Tan Gürpinar, Mehmet Akif Gulum, Melanie Martinelli
Enterprises today face increasing threats from cyberattacks, supply chain disruptions, and systemic market risks, making the enhancement of organizational resilience through advanced risk management frameworks increasingly critical. Traditional approaches often struggle to balance data privacy, cross-organizational collaboration, and real-time adaptability. While distributed ledger technologies (DLTs) initially enabled cryptocurrencies, they have evolved into a foundational infrastructure for decentralized AI applications. This study investigates how decentralized AI techniques, particularly federated learning, can support joint risk management processes in enterprise networks. First, a comprehensive review of decentralized AI methods is conducted to identify approaches suitable for enterprise risk management. Next, expert interviews are used to contextualize these insights, highlighting practical considerations, organizational challenges, and adoption constraints. Building on the literature and expert feedback, a decentralized framework is developed to allow organizations to securely share risk-related insights while preserving data privacy and control over proprietary information. The framework is validated through a technical prototype, combining architectural design with empirical proof-of-concept experiments on federated learning benchmarks. Results demonstrate the feasibility of achieving near-centralized model accuracy under privacy constraints, while also highlighting communication and governance issues that need to be addressed in real-world deployments. The study presents a structured comparison of decentralized AI techniques and a validated concept for enhancing supply chain risk prediction, fraud detection, and operational continuity across enterprise networks.
With the billions of devices that are connected in 5G-enabled Internet of Things (IoT) networks, industries have been revolutionized, due to high-speed data transmission, ultra-low latency as well as large scale connectivity that 5g offers. Yet, the growing number of IoT devices also creates serious security and privacy issues like unauthorized access, data breaches, and cyber-attacks. Security frameworks are dependent, centralized, and they cannot establish trust, scalability, and resilience against attack. With its potential to offer decentralized validation, secure authentication, and tamper-proof data storage, blockchain technology has therefore emerged as a promising solution to these concerns. Through this work, we investigate the combination of blockchain-based validation mechanisms with $\mathbf{5 G}$ Internet of Things networks to allow more secure, private, and integritypreserving networks. This usually involves lightweight consensus mechanisms like Proof-of-Stake (PoS) and Byzantine Fault Tolerance (BFT) to enhance transaction efficiency while lowering energy consumption. Edge computing along with blockchain also helps in processing of data at the spot while waiting to be encoded which saves time and decrease latency of IoT applications. Beyond that, AI-driven security models provide higher accuracy in threat detection and anomaly identification in $\mathbf{5 G}$ IoT networks available in blockchain. However, notwithstanding the benefits, issues such as privacy, scalability, regulatory compliance, and computational overhead are still emergent areas of research. In contrast, on the other hand, enhancing the security frameworks of $\mathbf{5 G ~} \mathbf{~ I o T}$ ecosystems built on blockchain through post-quantum cryptography, federated learning, and self-sovereign identity management will lay a crucial role in the developments of computer-based solutions coming in the future. Upon learning the state-of-the-art blockchain validation methods and their contributions to the Internet of Things security, we analyze and evaluate the future works and threats on the secure 5G Internet of Things in this study.
This paper proposes a distributed computing framework that integrates federated learning (FL) and blockchain-enabled smart contracts for automatic flood response management. FL is deployed to process time series sensor data locally for separate sensor devices by training a machine learning (ML) model and then aggregating the trained model parameters obtained from each sensor device to yield final predictions in terms of rainfall levels. The predicted precipitation levels are input into a predefined smart contract to automatically trigger mitigation strategies to be used by frontline safety and maintenance personnel. The results obtained using the proposed framework demonstrate both improved prediction accuracy and data privacy preservation. The validation effort shows that smart contracts can execute context-aware actions, thus enabling fast decision-making for flood response. The developed framework holds the potential to revolutionize decentralized data management, enhance efficient data processing, and ensure data privacy, transparent and secure data communication, and resilience against centralized failures, thereby enabling a more intelligent infrastructure management system to mitigate flood impacts.
