framework was implemented on a Hyperledger Fabric 2.5 network and evaluated using synthetic MCS data representing heterogeneous environmental, noise, and traffic sensing. The system achieved 98.76% accuracy, 57% latency reduction, and 40% computational cost savings compared with existing PoW- and PoA-based models. These results demonstrate that coupling blockchain consensus with adaptive deep trust modeling can significantly enhance the security, scalability, and efficiency of next-generation MCS systems, making the architecture suitable for real-time, large-scale IoT deployments.
Sufian Al majmaie, Ghazal Ghajari, Niraj Prasad Bhatta, Fathi Amsaad
The integration of Fog Computing with Flying Ad-Hoc Networks (FANETs) offers promising capabilities for decentralized, low-latency intelligence in UAV-based applications. However, the distributed nature, mobility, and resource constraints of FANETs expose them to significant security and privacy challenges, particularly against quantum threats. To address these issues, this work introduces a blockchain-based, AI-enhanced key management framework designed for fog-enabled FANETs. The proposed scheme employs a Post-Quantum Multivariate Identity-Based Signature Scheme (PQ-MISS) and Zero-Knowledge Proofs (ZKPs) to achieve secure key establishment, privacy-preserving data aggregation, and integrity verification. A polynomial composition-based encryption mechanism and an aggregate signature model support secure and efficient multi-device communication across fog and UAV layers. Fog servers construct partial blockchain blocks from validated UAV data. These blocks are completed and mined by Cloud Servers (CSs). AI algorithms then analyze the verified data to generate accurate predictions and insights. NS-3 simulations validate the efficiency of PQ-MISS in reducing communication overhead while improving the speed and reliability of data aggregation and verification. Comparative analysis demonstrates the proposed scheme's advantages over existing methods in computational cost, post-quantum security, and scalability, making it a robust solution for secure, intelligent, and future-ready FANET systems.
The proposed research is to provide a secure framework for transmitting and managing IoT sensor data using blockchain technology. The system involves different types of users, each with the ability to register, log in, and upload IoT sensor data. The data transmission is secured using the SHA-256 cryptographic algorithm, ensuring that sensor data is protected from unauthorized access. Both users can utilize Blockchain APIs or smart contracts to increase security and maintain transparency during data exchange. Additionally, users can decrypt the sensor data provided by each other, ensuring secure and trustworthy communication between devices within the IoT network. This system leverages blockchain decentralized nature to protect the data sensor data, prevent tampering and unauthorized modifications. The system’s performance is tested on different key sectors such as financial management, Health sector, supply chain and E-Governance data.
Remote examination platforms have experienced exponential growth, yet centralized architectures remain susceptible to data manipulation, unauthorized record alteration, and deficient audit mechanisms. This work introduces a federated, permissioned blockchain framework built upon Hyperledger Fabric, integrated within an AI-driven online examination platform designated as Evalon. The proposed architecture distributes ledger maintenance across multiple authorized institutional peers, recording cryptographic digests of examination lifecycle events—including candidate authentication, session boundaries, proctoring anomalies, and grade finalization—without exposing personally identifiable information on-chain. A Byzantine fault-tolerant ordering service coupled with endorsement policies ensures that no single administrative entity can unilaterally modify committed records. The blockchain substrate operates alongside a microservices backend deployed on serverless cloud infrastructure, facilitating real-time event validation through RESTful APIs and deterministic smart contracts. Complementing the integrity layer, computer vision models perform continuous behavioral analysis, detecting multi-face presence, gaze deviation, and anomalous motion patterns during live sessions. Experimental evaluation across 12,000 simulated examination sessions demonstrates a 99.7% hash verification success rate, sub-second ledger commit latency under concurrent loads of 500 transactions per second, and a 34% reduction in undetected integrity violations compared with conventional centralized logging. The combined framework establishes a tamper-resistant, auditable, and scalable ecosystem suitable for academic, certification, and enterprise assessment deployments.
