Soil monitoring plays an essential role in agricultural systems. Rather than deploying sensors' antennas above the ground, burying them in the soil is an attractive way to retain a non-intrusive aboveground space. Low Power Wide-Area Network (LPWAN) has shown its long-distance and low-power features for aboveground Internet-of-Things (IoT) communication, presenting a potential of extending to underground cross-soil communication over a wide area, which however has not been investigated before. The variation of soil conditions brings significant signal polarization misalignment, degrading communication reliability. In this paper, we propose Demeter, a low-cost low-power programmable antenna design to keep reliable cross-soil communication automatically. First, we propose a hardware architecture to enable polarization adjustment on commercial-off-the-shelf (COTS) single-RF-chain LoRa radio. Moreover, we develop a low-power programmable circuit to obtain polarization adjustment. We further design an energy-efficient heuristic calibration algorithm and an adaptive calibration scheduling method to keep signal polarization alignment automatically. We implement Demeter with a customized PCB circuit and COTS devices. Then, we evaluate its performance in various soil types and environmental conditions. The results show that Demeter can achieve up to 11.6 dB SNR gain indoors and 9.94 dB outdoors, 4× horizontal communication distance, at least 20 cm deeper underground deployment, and up to 82% energy consumption reduction per day compared with the standard LoRa.

In this paper, we proposed F²Key, the first earable physical security system based on commercial off-the-shelf headphones. F²Key enables impactful applications, such as enhancing voiceprint-based authentication systems, reliable voice assistants, audio deepfake defense, and the legal validity of artifacts. The key idea of F²Key is to establish a stable acoustic sensing field across the user's face and embed the user's facial structures and articulatory habits into a user-specific generative model that serves as a private key. The private key can decrypt the Channel Impulse Response (CIR) profiles provided by the acoustic sensing field into an inferred spectrogram that can match the real one calculated from the corresponding speech, provided that the user's CIR-spectrogram mapping relationship is consistent with the one embedded in the generative model. Extensive experiments demonstrate that F²Key resists 99.9%, 96.4%, and 95.3% of speech replay attacks, mimicry attacks, and hybrid attacks, respectively. We discussed and evaluated F²Key from different perspectives, such as the health consideration and identical twins study, to show the practicality and reliability.

Speech enhancement is regarded as the key to the quality of digital communication and is gaining increasing attention in the research field of audio processing. In this paper, we present EarSE, the first robust, hands-free, multi-modal speech enhancement solution using commercial off-the-shelf headphones. The key idea of EarSE is a novel hardware setting---leveraging the form factor of headphones equipped with a boom microphone to establish a stable acoustic sensing field across the user's face. Furthermore, we designed a sensing methodology based on Frequency-Modulated Continuous-Wave, which is an ultrasonic modality sensitive to capture subtle facial articulatory gestures of users when speaking. Moreover, we design a fully attention-based deep neural network to self-adaptively solve the user diversity problem by introducing the Vision Transformer network. We enhance the collaboration between the speech and ultrasonic modalities using a multi-head attention mechanism and a Factorized Bilinear Pooling gate. Extensive experiments demonstrate that EarSE achieves remarkable performance as increasing SiSDR by 14.61 dB and reducing the word error rate of user speech recognition by 22.45--66.41% in real-world application. EarSE not only outperforms seven baselines by 38.0% in SiSNR, 12.4% in STOI, and 20.5% in PESQ on average but also maintains practicality.

Recent years have shown substantial interest in revealing vulnerability issues of voice-controllable systems on smartphones and smart speakers. While significant prior works have leveraged inaudible signals to attack these smart devices, smart earbuds present unique challenges and vulnerabilities due to their extreme hardware constraints. In this paper, we present EchoAttack, a practical inaudible attack system for smart earbuds. The primary innovation of EchoAttack is the ability to leverage both indirect and direct paths to attack smart earbuds. To search for the optimal path, we design a path-searching algorithm based on the attenuation model of ultrasound. We also propose a novel approach to remove harmonics noise, which improves the attacking signal's SNR further. Finally, we propose using Zigbee radios to sniff the Bluetooth signal and enable a hidden feedback channel without the victim's awareness. We implement the EchoAttack prototype using off-the-shelf hardware components and evaluate the prototypes in four typical indoor and outdoor scenarios using six smart earbuds. Experimental results show that EchoAttack outperforms the pure direct-path attack by 75.8% on average in terms of attack success rate.

Accurate measurements of soil macronutrients (i.e., nitrogen, phosphorus, and potassium) and moisture play a key role in smart agriculture. However, existing commodity soil sensors are often expensive and the achieved accuracy is unsatisfactory. To address these issues, we present SoilCares, a low-cost soil sensing system enabling accurate and simultaneous monitoring of the concentration levels of soil moisture and macronutrients. SoilCares overcomes key challenges of accommodating diverse soil types and soil textures by introducing a novel membrane-based scheme. For moisture sensing, SoilCares leverages the multi-modal fusion of RF and NIR signals to significantly increase the sensing accuracy. Through delicate hardware design, we enable negligible-cost sensor data transmission using the existing sensing hardware, building up a complete end-to-end soil sensing system. SoilCares is cost-effective ($63.5), portable (0.5 kg), and low-power (236 μW), making it suitable for insitu deployment. On-site experimental results show that SoilCares achieves high macronutrient sensing accuracy with a low RMSE of 0.138, and extremely low moisture estimation error of 1%, outperforming the state-of-the-art research and expensive commodity moisture sensors on the market.