Gauhar Ali, Sajid Shah, Mohammed ElAffendi, Naveed Ahmad
Introduction Digital Twins (DT) have appeared as a significant tool in Industrial Internet of Things (IIoT) environments, allowing real-time monitoring, predictive maintenance, and maximizing device performance. However, integrating DTs with IIoT initiates serious security issues, specifically in the device’s authentication and authorization. The state-of-the-art mechanisms are exposed to insider threats, single points of failure, and privacy issues. Methods This study proposes a blockchain-based access control framework for cross-domain DTs. The blockchain (BC) integration eliminates reliance on the centralized authentication server. It uses platform verification from the manufacturer to validate IIoT device integrity and mitigate insider threats. Moreover, the authorization mechanism is implemented using smart contract and access control policies stored in BC. The proposed Non-Fungible Tokens enable role and permission delegation. Results and Discussion The integration of Hyperledger Fabric BC, platform hash verification, and NFT-based authorization in the proposed architecture enhanced its resilience against cyber-attacks i.e., replay, DoS/DDoS, insider, and spoofing attacks. Moreover, the proposed framework validates its viability with response times (approximately 300ms) for the authentication and authorization phases. Additionally, identity resolution attains 67 % depletion in latency compared to its counterpart.
Pfarelo Raliphada, Micheal O. Olusanya, Seun. Olukanmi
This study examines blockchain algorithms’ performance in a fog computing environments using data from the Bitcoin blockchain. As the demand for secure, low-latency solutions in IoT increases, blockchain offers integrity and decentralization, while fog computing ensures responsiveness. Key performance metrics like throughput, latency, and energy consumption were analyzed using statistical methods. The findings indicate significant variability in throughput and latency, along with high energy demands from traditional consensus mechanisms such as Proof of Work (PoW), making it unsuitable for fog environments. The study recommends adopting lightweight consensus protocols like Proof of Authority (PoA) and Delegated Proof of Stake (DPoS) to improve blockchain performance in edge environments.
IoT networks require secure coordination but cannot tolerate the heavy computational and energy burden of mainstream blockchain consensus mechanisms. This paper introduces an adaptive Proof-of-Probability (PoP) model designed for ultra-low-power devices. Unlike proof-of-work or stake-based models, PoP assigns block proposal probability based on device reliability, historical behavior, and real-time trust signals. Each node maintains a local trust vector updated through lightweight observations such as uptime, packet integrity, and peer confirmation. We design a probabilistic leader election protocol that minimizes message overhead and supports rapid convergence. Simulations across 10,000-node IoT clusters show PoP reduces energy consumption by 65–78% compared to PoS-lite variants, while maintaining strong resilience against Sybil and eclipse attacks. We also evaluate a real hardware deployment using ESP32 devices to measure runtime impact. Results show near-linear scalability. The paper concludes with security proofs and guidelines for practical deployments.
Yi-Jing Liu, L. Zhang, Xiaoqian Li, Hongyang Du · 8 authors
Integrated Sensing and Communication (ISAC) is driving the evolution of edge intelligence. In ISAC-enabled wireless edge networks, federated learning (FL) is crucial for realizing edge intelligence by supporting the networks with privacy protection, efficient data management, and dynamic adaptability. Specifically, FL allows distributed computing nodes (e.g., sensor devices) to first train local models by using data collected or sensed via ISAC and subsequently send them to one or multiple aggregation nodes for global model collaboration. However, traditional FL frameworks face significant challenges in the ISAC scenarios. For example, the privacy sensitivity of heterogeneous sensor data and the lack of transparency in model parameter exchange make it difficult to ensure the credibility of local and global models. Sharding distributed ledger technology (DLT), which divides the ledger into smaller and manageable shards, offers a potential solution to address these challenges by utilizing multi-node trust capabilities to facilitate distributed consensus during FL training. In this paper, we propose a trusted FL framework that incorporates sharding DLT within ISAC-enabled wireless edge networks to enhance both model training and consensus performance. Specifically, we develop a theoretical model to examine the interactions between model training performance and network capacities of sensing nodes (e.g., storage, computing, and communication capabilities) based on ISAC’s real-time channel state information. Based on this theoretical model, we design a trusted clustering scheme for aggregating local models. Numerical results demonstrate that in ISAC-enabled wireless edge networks, our proposed scheme significantly increases network throughput for model transmission while ensuring optimal model learning performance compared to some classical baselines.
