Ethical AI-Driven Automation Framework for SAP HANA-Based Cloud Ecosystems: Integrating Software-Defined Networks and Wireless Sensor Intelligence

Georgios Alexandros Papadopoulos

doi:10.15662/IJRAI.2023.0603007

Authors

Georgios Alexandros Papadopoulos IT Procurement Assistant, Greece Author

DOI:

https://doi.org/10.15662/IJRAI.2023.0603007

Keywords:

Ethical AI, Automation framework, SAP HANA cloud, Software-Defined Networking (SDN), Wireless Sensor Networks (WSNs), Sensor intelligence, Enterprise cloud ecosystem, Fairness, Transparency

Abstract

The rapid proliferation of cloud-based enterprise platforms, especially those anchored on in-memory databases such as SAP HANA, presents new opportunities and challenges for automation and intelligence. This paper proposes an ethical AI-driven automation framework tailored for SAP HANA-based cloud ecosystems, which moreover integrates programmable networking via Software‑Defined Networking (SDN) and sensor data intelligence derived from wireless sensor networks (WSNs). The framework delineates how AI agents can orchestrate business-process automation, network control, and real-time sensor data flows while embedding ethical principles (fairness, transparency, privacy, human oversight). The architecture comprises four layers: (i) a data-ingestion layer from WSNs and SDN controllers, (ii) a core SAP HANA analytics/automation layer, (iii) an AI governance & ethics layer, and (iv) automation execution and network orchestration layer. Methodologically, we adopt a design-science research approach, implementing a prototype in a simulated SAP HANA cloud sandbox, using SDN controllers and wireless sensor emulators. Key advantages include enhanced operational agility, programmable network responsiveness, sensor-based situational awareness, and ethical compliance. Disadvantages include complexity of integration, ethical governance overhead, dependency on sensor/ network reliability, and potential bias in AI automation. A discussion of results from performance trials — including automation latency, network adaptation speed and fairness metrics — is presented, followed by insights on practical deployment. The conclusion highlights the importance of embedding ethics into enterprise automation and offers directions for future work in standardization, robustness and cross-domain application.

References

1. Modieginyane, K. M., Letswamotse, B. B., Malekian, R., & Abu-Mahfouz, A. M. (2018). Software defined wireless sensor networks application opportunities for efficient network management: a survey. Computers & Electrical Engineering, 66, 274-287.

2. Kumar, R., Al-Turjman, F., Anand, L., Kumar, A., Magesh, S., Vengatesan, K., ... & Rajesh, M. (2021). Genomic sequence analysis of lung infections using artificial intelligence technique. Interdisciplinary Sciences: Computational Life Sciences, 13(2), 192-200.

3. Dong Wang, Lihua Dai (2022). Vibration signal diagnosis and conditional health monitoring of motor used in biomedical applications using Internet of Things environment. Journal of Engineering 5 (6):1-9.

4. Thambireddy, S., Bussu, V. R. R., & Pasumarthi, A. (2022). Engineering Fail-Safe SAP Hana Operations in Enterprise Landscapes: How SUSE Extends Its Advanced High-Availability Framework to Deliver Seamless System Resilience, Automated Failover, and Continuous Business Continuity. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(3), 6808-6816.

5. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2020). Artificial intelligence using TOPSIS method. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 3(6), 4305-4311.

6. Luo, T. T., Tan, H. P., & Quek, T. Q. S. (2012). Sensor OpenFlow: Enabling software-defined wireless sensor networks. IEEE Communications Letters, 16(11), 1896-1899. https://doi.org/10.1109/LCOMM.2012.092812.121712

7. Xu, Q., & Zhao, J. (2016). A WSN architecture based on SDN. In Advances in Computer Science Research – 4th International Conference on Information Systems and Computing Technology. Atlantis Press.

8. Hassan, M. A., Vien, Q.-T., & Aiash, M. (2017). Software defined networking for wireless sensor networks: A survey. Advances in Wireless Communications and Networks, 2017, Article 10. https://doi.org/10.11648/j.awcn.20170302.11

9. Vengathattil, S. (2019). Ethical Artificial Intelligence - Does it exist? International Journal for Multidisciplinary Research, 1(3). https://doi.org/10.36948/ijfmr.2019.v01i03.37443

10. Yahya, B., & Manaf, M. (2022). Security of Software-defined Wireless Sensor Networks. University of Waterloo.

11. Bonfim, M. S., Dias, K. L., & Fernandes, S. F. L. (2018). Integrated NFV/SDN architectures: a systematic literature review. arXiv preprint arXiv:1801.01516.

12. Hassan, M. A., Vien, Q.-T., & Aiash, M. (2017). Software defined networking for wireless sensor networks: A survey. Advances in Wireless Communications and Networks, 2017, Article 10. https://doi.org/10.11648/j.awcn.20170302.11

13. Modak, Rahul. "Distributed deep learning on cloud GPU clusters." (2022).

14. Tomovic, S., Radonjic, M., Pejanovic-Djurisic, M., & Radusinovic, I. (2017). Software-defined wireless sensor networks: opportunities and challenges. Journal of Electrical Engineering, 30, (special issue).

15. Yan, M., Casey, J., Shome, P., Oikonomou, G., Nejabati, R., & Sooriyabandara, M. (2015). ÆtherFlow: Principled wireless support in SDN. arXiv preprint arXiv:1509.04745.

16. Anadiotis, A.-C., Galluccio, L., Milardo, S., Morabito, G., & Palazzo, S. (2017). SD-WISE: A software-defined wireless sensor network. arXiv preprint arXiv:1710.09147.

17. Alam, T., & Aljohani, M. (2022). Software defined networks: Review and architecture. ITSDI, v1i2.

18. “Automating and optimizing business processes with SAP S/4HANA.” (2019). SAPinsider. Retrieved from article (date unspecified).

19. Amuda, K. K., Kumbum, P. K., Adari, V. K., Chunduru, V. K., & Gonepally, S. (2020). Applying design methodology to software development using WPM method. Journal ofComputer Science Applications and Information Technology, 5(1), 1-8.

20. Mohammed, A. A., Akash, T. R., Zubair, K. M., & Khan, A. (2020). AI-driven Automation of Business rules: Implications on both Analysis and Design Processes. Journal of Computer Science and Technology Studies, 2(2), 53-74.

21. Sugumar, R. (2022). Estimation of Social Distance for COVID19 Prevention using K-Nearest Neighbor Algorithm through deep learning. IEEE 2 (2):1-6.

22. Lu, Q., Zhu, L., Xu, X., Whittle, J., Zowghi, D., Jacquet, A., & Xu, … (2022). Responsible AI Pattern Catalogue: A collection of best practices for AI governance and engineering. arXiv Preprint.

23. Lu, Q., Zhu, L., Xu, X., Whittle, J., & Zowghi, D. (2022). Responsible-AI-by-Design: a pattern collection for designing responsible AI systems. arXiv Preprint.

24. Manda, P. (2022). IMPLEMENTING HYBRID CLOUD ARCHITECTURES WITH ORACLE AND AWS: LESSONS FROM MISSION-CRITICAL DATABASE MIGRATIONS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7111-7122.

25. Anand, L., Krishnan, M. M., Senthil Kumar, K. U., & Jeeva, S. (2020, October). AI multi agent shopping cart system based web development. In AIP Conference Proceedings (Vol. 2282, No. 1, p. 020041). AIP Publishing LLC.

26. Amjad, M., & Iradat, F. (2019). An active network-based open framework for IoT.