Sensors, wearables, implanted devices, and cloud platforms provide real-time monitoring, diagnosis, and clinical decision-making with the Internet of Medical Things (IoMT). Growth of IoMT infrastructure affects data privacy, cybersecurity, interoperability, and trust. IoMT systems, non-standard communication protocols, device capabilities, and medical network incursions are covered. We suggest data fusion, encryption, and decentralised trust enforcers for patient data. A decentralised, unchangeable, and secure blockchain. Blockchain-based solutions protect IoMT data flow, eliminate single-point-of-failure, and secure distributed medical device trust evaluation. Distributed ledger data integrity, authentication, and trust score storage improve active blockchain trust models. AI, data fusion, cloud/fog computing, smart hospitals, and blockchain secure IoMT. This chapter covers IoMT system design, cybersecurity, MULC frameworks, and blockchain-based health innovations. We discuss open research, regulation, and resilient, scalable, and dependable IoMT ecosystems.
Abuzar Khan, Ahmad Junaid, Abid Iqbal, Ghassan Husnain
This study proposes a Federated Cloud Intelligence for Privacy-Preserving AI, with new layered framework that can support secure and eco friendly learning across different cloud providers. Instead of centralizing data, our method trains models locally on varied client datasets and combines their updates using federated learning (FL) to stay compliant with data protection rules. The experiment have shown that the federated setup reached an average accuracy of 0.844 over five communication rounds, just slightly lower than the centralized baseline of 0.850. Meanwhile, the loss decreased from 0.367 to 0.285, coming close to the centralized value of 0.318. To build trust, a blockchain-based layer that permanently stored updates with little extra cost, adding blocks each round with an average consensus delay of 0.189 seconds. Tests showed that this consensus process reduced the impact of malicious client attacks, keeping accuracy stable around 0.827. Further it is then incorporated with zero-knowledge proofs (ZKM), where adds only 0.196 seconds of latency and 260–360 MB GPU memory overhead and showcases an accuracy up to 0.844. A reinforcement learning agent optimized workload scheduling by shifting the computation from AWS to GCP, reducing carbon scores by 20% with minimal accuracy trade-off. Finally, explainability analysis revealed balanced provider contributions from 0.021 to 0.023 and highlighted key features such as logPurchases and storePurchases.
As blockchain technology evolves from specialized financial tools to foundational infrastructure for Web3, the necessity for rigorous performance validation becomes paramount. Stress testing—defined as the evaluation of system stability under extreme workloads—is critical for identifying bottlenecks in consensus mechanisms and peer-to-peer communication. This survey provides an exhaustive analysis of web-based stress testing frameworks. Unlike traditional CLI-based tools, web-based frameworks provide real-time telemetry and distributed orchestration capabilities essential for modern decentralized applications. We categorize existing literature into three generations of benchmarking, evaluate ten prominent frameworks based on a multi-dimensional rubric, and identify significant research gaps including the lack of standardized cross-chain stress protocols and AI-integrated anomaly detection. This work aims to provide a roadmap for researchers and DevOps engineers to select and implement robust testing environments for enterprise-grade blockchain deployments.
Abdullah Abdullah, Nida Hafeez, Maryam Shabbir, Muhammad Ateeb Ather · 6 authors
The integration of blockchain technology with the Internet of Things (IoT) presents a paradigm shift in securing decentralized networks, yet it introduces critical trade-offs among security, privacy, and scalability. This systematic analytical review examines the inherent tensions within blockchain-enabled IoT systems, focusing on how consensus mechanisms, cryptographic primitives, and architectural choices affect these three pillars. Through a comprehensive analysis of the contemporary literature, we identify that no single blockchain configuration simultaneously optimizes security, privacy, and scalability. Instead, these properties exist in a triadic relationship where enhancing one dimension typically compromises at least one other. Our review categorizes existing solutions based on their approach to balancing these trade-offs, including sharding, layer-2 protocols, zero-knowledge proofs, and hybrid architectures. We further analyze the applicability of these solutions across different IoT domains, identifying context-specific optimal configurations. The findings reveal that while significant progress has been made in addressing individual challenges, integrated frameworks that holistically consider all three dimensions remain underdeveloped. This review contributes a novel analytical framework for evaluating blockchain–IoT systems and identifies critical research directions, including adaptive consensus mechanisms, privacy-preserving scalability solutions, and domain-specific architectural patterns. Unlike prior studies that primarily focus on conceptual discussions of blockchain–IoT integration, this work synthesizes insights from systematically reviewed literature to propose a conceptual lightweight blockchain framework tailored for resource-constrained IoT environments. This study combines a SLR with a conceptual and experimentally evaluated framework, where the review findings and the proposed solution are presented as distinct but complementary contributions.
