In this paper, we propose LoRaSeek, a lightweight and reliable LoRa denoising framework that enhances signal quality and robustness for neural-enhanced LoRa decoding. LoRaSeek integrates a hybrid architecture combining Convolutional Neural Networks (CNNs), Transformers, and a hierarchical U-Net to effectively capture multi-scale, multidimensional features of LoRa chirp signals. To maintain efficiency, we integrate a lightweight Transformer block that supports various LoRa configurations while keeping computational overhead low. Additionally, we incorporate dual attention-based skip connections to preserve chirp signal properties across different scales. Experiments across diverse LoRa configurations show that LoRaSeek achieves 2.04–3.86 dB signal-to-noise ratio (SNR) gains over standard decoding methods and up to 3.03 dB improvement over state-of-the-art neural-enhanced LoRa decoding methods while reducing model storage by up to 7.4× and inference time by up to 1.6×.

The Internet of Things (IoT) is revolutionizing our interaction with the physical world, significantly enhancing precision agriculture, infrastructure monitoring, forest fire prevention, environmental protection, and numerous other applications in rural areas. Deploying reliable and scalable wireless connectivity in rural areas is critical, but it presents distinct technical and economic challenges. Local area wireless networks, such as Wi-Fi and Bluetooth, provide limited coverage and consume high power. They are not suitable for rural scenarios. Cellular technologies like LTE and 5G offer wide-area coverage but involve high infrastructure costs and power consumption. Recently, Low-Power Wide-Area Networks (LPWANs) have become promising alternatives. They address rural IoT needs by offering long-range communication, low power consumption, and reduced deployment costs. Among LPWAN technologies, LoRa stands out due to its robustness, scalability, and affordability.

Accurate leaf wetness detection is essential to understanding plant health and growth conditions. The mmWave radar, with its sensitivity to subtle changes, is well-suited for leaf wetness detection. Existing mmWave-based approaches utilize the Synthetic Aperture Radar (SAR) algorithm to generate image-like inputs and rely on multi-modality fusion with an RGB camera to classify leaf wetness. However, the lack of understanding of SAR-based mmWave imaging limits its accuracy in various environments. This paper presents Proteus, a novel way of understanding mmWave SAR imaging. We design a noise reduction algorithm to reduce speckle noise and improve image clarity for SAR-based mmWave imaging. Then, we incorporate phase angle data to enrich SAR texture information to capture high-resolution surface details, increasing informative features for precise wetness assessment in complex plant structures. Additionally, we introduce a cross-modality Teacher-Student network, using an RGB-based teacher model to guide the mmWave SAR-based student model for feature extraction. This network transfers the explicit knowledge in the RGB image domain to the mmWave image domain. We use commercial-off-the-shelf mmWave radar to prototype Proteus. The evaluation results show that Proteus achieves up to 96.3% accuracy across varied environmental scenarios, outperforming state-of-the-art methods.

LoRa has been widely used for the Internet of Things (IoT) due to its low power consumption and long communication range. The standard LoRa demodulation process condenses the energy of LoRa chirps to combat noise. However, there is an intrinsic frequency jump in real-life LoRa signals that standard demodulation neglects, reducing communication range in practice. We thoroughly study the frequency jump phenomenon and observe that it affects LoRa demodulation mainly in two folds: First, it makes each section of the signal shorter than the standard FFT perception range, introducing additional noise; Second, it induces a random phase jump that causes destructive addition of signal power. To mitigate the influence of frequency jump on LoRa demodulation, we propose LoRaTrimmer, a novel, fast, and noise-resilient LoRa decoding algorithm that optimally condenses LoRa signal power. LoRaTrimmer contains two innovative designs: First, we trim the perception range of FFT at the frequency jump, trimming off the additional noise; Second, we bypass the phase jump induced by frequency jump by probabilistic modeling and add up signal power constructively. Furthermore, we performed theoretical analysis to guarantee the performance of our method. Thorough experiments in various real-life environments show 1.70 to 2.49 dB SNR gain over the state-of-the-art and 3.44 to 3.79 dB SNR gain over FFT-based methods, translating to at most 1.67 times gain of coverage area. LoRaTrimmer is also robust under complex noise patterns, and capable of real-time decoding, with the only overhead being a slight increase in computational cost (0.51 to 3.17 ms per packet, compared with 0.23 to 0.94 ms of baseline methods).

