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Shen, Sheng, Roy, Nirupam, Guan, Junfeng, Hassanieh, Haitham, Choudhury, Romit Roy.  2018.  MUTE: Bringing IoT to Noise Cancellation. Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication. :282–296.

Active Noise Cancellation (ANC) is a classical area where noise in the environment is canceled by producing anti-noise signals near the human ears (e.g., in Bose's noise cancellation headphones). This paper brings IoT to active noise cancellation by combining wireless communication with acoustics. The core idea is to place an IoT device in the environment that listens to ambient sounds and forwards the sound over its wireless radio. Since wireless signals travel much faster than sound, our ear-device receives the sound in advance of its actual arrival. This serves as a glimpse into the future, that we call lookahead, and proves crucial for real-time noise cancellation, especially for unpredictable, wide-band sounds like music and speech. Using custom IoT hardware, as well as lookahead-aware cancellation algorithms, we demonstrate MUTE, a fully functional noise cancellation prototype that outperforms Bose's latest ANC headphone. Importantly, our design does not need to block the ear - the ear canal remains open, making it comfortable (and healthier) for continuous use.

Chaman, Anadi, Wang, Jiaming, Sun, Jiachen, Hassanieh, Haitham, Roy Choudhury, Romit.  2018.  Ghostbuster: Detecting the Presence of Hidden Eavesdroppers. Proceedings of the 24th Annual International Conference on Mobile Computing and Networking. :337–351.
This paper explores the possibility of detecting the hidden presence of wireless eavesdroppers. Such eavesdroppers employ passive receivers that only listen and never transmit any signals making them very hard to detect. In this paper, we show that even passive receivers leak RF signals on the wireless medium. This RF leakage, however, is extremely weak and buried under noise and other transmitted signals that can be 3-5 orders of magnitude larger. Hence, it is missed by today's radios. We design and build Ghostbuster, the first device that can reliably extract this leakage, even when it is buried under ongoing transmissions, in order to detect the hidden presence of eavesdroppers. Ghostbuster does not require any modifications to current transmitters and receivers and can accurately detect the eavesdropper in the presence of ongoing transmissions. Empirical results show that Ghostbuster can detect eavesdroppers with more than 95% accuracy up to 5 meters away.
Jog, Suraj, Wang, Jiaming, Hassanieh, Haitham, Choudhury, Romit Roy.  2018.  Enabling Dense Spatial Reuse in mmWave Networks. Proceedings of the ACM SIGCOMM 2018 Conference on Posters and Demos. :18-20.

Millimeter Wave (mmWave) networks can deliver multi-Gbps wireless links that use extremely narrow directional beams. This provides us with a new way to exploit spatial reuse in order to scale network throughput. In this work, we present MilliNet, the first millimeter wave network that can exploit dense spatial reuse to allow many links to operate in parallel in a confined space and scale the wireless throughput with the number of clients. Results from a 60 GHz testbed show that MilliNet can deliver a total wireless network data rate of more than 38 Gbps for 10 clients which is 5.8× higher than current 802.11 mmWave standards.