With the proliferation of mobile devices such as smart phones, tablets, and laptops, consumers are creating and sharing large amounts of multimedia information such as images, videos, and documents. Traditional centralized systems have significant risks on data privacy and often share an unequal share of revenue with the intermediaries, which requires the creation of a secure, decentralized data trading and storage system. This paper presents a blockchain-based edge computing platform that leverages high capacity relay nodes to store encrypted and tamper-proof data to enable quick processing and maximize revenue to the data producers. A Rounding-based Data Placement Algorithm (RDPA) is a useful tool to locate cost-effective relay nodes to minimize storage and transmission costs, and a sustainable consensus mechanism, Proof-of-Data-Trading (PODT), is a synthesis of Proof-of-Work and Proof-of-Stake to safely publish blocks with the minimum use of energy. A set of encrypted data is stored on relay nodes and kept within a certain timeframe, and the metadata and location are recorded on the blockchain, which enables safe and open transactions between producers and consumers. Smart contracts make it possible to automate the data transaction, access, and verification process. A compression method is used to reduce the size of the large data items to make the most out of the relay nodes. The proposed solution will ensure decentralized storage of data in a way that is scalable, secure, and cost-effective which will enable the producers to support massive revenue potential and consumers to reliably retrieve data within a blockchain-based edge architecture.
The rapid expansion of IoT in smart hospitals enables continuous patient monitoring, automated diagnosis and real-time clinical decision support. However, centralized healthcare data systems remain vulnerable to unauthorized data modification, single points of failure and poor audit transparency threatening patient safety and regulatory compliance. This paper proposes a Block-chain-IoT (BC-IoT) framework built on a three-tier hierarchical architecture. The first tier connects heterogeneous medical devices (ECG monitors, glucose sensors, infusion pumps, pulse oximeters) through a lightweight IoT sensor layer. The second tier applies AI-based anomaly detection at edge computing nodes. The third tier employs a dual-block-chain approach, combining the IOTA Tangle protocol for feeless micro-transactions with a permissioned Hyperledger Fabric network for enterprise-grade data governance. Medical data is encrypted using AES-256 and TLS 1.3, screened for anomalies at edge nodes and stored on an immutable distributed ledger. Smart contracts enforce role-based access control, ensuring only authorized personnel can access or modify patient records. The Inter Planetary File System (IPFS) handles decentralized storage of large medical files, with cryptographic content identifiers stored on-chain for full traceability. Evaluation on a simulated smart hospital testbed with 350 IoT nodes across five department’s demonstrated strong results: 42ms transaction latency, 1,250 transactions per second, a 99.7% tamper detection rate and a 99.98% data integrity score outperforming existing block-chain-IoT healthcare systems across all key metrics. The BC-IoT framework offers a scalable, energy-efficient and standards-compliant solution for securing digital health infrastructure.
