Edge Native Analytics for Real-Time Enterprise Decision Intelligence using Distributed Generative AI Models
DOI:
https://doi.org/10.15680/IJMRSETM.2026.0209001Keywords:
edge-native analytics, real-time analytics, enterprise decision intelligence, generative artificial intelligence, distributed AI, edge computing, machine learning, large language models, federated learning, event-stream processing, intelligent automation, low-latency computingAbstract
The rapid growth of distributed enterprise systems, Internet of Things devices, cloud platforms, and real-time data streams has increased the need for decision-intelligence architectures capable of processing information with minimal latency. Conventional cloud-centric analytics can introduce communication delays, bandwidth constraints, privacy concerns, and dependency on centralized infrastructure, particularly when enterprise decisions must be made close to operational environments. Edge-native analytics provides an alternative by distributing data processing, machine-learning inference, and decision-support capabilities across edge devices, regional nodes, and cloud infrastructure. This study investigates an edge-native framework that integrates distributed generative artificial intelligence models with real-time enterprise decision intelligence. The proposed approach combines localized data processing, federated or distributed model execution, event-stream analytics, retrieval-augmented generation, and cloud coordination to transform heterogeneous operational data into contextual decision insights. Smaller specialized generative models can operate at edge locations, while larger models remain available through regional or cloud resources for computationally intensive tasks. The methodology evaluates latency, accuracy, resource utilization, scalability, reliability, privacy, and decision-support effectiveness under different workload conditions. The proposed architecture emphasizes adaptive model placement, continuous learning, secure communication, and human-centered decision support. The study provides a methodological foundation for enterprises seeking responsive and scalable intelligence systems capable of transforming real-time operational data into actionable insights without relying exclusively on centralized cloud processing
References
1. Ahmad, S. G., Iqbal, T., Munir, E. U., & Ramzan, N. (2023). Cost optimization in cloud environment based on task deadline. Journal of Cloud Computing, 12, 9. https://doi.org/10.1186/s13677-022-00370-x
2. Lingala, B. (2026). Artificial intelligence and society: Governance models and ethical risks. International Journal of Research and Applied Innovations, 9(1), 13707–13711.
3. Ramasamy, M. (2025). Secure and extensible platform architecture for enterprise network orchestration. International Journal of Science, Research and Technology, 8(1), 13534–13544.
4. Nabil, M. A., Akand, A. R., Datta, A., Akter, K. S., Hossain, I., & Prince, N. U. (2026, August). A Multi-Dimensional Supervised Machine Learning Framework for Cybersecurity-Focused Social Media Bot Detection. In 2026 7th International Conference On Computational Vision and Bio Inspired Computing (ICCVBIC) (pp. 88-93). IEEE.
5. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
6. Pasupuleti, N. S., Kapoor, S., Vedula, J., Sati, M. M., Shamilevna, G. S., & Khurmat, E. (2025, July). Implementation of a Deep Learning Model for Real-time Detection of Diabetic Retinopathy in Primary Care Clinics. In 2025 International Conference on Information, Implementation, and Innovation in Technology (I2ITCON) (pp. 1-6). IEEE.
7. Poranki, U. (2026). From Connectivity Monetization to Network Developer Ecosystems: A Conceptual Framework for Reclaiming the 6G Value Layer. International Journal of Computer Information Systems and Industrial Management Applications, 18(6s), 187-195.
8. Chundi, V. R. K., Agarwal, V., & Arya, P. (2025, November). Green AI for Sustainable Supply Chains: Challenges in Emerging Economies. In 2025 International Conference on Computational Engineering, Sensing Technology and Management (ICCETM) (pp. 1-5). IEEE.
9. Awopejo, T. E., Adigun, P. O., Oyekanmi, T. T., Azeez, N. A. A., Adekanye, M. A., & Obisesan, A. (2025). Machine learning-based prediction of magnetic properties from hysteresis curves: A comparative study of Random Forest, Gradient Boosting, XGBoost, LightGBM and Support Vector Regressions. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(6), 13456-13479.
10. Manda, P. (2026). Database modernization - Sybase to SQL Server migration. International Journal of Engineering & Extended Technologies Research, 8(1), 252–258.
11. Elliott, S., & Roy, S. (2026, April). Critical Transit Infrastructure in Smart Cities and Urban Air Quality: A Multi-City Seasonal Comparison of Ridership and PM 2.5. In 2026 IEEE Conference on Technologies for Sustainability (SusTech) (pp. 1-8). IEEE.
12. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
13. Tarakampet, S., Tatavarthi, S., & Ali, S. B. S. (2026, May). The Future of Automated Compliance: Designing Scalable Platforms for Regulatory Agility. In 2026 IEEE World AI IoT Congress (AIIoT) (pp. 0974-0980). IEEE.