Rural Internet of Things (IoT) systems connect sensors and actuators in remote areas, serving crucial roles in agriculture and environmental monitoring. Given the absence of networking infrastructure for backhaul in these regions, satellite IoT techniques offer a cost-effective solution for connectivity. However, current satellite IoT architectures often struggle to deliver high performance due to temporal and spatial link challenges. This paper presents SateRIoT, a new network architecture with temporal link estimation and spatial link sharing that fully exploits the capability of space low-cost low-earth-orbit (LEO) IoT satellites and ground low-power wide area (LPWA) IoT techniques in rural areas. First, we introduce a bursty link model that predicts the number of transmittable packets within a transmission window, reducing energy waste from failed uplink transmissions. Moreover, we enhance the model by selecting informative features and optimizing the window length. Additionally, we develop a multi-hop flooding protocol that enables gateways to buffer and share data packets across the network while incorporating a priority data queue to avoid duplicate transmissions. We implement SateRIoT with commercial-off-the-shelf (COTS) IoT satellite and LoRa radios, then evaluate its performance based on real deployment and real-world collected traces. The results show that SateRIoT can consume 3.3X less energy consumption for an individual gateway. Moreover, SateRIoT offers up to a 5.6X reduction in latency for a single packet and a 1.9X enhancement in throughput.

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.

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 to extend to underground cross-soil communication over a wide area, which has not been investigated before. The variation of soil conditions brings significant signal polarization misalignment, degrading communication reliability. 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 adjust polarization. We further design an energy-efficient heuristic calibration algorithm to keep signal polarization alignment automatically. We demonstrate Demeter in indoor environments. The antenna is buried in a plastic container filled with gardening soil to simulate the node underground. Meanwhile, we use the COTS LoRa gateway as a receiver to show the RSSI and SNR variations.

This paper introduces ChirpTransformer, a versatile LoRa encoding framework that harnesses broad chirp features to dynamically modulate data, enhancing network coverage, throughput, and energy efficiency. Unlike the standard LoRa encoder that offers only single configurable chirp feature, our framework introduces four distinct chirp features, expanding the spectrum of methods available for data modulation. To implement these features on commercial off-the-shelf (COTS) LoRa nodes, we utilize a combination of a software design and a hardware interrupt. ChirpTransformer serves as the foundation for optimizing encoding and decoding in three specific case studies: weak signal decoding for extended network coverage, concurrent transmission for heightened network throughput, and data rate adaptation for improved network energy efficiency. Each case study involves the development of an end-to-end system to comprehensively evaluate its performance. The evaluation results demonstrate remarkable enhancements compared to the standard LoRa. Specifically, ChirpTransformer achieves a 2.38 × increase in network coverage, a 3.14 × boost in network throughput, and a 3.93 × of battery lifetime.

With the advancement of AI-powered personal voice assistants, speaker authentication via earbuds has become increasingly vital, serving as a critical interface between users and mobile devices. However, existing audio-based speaker authentication methods fail to defend against voice spoofing threats such as replay and deep-fake attacks. To counteract these risks, we introduce PiezoBud, a pioneering multi-modal user authentication system that is truly practical and lightweight for earbuds. PiezoBud uses miniature piezoelectric sensors to detect micro-vibrations on the skin, extracting user-specific biometric data to authenticate legitimate access on the local smartphone and protect against malicious attacks. Our exploratory study, involving 85 participants, demonstrates the effectiveness of PiezoBud in various everyday scenarios, including ambient noise, body movement, and in-ear media playing. Using only 15 seconds of enrollment data, PiezoBud achieves an Equal Error Rate (EER) of 1.05% and attain a mean authentication latency of 0.06 seconds on mobile devices. We also evaluate PiezoBud's effectiveness in countering challenging adaptive attack scenarios and its overall performance in various real-world situations. Our evaluation highlights that PiezoBud stands out as a practical, resilient, responsive, and secure option for earbuds users.

