Autoencoder-Based Compressive Sensing for Adaptive IoT Sensor Networks

Abstract

Recently, energy efficient signal processing has emerged as a critical area of research, driven by the increasing demand for cost-effective solutions in modern communication systems, particularly in Internet of Things (IoT) sensor networks applications. At the same time, artificial intelligence (AI)-based approaches have gained significant popularity, extending their potential far beyond user-driven content creation. In this work, we explore the intersection of these emerging technologies, aiming to enable faster processing, lower power consumption, and reduced hardware complexity. We apply the combined potential of both frameworks to compressive sensing (CS) for structural health monitoring (SHM) applications. Specifically, we propose a conceptual sensing and monitoring framework incorporating an adaptive data compression strategy that jointly leverages traditional CS and deep-learning-based signal compression. This work represents a feasibility study toward the development of a low-power, low-rate, and cost-effective IoT-based sensor network for SHM capable of adapting to changing channel conditions. To support this framework, the study provides a comprehensive comparison between traditional CS methods and AI-based compression, focusing on compression efficiency and reconstruction capabilities when processing data streams from SHM systems.