Adaptive Learning in IoT-Based Smart City Applications

Abstract

Internet of things (IoT) based smart city applications rely on constant data collection and accurate data analytics, yet the fast-changing nature of such data often causes the performance of machine learning models to deteriorate over time. Adaptive learning has been increasingly utilized in these applications in recent years as a viable solution to this problem. Moreover, IoT applications are vulnerable to various security threats due to their large-scale deployment, resource-constrained devices, and diverse protocols. This has led to an increased interest in efficient security and intrusion detection mechanisms tailored for IoT environments. In this chapter, the authors first focus on methods to address the issue of concept drift in time series streaming data for IoT-based smart city applications, such as weather, flood, and energy consumption forecasting, through adaptive learning. Furthermore, the authors examine adaptive learning-based security solutions to various attacks in different domains of the dynamic smart city landscape.