Continual Learning Framework for Drift-Resilient and Autonomous IoT Intelligence

Abstract

The ubiquitous use of Internet of Things (IoT) system in dynamic and real-life contexts presents a high level of challenges because of the constant changes in the data distributions, otherwise known as concept drift. The conventional machine learning models that are implemented in IoT systems are usually trained in static mode and cannot respond to changing trends in data, which causes deterioration in performance as time goes by. The drawback restricts the reliability and independence of intelligent IoT applications in the long run. In order to overcome these challenges, this paper will present a omprehensive lifelong learning model that can be used to enable drift-robust and autonomous IoT intelligence. The framework proposed combines the real-time drift detection, incremental model adaptation with memory-based knowledge retention into a single framework. It allows the IoT systems to continuously learn stream data and retain the past knowledge, thus eliminating catastrophic forgetting. The architecture is edge deportable, which means that it is low-latency inference and less reliant on centralized retraining. This is proven by on-the-job evidence of the proposed approach; it has been shown to be able to sustain stable performance even when data distribution changes, it converts to concept drift faster and it has a higher level of robustness than the traditional batch and online approaches to learning. The framework offers a scalable and effective way of facilitating self-adaptive, resilient, and autonomous IoT systems in the new generation smart environments.