This paper introduces Wireless IoT-based Noise Cancellation (WINC) which defines a framework for leveraging a wireless network of IoT microphones to enhance active noise cancellation in noise-canceling headphones. The IoT microphones forward ambient noise to the headphone over the wireless link which travels a million times faster than sound and gives the headphone a future lookahead into the incoming noise. While leveraging wireless lookahead has been explored in past work, prior systems are limited to a single noise source. WINC, however, can simultaneously cancel multiple noise sources by using a network of IoT nodes. Scaling wireless lookahead aware noise cancellation is non-trivial because the computational and protocol delays can defeat the purpose of leveraging wireless lookahead. WINC introduces a novel algorithm that operates in the frequency domain to efficiently cancel multiple noise sources. We implement and evaluate WINC to show that it can cancel three noise sources and outperforms past work and state-of-the-art headphones without requiring completely blocking the users’ ears.

Molecular networks have the potential to enable bio-implants and biological nano-machines to communicate inside the human body. Molecular networks send and receive data between nodes by releasing molecules into the bloodstream. In this work, we explore how we can scale molecular networks from a single transmitter single receiver paradigm to multiple transmitters that can concurrently send data to a receiver. We identify unique challenges in enabling multiple access in molecular networks that prevent us from using standard multiple access protocols. These challenges include the lack of synchronization and feedback, the non-negativity of molecular signals, the extremely long tail of the molecular channel leading to high ISI (Inter-Symbol-Interference), and the limited types of molecules that can be used for communication. We present MoMA (Molecular Multiple Access), a protocol that enables a molecular network with multiple transmitters. We introduce packet detection, channel estimation, and encoding/decoding schemes that leverage the unique properties of molecular networks to address the above challenges. We evaluate MoMA on a synthetic experimental testbed and demonstrate that it can scale up to four transmitters while significantly outperforming the state-of-the-art.

Molecular communication has recently gained a lot of interest due to its potential to enable micro-implants to communicate by releasing molecules into the bloodstream. In this paper, we aim to explore the molecular communication channel through theoretical and empirical modeling in order to achieve a better understanding of its characteristics, which tend to be more complex in practice than traditional wireless and wired channels. Our study reveals two key new characteristics that have been overlooked by past work. Specifically, the molecular communication channel exhibits non-causal inter-symbol-interference and a long delay spread, that extends beyond the channel coherence time, which limit decoding performance. To address this, we design, μ-Link a molecular communication protocol and decoder that accounts for these new insights. We build a testbed to experimentally validate our findings and show that μ-Link can improve the achievable data rates with significantly lower bit error rates.

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.