In IoT applications such as environmental monitoring and industrial security surveillance, audio sensors are increasingly used, among which wireless sensors are preferred. In order to achieve a sustained transmission, low-power wireless technology such as backscatter has been widely considered. However, existing backscatter systems encounter difficulties in audio transmissions due to the high power consumption from the complicated digital processing and fast frequency-shifting clocks. In this paper, we propose Satori, the first-of-its-kind in-band analog backscatter system for audio transmission with ultra-low power consumption. Satori eliminates the need for in-place digital processing by directly embedding analog audio voltages into backscattered WiFi symbols through analog modulation. It also avoids the power consumption of the frequency-shifting clock by transmitting the audio within the excitation WiFi signal's band. We implement the Satori prototype and evaluate it under various settings. The results indicate that Satori can transmit audio at a sampling rate of 41.67 kHz and achieve a SNR exceeding 18 dB.

Whilesmalldrone video streaming systems create unprecedented video content, they also place a power burden exceeding 20% on the drone's battery, limiting flight endurance. We present${\sf Mighty}$, a hardware-software solution to minimize the power consumption of a drone's video streaming system by offloading power overheads associated with both video compression and transmission to a ground controller.${\sf Mighty}$innovates a high performance co-design among:(1)a ring oscillator-based, ultra-low power backscatter radio;(2)a spectrally-efficient, non-linear, low-power physical layer modulation and multi-chain radio architecture; and(3)a lightweight video compression codec-bypassing software design. Our co-design exploits synergies among these components, resulting in joint throughput and range performance that pushes the known envelope. We prototype${\sf Mighty}$on PCB board and conduct extensive field studies both indoors and outdoors. The power efficiency of${\sf Mighty}$is about 16.6 nJ/bit. A head-to-head comparison with aDJI Mini2drone's default video streaming system shows that${\sf Mighty}$achieves similar throughput at a drone-to-controller distance of up to 150 meters, with 34–55× improvement of power efficiency than WiFi-based video streaming solutions.

mmWave-based activity recognition technology has attracted widespread attention as it provides the ability of device-free, ubiquitous and accurate sensing. Recognition of human activities intrinsically demands to be real-time and continuous, but the state of the arts is still far limited with the capacity in this regard. The main obstacle lies in activity sequence segmentation, i.e., locating the boundaries between consecutive activities in an activity sequence. This is a daunting task, due to the unclear activity boundaries and the variable activity duration. In this paper, we proposeZuMa, the first mmWave-based approach to real-time continuous activity recognition. When resorting to a machine learning model for activity recognition, our insight is that the recognition confidence of the recognition model is highly correlated to the accuracy of activity sequence segmentation, so that the former can be utilized as a feedback metric to finely adjust the segmentation boundaries. Based on this insight,ZuMais a coarse-to-fine grained approach, which includes the fast coarse-grained activity chunk extraction and the find-grained explicit segmentation adjustment and recognition. We have implementedZuMawith the commercial mmWave radar and evaluated its performance under various settings. The results demonstrate thatZuMaachieves an average recognition error of 12.67%, which is 65.08% and 71.87% lower than that of the two baseline methods. The average recognition delay ofZuMais only 1.86 s.

WiFi-based device localization is a key technology for smart applications, while most of which rely on LoS signals to work. However, in real-world indoor environments, very few LoS signals are usable for accurate localization. This article presents Bifrost , a novel hardware-software co-design to cope with this practical problem. The core idea of Bifrost is to reinvent WiFi signals to provide sufficient LoS signals. Specifically, we present a low-cost plug-in design of leaky wave antenna (LWA) that can generate orthogonal polarized signals: On the one hand, LWA disperses signals of different frequencies to different angles, thus providing AoA information for the localized target. On the other hand, the target further leverages the antenna polarization mismatch to distinguish AoAs from different LWAs. Besides, fine-grained information in CSI is exploited to mitigate multipath and noise. Besides, a dedicated Kalman filter is proposed to facilitate the cooperation of Bifrost and SpotFi, a state-of-the-art approach, to enhance the availability and accuracy of SpotFi. The evaluation results show that the median localization error of Bifrost is 0.81 m, 52.35% less than that of SpotFi. When combined with Bifrost to work in realistic settings, SpotFi can reduce the localization error by 33.54%.

