Digital twins (DTs) are rapidly emerging as foundational enablers of 6G smart cities, offering real time monitoring, predictive analytics, and autonomous control across transportation, energy, healthcare, and industrial domains. Large scale DT adoption faces critical barriers including cybersecurity vulnerabilities, privacy risks, and the absence of standardized orchestration frameworks. This article presents Fed-DTOrch, a comprehensive end to end architecture that integrates federated intelligence, blockchain based audit trails, and AI governance to achieve secure and privacy preserving DT management. The proposed three tier architecture spans IoT and edge devices, domain specific twins, and a city level orchestrator, employing secure federated learning for model updates, lightweight cryptographic authentication, and tamper proof logging. We quantify the DT threat landscape, perform a standards gap analysis across ISO/IEC 27001, 3GPP TS 33.501, ITU-T IoT risk frameworks, and NIST AI RMF, and introduce a 6G ready security framework incorporating federated AI trust metrics, secure synchronization, and explainable AI audits. Cross domain evaluation across five smart city sectors demonstrates 35-60% privacy gain, 40-55% attack mitigation, 28-40% reliability uplift, 26-30% latency reduction, and >85% compliance readiness with <10% overhead. These results provide the first integrated blueprint that combines federated intelligence, blockchain-based auditability, and standards gap analysis to enable secure, standardized, and interoperable DT orchestration for trustworthy 6G ecosystems.
This paper addresses the growing challenge of implementing secure, reliable, and scalable federated intelligence on consumer Internet of medical things (IoMT) devices and healthcare enterprise systems. Current solutions generally trade off individual aspects, for example, learning accuracy, cryptographic strength, and blockchain auditability, and do not present an end‐to‐end framework that ensures privacy preservation, adversarial robustness, post‐quantum security, and enterprise governance. We address these shortcomings by introducing CeN‐CHAIN, a consumer–enterprise integrated framework that incorporates post‐quantum key distribution, homomorphic encryption with minimal computational overhead, differential privacy, blockchain‐ensured validation, and federated model lifecycle management into a single architectural model. CeN‐CHAIN enables on‐device learning at a secure level, incorporating auditing, aggregability, and model custodianship in a heterogeneous IoMT and enterprise setting. Extensive experimental assessment based on evaluated healthcare IoMT dataset shows that CeN‐CHAIN attains 96.1% global accuracy, 0.958 F1‐score, and convergence in 22 rounds, which simultaneously minimizes the attack success rate (ASR) below 5% and a privacy leakage rate (PLR) below 3%. Although the framework includes sophisticated security levels, it has an approachable overhead, and the blockchain anchoring latency is 22–43 ms, energy usage of 2.15–6.15 J/FL round, and CPU usage of less than 77% in IoMT devices. These findings substantiate the claim that CeN‐CHAIN provides a balanced trade‐off between learning performance and integrated security under the evaluated healthcare IoMT experimental configuration.
D. Dhinakaran, Chin-Shiuh Shieh· International Journal of Com...· 0 citations
BELS-IoT is proposed, a novel decentralized protection architecture that integrates a cryptocurrency-based blockchain layer with a multi-layer ensemble learning engine that rewards honest behavior and penalizes malicious activities while maintaining privacy through federated learning with blockchain-verified reputation scores.
Anwar Kalghoum, Leila Azouz Saidane· SN Computer Science· 0 citations
As IoT deployments rapidly expand, ensuring comprehensive end-to-end security across identity, authorization, communication, integrity, and auditability is critical. This paper presents BISF-IoT, a Blockchain-Integrated Security Framework that utilizes a permissioned ledger as a tamper-evident control plane while keeping high-volume telemetry and raw logs off-chain. BISF-IoT integrates decentralized identity (DID) management, capability-based authorization with explicit revocation under a freshness bound Δ, and secure-channel identity binding for MQTT/CoAP edge devices. Formal game-based proofs establish five core security properties: DID authenticity, authorization soundness, revocation safety, tamper-evident logging, and auditable anomaly alert non-repudiation. Performance evaluation through simulation with up to 10,000 devices demonstrates near-linear scalability, processing up to 160,000 transactions per day with a scaling efficiency of ~0.90–1.00. Authorization latency increases moderately from 120 ms at 100 devices to 650 ms at 10,000 devices. Therefore, it remains within practical operational limits. Blockchain storage grows steadily at approximately 37–42 MB/day by storing compact Merkle commitments and security artifacts while avoiding raw data bloat. Meanwhile Log verification time exhibits sub-linear growth, with verification cost per entry decreasing from 0.200 ms to 0.055 ms as log size increases from 100 to 10,000 entries, reflecting efficient Merkle inclusion proof mechanisms. These results confirm BISF-IoT’s capability to provide scalable, secure, and verifiable control-plane operations suitable for large-scale IoT environments.
