Convergent Intelligence: A Unified Ontological Framework for IoT-Blockchain-AI Integration in Next-Generation Cloud Ecosystems
Abstract
Cloud computing has developed to be a key technology in contemporary digital setups through allowing storage on a scale, shared computing, and on-demand provision of services. Over the last several years, the Internet of Things (IoT), blockchain, and Artificial Intelligence (AI) have emerged as technologies that can provide cloud platforms with many more functions. The IoT devices produce high volumes of data in real-time that can be easily stored and processed in cloud environments. AI can support smart data analytics and auto decision-making, whereas blockchain can support decentralised trust and secure transactions of data. Due to the combination of these technologies, there is a strong digital ecosystem that allows supporting more complex applications of smart cities, healthcare monitoring systems, industrial automation, and intelligent transportation networks. The given paper reviews the development of cloud computing within the framework of IoT, blockchain, and AI integration. It addresses the basics, architectural development and upcoming technological trends that are defining cloud ecosystems. The paper also outlines the open research challenges such as scalability constraints, interoperability problems, privacy issues and ethical issues. A methodological assessment and an analytical outcome prove that the combination of AI-based resource management and blockchain-related security systems can enhance the performance of cloud systems, their reliability, and trustworthiness. The results specify the need to create smart, safe, and scalable cloud structures with the capability to accommodate the upcoming generation of digital services.
References
S. S. Gill et al., “Transformative Effects of IoT, Blockchain and Artificial Intelligence on Cloud computing: Evolution, vision, Trends and Open Challenges,” Internet of Things, vol. 8, no. 1, p. 100118, Dec. 2019.
A. Botta, W. de Donato, V. Persico, and A. Pescapé, “Integration of Cloud computing and Internet of Things: A survey,” Future Generation Computer Systems, vol. 56, no. 2, pp. 684–700, Mar. 2016.
M. R. Dorsala, V. N. Sastry, and S. Chapram, “Blockchain-based solutions for cloud computing: A survey,” Journal of Network and Computer Applications, vol. 196, p. 103246, Dec. 2021.
D. Soni and N. Kumar, “Machine learning techniques in emerging cloud computing integrated paradigms: A survey and taxonomy,” Journal of Network and Computer Applications, p. 103419, May 2022.
S. Marston, Z. Li, S. Bandyopadhyay, J. Zhang, and A. Ghalsasi, “Cloud computing — The business perspective,” Decision Support Systems, vol. 51, no. 1, pp. 176–189, Apr. 2011.
R. Abraham, J. Schneider, and J. vom Brocke, “Data governance: a Conceptual framework, Structured review, and Research Agenda,” International Journal of Information Management, vol. 49, no. 2, pp. 424–438, Dec. 2019.
G. C. Saha et al., “IoT Based Smart Agricultural Crop Monitoring in Terms of Temperature and Moisture,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 11s, pp. 234–245, Mar. 2023.
Q. Duan, “Cloud service performance evaluation: status, challenges, and opportunities – a survey from the system modeling perspective,” Digital Communications and Networks, vol. 3, no. 2, pp. 101–111, May 2017.
Crosby M, Pattanayak P, Verma S, and Kalyanaraman V., “AIR Applied innovation review,” Applied Innovation Review, vol. 2, pp. 5-20, Jun. 2016.
R. Roman, J. Zhou, and J. Lopez, “On the features and challenges of security and privacy in distributed internet of things,” Computer Networks, vol. 57, no. 10, pp. 2266–2279, Jul. 2013.
A. Li, X. Yang, S. Kandula, And M. Zhang, “Cloudcmp,” Proceedings of The 10th ACM SIGCOMM Conference on Internet measurement, Nov. 2010.
