<title>Abstract</title> This study presents a comparative analysis of cloud-native and Distributed Ledger Technology (DLT)-based synchronization models for resilient geospatial data management in multi-cloud environments. With the rising demand for real-time geospatial data in applications such as smart cities, disaster response, and environmental monitoring, ensuring data consistency, availability, and integrity across distributed cloud infrastructures has become increasingly critical. Cloud-native models offer high throughput and scalability through managed replication and consistency protocols but may be limited by eventual consistency and reliance on provider-managed security. In contrast, DLT-based models, particularly those using blockchain, enhance data integrity and auditability through decentralized, tamper-proof synchronization, albeit at the cost of increased latency and operational complexity. To evaluate these trade-offs, we propose a composite performance framework encompassing resilience, synchronization efficiency, and operational cost. Using simulation-based analysis, we assess both models under various failure scenarios and performance conditions. Results highlight the strengths and limitations of each approach and underscore the value of a hybrid model—combining the speed of cloud-native systems with the trust guarantees of DLT—for mission-critical geospatial applications. This research offers practical recommendations for system designers and contributes to the evolving integration of blockchain, cloud, and AI technologies in secure, multi-cloud geospatial infrastructures.
Mohammad Alsaffar, Eman Abouelkheir, Wedad Alawad, Majed S. Alsayfi · 8 authors
The ultra-dense vehicle scenarios envisioned in 6G put high requirements on ultra-low latency, secure cooperation, and efficient task offloading decisions. Existing systems usually optimize latency or energy independently but ignore joint privacy problems and long-term trust sustainability. In this work, a distributed intelligence architecture based on the combination of federated learning (FL) and blockchain based trust management for vehicle-to-vehicle (V2V) edge computing is proposed. The proposed architecture enables collaborative prediction and decentralized incentive enforcement in a privacy-preserving manner without revealing raw vehicle data. In this paper, task allocation is defined as a multi-objective optimization problem, which jointly considers latency, energy consumption, communication stability and privacy exposure. The resultant problem is addressed by a learning-coupled primal-dual optimization, where the federated prediction is used to drive the offloading decisions and the dual update is used to impose the limitations of the system. A light-weight distributed ledger layer ensures secure coordination, automatic incentive allocation and reliable detection of fraudulent nodes. The extensive simulations in the integrated traffic-network-blockchain environments show that the proposed method outperforms the state-of-the-art baselines, achieving up to 30-40% reduction in the service latency, approximately 25% improvement in task completion rate, enhanced privacy preservation by the gradient-based learning, and up to 95% accuracy in detecting the malicious nodes. These results validate the efficacy of the suggested framework for attaining scalable, privacy-aware, and trustworthy distributed intelligence for next-generation 6G vehicular edge networks.
In response to problems such as a lack of trust, low resource utilization rates, conflicts due to multiple constraints, and security risks associated with sharing 5G wireless access network resources, this study proposes an efficient, trustworthy, and secure distributed resource sharing system and optimizes the resource allocation strategy. First, it performs virtual decoupling and atomic modeling for the three core computing resources: spectrum, security, and computing power. It also designs a five-layer distributed resource-sharing framework that integrates blockchain and software-defined networks. Additionally, it proposes an improved delegated proof-of-stake consensus mechanism, as well as an asymmetric encryption transaction authentication and resource status traceability mechanism. Second, for the multi-constraint conflict issue, it designs a multi-agent deep deterministic strategy gradient secure resource allocation algorithm integrating long-term and short-term memory state prediction. The verification experiments were carried out based on the 5G-RAN public resource scheduling dataset in accordance with the 3GPP TR38.901 protocol specification. The experimental hardware was equipped with Intel Core i9-13900K processor, NVIDIA RTX 4090 graphics card, etc. The simulation platform was built on the Ubuntu 22.04 LTS system using the PyTorch 2.1.0 deep learning framework and the NS-3 3.36 simulation tool. The comparison benchmarks were mainstream centralized resource allocation schemes, blockchain, federated deep reinforcement learning schemes, and consortium chain hierarchical cross-slice schemes. The experimental results showed that the resource utilization rate of this framework reached 89.3%, the transaction delay was only 21.8 ms, the service quality satisfaction and security compliance rate were 96.7% and 98.2% respectively, the double-spend attack resistance rate and resource status traceability accuracy rate both reached 99.9%, and all related indicators were significantly superior to the existing comparison schemes. This study provided technical support for 5G resource collaboration in scenarios such as industrial internet and vehicle networking, effectively solving the trust bottleneck and scheduling problems in distributed environments. However, the research has not fully considered the adaptability of resource scheduling in extreme network environments. The computational power consumption of the algorithm in large-scale node deployment scenarios must be optimized further. The computational cost of the blockchain and multi-agent deep reinforcement learning components is high. Additionally, the system’s scalability in ultra-dense 5G scenarios must be improved. To a certain extent, this framework’s immediate large-scale practical application in complex 5G network environments is limited.