Z. Liang, Bin Chen, Litao Ye, Chen Sun · 6 authors
The ERC4907 standard enables rentable Non-Fungible Tokens (NFTs) but is limited to single-user, single-time-slot authorization, which severely limits its applicability and efficiency in decentralized multi-slot scheduling scenarios. To address this limitation, this paper proposes Multi-slot ERC4907 (M-ERC4907) extension method. The M-ERC4907 method introduces novel functionalities to support the batch configuration of multiple time slots and simultaneous authorization of multiple users, thereby effectively eliminating the rigid sequential authorization constraint of ERC4907. The experiment was conducted on the Remix development platform. Experimental results show that the M-ERC4907 method significantly reduces on-chain transactions and overall Gas consumption, leading to enhanced scalability and resource allocation efficiency.
The integration of decentralized security mechanisms into smart drone surveillance systems marks a transformative advancement in the field of unmanned aerial monitoring. Traditional centralized architectures are often vulnerable to single points of failure, data breaches and latency issues specifically in case of operation in hostile or remote environments. By leveraging blockchain technology, drone networks can establish a tamper-proof, distributed ledger that ensures the integrity and authenticity of surveillance data in real time. Internet of Drones is a decentralized network linking drones access to controlled airspace, providing high adaptability to complex scenarios and services to various drone applications such as package delivery, traffic surveillance and rescue including navigation services. One of the potential methods to enhance user privacy, data security and authentication, especially in peer-to-peer UAV networks is blockchain technology, which has now been gained prominence.
Rutuja Chirwatkar, Beemkumar Nagappan, L. P. Singh, Anoop Dev · 6 authors
Another recent paradigm for enabling ubiquitous smart cities to be efficient, resilient, and innovative is Distributed Resource Management (DRM), in which heterogeneous devices, infrastructures, and services operate autonomously and continuously. This paper discusses more advanced concepts of edge-cloud synergy, decentralized coordination, cyber-physical integration, and context-aware optimization to address the increasing burden on urban energy, transportation, communication, and environmental systems. These are the main objectives: (1) to create scalable DRM frameworks with the features of real-time decision, (2) to enhance the interoperability of distributed heterogeneous resources, and (3) to enhance sustainability and service quality as a result of flexible allocation schemes. The proposed solutions will be the multi-agent systems, distributed ledger technologies (DLT), machine-learning-based prediction systems, and dynamic resource-orchestration algorithms. A hybrid simulation-prototype was applied to test the performance based on the metrics of latency, reliability, load balancing, and energy efficiency. Results suggest that significant improvements (up to a 35 percent reduction in resource contention, a 28 percent reduction in response time, and a 20 percent increase in system robustness under high-density urban workloads) have been achieved. A qualitative measure also fosters greater transparency and trust in cross-domain operations. In totality, the paper identifies that the disruptive potential of decentralized management systems can make smart-city ecologies adaptive, secure, and sustainable.
The proliferation of Internet of Things (IoT) applications and on-demand logistics has fostered crowdsourced delivery systems where dynamic coordination among senders, couriers, and receivers enables efficient lastmile logistics. However, centralized dispatching exposes privacy and reliability risks, including single points of failure and leakage of routing and transaction data. Blockchainbased Payment Channel Networks (PCNs) address these limitations by moving frequent interactions off-chain while maintaining verifiable settlement on-chain. This paper presents a blockchain-anchored privacy-preserving path optimization protocol that supports variable-amount multihop payments over PCNs. By combining Pedersen commitments and lightweight zero-knowledge proofs (ZKPs), the protocol verifies transaction correctness without revealing amounts, and employs blind-channel operations to prevent intermediaries from accessing sensitive data. Experimental results show that the proposed scheme achieves strong privacy protection and scalability with low computational cost, making it suitable for blockchain-based IoT delivery environments.
