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By: Heena T Shaikh and Kazi Kutubuddin Kazi Kutubuddin.
1.Assistant Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
2.Professor and Head Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
As the Internet of Things (IoT) ecosystem expands into complex industrial and urban environments, the demand for high-fidelity data acquisition and near-instantaneous edge processing has reached a critical juncture. Traditional optical and infrared sensors often falter under adverse conditions, such as dense smoke, occlusions, or low-light environments. This paper explores the transformative potential of microwave signals—leveraging their unique ability to penetrate non-metallic materials and operate independently of ambient lighting—as the foundational data layer for AI-driven IoT decision-making systems. We examine the integration of Frequency Modulated Continuous Wave (FMCW) radar and ultra-wideband (UWB) microwave sensing with deep learning architectures, specifically focusing on how spatio- temporal signal patterns can be translated into actionable intelligence. By deploying lightweight convolutional neural networks (CNNs) at the edge, we demonstrate how microwave-based IoT systems can enable non-invasive human activity recognition, structural health monitoring, and precise asset tracking with sub-millimeter accuracy, effectively closing the loop between real- world physical dynamics and autonomous system responses. Microwave signals are rapidly evolving from a communication medium to a pervasive perception modality for AIdriven IoT ecosystems. Microwave sensors leverage electromagnetic waves (0.3–300GHz) to detect movement, moisture, or structural changes, while AI algorithms analyze this data to automate actions and enhance operational efficiency.
Keyword – Microwave, Sensing, AI Driven IoT, Decision making, Harmonic Intelligence
Citation:
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