AI Powered Real Time Business Process Integration and Privacy Preserving Cloud IoT Networks
DOI:
https://doi.org/10.15680/IJCTECE.2020.0405007Keywords:
Artificial Intelligence (AI), Real-Time Data Processing, Business Process Integration, Privacy-Preserving Computing, Cloud Computing, Internet of Things (IoT), Software-Defined Networking (SDN), Data Mining, Deep Learning, Artificial Neural Networks (ANN),, Agile Methodology, Secure Network Architecture, Edge Computing, Distributed Systems, Intelligent Decision Support SystemsAbstract
The rapid expansion of Cloud Computing and Internet of Things (IoT) technologies has transformed modern enterprises into highly interconnected digital ecosystems. However, real-time business process integration across heterogeneous IoT environments introduces significant challenges related to scalability, interoperability, latency, and data privacy. This research explores an Artificial Intelligence (AI)–powered framework for real-time business process integration within privacy-preserving Cloud IoT networks. The proposed model leverages intelligent orchestration, federated learning, edge computing, and secure multi-party computation to ensure seamless workflow automation while maintaining strong data protection standards. AI-driven analytics engines process streaming IoT data to optimize decision-making, automate process coordination, and dynamically adapt workflows across distributed cloud infrastructures. Privacy-preserving mechanisms such as encryption, anonymization, blockchain-based audit trails, and zero-trust architectures are incorporated to mitigate data leakage and unauthorized access risks. The framework aims to enhance operational efficiency, reduce latency, and strengthen compliance with global data protection regulations. Experimental simulations demonstrate improvements in throughput, decision accuracy, and data confidentiality compared to conventional cloud-centric IoT architectures. This study contributes a scalable, intelligent, and secure integration model that supports digital transformation across industries including healthcare, manufacturing, logistics, and smart cities.Artificial Intelligence (AI),
References
1. Ananth, S., Radha, D. K., Prema, D. S., & Nirajan, K. (2019). Fake news detection using convolution neural network in deep learning. International Journal of Innovative Research in Computer and Communication Engineering, 7(1), 49–63.
2. Keezhadath, A. A., Sethuraman, S., & Das, D. (2021). Cost-Efficient Cloud Data Processing: Strategies for Enterprise-Wide Cost Optimization. American Journal of Data Science and Artificial Intelligence Innovations, 1, 135-168.
3. Gopalan, R., & Chandramohan, A. (2018). A study on Challenges Faced by IT organizations in Business Process Improvement in Chennai. Indian Journal of Public Health Research & Development, 9(1), 337–341.
4. Ponlatha, S., Umasankar, P., Balashanmuga Vadivu, P., & Chitra, D. (2021). An IOT‐based efficient energy management in smart grid using SMACA technique. International Transactions on Electrical Energy Systems, 31(12), e12995.
5. Vaidya, S., Shah, N., Shah, N., & Shankarmani, R. (2020, May). Real-time object detection for visually challenged people. In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 311–316). IEEE.
6. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
7. Rajurkar, P. (2018). Process integration strategies for reducing hazardous waste in membrane-based chlor-alkali production. International Journal of Innovative Research in Science, Engineering and Technology, 7(3), 3001–3009.
8. Surisetty, L. S. (2021). Zero-Trust Data Fabrics: A Policy-Driven Model for Secure Cross-Cloud Healthcare and Financial Data Exchanges. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 4(2), 4548–4556.
9. Krishnan, S., Umasankar, P., & Mohana, P. (2020). A smart FPGA based design and implementation of grid connected direct matrix converter with IoT communication. Microprocessors and Microsystems, 76, 103107.
10. Anand, L., & Neelanarayanan, V. (2019). Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6434–6439.
11. S. Vishwarup et al., "Automatic Person Count Indication System using IoT in a Hotel Infrastructure," 2020 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2020, pp. 1-4, doi: 10.1109/ICCCI48352.2020.9104195
12. Inbavalli, M., & Arasu, T. (2015). Efficient Analysis of Frequent Item Set Association Rule Mining Methods. International Journal of Scientific & Engineering Research, 6(4).
13. Lakshmi, C. S., & Nagarajan, C. (2021). Comparison of shunt active filter controllers for harmonic elimination. Suraj Punj Journal for Multidisciplinary Research, 11(4), 674–678.
14. Keezhadath, A. A., Kota, R. K., & Selvaraj, A. (2021). Dynamic Pricing Optimization for Global Hospitality: Real-Time Data Integration and Decision Making. American Journal of Autonomous Systems and Robotics Engineering, 1, 131–165.
15. Prasanna, D., & Santhosh, R. (2018). Time Orient Trust Based Hook Selection Algorithm for Efficient Location Protection in Wireless Sensor Networks Using Frequency Measures. International Journal of Engineering & Technology, 7(3.27), 331–335.
16. Yashwanth, K., Adithya, N., Sivaraman, R., Janakiraman, S., & Rengarajan, A. (2021, July). Design and Development of Pipelined Computational Unit for High-Speed Processors. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.
17. Sudha, N., Kumar, S. S., Rengarajan, A., & Rao, K. B. (2021). Scrum Based Scaling Using Agile Method to Test Software Projects Using Artificial Neural Networks for Block Chain. Annals of the Romanian Society for Cell Biology, 25(4), 3711–3727.
18. Ananth, S., Kalpana, A. M., & Vijayarajeswari, R. (2020). A dynamic technique to enhance quality of service in software-defined network-based wireless sensor network (DTEQT) using machine learning. International Journal of Wavelets, Multiresolution and Information Processing, 18(01), 1941020.
19. Ponlatha, S., Umasankar, P., Balashanmuga Vadivu, P., & Chitra, D. (2021). An IOT‐based efficient energy management in smart grid using SMACA technique. International Transactions on Electrical Energy Systems, 31(12), e12995.
20. Yashwanth, K., Adithya, N., Sivaraman, R., Janakiraman, S., & Rengarajan, A. (2021, July). Design and Development of Pipelined Computational Unit for High-Speed Processors. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.
21. Adari, V. K. (2020). Intelligent Care at Scale AI-Powered Operations Transforming Hospital Efficiency. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(3), 1240–1249.
22. Ramsugeerthi, A., Neela Madheswari, A., Umamaheswari, A., & Prassana, D. (2020). Location navigation assistance for educational institutions using augmented reality. Journal of Xidian University, 14(4), 1342–1347. https://doi.org/10.37896/jxu14.4/156
23. Jaikrishna, G., & Rajendran, S. (2020). Cost-effective privacy preserving of intermediate data using group search optimisation algorithm. International Journal of Business Information Systems, 35(2), 132–151.
24. Singh, A. (2021). Unlocking Mesh Networks: Tackling Scalability in Dynamic Environments. IJSAT-International Journal on Science and Technology, 12(1).
25. Vimal Raja, G., K. K. Sharma (2014). Analysis and Processing of Climatic data using data mining techniques. Envirogeochimica Acta, 1(8), 460–467.
26. Krishnan, S., Umasankar, P., & Mohana, P. (2020). A smart FPGA based design and implementation of grid connected direct matrix converter with IoT communication. Microprocessors and Microsystems, 76, 103107.
27. Aashiq Banu, S., Sucharita, M. S., Soundarya, Y. L., Nithya, L., Dhivya, R., & Rengarajan, A. (2020). Robust Image Encryption in Transform Domain Using Duo Chaotic Maps—A Secure Communication. In Evolutionary Computing and Mobile Sustainable Networks: Proceedings of ICECMSN 2020 (pp. 271-281). Singapore: Springer Singapore.

