Smart homes, equipped with interconnected IoT devices such as locks, cameras, and sensors, face critical security challenges due to the limitations of static access control mechanisms like Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC), which lack adaptability to dynamic, multi-user environments and evolving threats. To address this problem, this research introduces a hybrid Blockchain–Machine Learning (ML) framework that ensures secure, adaptive, and context-aware access control for smart home ecosystems. The proposed system integrates IoT devices with ML algorithms, including Support Vector Machines (SVM) and Neural Networks, to predict user behaviours and dynamically adjust access permissions in real time, while Blockchain ensures immutable, decentralized, and tamper-proof logging of access events. The methodology employed a mixed approach, beginning with an extensive literature review to identify shortcomings in existing static models, followed by system design using smart contracts, caching strategies to reduce latency, and a user perception survey involving 25 participants to validate acceptance and usability. Results demonstrated high user trust and readiness to adopt the proposed system, with 96% of respondents favouring Blockchain-ML-enabled dynamic access control over conventional methods despite concerns about privacy risks, costs, and implementation complexity. This work contributes to society by offering a scalable and intelligent smart home security solution that enhances trust, improves user experience, and strengthens resilience against cyber threats, ultimately supporting safer and smarter living environments.
Atikah Balqis Binti Basri, Mohd Izzuddin Mohd Tamrin, Mohd Khairul Azmi Hassan et al.· International Journal of Inn...· 0 citations
The digital transformation of industrial systems under the Industry 4.0 paradigm has introduced cyber-physical systems (CPS) as a core enabler of vertical integration and data-driven production environments. The convergence of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has accelerated this transformation, fostering the development of the Industrial Internet of Things (IIoT) and creating smart industries characterized by real-time monitoring, automation, and human–robot collaboration (HRC). While these advancements establish a digital ecosystem capable of optimizing production through intelligent data analysis and decision-making, their practical implementation remains constrained by unresolved challenges and gaps in validation. With the emergence of Industry 5.0, the focus shifts toward human-centric, sustainable, and resilient industrial ecosystems, where AI-driven cognitive computing further enhances interaction between humans and machines. This review examines the application of AI–IoT integrated technologies across multiple industrial domains to identify their strengths, limitations, and recurring challenges. By categorizing existing literature into key application areas, the study highlights both the opportunities and risks inherent in current approaches, bridging the conceptual design of smart industries with their real-world realizations. The findings underscore the importance of scalable, secure, and efficient frameworks to ensure the safe and reliable adoption of AI–IoT in the industrial ecosystem.
Asmarani Ahmad Puzi, Ahmad Anwar Zainuddin, Muhammad Afham Anuar et al.· International Journal of Inn...· 0 citations