LoRa and its enabled LoRa wide-area network (LoRaWAN) have been seen as an important part of the next-generation network for massive Internet-of-Things (IoT). Due to LoRa's low-power and long-range nature, LoRa signals are much weaker than the noise floor, particularly in complex urban or semi-indoor environments. Therefore, weak signal decoding is critical to achieve the desired wide-area coverage in general. Existing work has shown the advantages of exploring deep neural networks (DNN) for weak signal decoding. However, the existing single-gateway based DNN decoder is hard to fully leverage the spatial information in multi-gateway scenarios. In this paper, we propose SRLoRa, an efficient DNN LoRa decoder that fully utilizes the spatial information from multiple gateways to decode extremely weak LoRa signals. Specifically, we design interleaving denoising and merging layers to improve signal quality at ultra-low SNR. We develop efficient merging on feature maps extracted by denoising DNNs to tolerate time misalignments among different signals. We define max and min operations in the merging layer to efficiently extract salient features and reduce noise, merging the features extracted from multiple gateways to guide future DNN layers to gradually improve signal quality. We implement SRLoRa with USPR N210 and commercial LoRa nodes and evaluate its performance indoors and outdoors. The results show that with four gateways, SRLoRa achieves SNR gain at 4.53--4.82 dB, which is 2.51× of Charm, leading to a 1.84× coverage area compared to standard LoRa in an urban deployment.

This article investigates how the expansion of array size may improve the spatial diversity of state-of-the-art Wi-Fi system and increase its throughput. With comprehensive Wi-Fi measurement studies with augmented antennas, we identify the potential performance gain atop spatial diversity gains from existing technologies like MIMO and beamforming. We propose WINAS, a general Wi-Fi intelligent antenna selection scheme with full system implementation that can be easily integrated with commodity Wi-Fi AP. WINAS provides substantially improved throughput for downlink traffics. Our experimental evaluation suggests that WINAS improves Wi-Fi throughput up to 1.56x, and 1.47x in average, in real user-based evaluation.

Internet-of-Things (IoT) aims to connect billions of low-date rate and energy-constrained end-devices in the near future. Although many IoT systems have been commercialized, most of them focus on home and body scale applications. To establish a low-cost IoT at the city scale, LoRa Wide Area Networks (LoRaWAN) have become attractive in recent years due to their desirable kilometer or even longer communication distance with low energy consumption. However, due to the expensive cost of densely deploying end-nodes, the understanding of LoRa link behavior is still coarse-grained, and hard to fully realize the link dynamics, networking coverage, and localization accuracy of LoRaWAN in an urban environment. This paper shows a fine-grained LoRa link-level measurement via mobile end-nodes. We deploy two gateways and six mobile end-nodes and collect data packets over four months at a$6\times 6\ km^{2}$urban area. The evaluation mainly focuses on answering three questions: 1) Does a LoRa link stably perform in both spatial and temporal dimensions? 2) How large area can be covered for reliable communication by each gateway in the urban environment? 3) What accuracy can be achieved to localize an end-node through LoRa links? According to our measurement, our key findings are 1) The spatial and temporal behavior of LoRa links is quite dynamic due to the different types of land covers and the frequent micro-environment changes in the urban areas; 2) Each gateway can cover about 11.3 km2area and marginal SNR gains (e.g., 2 dB) of LoRa links are efficient enough to enlarge 32.6% coverage area of a gateway; and 3). The median localization error is about 400 m. Without densely deployed LoRa gateways, the SOTA LoRa localization can support road-level localization, even when an end node is close to one of the gateways.