With the rapid development of backscatter technology, the limitation of downlink communication has become one of the key bottlenecks hindering its scalability. Traditional downlink communication requires carrier down conversion and digital sampling, which is energy-intensive and unaffordable for backscatter tags. To address this challenge, we present DUET (Downlink Ultra-low-power Extensive Transmission), a system for long-range downlink communication with low-power backscatter tags. DUET leverages commodity LoRa nodes for signal transmission and implements an innovative over-the-air demodulation mechanism, enabling direct decoding of downlink LoRa signals by low-power backscatter receivers. We address key challenges such as weak signal amplification and low-power wake-up for enhancing the range of downlink communications. To achieve low-power demodulation, we extract the signal envelope as a low-frequency carrier and design a closed-loop amplifier and resonant circuit for signal amplification. We prototype DUET using commercial LoRa hardware and evaluate its performance. The results show that DUET achieves a downlink data rate of 1 kbps at 175 meters, with 61.6 μW power consumption and -55 dBm sensitivity. DUET improves sensitivity by 7 dB and extends the range by 1.7× compared to state-of-the-art technologies.

Health monitoring is a critical component of modern healthcare, requiring continuous or periodic measurement of physiological parameters to accurately assess personal health status. Advances in wearable technology have significantly improved the accessibility and convenience of such monitoring. Among various form factors, earables offer unique advantages: they can capture rich biosignals, provide stable and motion-resistant measurements, ensure long-term comfort, maintain discreteness, and integrate seamlessly with everyday audio functionalities. By investigating the latest technological advances and application cases in ear-worn devices, this survey reviews the current state of earable technology in health monitoring, identifies gaps and opportunities, and suggests directions for future research and development. We first explore the multifaceted role of earables in health monitoring, including measurement of physiological parameters, activity monitoring, and healthcare applications. We then summarize the challenges of robustness, context-awareness, and signal fidelity, and outline six future directions-dynamic monitoring, context-aware processing, multimodal fusion, semantic activity understanding, personalized adaptation, and explainable AI-to advance earable health monitoring.

Rotation is a fundamental form of motion and rotation speed measurement holds paramount importance for assessing the health and performance of machinery with rotating components. However, existing measurement systems often face challenges such as limited measurement distance, low accuracy, and complex installation or maintenance processes. In this paper, we propose RoLEX, a LoRa-based rotation speed measurement system for long-distance and contactless monitoring of rotating machinery in ubiquitous scenarios. RoLEX employs a novel Signal Selection method to eliminate chirp interference and adapt to varying rotation speeds, along with a Boost Sensing method to enhance sampling rates and an advanced feature processing algorithm for precise rotation speed estimation and tracking. Comprehensive experiments validate that RoLEX achieves a measurement distance of 50 m, approximately 17 times farther than the latest wireless rotation speed measurement systems. Moreover, RoLEX is robust to interference and obstructions (including through-wall scenarios) and achieves an average measurement error less than 0.69% across different rotation speeds (100 - 5100 Revolutions Per Minute). For tracking performance, RoLEX achieves a relative error less than 2.8% in 90% of cases. We also present a case study to highlight RoLEX's practical applicability in real-world scenarios.

LoRa enables long-range Internet of Things (IoT) connectivity but suffers from collision issues in dense deployments, where concurrent transmissions overlap at gateways, leading to decoding errors. Existing solutions rely on time/frequency separation or protocol modifications, requiring either hardware changes or dedicated codings, and failing to resolve collisions that are completely aligned in time or frequency. We present CD-LoRa, a Channel-Division based LoRa parallel transmission scheme for LoRa collisions. CD-LoRa exploits distinct channel signatures as inherent orthogonal fingerprints, enabling parallel decoding even when collisions are completely aligned. We present a phase calibration model that decouples genuine channel features from hardware imperfections and payload modulation distortions. We enhance low-SNR LoRa signals through energy-concentration processing. We address channel variations in mobile scenarios with a dynamic temporal sequence based clustering design. We implement CD-LoRa on commodity LoRa devices and evaluate its performance in real-world deployments. Experimental results show that CD-LoRa effectively decodes up to 8 time-frequency-aligned packets, and improves network throughput by 1.72× compared to state-of-the-art methods.

LoRa, as a widely used Low-Power Wide-Area Network (LP-WAN) technology, is designed for long-term use. However, in practice, the battery life of LoRa devices often falls far short of the expected decade-long duration. We propose OptSamp, a software-based approach that reduces the energy consumption of LoRa devices by lowering their physical-layer sampling rate, thereby extending battery life. To address the challenge of frequency aliasing caused by down-sampling, we embed a specially designed feature into each modulated symbol, enabling the OptSamp receiver to accurately recover the distorted signal. In addition, we design an adaptive sampling-rate selection mechanism to balance link reliability and energy efficiency. We further propose OptSamp+, which compresses symbol duration to shorten uplink transmission time, thereby reducing transmitter energy consumption and enhancing spectral efficiency. We prototype OptSamp and OptSamp+ on software-defined LoRa platforms and evaluate them in both indoor and outdoor environments. Results show that OptSamp reduces the receiver-side sampling rate to 1/16 of the Nyquist rate, cutting downlink energy consumption by 68%, and OptSamp+ reduces uplink transmission time by up to 1/32 compared to traditional LoRa.