The Internet of Vehicles (IoV) is changing the contemporary mobility, as it allows real-time communication between vehicles, infrastructure, and cloud services. Nevertheless, such growing connectivity brings on serious privacy, regulatory, and trust issues especially because sensitive behavioral and location information is exposed. The current IoV-security systems tend to be based on identity-based checks, or centralized trust authorities, which can lead to infringement of user privacy and cause surveillance and profiling threats. The paper is inspired by privacy-preserving architectures in the Metaverse to suggest a decentralized trust system of IoV systems on the basis of zero-knowledge proofs, namely zk-SNARKs. The suggested solution allows vehicles to cryptographically verify that they meet regulatory or operational regulations- i.e. valid insurance, safety test, or emissions- without revealing personal identifiers or raw information. The framework enables building scalable, low-latency and audible trusts and following data minimization principles through combining zk-SNARK verification and Layer 2 blockchain solutions.
One area of application for distributed ledger technologies is the Internet of Things. These technologies can provide an effective solution to many problems in this field. The consensus layer is a crucial architectural component of distributed ledger systems. Modern IoT networks place increased demands on the consensus mechanisms used in blockchain systems. There are many consensus protocols with different properties and purposes, including those for IoT blockchain networks. Selecting an appropriate consensus protocol for a specific IoT blockchain system is an important and complex task. Multi-criteria decision analysis methods are widely used in such problems, as they allow for the consideration of multiple conflicting criteria and provide a balanced approach to evaluating alternatives. Given the variability of network parameters and requirements of consensus mechanisms, multi-criteria decision-making methods can support more informed protocol selection. This paper presents a decision support framework for selecting a consensus protocol for blockchain-based Internet of Things networks. The system is an implementation of a previously developed conceptual model for a consensus protocol selection framework. A case study is also provided to demonstrate the application of the system.
Guilin Guan, Yang Cao, Zhenqiang Xie, Shu Yang · 5 authors
Abstract Industrial Internet of Things (IIoT) devices continuously generate large volumes of privacy-sensitive operational data. Federated Learning (FL) enables distributed model training without exposing raw data to external parties. However, existing FL solutions suffer from critical limitations, including single points of failure from centralized servers, insufficient verifiable defenses against gradient poisoning attacks, and poor adaptability to dynamic device churn. To address these challenges, we propose DVFL-IIoT, a fully decentralized and dynamic secure aggregation protocol tailored for IIoT environments. Our framework eliminates centralized trust assumptions using Pedersen Distributed Key Generation (DKG), supports seamless device joining and leaving without full system reinitialization through a key insulation mechanism, and ensures end-to-end verifiability via dual non-interactive zero-knowledge proofs (NIZKs). Formal security analysis proves that DVFL-IIoT achieves IND-CCA2 privacy, information-theoretic collusion resistance, and computational verifiability. Extensive experiments on two real-world IIoT intrusion detection benchmarks, ToN-IoT and Edge-IIoTset, achieve test accuracies of 98.81\% and 98.35\%, respectively, significantly outperforming state-of-the-art methods while maintaining strong robustness against poisoning attacks and dynamic device churn.
