Wireless connectivity between aerial and underwater networks has inherently been a major challenge, as no single carrier operates well across the boundary. Traditional principles, such as radio in air and acoustics underwater, are fundamentally limited at the cross-media interface because of a stark mismatch in electromagnetic and acoustic properties, including impedance, between the two media. To bridge this gap, we propose AquaTera, a new communication architecture that transforms the interface itself into a natural transducer to establish a direct air-to-water link.

The soaring flood risk, especially from intense rainfall, has become a major global challenge. Despite national meteorological forecasting and public, albeit limited, emergency sirens, the lack of a local, i.e., near-residence, flood sensing and alerting can lead to devastating consequences, as witnessed during the recent overnight flood in Kerr County, Texas, which took the lives of hundreds of residents. In this work, we propose MUFASA, a novel architecture for distributed, residence-scale flood sensing and early warning. We design an early prototype of MUFASA using off-the-shelf millimeter-wave radar and present promising results.  [DEMO]

Among the many challenges in realizing wireless networks above 100 GHz, multi-user access remains one of the least explored. Traditionally, multi-user multiplexing at lower frequencies requires the implementation of one RF chain per data stream (user). Adapting such technology to higher frequencies above 100 GHz incurs prohibitively complex design and fabrication challenges, especially when scaling to many users. Here, we propose a fundamentally new approach that enables sub-terahertz multi-user access without any RF chains.

Wireless eavesdropping on phone conversations has become a major security and safety concern, especially with advancements toward 5G and beyond featuring higher frequencies and higher sensing resolution. As demonstrated recently, attackers can remotely detect even micron-scale acoustic vibrations emanating from a smartphone's earpiece via off-the-shelf millimeter-wave radar for audio information eavesdropping, all without the victim ever noticing. Here, we present a new architecture, MiSINFO, that not only thwarts such attacks but also enables the victim to counter-attack by spoofing eavesdroppers with audio misinformation.

Privacy-invading biometrics monitoring is becoming a prominent security threat as modern sensing systems move to higher operating frequencies (mmWave, sub-THz), increasing sensing resolution and accuracy. As such, developing systems that can protect or obfuscate biometrics from adversarial intrusion becomes pivotal to preserving user privacy. In this work, we develop and implement MetaHeart, a real-time biometrics misinformation system based on reflective, programmable metasurfaces and dynamic phase-front manipulation of radar inferences. 

Active metasurfaces operating in the millimeter-wave and sub-terahertz spectral ranges have become a significant focus of recent research, especially with advancements toward 6G wireless systems. Such devices, which are generally configured in an array-of-subarrays architecture, are envisioned as valuable tools for manipulating millimeter-wave or terahertz beams, or for modulation [or high-speed wavefront manipulation. We demonstrate a wireless security application to protect the weakest link in phone-to-phone communication, using a terahertz metasurface. To our knowledge, this is the first example of an eavesdropping countermeasure in which the attacker is actively misled.

Wireless backhaul links, already ubiquitous and expanding further with 5G and beyond, are employed for many critical functions, such as financial trading on Wall Street. In this work, we demonstrate for the first time that such links are acutely vulnerable to a new class of aerial metasurface attacks. In particular, we show how an adversary Eve designs and employs MetaFly to covertly manipulate the electromagnetic wavefront of the signals and remotely eavesdrop on highly directional backhaul links.  Exploring the foundation of the attack, we demonstrate Eve’s strategy for generating eavesdropping diffraction beams by inducing phase profiles at the aerial metasurface interface.

In this work, we propose FloatSense, a novel balloon-based UAV network system for efficient and robust air pollution monitoring. Unlike prior related work commonly leveraging rotary-wing drones, FloatSense UAVs mainly exploit helium balloons to maintain elevation and use small lightweight normally-off fans as a propulsion mechanism. The proposed design enables as a result extended environmental sensing missions by staying afloat for weeks. We reveal that although balloon-based UAVs are prone to drifting off due to external forces like wind, FloatSense outperforms traditional drones even in the presence of considerable wind speeds.

We demonstrate for the first time security vulnerabilities of wireless backhaul links to aerial metasurfaces. Considering over-the-air threats and a strong adversary, we define and experimentally demonstrate the “Remotely Positioned MetaSurface-Drone” (RMD) attack. In the attack, the adversary Eve remotely approaches hard-to-reach wireless backhaul links, e.g., between towers and rooftops, and stealthily manipulates highly directive backhaul transmissions on-the-fly, enabling remote eavesdropping. Our results reveal that the RMD attacker can intercept backhaul transmissions with nearly zero BER while maintaining minimal impact on legitimate communication.

Metasurfaces enable controllable manipulation of electromagnetic waves and have been shown to improve wireless communications in many diverse ways. In this paper, we define and experimentally demonstrate for the first time a “MetaSurface-in-the-Middle” (MSITM) attack. In this attack, the adversary Eve places a metasurface in the path of a directive transmission between Alice and Bob and targets to re-direct a portion of the signal towards herself, without being detected. We show how Eve can design a metasurface that induces abrupt phase changes at the interface of the metasurface to controllably diffract directional links and establish furtive eavesdropping links. [DEMO]

We present FALCON, a novel autonomous drone network system for sensing, localizing, and approaching RF targets/sources such as smartphone devices. Potential applications of our system include disaster relief missions in which networked drones sense the Wi-Fi signal emitted from a victim’s smartphone and dynamically navigate to accurately localize and quickly approach the victim, for instance, to deliver the time-critical first-aid kits. For that, we exploit Wi-Fi’s recent fine time measurement (FTM) protocol to realize the first on-drone FTM sensor network that enables accurate and dynamic ranging of targets in a mission. 

We present the design, implementation, and experimental evaluation of ASTRO, a modular end-to-end system for distributed sensing missions with autonomous networked drones. We introduce the fundamental system architecture features that enable agnostic sensing missions on top of the ASTRO drones. We demonstrate the key principles of ASTRO by using on-board software-defined radios to find and track a mobile radio target. We show how simple distributed on-board machine learning methods can be used to find and track a mobile target, even if all drones lose contact with ground control. [DEMO]