Low-cost passive sensing techniques for industrial applications

This thesis addresses sustainability challenges in digitalization by leveraging passive RFID technology for industrial sensing applications. While digitalization drives progress, electronic waste and energy consumption pose environmental risks, particularly from batteries in IoT devices. In this work, COTS UHF RFID systems, traditionally used for identification, are repurposed for low-cost, battery-free sensing by analyzing RF signal variations when antennas interact with different materials. Key contributions include the development of the WiLD system, a patented moisture-detection method for moving vehicles (e.g., automotive assembly lines), achieving 100% leak detection (≥1.5 mL) through differential analysis of shielded and exposed RFID tags. Additionally, a fabric classification technique with over 92% accuracy (cotton/polyester) is introduced using RF signals during drying, enabling large-scale recycling. Experimental validations in the automotive and textile sectors demonstrate real-world feasibility. This research leverages existing RFID infrastructure to provide scalable, sustainable solutions for industrial digitalization, reducing e-waste and bridging the gap between academic research and commercial adoption.