Large-language-model (LLM) inference is increasingly concentrated in dedicated GPU data centres and closed API platforms, raising barriers for institutions that want to run, study, or contribute to AI infrastructure. We argue that democratizing inference requires an architecture in which smaller organizations can participate as operators, builders, and researchers rather than only as customers. We propose OpenPrism Network, an open, UMA-first distributed inference architecture in which transformer layers are statically owned by nodes so that weights remain resident and only activations transit the network; a blockchain layer is restricted to settlement, reputation, and payment and never to compute; output integrity is established by multi-node redundancy with tolerance-banded fingerprinting rather than zero-knowledge proofs; and routing is locality-aware, keeping inference within metro-area clusters. The network is explicitly scoped to batch- and throughput-oriented, latency-tolerant workloads. We describe two deployment models: a distributed mesh harvesting idle institutional capacity, and a purpose-built UMA micro data center deployable by resource-constrained organizations as a sovereign inference facility. We also describe an open participation model in which node operators, runtime implementers, benchmark maintainers, and application integrators can contribute through published interfaces and open-source reference components. This is a position and architecture paper: we claim no original experimental results, and all quantitative figures are drawn from publicly available benchmarks and published specifications, cited explicitly. We report performance per watt honestly, including the threefold cost of consensus, and find that UMA nodes lose on operational efficiency against batched data-centre GPUs in the scoped regime; the architecture's advantage is therefore established on capital in the harvested-capacity model, participation, and data sovereignty, while total cost of ownership for the purpose-built micro data center is mixed and strongly pricing-regime dependent, not universally favorable. We frame two problems as genuinely unsolved: a consensus protocol for ML output verification under floating-point non-determinism, and a dynamic layer-assignment protocol that rebalances ownership as nodes join and leave without full weight redistribution. We also state a concrete validation roadmap, including prototype scope, baselines, and evaluation metrics.
With the increasing adoption of mobile applications, data in the mobile cloud faces numerous security threats and privacy breaches. To overcome cyberattacks, ensuring confidentiality and data security for users’ sensitive data is pivotal in mobile cloud computing. Traditional security mechanisms involve data leakage during the verification process, while blockchain-dependent solutions lead to high resource consumption and latency. Additionally, collaborative data processing during data transactions can result in potential privacy attacks on users. This paper proposes a novel approach for maintaining a security framework for Microservice-based Mobile Cloud Computing (MSCMCC) using hybrid cryptographic frameworks such as Zero-Knowledge Proof (ZKP) and Secure Multi-Party Computation (SMPC). The proposed model validates users’ offloaded data using zk-SNARK and Groth16 for task verification and enables data analysis from multiple users without exposing raw data. SMPC is employed for privacy preservation during collaborative multi-party computation. Experimental results demonstrate that the proposed framework reduces power consumption, improves energy efficiency during processing by 30–35%, lowers computational costs, enhances security and privacy, and effectively manages dynamic load balancing compared to traditional cryptographic techniques.
Agricultural trade in developing countries continues to depend on informal agreements, multi-tier intermediary networks, and centralized payment mechanisms, resulting in payment delays of 30–90 days, information asymmetry, and weakened bargaining power for smallholder farmers. This paper presents AgriHandshake, a blockchain-based smart contract platform enabling direct crop trading between farmers and vendors through an automated escrow payment mechanism deployed on the Ethereum network. The system employs a hybrid architecture that stores cryptographic state hashes and escrow logic onchain while offloading trade metadata and delivery documentation to the InterPlanetary File System (IPFS), reducing average transaction gas costs to approximately 85,000–210,000 gas units per operation. A Solidity-based escrow contract enforces a structured four-state machine (CREATED→FUNDED→DELIVERED→COMPLETED/DISPUTED) with a 72-hour automatic payment-release timer implemented via block.timestamp. Experimental evaluation on the Ethereum Sepolia testnet demonstrates average smart contract function execution latency under 15 seconds, end-to-end trade confirmation within 3–8 minutes including IPFS upload, and strong resistance to reentrancy and front-running attacks. Comparative analysis against eNAM, FarMarket, and AgriOnBlock confirms that AgriHandshake is the first platform to combine a dedicated escrow payment guarantee, decentralized off-chain storage, and a farmer-centric usability model within a single deployable framework
Smart cities require the efficient and secure integration of key infrastructure domains such as water, energy, transportation, smart lighting and waste management deploying a myriad of data-generating IoT sensors and devices. Current management systems feature single points of failure, lack of auditability, insufficient privacy protection and lack of scalability as IoT nodes increase in density. In this paper, we present SmartChain, a three-tier multilayered blockchain based architecture that incorporates a permissioned distributed ledger, an AIbased anomaly detection module (ADM) and a dual-layered privacy preservation approach that combines Zero-Knowledge Proofs (ZKP) and Ciphertext-Policy Attribute-Based Encryption (CP-ABE). SmartChain is tested on a large dataset - 2000 timestamped transactions involving Smart Cities’ five infrastructure types across several zones of a city. The results show a mean throughput of 3,421 transactions per second (TPS), a mean transaction latency of 3,847 milliseconds and a mean privacy score of 82.4 out of 100. The machine learning based anomaly module produces an F1-Score of over 97% and an AUROC score of 0.991 with Random Forest as the classifier. Benchmarking against Hyperledger Fabric 2.5, Ethereum 2.0 and the IOTA Tangle demonstrate the scalability, security, privacy and efficiency of SmartChain. This research renders SmartChain a practical production-level platform for management of new smart city infrastructure.