The Proof of Stake (PoS) consensus mechanism is increasingly used in blockchain systems; however, resource allocation for PoS-based mobile blockchain networks remains underexplored, particularly given the constraints of mobile devices. This work introduces MEC-Chain, a new framework that integrates Mobile Edge Computing (MEC) with mobile blockchain to support efficient validator-node execution under PoS. MEC-Chain formalizes a multi-objective resource-allocation problem that jointly considers latency, reliability, and cost from both the validator and MEC-provider perspectives. To address this challenge, we develop a deep reinforcement learning-based allocation agent using the Proximal Policy Optimization (PPO) algorithm. Experimental results show that PPO achieves a 30–40% reduction in total execution time, 25–35% lower transmission latency, and 10–15% higher reliability compared to A2C (Advantage Actor–Critic) and DQN (Deep Q-Network), while offering comparable cost savings across all methods. These results demonstrate the effectiveness of MEC-Chain in enabling low-latency, reliable, and resource-efficient PoS validation within mobile blockchain environments.
The deployment of cyber-physical systems (CPS) in smart manufacturing has advanced industrial processes via live data analytics, automation, and intelligent decision-making. However, these interrelated systems can no longer avoid critical issues surrounding data trust, integrity, and security across heterogeneous devices and networks. The issue is to facilitate a secure, transparent, and efficient method of performing consensus in a distributed CPS environment while keeping performance and resilience in the face of cyber-attacks. This paper outlines a Cognitive Blockchain Consensus Algorithm (CBCA) for dealing with these concerns, that integrates the decentralized ledger property of blockchain with cognitive computing principles. CBCA will apply a adaptive learning model to determine consensus parameters based on real-time information in order to minimize computation overhead to reach consensus, and latency, while being able to detect threats and respond to anomalous behavior. The cognitive layer observes behavior across the network continually and makes autonomous changes to the consensus mechanism on behalf of CPS, optimizing the trust vs security tradeoff. Results from experimental simulations conducted using a smart manufacturing testbed show CBCA increasing throughput transactions by 23%, reducing consensus delay by 17%, and malicious node detection accuracy by 96% when compared to traditional Proof of Work and Proof of Stake methods.
Federated learning (FL) enables collaborative model training across edge devices without centralizing raw data, but existing frameworks remain ill-equipped to support data privacy regulations mandated by GDPR, HIPAA, and CCPA. Once user data has influenced training, its verifiable removal becomes prohibitively expensive, particularly in non-IID and resource-constrained edge environments. This paper introduces a modular and scalable federated unlearning framework that unifies three complementary strategies: gradient subtraction, knowledge distillation, and checkpoint rollback, within an adaptive decision layer. A resource-aware checkpoint manager reduces storage costs through compression and pruning, while a privacy and trust layer integrates zero-knowledge proofs, differential privacy, and Merkle-based audit logs to provide verifiable guarantees of deletion. A non-IID-aware aggregator further preserves fairness across heterogeneous clients. Unlike prior approaches, our proposed framework systematically integrates rollback efficiency with formal privacy protections and auditability, offering a practical path toward trustworthy and regulation-compliant unlearning in domains such as healthcare, transportation, and smart agriculture.
Cloud computing has emerged as the dominant platform for contemporary data management and service provision. However, its centralized nature poses significant risks to security, privacy, and trust. Distributed systems can enhance data integrity and auditability by incorporating blockchain technology, which offers a decentralized and tamper-resistant approach. Nevertheless, the inherent transparency of blockchain conflicts with the confidentiality requirements of cloud environments. This review paper analyzes existing studies on privacy-preserving blockchain architectures designed to secure cloud-based information systems. A systematic literature review methodology was adopted, examining forty-eight peer-reviewed studies published between 2018 and 2024. The findings reveal that researchers have explored approaches such as encryption, zero-knowledge proofs, homomorphic encryption, and hybrid on/off-chain models to balance transparency and privacy. Scalability, interoperability, and regulatory compliance remain key challenges, particularly in permissioned blockchains, which nevertheless offer advantages in governance and compliance. The study identifies research gaps and future directions, including the development of common privacy frameworks, integration of confidential computing, and establishment of standardized evaluation metrics. Overall, privacy-sensitive blockchain architectures hold strong potential for creating trustworthy and secure cloud systems.
