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Position-Based Medium Access Control for Ultra-wideband Sensors and Actuators
Position-Based Medium Access Control for Ultra-wideband Sensors and Actuators
PBMAC is a novel medium access control (MAC) protocol that replaces traditional device-ID–based addressing with position-based addressing. Instead of communicating with specific MAC addresses, PBMAC enables a wireless network to send commands to or collect data from devices located within a specified spatial region. This reimagines location as a first-class primitive in wireless communication.
Built on ultra-wideband (UWB) radios, PBMAC leverages passive time-difference-of-arrival (TDoA) localization so client devices can infer their position without per-device ranging overhead. The protocol also introduces a nanosecond-scale randomized backoff mechanism that enables collision detection using channel impulse response (CIR) analysis, allowing reliable uplink communication even when multiple spatially co-located devices transmit concurrently.
We implemented PBMAC on a 20-node UWB testbed and demonstrated position-based sensing and actuation, including dynamic light displays and spatial data collection. PBMAC opens up new possibilities in wireless sensor networks, industrial IoT, vehicular systems, swarm robotics, and large-scale spatially-programmed displays.
This project was advised by Prof. Ashutosh Dhekne and is still on-going. Tune in for more updates on this project later!
Ultra-wideband Wireless Solid-Liquid State Transition Sensor to Detect Thawing of Food
Ultra-wideband Wireless Solid-Liquid State Transition Sensor to Detect Thawing of Food
We explore how ultra-wideband (UWB) wireless sensors can detect when food transitions between frozen and thawed states. The key idea is that a food item’s dielectric properties (complex permittivity) change dramatically during the solid–liquid transition at microwave frequencies (around 4 GHz). Using this effect, we demonstrate a non-invasive monitoring system that can work through common food packaging, making it practical for real-time tracking without opening or touching the package.
Compared with traditional time- and temperature-based methods used in homes, kitchens, and the cold chain, our approach can provide more direct insight into the actual state of the food. Results from raw UWB channel impulse responses (CIR) and similarity-score analysis show strong potential and support the feasibility of the system for real-world frozen food safety and logistics applications.
This project was advised by Prof. Ashutosh Dhekne. Exploratory work has is on-going using mmWave to detect crystals and surface level detection of food.
BS4LIES: Backscatter 4 Low-power IoT Environmental Sensing
BS4LIES: Backscatter 4 Low-power IoT Environmental Sensing
Built a low-power backscatter system (<100 uW) with practical ranges of ~100s+ meters by applying digital communication techniques such as forward error correction and spread-spectrum modulation, to improve reliability and range under severe power constraints. Additionally, we have integrated an environmental temperature sensor to support urban heat island monitoring, targeting deployment in Atlanta urban environments.
This project was a group project done by me and the following team members: Eric Greenlee, Aadesh Madnaik, and Jason Cox.
Outdoor Point Cloud Reconstruction Using Drone-based Millimeter-Wave Systems
Outdoor Point Cloud Reconstruction Using Drone-based Millimeter-Wave Systems
This project proposes to use a frequency-modulated continuous wave (FMCW) radar implemented with a Millimeter-Wave transceiver integrated on a drone platform with depth sensors for ground truth. Using properties of FMCW and constant false-alarm rate (CFAR) detection techniques we are able to generate small-scale PCDs in outdoor environments.
This project was advised by Prof. Sanjib Sur and the work was co-authored with Ian McDowell
[Poster]
Theia: Outdoor Millimeter-Wave Picocell Placement using Drone-based surveying and Machine Learning
Theia: Outdoor Millimeter-Wave Picocell Placement using Drone-based surveying and Machine Learning
Theia is an extension to MilliDrone which uses a drone-based system that predicts outdoor Millimeter-Wave (mmWave) Signal Reflection Profiles (SRPs) and facilitates picocell placement for optimal network coverage. The drone platform integrates optical systems and a mmWave transceiver to collect depth images and mmWave SRPs of the environment. The datasets are fed into a machine learning model that maps the depth data to SRPs, allowing SRPs to be predicted at previously unseen parts of the environment. Theia then leverages these predictions to identify optimal picocell locations that maximize network coverage and minimize link outages. Theia has been evaluated in three large-scale outdoor environments and demonstrates that the proposed design can generalize the deployment method with a little refinement of the model.
This project was advised by Prof. Sanjib Sur and the work was co-authored with Ian McDowell and Hem Regmi.
MilliDrone: A Drone Platform to Facilitate Scalable Survey of Outdoor Millimeter-Wave Signal Propagation
MilliDrone: A Drone Platform to Facilitate Scalable Survey of Outdoor Millimeter-Wave Signal Propagation
MilliDrone is a Drone-based system equipped with a mmWave transceiver and a Guidance platform, and is synchronized to collect depth, grayscale, and mmWave reflection profiles by following a specified programmed path in an outdoor environment.
This project was advised by Prof. Sanjib Sur and the work was co-authored with Ian McDowell.
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