Edge Computing-based Adaptive Machine Learning Model for Dynamic IoT Environment

With the advent of IoT and smart systems, edge computing coupled with machine learning (ML) techniques are becoming imperative to locally process and analyze the heterogeneous data generated from various IoT devices in real-time. The most common problem in dynamic IoT environment is performance degradation, mainly due to virtual concept drift (VCD). The issue of VCD often incurs, in dynamic IoT environment, when statistical properties of the input features change over time and make existing ML models obsolete or degrade models' performance and efficiency. Thus, the need for adaptive ML models. To facilitate this endeavor, our main objective is to create an adaptive ML model for resource-constrained edge computing devices to address the VCD issues in dynamic IoT environment. In this paper, we present a proof-of-concept adaptive ML model for edge computing based on a CNN single classifier with a real-time transfer learning method using fine-tuning. We also present our problem formulation and preliminary experimental results for problem validation.