The increasing demand for real-time decision analytics in modern enterprises has accelerated the development of edge-to-cloud data pipelines, which integrate distributed computing resources to enable instantaneous insights. Traditional centralized cloud architectures struggle with latency and bandwidth limitations, making them unsuitable for applications requiring immediate decision-making. Edge-to-cloud pipelines overcome these barriers by combining localized data processing with cloud-based intelligence, creating a continuous, adaptive flow of analytical information. This review examines the architectural principles, technological enablers, and analytical impacts of edge-to-cloud data pipelines on real-time decision-making. It explores how distributed processing, stream analytics, and AI-driven orchestration enhance responsiveness, reliability, and scalability across diverse environments. Technologies such as 5G, machine learning, and containerized orchestration platforms are discussed as key drivers of this transformation. The study also identifies challenges including data synchronization, security, interoperability, and energy efficiency at the edge. Addressing these issues is essential for realizing seamless, end-to-end analytics across hybrid ecosystems. Future directions highlight the potential of autonomous, decentralized, and quantum-enhanced data pipelines to deliver self-optimizing intelligence at global scale.Ultimately, this review concludes that edge-to-cloud data pipelines are foundational to achieving context-aware, predictive, and autonomous analytics, enabling organizations to transition from reactive operations to real-time, intelligent decision ecosystems.
The purpose of this study is to address the persistent security and privacy challenges in cloud-based Electronic Health Record (EHR) sharing by proposing a blockchain-enabled architecture that integrates decentralized storage and smart contracts. Traditional mobile cloud solutions improve data accessibility but rely on centralized control, making them vulnerable to unauthorized access, single points of failure, and limited patient transparency. To overcome these limitations, this research designs a user-centric access control framework that leverages the Ethereum blockchain, smart contracts, and the InterPlanetary File System (IPFS) within a mobile cloud environment. The methodology involves developing and deploying a prototype on Amazon Web Services, supported by an Android-based mobile application that enables healthcare providers and patients to interact with the blockchain network. Experimental evaluation was conducted using wearable sensor data to test the performance, scalability, and resilience of the proposed system. The findings indicate that the framework ensures secure EHR exchange, enforces fine-grained access policies, and achieves reduced latency compared to conventional centralized approaches. Unauthorized requests were reliably detected and blocked through the smart contract mechanism, while authorized users accessed records with minimal delay. The results also confirm the lightweight overhead of the system, making it practical for mobile healthcare environments. The practical implications of this work lie in offering a tamper-resistant, transparent, and patient-centric solution for medical data sharing, thereby improving trust, reducing administrative overhead, and supporting real-time healthcare services in distributed and resource-constrained settings.
This study addresses the critical challenge of securely transmitting digital cheques in blockchain-based agricultural platforms operating under intermittent connectivity constraints, particularly in Guinea, where only 34% of the population has access to the Internet. We propose an innovative protocol that cryptographically embeds the seller’s address into the digital cheque signature, thereby creating an autonomous notification system independent of external communication infrastructures. The approach introduces three key innovations: address-integrated signatures, asynchronous validation protocols, and distributed notification mechanisms operating directly on the blockchain. The experimental implementation employs Ganache as an Ethereum simulator with a React.js DApp interface, enabling buyers to issue cryptographically signed cheques and sellers to automatically receive notifications through blockchain events. The results demonstrate technical feasibility with a secure transfer of 2 ETH validated by balance variations. The protocol ensures integrity, non-repudiation, and resilience to network disconnections, outperforming existing solutions that require stable connectivity. This contribution paves the way for fully autonomous decentralized agricultural markets adapted to the infrastructural constraints of West Africa.
