EDGE COMPUTING FRAMEWORK FOR REAL-TIME IOT SENSOR DATA PROCESSING

The study created an edge computing system of real-time IoT sensor data processing that mitigates traditional challenges of cloud computing such as latency, bandwidth, and security concerns. Decentralizing the architecture also improved performance with savings in latency and bandwidth utilization, better energy efficiency, more accuracy. This facilitates immediate processing and decision-making, which are integral to health care delivery, industrial automation, and smart cities. Light On-Premise Model Hosting for Interactive Predictive Analytics with Machine Learning and Anomaly Detection at the Edge Non-Intrusive Smart Processing Capability — Logic-driven approaches to critical diagnosis and decision making in business processes. Encryption, authentication and intrusion detection are very crucial for both data protection and privacy but their limitations remain as well, such as resource crunches and interoperability. In general this research depicts the edge computing as a capable option for the breakthrough of limits on cloud based systems and to move towards new approaches in developing IoT and Distributed Computing more smartly paradigms.

1.               Ahmed, H. (2018). Security in IoT systems. Journal of Network Security, 12(3), 145–156. 2.               Brown, M. (2015). Data analytics in IoT. International Journal of Data Science, 8(2), 101–110. 3.               Chen, M. (2016). Edge computing frameworks. IEEE Transactions on Cloud Computing, 4(1), 45–55. 4.               Chen, E. (2019). Smart computing systems. Journal of Smart Technologies, 10(2), 88–97. 5.               Garcia, M. (2017). Distributed computing models. Computer Systems Journal, 9(1), 33–42. 6.               Hassan, A. (2018). IoT security challenges. Cybersecurity Review, 6(4), 120–130. 7.               Kim, D. (2022). Edge-cloud integration. Journal of Cloud Computing, 11(2), 210–220. 8.               Kumar, R. (2019). Distributed IoT frameworks. International Journal of Computing, 14(3), 155–165. 9.               Lee, T. (2016). Network architectures for IoT. Networking Journal, 7(2), 60–70. 10.            Liu, W. (2020). Edge AI systems. AI and Computing Journal, 15(1), 25–35.