Cognitive Edge Computing: Machine Learning Strategies for IoT Data Management

Hari Priya Kommineni

doi:10.18034/ajase.v8i1.123

Authors

Hari Priya Kommineni Software Engineer, Hadiamondstar Software Solutions LLC, 9477 B, Silver King Ct, Fairfax, VA 22031, USA

Keywords:

Cognitive Edge Computing

Machine Learning

IoT Data Management

Edge Computing

Real-Time Data Processing

Anomaly Detection

Data Privacy

Abstract

This research examines how Cognitive Edge Computing (CEC) and machine learning improve IoT data management. The main goal is to study how CEC might increase IoT system efficiency, Scalability, and real-time responsiveness via local data processing and intelligent decision-making at the edge. Secondary data examines the literature on CEC uses, difficulties, and future directions in IoT contexts. We found that CEC improves IoT applications like predictive maintenance, anomaly detection, and autonomous systems by processing real-time data, reducing latency, and optimizing bandwidth. Scalability, resource constraints, security, and energy efficiency hinder wide-scale adoption. The intriguing answers include Federated learning, AI-driven edge orchestration, and 5G connection. The research also emphasizes energy-efficient models, device security, data privacy, and edge device authentication standards. Regulators must ensure safe deployment, fair resource access, and standardization of edge machine learning. In conclusion, machine learning-powered CEC can potentially improve IoT data management, but overcoming its limits and resolving regulatory issues are essential for sustainable and safe adoption.

References

Barik, R., Priyadarshini, R., Dubey, H., Kumar, V., Mankodiya, K. (2018). FogLearn: Leveraging Fog-Based Machine Learning for Smart System Big Data Analytics. International Journal of Fog Computing, 1(1), 15-34. https://doi.org/10.4018/IJFC.2018010102 DOI: https://doi.org/10.4018/IJFC.2018010102

Cui, L., Yang, S., Chen, F., Ming, Z., Lu, N. (2018). A Survey on Application of Machine Learning for Internet of Things. International Journal of Machine Learning and Cybernetics, 9(8), 1399-1417. https://doi.org/10.1007/s13042-018-0834-5 DOI: https://doi.org/10.1007/s13042-018-0834-5

Gupta, B. B., Agrawal, D. P., Yamaguchi, S. (2019). Deep Learning Models for Human Centered Computing in Fog and Mobile Edge Networks. Journal of Ambient Intelligence and Humanized Computing, 10(8), 2907-2911. https://doi.org/10.1007/s12652-018-0919-8 DOI: https://doi.org/10.1007/s12652-018-0919-8

Hossain, K., Rahman, M., Roy, S. (2019). IoT Data Compression and Optimization Techniques in Cloud Storage: Current Prospects and Future Directions. International Journal of Cloud Applications and Computing, 9(2), 43-59. https://doi.org/10.4018/IJCAC.2019040103 DOI: https://doi.org/10.4018/IJCAC.2019040103

Kadhum, M., Manaseer, S., Dalhoum, A. L. A. (2019). Cloud-Edge Network Data Processing based on User Requirements using Modify MapReduce Algorithm and Machine Learning Techniques. International Journal of Advanced Computer Science and Applications, 10(12). https://doi.org/10.14569/IJACSA.2019.0101242 DOI: https://doi.org/10.14569/IJACSA.2019.0101242

Karanam, R. K., Natakam, V. M., Boinapalli, N. R., Sridharlakshmi, N. R. B., Allam, A. R., Gade, P. K., Venkata, S. G. N., Kommineni, H. P., & Manikyala, A. (2018). Neural Networks in Algorithmic Trading for Financial Markets. Asian Accounting and Auditing Advancement, 9(1), 115–126. https://4ajournal.com/article/view/95

Kenda, K., Kazic, B., Novak, E., Mladenic, D. (2019). Streaming Data Fusion for the Internet of Things. Sensors, 19(8). https://doi.org/10.3390/s19081955 DOI: https://doi.org/10.3390/s19081955

Kundavaram, R. R., Rahman, K., Devarapu, K., Narsina, D., Kamisetty, A., Gummadi, J. C. S., Talla, R. R., Onteddu, A. R., & Kothapalli, S. (2018). Predictive Analytics and Generative AI for Optimizing Cervical and Breast Cancer Outcomes: A Data-Centric Approach. ABC Research Alert, 6(3), 214-223. https://doi.org/10.18034/ra.v6i3.672 DOI: https://doi.org/10.18034/ra.v6i3.672

Rehman, M. H. U., Jayaraman, P. P., Malik, S. U. R., Khan, A. U. R., Gaber, M. M. (2017). RedEdge: A Novel Architecture for Big Data Processing in Mobile Edge Computing Environments. Journal of Sensor and Actuator Networks, 6(3), 17. https://doi.org/10.3390/jsan6030017 DOI: https://doi.org/10.3390/jsan6030017

Rodriguez, M., Mohammed, M. A., Mohammed, R., Pasam, P., Karanam, R. K., Vennapusa, S. C. R., & Boinapalli, N. R. (2019). Oracle EBS and Digital Transformation: Aligning Technology with Business Goals. Technology & Management Review, 4, 49-63. https://upright.pub/index.php/tmr/article/view/151

Shukla, S., Hassan, M. F., Khan, M. K., Jung, L. T., Awang, A. (2019). An Analytical Model to Minimize the Latency in Healthcare Internet-of-things in Fog Computing Environment. PLoS One, 14(11), e0224934. https://doi.org/10.1371/journal.pone.0224934 DOI: https://doi.org/10.1371/journal.pone.0224934

Sittón-Candanedo, I., Alonso, R. S., García, Ó., Muñoz, L., Rodríguez-González, S. (2019). Edge Computing, IoT and Social Computing in Smart Energy Scenarios. Sensors, 19(15). https://doi.org/10.3390/s19153353 DOI: https://doi.org/10.3390/s19153353

Wei, J., Han, J., Cao, S. (2019). Satellite IoT Edge Intelligent Computing: A Research on Architecture. Electronics, 8(11), 1247. https://doi.org/10.3390/electronics8111247 DOI: https://doi.org/10.3390/electronics8111247