An Adaptive Machine Learning Framework for Real Time IoT Data Classification and Prediction

Abstract

The analysis of large amounts of data generated in real time by applications and services of the Internet of Things (IoT) has become a real challenge, since the data streams are continuous and non-stationary, therefore they require a high quality classification and prediction. Therefore, the use of static machine learning models becomes inefficient, since the behavior of the devices and the distribution of the sensors can change over time, and they require an adaptive model that can be updated in real time. In this work, an adaptive machine learning framework for real-time device activity recognition and future-state prediction is proposed. It is tested on a simulated IoT stream with 30,000 observations and 5 different device states (sleep, sensing, processing, transmitting, idle) under different types of concept drift (abrupt, gradual, incremental, and recurring). The framework consists of an online stochastic gradient descent (SGD) classifier, a recent-window random forest, dynamic model weighting, and Page–Hinkley drift detection. A prequential test-then-train approach is used after an initial set of 2,000 observations. For the task of activity recognition, the framework achieves an accuracy of 93.92% and a corresponding macro-F1 score of 93.81%, outperforming a static random forest as well as a non-adaptive online SGD model by 9.39% and 1.83% percentage points, respectively. For the task of one-step-ahead state prediction, it achieves an accuracy of 68.32% and corresponding macro-F1 score of 68.27%. Average processing latency per observation is below 0.5 ms. The results of this work show that adaptive online learning can efficiently support reliable IoT classification and prediction in changing operational scenarios, while keeping low computational latency.