Wave-based human tracking is a key enabling technology for smart applications. Most of the existing works on this topic employ the conventional approach of device-free object localization, which treat any person as a general moving target rather than distinguish different persons. As a result, the existing approaches have poor performance in the scenarios of multi-person tracking, especially when there are crossovers among different persons’ trajectories. This paper presents mMTAI, a novel approach for multi-person tracking with a mmWave radar. By exploiting mmWave sensing to capture a human’s biometric features, MMTAI augments mmWave radar based human tracking with the ability of identifying different persons. Specifically, MMTAI is able to sense persons’ scalp responses to the signals and their head-shoulder distances, which are then continuously mapped to their trajectories using a bipartite matching algorithm. We implement MMTAI with a commercial mmWave radar and evaluate its performance under various settings. The results show that in the multi-person tracking scenarios, mmTAI has a median tracking error of 12.33 cm, which is $35.88 \%$ lower than that of the state-of-the-art approach.

With the increasing prevalence of IoT devices and smart systems in daily life, there is a growing demand for new modalities in Human-Computer Interaction (HCI) to improve accessibility, particularly for users who require hands-free and eyes-free interaction in contexts like VR environments, as well as for individuals with special needs or limited mobility. In this paper, we propose teeth gestures as an input modality for HCI. We find that teeth gestures, such as tapping, clenching, and sliding, are generated by various facial muscle movements that are often imperceptible to the naked eye but can be effectively captured using mm-wave radar. By capturing and analyzing the distinct patterns of these muscle movements, we propose a hands-free and eyes-free HCI solution based on three different gestures. Key challenges addressed in this paper include user range identification amidst background noise and other irrelevant facial movements. Results from 16 volunteers demonstrate the robustness of our approach, achieving 93% accuracy for up to a 2.5m range.

WiFi-based device localization is a key enabling technology for smart applications, which has attracted numerous research studies in the past decade. Most of the existing approaches rely on Line-of-Sight (LoS) signals to work, while a critical problem is often neglected: In the real-world indoor environments, WiFi signals are everywhere, but very few of them are usable for accurate localization. As a result, the localization accuracy in practice is far from being satisfactory. This paper presents Bifrost, a novel hardwaresoftware co-design for accurate indoor localization. The core idea of Bifrost is to reinvent WiFi signals, so as to provide sufficient LoS signals for localization. This is realized by exploiting the dispersion effect of signals emitted by the leaky wave antenna (LWA). We present a low-cost plug-in design of LWA that can generate orthogonal polarized signals: On one hand, LWA disperses signals of different frequencies to different angles, thus providing Angle-of-Arrival (AoA) information for the localized target. On the other hand, the target further leverages the antenna polarization mismatch to distinguish AoAs from different LWAs. In the software layer, fine-grained information in Channel State Information (CSI) is exploited to cope with multipath and noise. We implement Bifrost and evaluate its performance under various settings. The results show that the median localization error of Bifrost is 0.81m, which is 52.35% less than that of SpotFi, a state-of-the-art approach. SpotFi, when combined with Bifrost to work in the realistic settings, can reduce the localization error by 33.54%.

In this paper, we analyze the performance of two-way relaying (TWR) protocol in Rayleigh fading channels, where the terminals and relay are interfered by a finite number of co-channel interferers. The relay is assumed to operate in the decode-and-forward mode. The average symbol error rate (SER) performance for binary phase shift keying (BPSK) is analyzed. To make the analysis mathematically tractable, two approximations are adopted to deal with the problem of correlations between the received SINRs of the relay and terminal and a tight approximate expression of the average SER is derived in closed-form. Moreover, it can be shown that the result can be simply applied in straightforward network coding protocol with co-channel interference. Based on the analytic results, we study the impacts of system parameters, such as interference power, number of interferers and relay placement, on the average SER performance. Finally, the correctness of our analytic results is validated through Monte Carlo simulations.