Shahid Imran, Kalsoom Safdar, Muhammad Usman Younus· International Journal of Inn...· 0 citations
The integration of artificial intelligence (AI), the Internet of Things (IoT), and blockchain may provide a viable approach to tackle persistent operational and informational challenges in cleaner production. This conceptual review synthesizes existing literature and presents an integrated AI-IoT-blockchain framework mapped across the four sequential stages of cleaner production: source reduction, process control, end-of-pipe treatment and recycling, and full-chain traceability. The literature indicates that IoT enables real-time sensing, AI drives predictive and prescriptive analytics, and blockchain ensures tamper-proof record-keeping and stakeholder trust. Together, these technologies may help address long-standing barriers including fragmented data, delayed responses, and a lack of verifiability. Despite challenges such as high costs, technical fragmentation, and organizational resistance, several emerging strategies have been proposed in the literature to address these challenges. These include modular deployment, federated learning, permissioned blockchains, and regulatory sandboxes. The framework’s underlying architecture appears transferable across sectors, subject to industry-specific adaptation, supporting sustainable manufacturing, the circular economy, and low-carbon development.
Edge-cloud big data ecosystems are changing many areas, such as healthcare, smart cities, manufacturing IoT settings, cybersecurity, self-driving systems, mobile platforms, and financial analytics. Additionally, these groups produce a lot of data that is dispersed, unique, changes based on the situation, is sensitive to delays, and is usually private. Classical centralized cloud analytics are having more and more issues with bandwidth, latency, single points of failure, and keeping private data and power to make decisions in one place. Multiple edge clients can work on the same model at the same time with federated learning, and the raw data stays close by. Even so, federated learning doesn't guarantee accurate knowledge by itself. Although model changes can keep private data safe, hacked clients can ruin global learning, non-IID data can lead to performance gaps, and choices made by many people may still be hard to explain, audit, or control. This article suggests a trustworthy way to think about shared big data information that can be communicated in secure edge-cloud settings. Additionally, it includes studies on edge-cloud computing, shared learning, AI that can be explained, privacy-preserving analytics, security, and AI control. Additionally, the paper includes a study plan, an assessment tool, a trust-risk mapping, evaluation factors, and governance development standards. When making a case, the main point is that trustworthy federated intelligence is more than just distributed learning that protects privacy. It's a sociotechnical paradigm that includes privacy protection, security robustness, explanation quality, accountability, auditability, fairness, the ability to be deployed, and meaningful human oversight.
Khaled Mohamed Mohamed Khalifa· International journal of com...· 0 citations
This WSNs coupled with the Internet of Things (IoT) is very much needed in facilitating smart environment like smart cities, industrial automation, healthcare monitoring and environmental monitoring. Nevertheless, WSN-IoT systems are extremely susceptible to security risks such as denial-of-service attacks, malicious node behaviors, manipulation of routing, data manipulation, and privacy breaches due to their distributed, heterogeneous, and resource-constrained nature. Conventional centralized security models are usually insufficient to deal with these dynamic and scale cyber threats. The paper describes a detailed overview of blockchain-based and artificial intelligence (AI)-based security solutions to the distributed WSN-IoT scenarios. The paper examines the security dangers at varying layers of a IoT architecture and surveys the recent frameworks that incorporate machine learning, deep learning, and federated learning along with blockchain technology in managing decentralized trust and identify intrusions. Moreover, performance trade-offs on the effectiveness of security, energy use, latency, and scalability are discussed. Lastly, the research indicates the presence of an open challenge and future research topics of creating secure, scalable, and intelligent WSN-IoT infrastructure. The paper also suggests a solution of integrative AI-blockchain security framework that would have a combination of AI-based intrusion detection and decentralized blockchain trust management.
S. Madhuri, D. Dhevi· International Conference Com...· 0 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.