Jul 2026· International Journal of Innovative Computing· 0 citations· 30 references
TL;DR
This review examines the application of AI–IoT integrated technologies across multiple industrial domains to identify their strengths, limitations, and recurring challenges and underscores the importance of scalable, secure, and efficient frameworks to ensure the safe and reliable adoption of AI–IoT in the industrial ecosystem.
Abstract
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.
This study critically analyzes existing literature to pinpoint current achievements, implementation strategies, and technological progress across various civil engineering fields, and highlights several key research challenges, such as data interoperability, cybersecurity, computational complexity, scalability, standardization, data quality, and model validation.
M. M., M. C. S.· Journal of Recent Activities...· 0 citations
Next-generation technologies are transforming the landscape of scientific innovation by enabling intelligent, connected, and data-driven approaches to research and problem-solving. Artificial Intelligence (AI), the Internet of Things (IoT), and intelligent systems are increasingly being integrated into scientific processes to enhance data analysis, experimentation, prediction, automation, and decision-making. AI facilitates advanced computational analysis and pattern recognition, while IoT enables real-time collection and exchange of data through interconnected devices and sensors. Intelligent systems integrate these capabilities to support autonomous operations and adaptive scientific applications. This research examines the role of AI, IoT, and intelligent systems in advancing scientific innovation across diverse domains, including healthcare, environmental science, engineering, agriculture, and industrial research. It further explores their potential benefits, challenges, ethical implications, and future prospects. The study argues that effective integration of these technologies can accelerate scientific discovery, improve research efficiency, and contribute to sustainable and evidence-based technological development.
Mrs. Vinita Edlabadkar, Mrs. Bhushana Thakur, Sonu, CS Khushboo Lalit Shah, Adv. Hardik Goradiya· International Journal of Adv...· 0 citations
Findings indicate that AI-enabled digital twins significantly improve equipment reliability, reduce unexpected failures, enhance resource utilization, and enable proactive manufacturing strategies, however, challenges related to interoperability, cybersecurity, computational complexity, data quality, and governance remain critical barriers to widespread industrial adoption.
H. Mahmood· European International Journ...· 0 citations
Detailed analysis of smart manufacturing systems that utilize the IoT and automation technology indicates a high improvement in operational performance, predictive accuracy, and decision-making ability when compared to the traditional manufacturing system.
Aiko Yamamoto· International Journal of Mod...· 0 citations
The Internet of Things (IoT) and artificial intelligence have advanced quickly, and with shifting consumer behaviors, the smart home has become the most promising field of IoT application. Rapid growth, however, has exposed persistent weaknesses in interoperability, security, and reliability that slow adoption. This paper analyses the deployment of smart home IoT and the obstacles it faces. The paper first clarifies the concept of the smart home and its underlying IoT infrastructure, then reviews the industry's evolution, and finally assesses the domestic market from four angles: overall scale, penetration rate, major platforms and ecosystems, and typical application scenarios. It then identifies five recurring problems—limited interoperability and ecosystem fragmentation, security and privacy hazards, conflicting automation logic and unreliable operation, a weak user experience, and high cost and energy use alongside lagging standards—and proposes strategies built around unified standards, security-oriented design, conflict detection and mitigation, edge computing with AI, and aging-friendly design. Methodologically, the study combines literature review, case analysis, and industry statistics. It concludes that China's smart home market, though large and fast-growing, remains in a growth phase, and that its tensions are best resolved through stronger standards, a security-first stance, and a systems perspective. Finally, since closed commercial ecosystems provide no usable means of detecting rule conflicts, the study put forward a black-box, log-driven approach to conflict identification and severity assessment that requires neither platform source code nor proprietary interfaces. Unlike techniques that depend on open APIs or runtime instrumentation, it can inform risk governance in multi-brand smart homes and the drafting of technical standards.
Hao-Xiang Shi· Applied and Computational En...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