Location privacy insparseInternet of Vehicles is difficult to ensure due to limited anonymity, predictable mobility, and prolonged tracking windows. Existing silent-period and pseudonym-based schemes generally assume dense traffic and thus degrade under low-density conditions. This work proposes ZK-V2XChain, a lightweight privacy-preserving framework that integrates a Random Silent Period (RSP) mechanism with blockchain-based Identity Token (IT) authentication and Zero-Knowledge Proof (ZKP) validation. The framework explicitly models sparse-network behavior and enables adaptive, verifiable privacy without compromising efficiency. We design a privacy-preserving IT issuance process using simulated smart contracts and implement decentralized IT verification through RSU–blockchain interaction. Using SUMO mobility traces, ns-3.45 simulations, and MATLAB-based privacy analytics, results show that ZK-V2XChain achieves higher entropy than SAP, RFPM, GLS, and CPS, and approaches the performance of OBS. The maximum anonymity-set size reaches 10.88, and communication overhead remains low (940 bytes). Ablation studies highlight the complementary roles of RSP, ZKP, and blockchain in balancing uncertainty, responsiveness, and issuance stability.
In the deployment of smart home IoT there is a critical security Loopholes that centralized management system are not able to solve. We present LiDIA, a lightweight Delegated Proof-of-Stake (DPoS) blockchain framework integrating Inter Planetary File System (IPFS) distributed storage and zeroknowledge authentication for residential IoT. we performed various simulation and result demonstrate end-to-end confirmation averaging 18 ms, throughput exceeding 1,200 Transactions Per Second (TPS), device-level energy reductions exceeding 99% over Proof-of-Work and AI-driven threat detection accuracy of 87.3% 92.5%, establishing a scalable and secure foundation for smart home environments.
Augusto Magalhães Pinto de Mendonça, Filipe Pessôa Sousa, fellipe souza pessanha, Igor Machado Coelho
Fitness apps help users track their exercises and health. Wearable sensors provide increasing amounts of data, bringing motivation but also challenges to privacy. Data shared by users’ devices may exceed what they want to share for the desired results. Health insurers, corporate wellness programs, and gamified apps all want proof of exercise, but current systems require users to disclose raw activity logs, heart rate, and location data. This paper presents a system that lets users prove they met exercise goals without revealing raw sensor data. We embed a Decision Tree classifier inside a zero-knowledge proof circuit using the Groth16 protocol. The classifier distinguishes rest, moderate, and vigorous activity from accelerometer and gyroscope readings. The model reaches 97.19% holdout accuracy on the MHEALTH dataset, validated through $\mathbf{1 0}$-fold crossvalidation. The circuit uses 10,447 constraints, small enough for smartphones and wearables. Proofs take about 500 ms on desktop and 1.1 seconds on mobile, with only 800 bytes each. Unlike systems where classification runs outside the proof, our approach makes the classification itself verifiable, so label injection attacks become computationally infeasible. We tested 30 cases covering all intensity classes and boundary conditions, and all proofs were generated and verified correctly. The system provides cryptographic guarantees while remaining practical for real-world deployment.
Today, many computing workloads are executed in loosely coupled, geographically distributed environments where resources are owned by different organizations. Examples include inter-institutional research infrastructures, community-operated clusters, and edge deployments. As disconnections are frequent in such environments, ensuring reliable task execution remains a fundamental challenge. Kubernetes, the de facto standard for cluster orchestration, provides centralized control and strong consistency, but suffers from slow recovery when node failures occur frequently. At the opposite extreme, blockchain-based orchestration removes centralized control but incurs substantial latency due to global consensus, making it unsuitable for time-sensitive task scheduling. This paper presents Mutual Cloud, a decentralized orchestration framework that operates between these two extremes. Mutual Cloud adopts a hybrid architecture where task admission and queue management are handled in a centralized manner similar to conventional public clouds, whereas most scheduling functions, including execution-node selection and failure handling, are performed in a decentralized manner by autonomous agents using a distributed hash table. We implement a prototype of Mutual Cloud and evaluate its performance through large-scale simulation studies. The results show that Mutual Cloud maintains stable performance comparable to centralized baselines under normal conditions while achieving approximately five-second-level recovery latency under substantial node failures.
