Pairing of wireless devices, such as smartphones, suffers from a plethora of practical challenges if strong security guarantees via integrity codes are desired. Acoustic schemes are easy to deploy, but suffer from limited data rates and high sensitivity to environmental noise. Existing Wi-Fi-based methods, however, lack the necessary timing precision and signal flexibility to implement integrity codes on off-the-shelf devices. In this paper, we introduce Pair-Fi, a novel method that overcomes these limitations by leveraging integrity codes transmitted using software-defined radio (SDR)-like capabilities enabled through firmware modifications directly on smartphone-integrated Wi-Fi chips. Pair-Fi demonstrates both transmission and reception of raw IQ samples directly on commodity smartphone hardware, allowing for precise on-off keying modulation with timing resolution as low as 4 µs slots. By bypassing the constraints of conventional Wi-Fi frame timings, our approach significantly improves pairing speed, reliability, and resistance to interference. We validate Pair-Fi experimentally on recent smartphone models, such as the Google Pixel 7, showing robust performance in realistic environments. Our results indicate that SDR-enabled integrity code pairing via smartphone Wi-Fi chips provides a practical, secure, and efficient alternative to existing device pairing mechanisms, opening new avenues for secure and seamless device interactions in everyday scenarios.

Wi-Fi sensing applications have achieved remarkable results over the last decade, offering accurate device-free localization and gesture recognition capabilities. Indeed, Wi-Fi sensing has quickly become a critical field of research for future communication systems under the paradigm known as joint communication and sensing. However, device-free wireless sensing can also be exploited for malign purposes against unaware victims, and the omnipresence of Wi-Fi transceivers poses a significant threat to people’s privacy. Therefore, it is essential to develop functional solutions that can effectively thwart wireless sensing. All the current attempts to hinder illegitimate wireless sensing rely on specialized hardware deployed in the environment, but their cost and complexity can undermine widespread deployment. In this paper, we explore the possibility of using native capabilities of Wi-Fi systems, namely beamforming, to thwart wireless sensing. To this end, we propose for the first time a solution that enables complete control over the beamforming in commercial Wi-Fi devices. On top of that, we build BeamDancer, which randomizes beamforming vectors to inhibit channel fingerprinting. We empirically demonstrate the effectiveness of the proposed solution against three different wireless sensing techniques, both data-driven and model-based, while preserving almost entirely the legitimate Wi-Fi traffic at the same time.

We present a novel multi-hop data dissemination protocol for wireless networks that minimizes the total energy consumption across an entire network by minimizing the transmission power at each hop. It is based on a game-theoretic model, constructs a spanning tree topology in a decentralized manner, and is usable in practice. We evaluate the protocol via simulation and a pratical implementation on a testbed of 75 Raspberry Pis, demonstrating that a total energy reduction of up to 90% can be achieved compared to a simple broadcast protocol.

Context-based copresence detection schemes are a necessary prerequisite to building secure and usable authentication systems in theInternet of Things (IoT). Such schemes allow one device to verify proximity of another device without user assistance utilizing their physical context (e.g., audio). The state-of-the-art copresence detection schemes suffer from two major limitations: (1) They cannot accurately detect copresence in low-entropy context (e.g., empty room with few events occurring) and insufficiently separated environments (e.g., adjacent rooms), (2) They require devices to have common sensors (e.g., microphones) to capture context, making them impractical on devices with heterogeneous sensors. We address these limitations, proposingNext2You, a novel copresence detection scheme utilizing channel state information (CSI). In particular, we leverage magnitude and phase values from a range of subcarriers specifying a Wi-Fi channel to capture a robust wireless context created when devices communicate. We implementNext2Youon off-the-shelf smartphones relying only on ubiquitous Wi-Fi chipsets and evaluate it based on over 95 hours of CSI measurements that we collect in five real-world scenarios.Next2Youachieves error rates below 4%, maintaining accurate copresence detection both in low-entropy context and insufficiently separated environments. We also demonstrate the capability ofNext2Youto work reliably in real-time and its robustness to various attacks.

Despite the growing interest in cross-technology communication, its application to real-world systems is still limited, as existing schemes are mostly unidirectional and technology-specific. The lack of generic solutions as well as the complexity of their integration reduces the applicability in a broader scope. In this paper, we propose a solution to augment Wi-Fi, BLE, and ZigBee devices with the ability to transmit and receive cross-technology broadcast frames alongside their existing functionality. After experimentally evaluating the performance of our solution on a variety of hardware platforms, we leverage it to build a gateway-free smart home, where a smartphone can simultaneously control heterogeneous smart objects. The smart objects, which include an off-the-shelf ZigBee light bulb and a BLE-enabled door lock from different vendors, perform cross-technology communication while retaining their original functionality and can maintain duty-cycled operations.

Wi-Fi chips offer vast capabilities, which are not accessible through the manufacturers' official firmwares. Unleashing those capabilities can enable innovative applications on off-the-shelf devices. In this work, we demonstrate how to transmit raw IQ samples from a large buffer on Wi-Fi chips. We further show how to extract channel state information (CSI) on a per frame basis. As a proof-of-concept application, we build a covert channel on top of Wi-Fi to stealthily exchange information between two devices by prefiltering Wi-Fi frames prior to transmission. On the receiver side, the CSI is used to extract the embedded information. By means of experimentation, we show that regular Wi-Fi clients can still demodulate the underlying Wi-Fi frames. Our results show that covert channels on the physical layer are practical and run on off-the-shelf smartphones. By making available our raw signal transmitter, the CSI extractor, and the covert channel application to the research community, we ensure reproducibility and offer a platform for further innovative applications on Wi-Fi devices.