We designed, deployed, and performed a vast set of experiments on ASTRO, a platform for 3-D data-driven mobile sensing via networked drones. Our initial proof-of-concept demonstrations and extensive experiments were focused in the neighborhood surrounding our already deployed tower and wireless network at Technology For All (TFA) headquarters, in Houston, Texas. Our advanced sensing algorithms using 5G infrastructure were performed on the Rice University Campus. Our final advances were demonstrated in an urban rooftop scenario in Boston.

First, we deployed an Aerial/Ground pollution distributed real-time monitoring platform. In order to provide high resolution of air pollution sensing: (i) we deployed our reference ground sensor (FROG) in Houston’s East End with TFA as base station, (ii) we performed multiple drone missions in that same neighborhood and are currently analyzing the data collected using our custom-built drones, and (iii) we implemented a mobile app that provides the community with real-time collected data of both ground sensors and drones. This neighborhood not only demonstrated these methods in a real operational environment, but it also showcased environmental inequalities. Namely, the TFA low-income community is a short distance from Houston’s petrochemical industry and extreme events are common placed. Our research demonstrated a method to get real-time information to neighborhood residents via the AirSafe App.

Second, we developed a method for 5G infrastructure to rapidly localize drones.  In particular, we used Rice’s RENEW platform (Reconfigurable Ecosystem for Next-Generation End-to-End Wireless) with a stadium-mounted programmable Massive MIMO base station. We showed how the large 2-D array enabled redundant azimuth and elevation observations for accurate and real-time angle-of-arrival estimation. The research showed how to overcome challenges due to drone mobility, wind, and rapidly changing multi-path channel conditions.

Third, the project showed how an ASTRO drone armed with a printed metasurface can be used to intercept a highly directional roof-top backhaul link. In particular, we showed how an adversary Eve designs and employs an ASTRO drone to covertly manipulate the electromagnetic wavefront of the signals and remotely eavesdrop on highly directional backhaul links. Exploring the foundation of the attack, we demonstrated Eve’s strategy for generating eavesdropping diffraction beams by inducing pre-defined phase profiles at the aerial metasurface interface. We showed how Eve’s flight navigation approach can dynamically shape radiation patterns based on drone mobility via a wavefront-tailored flight refinement principle. We implemented the attack and performed a suite of over-the-air experiments in both a large indoor atrium and outdoor rooftops in a large metropolitan area. The results reveal that Eve can intercept backhaul transmissions with nearly zero bit error rate while maintaining minimal impact on legitimate communication.

Lastly, the project yielded peer-reviewed publications and demonstrations in top conferences and journals. The data sets collected have been anonymized and made publicly available to serve as a unique resource for the research community. Together, the project's workshops, data sets, and open-source code and platforms have promoted community-wide efforts. The project has provided research opportunities for undergraduate and graduate students from a variety of disciplines.  The project team included multiple female and Hispanic Ph.D. students. Our project has provided valuable environmental information via a custom App to an underserved and primarily Hispanic community, and the project has included outreach to underserved communities centered in the deployment’s footprint.

Last Modified: 11/14/2023
Modified by: Edward W Knightly