The current methods don't meet the security and performance needs of Internet of Vehicles (IoV) apps, and they also don't give the end user a low-latency, secure edge-computing service at the same time, while in the context of vehicles. This study presents a blockchain-enabled edge computing architecture that employs Double Deep Q-Network (DDQN) for reinforcement learning and lightweight Practical Byzantine Fault Tolerance (PBFT) for consensus, aiming to simultaneously enhance latency, energy efficiency, and security. The containerised architecture uses Hyperledger Fabric with Kubernetes to efficiently manage micro-services and move tasks off of them. In urban, suburban, and highway settings, the framework consistently outperforms baseline algorithms, with a 30–45% improvement in end-to-end latency and a 55% reduction in energy use under moderate to heavy loads. The system finished more than 95% of its tasks while keeping block consensus times under 1.2 seconds at peak loads. The architecture also showed consistent performance with different levels of vehicle density and used zero-knowledge proofs with attribute-based security to protect data from cyber threats from bad actors. These findings indicate that the integration of DDQN and blockchain will mitigate security issues in the Internet of Vehicles (IoV) by enabling secure edge computing for future vehicular networks.
The fast move toward sixth-generation (6G) distributed networks is making it possible to create highly dynamic, intelligent, and collaborative service environments for a wide range of use cases, including smart cities, autonomous systems, industrial IoT, immersive communication, and edge intelligence. But working together on a large scale in 6G environments comes with a lot of technical problems, such as the need for instant access to resources, coordinating different types of services, exchanging data that can grow, and making sure that security, trustworthiness, and privacy are all strong. Traditional centralised architectures have trouble meeting these needs because they have single points of failure, limited transparency, and problems with managing trust. Blockchain technology provides decentralisation, immutability, and the establishment of trust; however, its fundamental limitations in throughput and storage capacity impede its direct implementation in extensive 6G distributed collaboration systems. This paper presents a universal blockchain-based collaboration architecture specifically designed for 6G distributed networks, accompanied by an end-to-end collaboration mechanism aimed at delivering efficient, secure, and reliable resource-sharing functionalities. The proposed architecture combines service-oriented design ideas with adaptive blockchain improvements to get around problems with scalability. To address the throughput constraints of traditional blockchain systems, a service-oriented, capacity-adaptive blockchain sharding framework is proposed. In this framework, network nodes with different levels of consensus efficiency are dynamically split into different shards using a strategy that rates nodes based on their reputation. The assessment checks the performance of nodes by looking at things like their computational power, communication delay, reliability, and past behaviour. This is to make sure that shard formation is fair and reliable. Also, transactions are grouped by service type and sent to the right shards, which have the right level of consensus for the service. This service-aware transaction assignment makes sure that high-performance shards handle services that need to be processed quickly, while shards with moderate consensus capabilities handle services that don't need to be processed as quickly. This kind of adaptive alignment between service characteristics and shard performance greatly improves the overall throughput of the system and the efficiency of resource use. To make consensus even more efficient when workloads change, a load-sensitive Practical Byzantine Fault Tolerance (PBFT) mechanism is suggested for intra-shard consensus. The proposed load-aware enhancement dynamically changes consensus parameters based on shard load conditions, which is different from regular PBFT, which may slow down when there are a lot of transactions. This adaptive approach cuts down on communication overhead, makes the system more fault-tolerant, and keeps consensus performance stable even when many people are working together. So, the architecture makes sure that transactions are always valid while still meeting the ultra-low latency and high reliability needs of 6G apps. Along with throughput issues, storage scalability is still a big problem for blockchain-based systems because the ledger size keeps getting bigger. The paper proposes a hybrid storage policy that combines both on-chain and off-chain storage methods to get around this problem. To keep things immutable and trustworthy, important metadata, transaction proofs, and security-related records are kept on-chain. Large amounts of service data and information about sharing resources are kept off-chain using distributed storage solutions. Secure cryptographic connections between on-chain and off-chain parts make sure that data is accurate and can be verified without putting too much strain on the blockchain ledger. This mixed strategy greatly reduces the pressure on storage while keeping things clear and traceable. A lot of simulations are done to see if the proposed architecture and mechanisms are possible, can be scaled up, and will work better than other options. The results show that this new way of working together on a blockchain has a lot better throughput, less consensus latency, more balanced shard usage, and better storage efficiency than traditional blockchain-based collaboration models. Also, the proposed framework offers strong security guarantees and is resistant to bad behaviour in networks with different types of devices. In general, the suggested universal blockchain-based collaboration architecture is a scalable, secure, and adaptable way to make resource sharing in 6G distributed networks more efficient. The framework effectively solves blockchain scalability problems while also meeting the strict performance needs of next-generation distributed communication systems by combining service-aware sharding, load-sensitive consensus optimisation, and hybrid storage design. The results show that the architecture has the potential to be a key part of trustworthy and smart collaboration in future 6G ecosystems.
