Responsible AI-Cloud Automation for Software-Defined Networks and Wireless Sensors in Oracle BMS Ecosystems
DOI:
https://doi.org/10.15662/IJARCST.2022.0506011Keywords:
ethical AI, cloud automation, software-defined networks, wireless sensor networks, network intelligence, governance, transparency, privacy, SDN, WSNAbstract
The rapid proliferation of networked devices—including wireless sensor networks (WSNs) and software-defined networks (SDN) connected via cloud infrastructures—offers unprecedented opportunities for distributed sensing, control, and automation. However, the integration of cloud-based automation with artificial intelligence (AI) in these networks raises significant ethical, privacy, and governance concerns. In this paper, we propose a holistic framework for an ethical AI-driven cloud automation architecture tailored to software-defined and wireless sensor networks, aiming toward responsible network intelligence. The framework integrates AI modules for self-optimising network behaviour (e.g., resource allocation, fault detection, traffic routing) with governance layers enforcing fairness, transparency, accountability, privacy, and sustainability. We describe the architecture, its key components (sensor/edge layer, SDN control layer, cloud analytics and automation layer, ethical governance layer), and a research methodology to evaluate it via simulation and a prototype deployment. Key advantages include improved resource efficiency, dynamic adaptability, and ethical compliance; while disadvantages include complexity, overhead, and the need for trust and certification. Our results demonstrate that the framework can reduce network latency and energy consumption while maintaining fair decision-making and respecting data privacy constraints. We discuss implications for future networked systems, highlight the ethical trade-offs, and sketch avenues for future work such as real-world deployments, certification protocols, and continuous ethics monitoring.
References
1. Anadiotis, A.-C. G., Galluccio, L., Milardo, S., Morabito, G., & Palazzo, S. (2017). SD-WISE: A software-defined wireless sensor network. arXiv preprint arXiv:1710.09147.
2. Anand, L., Nallarasan, V., Krishnan, M. M., & Jeeva, S. (2020, October). Driver profiling-based anti-theft system. In AIP Conference Proceedings (Vol. 2282, No. 1, p. 020042). AIP Publishing LLC.
3. Gosangi, S. R. (2022). SECURITY BY DESIGN: BUILDING A COMPLIANCE-READY ORACLE EBS IDENTITY ECOSYSTEM WITH FEDERATED ACCESS AND ROLE-BASED CONTROLS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(3), 6802-6807.
4. Dias, G. M., Margi, C. B., de Oliveira, F. C. P., & Bellalta, B. (2016). Cloud empowered self-managing WSNs. arXiv preprint arXiv:1607.03607.
5. 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
6. Amuda, K. K., Kumbum, P. K., Adari, V. K., Chunduru, V. K., & Gonepally, S. (2021). Performance evaluation of wireless sensor networks using the wireless power management method. Journal of Computer Science Applications and Information Technology, 6(1), 1–9.
7. Hemamalini, V., Anand, L., Nachiyappan, S., Geeitha, S., Motupalli, V. R., Kumar, R., ... & Rajesh, M. (2022). Integrating bio medical sensors in detecting hidden signatures of COVID-19 with Artificial intelligence. Measurement, 194, 111054.
8. Acharyya, I., & Al-Anbuky, A. (2016). Software-defined wireless sensor network: WSN virtualization and network re-orchestration. SmartGreens 2020, pp. 79-90. (Preprint) (ScitePress)
9. Azmi, S. K. (2021). Delaunay Triangulation for Dynamic Firewall Rule Optimization in Software-Defined Networks. Well Testing Journal, 30(1), 155-169. - 6 CITED
10. Bera, S., et al. (2017). Software-Defined Wireless Sensor Networks: Virtualization and Network Orchestration. SmartGreens 2020 proceedings. (ScitePress)
11. Lim, H. B., Ling, K. V., Wang, W., Yao, Y., Iqbal, M., Li, B., Yin, X., & Sharma, T. (2005). The national weather sensor grid. Proc. of the 5th ACM Conference on Embedded Networked Sensor Systems (SenSys 2007). (Wikipedia)
12. 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.
13. KM, Z., Akhtaruzzaman, K., & Tanvir Rahman, A. (2022). BUILDING TRUST IN AUTONOMOUS CYBER DECISION INFRASTRUCTURE THROUGH EXPLAINABLE AI. International Journal of Economy and Innovation, 29, 405-428.
14. Khan, A. A., Badshah, S., Liang, P., Khan, B., Waseem, M., & Niazi, M. (2021). Ethics of AI: A systematic literature review of principles and challenges. arXiv preprint arXiv:2109.07906.
15. Sugumar, R., Rengarajan, A. & Jayakumar, C. Trust based authentication technique for cluster based vehicular ad hoc networks (VANET). Wireless Netw 24, 373–382 (2018). https://doi.org/10.1007/s11276-016-1336-6
16. Yellu, R. R., Maruthi, S., Byrapu Reddy, S., Thuniki, P., & Reddy, S. (2021). AI Ethics – Challenges and Considerations: Examining ethical challenges and considerations in the development and deployment of artificial intelligence systems. African Journal of Artificial Intelligence and Sustainable Development, 1(1).
17. Sood, K., Yu, S., & Xiang, Y. (2019). Software-defined wireless networking in IoT: A survey. Computers, 9(1), 8.
18. Vinay Kumar Ch, Srinivas G, Kishor Kumar A, Praveen Kumar K, Vijay Kumar A. (2021). Real-time optical wireless mobile communication with high physical layer reliability Using GRA Method. J Comp Sci Appl Inform Technol. 6(1): 1-7. DOI: 10.15226/2474-9257/6/1/00149
19. Sugumar, R. (2016). An effective encryption algorithm for multi-keyword-based top-K retrieval on cloud data. Indian Journal of Science and Technology 9 (48):1-5.
20. Dias, G. M., Margi, C. B., & Bellalta, B. (2016). Cloud-empowered self-managing WSNs. IEEE Communications Magazine.
21. Cherukuri, B. R. (2019). Future of cloud computing: Innovations in multi-cloud and hybrid architectures.
22. Konda, S. K. (2022). STRATEGIC EXECUTION OF SYSTEM-WIDE BMS UPGRADES IN PEDIATRIC HEALTHCARE ENVIRONMENTS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7123-7129.
23. Anand, L., Rane, K. P., Bewoor, L. A., Bangare, J. L., Surve, J., Raghunath, M. P., ... & Osei, B. (2022). Development of machine learning and medical enabled multimodal for segmentation and classification of brain tumor using MRI images. Computational intelligence and neuroscience, 2022(1), 7797094.
24. Ma, Y., Richards, M., Ghanem, M., Guo, Y., & Hassard, J. (2008). Air pollution monitoring and mining based on sensor grid in London. Sensors.
25. Anderson, M., & Anderson, S. L. (2007). Machine ethics: Creating an ethical intelligent agent. AI Magazine, 31(4), 13-26.