Sixth-generation (6G) networks are expected to provide ubiquitous connectivity, AI-native orchestration, and seamless integration across terrestrial and non-terrestrial infrastructures. However, these capabilities introduce new privacy challenges related to the classification and protection of personal, quasi-personal, and non-personal data in complex data-driven environments. This paper presents a systematic review of 78 peer-reviewed studies published between 2019 and 2025. Following a PRISMA-based methodology, this review analyzes privacy-enhancing technologies (PETs), regulatory compliance frameworks, and architectural patterns for privacy preservation in 6G networks. The findings show that differential privacy (DP) and federated learning (FL) dominate current research, accounting for nearly 52% of the reviewed studies. Blockchain auditing and zero-knowledge proofs (ZKPs) collectively represent approximately 30%, while the remaining mechanisms, including physical-layer security (PLS), trusted execution environments (TEEs), homomorphic encryption (HE), secure multi-party computation (SMPC), and anonymization, account for roughly 18%. These mechanisms exhibit varying levels of privacy strength, utility preservation, latency, and energy cost. At the same time, evolving regulatory frameworks, including GDPR, PDPL, CCPA/CPRA, LGPD, and PIPL, increasingly extend privacy obligations to quasi-personal and aggregated data. Building on these findings, this paper proposes a unified taxonomy that clarifies the boundary between personal and non-personal data. It also provides a cross-layer mapping between PETs and compliance requirements across the Core/SBA, RAN, Edge/MEC, and NTN layers. Finally, this paper presents a forward-looking roadmap for 2025–2030, highlighting hybrid PET pipelines, post-quantum auditability, and AI-driven compliance automation as key directions for privacy-preserving 6G standardization.
Introduction: The integration of blockchain technology with Internet of Things (IoT) devices in smart city deployments presents significant technical challenges, particularly regarding consensus mechanism design. Traditional blockchain consensus protocols such as Proof of Work (PoW) and standard Proof of Stake (PoS) impose computational, energy, and latency requirements that exceed the capabilities of resource-constrained IoT devices, motivating the development of lightweight alternatives. Materials and methods: This paper examines lightweight consensus mechanisms specifically designed for edge-optimised blockchain deployments, evaluates hybrid on-chain/off-chain architectural patterns, and presents a comparative analysis of emerging solutions. We propose four research hypotheses (H1–H4) regarding hierarchical consensus performance and validate them through extensive discrete-event simulation across five smart city domains, using a 500-node testbed calibrated to representative urban workloads. A novel Business Process Model and Notation (BPMN)-based process model formalises the three-tier consensus workflow, and security analysis quantifies Byzantine fault tolerance guarantees. Results: The proposed three-tier framework achieves a 94.7% ± 2.3% reduction in on-chain transactions while maintaining cryptographic auditability, with consensus latency under 500 ms for district-level operations (p < 0.001 vs. single-tier baseline). District-level throughput reaches 1247 ± 89 transactions per second (TPS), representing an 8.0× improvement over city-wide consensus, with energy consumption per transaction at the edge tier 95% lower than single-tier implementations. The simulation of 10,000 Byzantine attack attempts yielded a 99.97% detection rate. All improvements are statistically significant (p < 0.001, Cohen’s d > 0.8). Conclusions: Hierarchical consensus architectures, combined with selective off-chain processing, offer the most promising pathway towards scalable, secure blockchain–IoT integration in smart city contexts. Through an examination of five practical smart city use cases, we demonstrate that tailored consensus approaches can achieve the security guarantees necessary for critical urban infrastructure while respecting the computational limitations of deployed sensor networks. Remaining challenges in dynamic validator management, cross-chain interoperability, quantum resistance, and regulatory compliance are identified as priorities for future work.