Federated unlearning enables clients to withdraw their contributions from a global model.However, enabling clients to verify whether the server has honestly and effectively removed their contributions remains a critical challenge. To address this aspect, which has been largely overlooked in existing literature, a verification model based on zero-knowledge proofs was constructed, and a comprehensive framework for verifiable federated unlearning was proposed. Combined with a dynamically updated Merkle tree structure, a novel verifiable federated unlearning scheme was presented characterized by its zero-knowledge property. This allows for the efficient generation of cryptographic proofs for server unlearning operations while rigorously protecting the data privacy of other clients. We evaluate the effectiveness and computational overhead of the proposed scheme. Comparative experiments with Rivest-Shamir-Adleman (RSA) accumulator-based and Hash chain-based schemes demonstrate that, when the model parameter size reaches the order of <inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><msup><mrow><mn mathvariant="normal">10</mn></mrow><mrow><mn mathvariant="normal">5</mn></mrow></msup></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/B6D6E598-14B1-468e-9A32-73199F9CD69E-M002.jpg"><?fx-imagestate width="4.23333359" height="2.53999996"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/B6D6E598-14B1-468e-9A32-73199F9CD69E-M002c.jpg"><?fx-imagestate width="4.23333359" height="2.53999996"?></graphic></alternatives></inline-formula>, the proposed scheme improves proof generation speed by approximately two orders of magnitude and verification speed by 13.2 times compared to the RSA-based scheme. Furthermore, it effectively avoids the scalability bottleneck of data linear growth in verification overhead inherent in Hash chain-based schemes.
The proliferation of Internet of Things (IoT) devices creates unprecedented security, privacy, and transparency challenges in distributed systems. Traditional encryption-based approaches provide privacy but impose significant computational overhead, storage bloat, and key management complexity. This paper presents CIPHER-IoT, a blockchain-based framework that integrates Zero-Knowledge Proofs (ZKPs) with Hyperledger Fabric for privacy-preserving IoT data management. Unlike encryption-based approaches that store encrypted data on-chain, CIPHER-IoT utilises Groth16 zk-SNARKs to generate cryptographic proofs of data validity while storing only commitments on the blockchain, achieving stronger privacy guarantees with lower storage overhead. The framework employs Ed25519 for lightweight digital signatures and implements comprehensive chaincode for ZKP verification, commitment uniqueness checking, and access control enforcement. CIPHER-IoT targets gateway/edge IoT deployments with moderate computational capacity (ARM processors 500 MHz+) rather than ultra-constrained sensors. We evaluate CIPHER-IoT against two baseline systems, SPAS (homomorphic encryption-based) and SPAS-H (AES encryption with Hyperledger Fabric), using realistic simulation with 50–500 devices and transaction rates of 10–75 TPS. Experimental results demonstrate superior privacy (98% confidentiality vs. 80–95% for encryption-based approaches) alongside competitive performance: read latency improves 37% (p < 0.001), throughput increases 14.6% (p < 0.001), memory reduces 21.4%, network bandwidth saves 47%, and disk I/O reduces 37.8%. The system maintains zero data loss under failure scenarios and scales linearly to 500 devices with minimal degradation (9.9%). CIPHER-IoT demonstrates that verification-based privacy mechanisms can achieve stronger privacy and better performance than transformation-based approaches in distributed validation contexts, particularly suitable for enterprise IoT deployments requiring coordinated privacy-preserving infrastructure.