Edison A. Arteaga López, Gustavo A. RamÃrez González, Carlos Alberto Astudillo
The growth of the Internet of Things (IoT) has highlighted the limitations of centralized data platforms, particularly in terms of security and scalability. Distributed Ledger Technologies (DLT) offer a solution, but traditional DLT architectures, such as blockchain, are often incompatible with low-power wireless IoT networks (LPWAN) due to their latency and operational cost. This paper presents a comparative and empirical performance analysis of two end-to-end data oracle systems designed to record data from a LoRaWAN sensor network. The first implementation uses IOTA Tangle, while the second is based on a blockchain compatible with the Ethereum Virtual Machine (EVM). By evaluating key indicators such as latency and transaction costs, our results demonstrate that the IOTA system offers significant superiority, with a predictable median latency of 4.47 seconds and transaction costs that are economically negligible. In contrast, the blockchain implementation incurred measurable gas costs and demonstrated an architecture with inherently higher and extremely unpredictable latency, with a median of 11.52 seconds and outliers exceeding 200 seconds. We conclude that the IOTA Tangle architecture is technologically and economically better prepared to support scalable and sustainable IoT applications over wireless infrastructures.
K. C. Krishnachalitha, Dikshit Sharma, Samaksh Goyal, K. Yuvaraj · 6 authors
The present research combines Blockchain, 5G, and Green Computing to create secure, energy-efficient, and sustainable digital environments through a careful investigation and simulation-based analysis. Using the PRISMA approach, we choose 67 of the initial 312 papers that were related to our research. We choose these studies because they were related to our research. We created a multi-objective optimization model: f(x) = αC(x) + βE(x) + γL(x). By using this strategy, they keep our expenses, energy use, and latency low while also keeping safety and flexibility high. We were able to simulate the mixed architecture that was demonstrated in MATLAB and NS-3 by using evidence-of-stake and PBFT consensus approaches, edge computing, and ecologic routing. The results indicate that these systems have 37% less latency, 24% more energy efficiency, and 18% less carbon emissions than systems that simply use 5G. The results show that using these three in combination helps to build solid basis for Internet of Things, medical facilities, and smart towns. This, in turn, leads to the building of facilities that are not just incredibly effective but also last for a very long period. The structure also sets the stage for Industry 5.0, that will lead to greater research in areas like quantum-proof Bitcoin, artificial intelligence-based control, and sustainability made feasible by 6G infrastructure.
Shaista Ashraf Farooqi, Aedah Abd Rahman Rahman, Amna Saad
The growing integration of the Internet of Medical Things (IoMT) into healthcare has amplified the need for secure and privacy-preserving artificial intelligence. Federated Learning (FL) has emerged as a pivotal paradigm for decentralized medical data processing; however, it still faces challenges concerning data confidentiality, trust management, and scalability. This review presents an extended theoretical comparison of two prominent privacy-preserving frameworks—Federated Learning with Differential Privacy (FL-DP) and Federated Learning with Blockchain (FL-BC)—to assess their suitability for ensuring data security, transparency, and regulatory compliance in IoMT environments. The FL-DP framework safeguards patient data through noise injection during model updates, offering mathematically proven privacy guarantees. Conversely, the FL-BC framework reinforces trust and integrity via immutable ledgers and consensus mechanisms such as Proof of Stake (PoS) and Byzantine Fault Tolerance (BFT). Reviewing literature published between 2021 and 2025, this study examines trade-offs in privacy, scalability, latency, and energy efficiency, while highlighting emerging hybrid architectures that integrate both approaches. The findings reveal that FL-DP provides stronger privacy control, whereas FL-BC ensures verifiable trust and traceability—together forming the foundation for next-generation secure and trustworthy federated learning systems in IoMT-driven healthcare.
