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The advent of radio sensing that works through walls & obstacles challenges the notion of indoor privacy. An eavesdropper can deploy such sensing to snoop on their neighbors and a smart sensor embedded with such sensing capabilities can perform large scale behavioral and health data mining. We present RF-Protect, a new framework that enables privacy by injecting fake humans in the sensed data. RF-Protect consists of a novel hardware reflector design that modifies radio waves to create reflections at arbitrary locations in the environment and a new generative mechanism to create realistic human trajectories. RF-Protect’s design doesn’t require any high bandwidth hardware or physical motion. We implement RF-Protect using commodity hardware and validate its ability to generate fake human trajectories.

Earth observation Low Earth Orbit (LEO) satellites collect enormous amounts of data that needs to be transferred first to ground stations and then to the cloud, for storage and processing. Satellites today transmit data greedily to ground stations, with full utilization of bandwidth during each contact period. We show that due to the layout of ground stations and orbital characteristics, this approach overloads some ground stations and underloads others, leading to lost throughput and large end-to-end latency for images. We present a new end-to-end scheduler system called Umbra, which plans transfers from large satellite constellations through ground stations to the cloud, by accounting for both spatial and temporal factors, i.e., orbital dynamics, bandwidth constraints, and queue sizes. At the heart of Umbra is a new class of scheduling algorithms called withhold scheduling, wherein the sender (i.e., satellite) selectively under-utilizes some links to ground stations. We show that Umbra’s counter-intuitive approach increases throughput by 13–31% & reduces P90 latency by 3–6 \texttimes.

Implantable and edible medical devices promise to provide continuous, directed, and comfortable healthcare treatments. Communicating with such devices and localizing them is a fundamental, but challenging, mobile networking problem. Recent work has focused on leveraging near field magnetism-based systems to avoid the challenges of attenuation, refraction, and reflection experienced by radio waves. However, these systems suffer from limited range, and require fingerprinting-based localization techniques. We present InnerCompass, a magnetic backscatter system for in-body communication and localization. InnerCompass relies on new magnetism-native design insights that enhance the range of these devices. We design the first analytical model for magnetic-field-based localization, that generalizes across different scenarios. We’ve implemented InnerCompass and evaluated it in porcine tissue. Our results show that Inner-Compass can communicate at 5 Kbps at a distance of 25 cm, and localize with an accuracy of 5 mm.