14. Chinnam, N. B. (2025). Intelligent distribution center integration for transportation modernization. International Journal of Research and Applied Innovations, 8(2), 11283–11288.
15. Soundappan, S. J. (2021). Integrated Artificial Intelligence Framework for Enterprise Cloud Modernization and Intelligent Threat Management Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(4), 5274-5284.
16. Shaik, N. (2025). Securing Kubernetes: An integrated approach to AI-driven threat detection and eBPF-based security monitoring. International Journal of Research and Applied Innovations, 8(2), 11289–11294.
17. Singh, V. (2026). Real-Time Financial Data Validation Platform for Enterprise Accounting Systems. International Journal of Computer Information Systems and Industrial Management Applications, 18(10s), 467-481.
18. Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of medical systems, 43(4), 96.
19. Mohile, A., Yadav, A. L., Mukherjee, U., Kapoor, R., Attri, V., & Reddy, R. R. (2026, May). Ethical AI Framework for Protecting Human Rights in Digital Surveillance Systems. In 2026 International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE) (pp. 1-6). IEEE.
20. Nabil, M. A., Akand, A. R., Datta, A., Akter, K. S., Hossain, I., & Prince, N. U. (2026, August). A Multi-Dimensional Supervised Machine Learning Framework for Cybersecurity-Focused Social Media Bot Detection. In 2026 7th International Conference On Computational Vision and Bio Inspired Computing (ICCVBIC) (pp. 88-93). IEEE.
21. Raja, G. V. (2022). AI Enabled Cloud and Data Engineering Frameworks for Secure, Scalable, and Intelligent Cyber Physical Analytics Systems. International Journal of Research and Applied Innovations, 5(4), 7395-7409.
22. Ahuja, D. (2025). Securing Container Isolation in Multi-Tenant Environments. Journal of Computer Science and Technology Studies, 7(10), 225-232.
23. Chidambaranathan, S., Mudunuri, L. N. R., Banu, S. S., Anitha, V., Praveen, R., & Golla, S. (2024, November). Effects of Water Quality Deterioration and the Application of Artificial Intelligence and Blockchain Technology. In 2024 International Conference on Advances in Computing, Communication and Materials (ICACCM) (pp. 1-6). IEEE.
24. Pothuri, M. K. (2025). AI-Driven Reusable Unified Extract for Multi-State Medicaid and Federal Reporting-a Product that saves Millions of Taxpayer Money through process efficiency and reusability. International Journal of AI, BigData, Computational and Management Studies, 6(4), 211-216.
25. Polamarasetty, V. K. (2021). Modernizing SAP sales and distribution systems through ABAP-based enterprise solutions. International Journal of Research and Applied Innovations, 4(2), 4925–4930.
26. Ramasamy, M. (2026). Microservices-based architectures for enterprise network orchestration and assurance. International Journal of Research and Applied Innovations, 9(2), 156–166.
27. Mathew, A., & Sydney, W. (2026). Cybersecurity 5.0: From firewalls to fully autonomous digital protection. ISRG Journal of Engineering and Technology, 2(2), 6–9.
28. Rekulapalli, K., Kagga, S. R., Ayyagari, V., Akula, A., & Raj Akula, R. (2026). AI in HRM: Enhancing workforce competency and organizational effectiveness. Proceedings of the International Conference on Innovative Computing and Communication (ICICC 2026).
29. Das, P., Gogineni, A., Patel, K., & Jain, A. (2025, May). Integration of IoT and Machine Learning to Monitor the Air Quality by Measuring the Block Carbon Levels. In 2025 International Conference on Engineering, Technology & Management (ICETM) (pp. 1-6). IEEE.
30. Gopinathan, V. R. (2023). Modernizing Enterprise Applications Using Intelligent API Frameworks Machine Learning and Cloud Native Computing. International Journal of Science, Research and Technology, 6(5), 10690-10697.
31. Ravichandran, S., & Kandasamy, V. (2025). Optimized Attention Augmented Residual Convolutional Neural Network with Fa-Resnet for Fabric Defect Detection. Journal of Control Engineering and Applied Informatics, 27(4), 3-15.
32. Sugumar, R. (2022). Federated Learning and Distributed AI Architectures for Cloud-Based Cyber Healthcare Data Collaboration Systems. International Journal of Future Innovative Science and Technology (IJFIST), 5(5), 9232.
33. Bitragunta, S. L. V. (2024). AI-driven deep learning-based animal intrusion detection and early warning system for railroad tracks. International Research Journal of Innovative Engineering, 8(1), 13909–13918.
34. 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.
35. Zhang, B., Ding, G., Zheng, Q., Zhang, K., & Qin, S. (2024). Iterative updating of digital twin for equipment: Progress, challenges, and trends. Advanced Engineering Informatics, 62, 102773. https://doi.org/10.1016/j.aei.2024.102773