Aleksei Olkhovikov, Yash Madhwal, Arsen Andrian, Hamza Imran · 8 authors
• Prototype system with Raspberry Pi and dual ultrasonic sensors for data acquisition. • Real-time data signing and blockchain submission using web3.py and EVM chain. • Smart contract for secure data logging, access control, and gas-efficient events. • Frontend with Streamlit MVP and Vue3 dashboard supporting secure user login. • Experiments on 15M-record dataset to evaluate gas cost, batching, and scalability. Integrity and traceability of sensor data in oilfield operations are essential for safe, efficient, and compliant resource extraction. This paper presents a blockchain-enabled proof-of-concept (PoC) IoT framework that facilitates decentralized, tamper-evident monitoring of oil extraction infrastructure. The system integrates field-deployed sensors with a Raspberry Pi-based edge controller to capture, buffer, and cryptographically sign telemetry data, which is then submitted to an EVM-compatible blockchain using smart contracts. The PoC demonstrates historical and real-time data visualization through a web-based dashboard that authenticates and displays blockchain event streams. A real-world drilling data set comprising more than 15 million records is used for the experimental evaluation of the prototype. Gas consumption metrics are analyzed under varying payload sizes and batching strategies, revealing linear scalability with respect to parameter volume and significant efficiency gains through transaction batching. These results demonstrate measurable improvements in resource utilization and operational cost, confirming the framework’s efficiency and robustness for large-scale industrial telemetry. The architecture supports secure access control, structured metadata annotation, and transparent logging without reliance on centralized intermediaries. By addressing key challenges in data authenticity and operational visibility, the proposed solution establishes a scalable foundation for secure telemetry in oil and gas operations, with potential applicability to other critical infrastructure domains such as energy grids, mining, and water resource management. Unlike prior blockchain-IoT frameworks focusing primarily on architectural design or off-chain coordination, the proposed system demonstrates an end-to-end implementation directly linking field-level sensors to on-chain storage and visualization. Through large-scale validation on a 15 M-record drilling dataset, this work provides one of the first empirical analyses of gas-efficient, real-time telemetry submission in industrial settings.
The fast growth of Internet of Things (IoT) technologies has turned smart cities into big data ecosystems for intelligent mobility, energetic efficiency and public services. But this increasing reliance on IoT data raises significant privacy issues because of the perpetually gathered sensor readings, inter-organisational sharing and algorithmic analyses. In this paper, we focus on the state-of-the-art IoT data-sharing methods that preserve privacy and protect recent progress in preserving privacy while sharing data in the IoT personal record by preserving statistical value. It combines traditional approaches, including anonymisation, differential privacy, federated learning, secure multiparty computation and homomorphic encryption with new technologies (e.g., blockchain-enabled governance, edge intelligence or zero-knowledge proofs) (Nguyen et al., 2023; Alrawais et al., 2024; Lin & Kuo, 2025). The paper analyses the impact of hybrid architectures combining edge-cloud cooperation and decentralised access control for improving data protection, in terms of not losing performance or interoperability. Conclusions: Summary of the main findings, Barriers to Implementation. This paper identifies several ongoing barriers, including computational expense, related to past research. Personal data while preserving its analytical worth. It combines cutting-edge technologies like blockchain-enabled governance, edge intelligence, and zero-knowledge proofs with traditional strategies like anonymisation, differential privacy, federated learning, secure multiparty computation, and homomorphic encryption (Nguyen et al., 2023; Alrawais et al., 2024; Lin & Kuo, 2025). The study investigates how hybrid architectures that incorporate decentralised access control and edge-cloud collaboration can improve data security without compromising interoperability or performance. The results point to enduring obstacles, such as interoperability, computational overhead, and regulatory compliance, especially in urban settings with limited resources. In order to integrate privacy-by-design principles into IoT analytics for smart city governance, the study suggests a multi-layered conceptual framework. To maintain public confidence in urban digital transformation, this framework places a strong emphasis on open data policies, citizen consent procedures, and the incorporation of cutting-edge cryptographic techniques. The information adds to the current discussion on how to balance privacy and innovation in smart cities and provides guidance for system architects, legislators, and municipal IT leaders who want to adopt IoT responsibly.