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

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1. Steer MB, Bandler JW, Snowden CM. Computer-aided design of RF and microwave circuits and systems. IEEE Transactions on Microwave Theory and Techniques. 2002 Aug 7;50(3):996- 1005.
2.  Wang F, Sarabandi K. An enhanced millimeter-wave foliage propagation model. IEEE Transactions on Antennas and Propagation. 2005 Jul 31;53(7):2138-45.
3. Taylor JD. Ultra-wideband radar overview. InIntroduction to ultra-wideband radar systems 2020 Sep 23 (pp. 1-10). CRC Press.
4. Wang S, Chen J, Liu J, Li Q, Yuan S, Kuan YC, Yu X, Song C, Gu QJ, Xu Z. A low-power 23–25.5-GHz FMCW radar transceiver in 65-nm CMOS for AIOT applications. IEEE Transactions on Microwave Theory and Techniques. 2024 Mar 19;72(4):2560-76.
5. Yao Y, Liu C, Zhang H, Yan B, Jian P, Wang P, Du L, Chen X, Han B, Fang Z. Fall detection system using millimeter-wave radar based on neural network and information fusion. IEEE Internet of Things Journal. 2022 May 17;9(21):21038-50.
6. Gan L, Liu Y, Li Y, Zhang R, Huang L, Shi C. Gesture recognition system using 24 GHz FMCW radar sensor realized on real-time edge computing platform. IEEE Sensors Journal. 2022 Mar 30;22(9):8904-14.
7. Ashok K, Kishore R, Sudha T, Gupta M, BV SK, Bhaskar SV. Deep Sequence and Graph- Based Channel State Prediction for Intelligent 6G Wireless Networks in Dynamic Environments. In2025 International Conference on Communication, Computer, and Information Technology (IC3IT) 2025 Oct 24 (pp. 1-6). IEEE.
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9. Garavagno AM, Ragusa E, Frisoli A, Gastaldo P. An affordable hardware-aware neural architecture search for deploying convolutional neural networks on ultra-low-power computing platforms. IEEE Sensors Letters. 2024 Apr 10;8(5):1-4.
10. Vahidi M, Shafian S, Frame WH. Multi-depth soil moisture estimation via 1D convolutional neural networks from drone-mounted ground penetrating Radar data. Computers and Electronics in Agriculture. 2025 May 1;232:110104.
11. Pittala RB, Ramakrishna B, Ravi CV, Kiran MA, Sharma N, Thaile M, Surendra G, Bhargavi YK. Integrating Vision Transformers and CNNs for High-Accuracy Weed Detection in Crop Fields. Engineering Letters. 2026 Jan 1;34(1).
12. Delgoda D, Malano H, Saleem SK, Halgamuge MN. Irrigation control based on model predictive control (MPC): Formulation of theory and validation using weather forecast data and AQUACROP model. Environmental Modelling & Software. 2016 Apr 1;78:40-53.
13. Kim SH, Han GT. 1D CNN based human respiration pattern recognition using ultra wideband radar. In2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) 2019 Feb 11 (pp. 411-414). IEEE.
14. Momenroodaki P, Haines W, Fromandi M, Popovic Z. Noninvasive internal body temperature tracking with near-field microwave radiometry. IEEE Transactions on Microwave Theory and Techniques. 2017 Dec 12;66(5):2535-45.

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

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Steer MB, Bandler JW, Snowden CM. Computer-aided design of RF and microwave circuits and systems. IEEE Transactions on Microwave Theory and Techniques. 2002 Aug 7;50(3):996- 1005.
2.  Wang F, Sarabandi K. An enhanced millimeter-wave foliage propagation model. IEEE Transactions on Antennas and Propagation. 2005 Jul 31;53(7):2138-45.
3. Taylor JD. Ultra-wideband radar overview. InIntroduction to ultra-wideband radar systems 2020 Sep 23 (pp. 1-10). CRC Press.
4. Wang S, Chen J, Liu J, Li Q, Yuan S, Kuan YC, Yu X, Song C, Gu QJ, Xu Z. A low-power 23–25.5-GHz FMCW radar transceiver in 65-nm CMOS for AIOT applications. IEEE Transactions on Microwave Theory and Techniques. 2024 Mar 19;72(4):2560-76.
5. Yao Y, Liu C, Zhang H, Yan B, Jian P, Wang P, Du L, Chen X, Han B, Fang Z. Fall detection system using millimeter-wave radar based on neural network and information fusion. IEEE Internet of Things Journal. 2022 May 17;9(21):21038-50.
6. Gan L, Liu Y, Li Y, Zhang R, Huang L, Shi C. Gesture recognition system using 24 GHz FMCW radar sensor realized on real-time edge computing platform. IEEE Sensors Journal. 2022 Mar 30;22(9):8904-14.
7. Ashok K, Kishore R, Sudha T, Gupta M, BV SK, Bhaskar SV. Deep Sequence and Graph- Based Channel State Prediction for Intelligent 6G Wireless Networks in Dynamic Environments. In2025 International Conference on Communication, Computer, and Information Technology (IC3IT) 2025 Oct 24 (pp. 1-6). IEEE.
8. Weber VI, Kuprits VY. Efficiency of YOLO neural network models applied for object recognition in radar images. RUSSIAN. 2025 Jul 31:25.
9. Garavagno AM, Ragusa E, Frisoli A, Gastaldo P. An affordable hardware-aware neural architecture search for deploying convolutional neural networks on ultra-low-power computing platforms. IEEE Sensors Letters. 2024 Apr 10;8(5):1-4.
10. Vahidi M, Shafian S, Frame WH. Multi-depth soil moisture estimation via 1D convolutional neural networks from drone-mounted ground penetrating Radar data. Computers and Electronics in Agriculture. 2025 May 1;232:110104.
11. Pittala RB, Ramakrishna B, Ravi CV, Kiran MA, Sharma N, Thaile M, Surendra G, Bhargavi YK. Integrating Vision Transformers and CNNs for High-Accuracy Weed Detection in Crop Fields. Engineering Letters. 2026 Jan 1;34(1).
12. Delgoda D, Malano H, Saleem SK, Halgamuge MN. Irrigation control based on model predictive control (MPC): Formulation of theory and validation using weather forecast data and AQUACROP model. Environmental Modelling & Software. 2016 Apr 1;78:40-53.
13. Kim SH, Han GT. 1D CNN based human respiration pattern recognition using ultra wideband radar. In2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) 2019 Feb 11 (pp. 411-414). IEEE.
14. Momenroodaki P, Haines W, Fromandi M, Popovic Z. Noninvasive internal body temperature tracking with near-field microwave radiometry. IEEE Transactions on Microwave Theory and Techniques. 2017 Dec 12;66(5):2535-45.