Mark C. Ballandies, Florian Spychiger, Uwe Serdült, Claudio J. Tessone
We propose DAO-enabled decentralized physical AI (DePAI), a democratic architecture for coordinating humans and autonomous machines in the operation and governance of physical-digital systems. We (1) synthesize foundations in blockchains, decentralized autonomous organizations (DAOs), and cryptoeconomics; (2) connect DAO design with digital-democracy research on deliberation and voting, showing how each can advance the other; (3) position DAO-governed decentralized physical infrastructure networks (DePIN) within a vertically integrated stack that links energy and sensing to connectivity, storage/compute, models, and robots; (4) show how these elements specify workflows that couple machine execution with human oversight, enabling enhanced self-organization of techno-socio-economic systems, which we call DePAI; and (5) analyze risks, including security, centralization, incentive failure, legal exposure, and the crowding-out of intrinsic motivation, and argue for value-sensitive design and continuously adaptive governance. DePAI offers a path to scalable, resilient self-organization that integrates physical infrastructure, AI, and community ownership under transparent rules, on-chain incentives, and permissionless participation, aiming to preserve human autonomy.
This seminar paper presents a comprehensive study on blockchain interoperability, focusing on enabling communication between independent blockchain networks. It examines key techniques such as cross-chain bridges, atomic swaps, relay chains, and oracle-based solutions. The paper also analyzes major platforms like Polkadot, Cosmos, and Chainlink, highlighting their roles in improving scalability and efficiency. Additionally, it discusses the challenges, security concerns, and limitations of interoperability while proposing a hybrid framework to enhance secure and reliable cross-chain communication. The study emphasizes the importance of interoperability in advancing decentralized applications and the future of Web3 technologies.
The Internet of Things (IoT) has become a major issue that has gained significant attention in the research community. Advances in IoT technologies have resulted in the emergence of various security issues and raised concerns about potential privacy breaches of IoT data. Utilizing Blockchain (BC) is seen as a promising solution for addressing security issues in the IoT. This paper offers a clear overview of IoT security threats, including the related security characteristics and the challenges that come with integrating BC with IoT. A brief discussion of various consensus protocols and existing security techniques is presented. A comparative study of several Distributed Ledger Technology (DLT) platforms based on both qualitative and quantitative evaluation criteria is also presented. This paper explores the role of BC Technology in improving security in Intrusion Detection Systems (IDS) and other applications in the IoT environment. Additionally, the paper identifies open issues and highlights potential research opportunities that can benefit future studies.
The rapid expansion of Artificial Intelligence Data Centers (AIDC) faces severe physical constraints, notably the linear O(n) scaling of power consumption, cooling requirements, and latency. In this paper, we propose the Lattice Swarm Protocol, a paradigm shift in distributed edge computing utilizing an O(1) constant memory architecture combined with the Virtual-to-Materialization (V2M) engine. We mathematically demonstrate that when interconnected via high-speed 400G/800G optical networks, multiple 1MW ultra-low-power edge nodes do not compute independently. Instead, they share Spatiotemporal Environmental Hashes across a 9192-D Lattice network. This mechanism exponentially reduces the computational load of the entire network as node count increases, creating a single 300MW-equivalent "Hyper-Organism" from merely 30 distributed 1MW nodes. We empirically validate this architecture through the implementation of zero-latency Stateless Custody protocols and interstellar acoustic materialization (Voyager 1), both audited by Google DeepMind Antigravity. This infrastructure establishes a new global standard for Autonomous Driving and Urban Air Mobility (UAM).Version 2 Update: Integrated Zero-Knowledge Proof (ZKP) mechanisms and Stateless Key Vaporization (0.024s), aligned with KIPO Patent No. 10-2026-0079266.
Interoperability of patient files between hospitals continues to present significant obstacles. Health systems frequently utilize central EHR systems that could suffer malfunctions, data breaches, and unauthorized access by third parties. Not only does this jeopardize patient confidentiality, but it also hinders the efficient operations of hospital processes.Blockchain technology is viewed as a prospective remedy for the issue. Blockchain keeps its data differently, allowing users to store data securely and make changes difficult. In this study, we analyze research works published between 2016 and 2023 regarding blockchain-based hospital-to-hospital data exchange.The methodologies differ widely: there are cases where researchers use smart contracts in Ethereum, build a system on Hyperledger Fabric, and deploy IPFS. Moreover, certain studies incorporate encryption methods, machine learning algorithms, and more. In summary, the results show that blockchain allows for improved data protection and transparency while giving patients more control over their personal information. Still, some issues persist, such as scalability, expenses, integration with existing infrastructure, and adherence to GDPR and HIPAA requirements. For future work, more improvements are necessary. For instance, zero-knowledge proofs, cybersecurity measures for new technologies, and using artificial intelligence to audit and validate smart contracts may be promising solutions.
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.
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.
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.
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.