We engineered a wearable microphone jammer that is capable of disabling microphones in its user's surroundings, including hidden microphones. Our device is based on a recent exploit that leverages the fact that when exposed to ultrasonic noise, commodity microphones will leak the noise into the audible range.

Spectrum management in cellular networks is a challenging task that will only increase in difficulty as complexity grows in hardware, configurations, and new access technology (e.g. LTE for IoT devices). Wireless providers need robust and flexible tools to monitor and detect faults and misbehavior in physical spectrum usage, and to deploy them at scale. In this paper, we explore the design of such a system by building deep neural network (DNN) models1 to capture spectrum usage patterns and use them as baselines to detect spectrum usage anomalies resulting from faults and misuse. Using detailed LTE spectrum measurements, we show that the key challenge facing this design is model scalability, i.e. how to train and deploy DNN models at a large number of static and mobile observers located throughout the network. We address this challenge by building context-agnostic models for spectrum usage and applying transfer learning to minimize training time and dataset constraints. The end result is a practical DNN model that can be easily deployed on both mobile and static observers, enabling timely detection of spectrum anomalies across LTE networks.

While crowdsourcing is an attractive approach to collect large-scale wireless measurements, understanding the quality and variance of the resulting data is difficult. Our work analyzes the quality of crowdsourced cellular signal measurements in the context of basestation localization, using large international public datasets (419M signal measurements and 1M cells) and corresponding ground truth values. Performing localization using raw received signal strength (RSS) data produces poor results and very high variance. Applying supervised learning improves results moderately, but variance remains high. Instead, we propose feature clustering, a novel application of unsupervised learning to detect hidden correlation between measurement instances, their features, and localization accuracy. Our results identify RSS standard deviation and RSS-weighted dispersion mean as key features that correlate with highly predictive measurement samples for both sparse and dense measurements respectively. Finally, we show how optimizing crowdsourcing measurements for these two features dramatically improves localization accuracy and reduces variance.

We describe our efforts to empirically validate a distributed spectrum monitoring system built on commodity smartphones and embedded low-cost spectrum sensors. This system enables real-time spectrum sensing, identifies and locates active transmitters, and generates alarm events when detecting anomalous transmitters. To evaluate the feasibility of such a platform, we perform detailed experiments using a prototype hardware platform using smartphones and RTL dongles. We identify multiple sources of error in the sensing results and the end-user overhead (i.e. smartphone energy draw). We propose and implement a variety of techniques to identify and overcome errors and uncertainty in the data, and to reduce energy consumption. Our work demonstrates the basic viability of user-driven spectrum monitoring on commodity devices.

Dynamic spectrum redistribution---under which spectrum owners lease out under-utilized spectrum to users for financial gain---is an effective way to improve spectrum utilization. Auction is a natural way to incentivize spectrum owners to share their idle resources. In recent years, a number of strategy-proof auction mechanisms have been proposed to stimulate bidders to truthfully reveal their valuations. However, it has been shown that truthfulness is not a necessary condition for revenue maximization. Furthermore, in most existing spectrum auction mechanisms, bidders may infer the valuations---which are private information---of the other bidders from the auction outcome. In this paper, we propose a Differentially privatE spectrum auction mechanism with Approximate Revenue maximization (DEAR). We theoretically prove that DEAR achieves approximate truthfulness, privacy preservation, and approximate revenue maximization. Our extensive evaluations show that DEAR achieves good performance in terms of both revenue and privacy preservation.