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Project # 89
Wi-Fi Payload Extraction on Low-Power IoT Devices Enabling Bistatic Backscatter Communication
| Team Lead: | Angelina Abuhilal |
| Other Team Members: | Ivan Ko |
| Faculty Advisor(s): | Dr Yoon Chae |
| Department: | CSE |
| Project Type: | REU |
| Title: | Wi-Fi Payload Extraction on Low-Power IoT Devices Enabling Bistatic Backscatter Communication |
| Abstract: | Low-power IoT devices are attractive due to their minimal energy consumption, but this efficiency imposes hardware constraints that significantly limit wireless communication capabilities. Slow oscillators and low sampling rates prevent these devices from decoding high-rate Wi-Fi signals, particularly when operating below the Nyquist frequency. While prior work has demonstrated that transmission speed can be improved by embedding backscatter data onto commodity Wi-Fi signals, these approaches are largely unidirectional and do not enable low-power devices to decode standard Wi-Fi packets. Existing Wi-Fi decoding techniques typically require high sampling rates, specialized hardware, or protocol modifications, limiting scalability and practical deployment. This project proposes a Wi-Fi-compliant modulation framework that enables low-power IoT devices to decode information at the OFDM symbol level using only an RF power detector operating at sub-Nyquist sampling rates. The method embeds carefully designed low-correlation OFDM symbols within standard Wi-Fi payloads, allowing symbol classification through autocorrelation of the detected power envelope. We will design robust symbol mappings, develop packet detection and decoding algorithms for constrained hardware, and evaluate performance under realistic impairments including carrier frequency offset, noise, and multipath fading. By enabling bidirectional communication using commodity Wi-Fi infrastructure without hardware modification, this work advances scalable and energy-efficient IoT networking. |
| Poster: | View Poster #89 (PDF) |
| Judges Scoring: | Submit Evaluation (judges only) |