Cognitive Edge Computing: Machine Learning Strategies for IoT Data Management

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.