WiFi backscatter localization is a promising technology for the Internet of Things. However, existing works cannot work well for large-scale and low-cost tags with commodity WiFi devices. We present Willow, which provides accurate localization for parallel backscatter tags with commodity WiFi devices. We design a packet-level orthogonal backscatter modulation method to generate multiple orthogonal backscatter signals and support in-band backscatter with ambient WiFi. We show that backscatter signals can be effectively extracted even under strong in-band interference. To work in real WiFi traffic, we propose adaptive packet selection-based modulation to guarantee the orthogonality of backscatter signals. For parallel localization, we propose an iterative inter-tag interference cancellation method and a location filtering method to remove location ambiguity. We theoretically analyze the effectiveness of our method in supporting parallel tags. We prototype Willow tags using low-cost hardware and implement Willow AP on commodity WiFi NIC AX200. Through extensive experiments, we show that Willow achieves a median localization error of 27 cm and supports 51 parallel tags, which is 2× and 17× better than the state-of-the-art method.

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.

LoRa, as a representative Lower Power Wide Area Network (LPWAN) technology, shows great potential in providing low power and long range wireless communication. Real LoRa deployments, however, suffer from severe collisions. Existing collision decoding methods cannot work well for low SNR LoRa signals. Most LoRa collision decoding methods process collisions offline and cannot support real-time collision decoding in practice. To address these problems, we propose Pyramid, a real-time LoRa collision decoding approach. To the best of our knowledge, this is the first real-time multi-packet LoRa collision decoding approach in low SNR. Pyramid exploits the subtle packet offset to separate packets in a collision. The core of Pyramid is to combine signals in multiple windows and transfers variation of chirp length in multiple windows to robust features in the frequency domain that are resistant to noise. We address practical challenges including accurate peak recovery and feature extraction in low SNR signals of collided packets. We theoretically prove that Pyramid incurs a very small SNR loss (< 0.56 dB) to original LoRa transmissions. We implement Pyramid using USRP N210 and evaluate its performance in a 20-nodes network. Evaluation results show that Pyramid achieves real-time collision decoding and improves the throughput by 2.11×.

As a representative Low Power Wide Area Network (LPWAN) technology, LoRa is expected to connect devices for various Internet of Things (IoT) applications. Many IoT applications require both long-range communications and high precise sensing at the same time, while state of the art approaches fail to achieve this. We propose LoSense, which enables LoRa movement sensing alongside the regular data transmissions. LoSense recovers the fine-grained trajectory of a LoRa transmitter only based on its communication signals during the data transmission period. We address practical challenges for LoSense designs. We propose the active tracking model for detecting movements of active LoRa transmitters. We use dual antennas at the receiver to eliminate synchronization offsets between LoRa transmitters and the receiver. We design feature amplification and signal enhancement schemes to combat noise and interference. We prototype LoSense with commodity LoRa transmitters and USRP receivers, and extensively evaluate its performance. The results show that LoSense tracks movements of active LoRa transmitters with 2.32 cm distance accuracy and 0.089 Hz frequency accuracy from a sensing range of 150m, supporting regular data communication at the same time.

LoRa, as a representative Low-Power Wide-Area Network (LPWAN) technology, holds tremendous potential for various Internet of Things (IoT) applications. However, as there are few real large-scale deployments, it is unclear whether and how well LoRa can eventually meet its prospects. In this paper, we demystify the real performance of LoRa by deploying LoRa systems in both campus-scale testbeds and citywide applications. Our LoRa network consisting of 100 gateways and 19,821 LoRa end nodes, covering an area of 130 km2 for 12 applications. Our measurement focuses on following perspectives: (i) Coverage performance of the LoRa network; (ii) Gateway efficiency and deployment optimization; (iii) Validation of two LoRa optimization mechanisms. The results reveal that LoRa performance in urban settings is bottlenecked by the prevalent blind spots, and there is a gap between the gateway efficiency and network coverage for gateway deployment. Our measurement provides insights for large-scale LoRa network deployment and also for future academic research to fully unleash the potential of LoRa.

LoRa, as a representative Low-Power Wide-Area Network (LPWAN) technology, holds tremendous potential for various city and industrial applications. However, as there are few real large-scale deployments, it is unclear whether and how well LoRa can eventually meet its prospects. In this paper, we demystify the real performance of LoRa by deploying and measuring a citywide LoRa network, named CityWAN, which consists of 100 gateways and 19,821 LoRa end nodes, covering an area of 130 km2 for 12 applications. Our measurement focuses on the following perspectives: (i) Performance of applications running on the citywide LoRa network; (ii) Infrastructure efficiency and deployment optimization; (iii) Physical layer signal features and link performance; (iv) Energy profiling and cost estimation for LoRa applications. The results reveal that LoRa performance in urban settings is bottlenecked by the prevalent blind spots, and there is a gap between the gateway efficiency and network coverage for the infrastructure deployment. Besides, we find that LoRa links at the physical layer are susceptible to environmental variations, and LoRa and other LPWANs show diverse costs for different scenarios. Our measurement provides insights for large-scale LoRa network deployment and also for future academic research to fully unleash the potential of LoRa.

Social network events are related to the national economy and people’s livelihood, so timely perception and processing of massive social network events data are becoming increasingly important for public opinion analysis. How to fully exploit the access feature of event information to store and manage social network events is of great significance for accurate and real-time query analysis. We propose a social network event storage management method based on microblog text clustering and hot/cold data classification. First, for the microblog text data, we construct a keyword provenance graph by using the information entropy to measure the weight of the edge between keyword nodes. Then, we cluster the events using the provenance-based community partition (PCP) with local modularity to improve the event clustering accuracy. In addition, we can further filter noisy data via incremental clustering, enable hot/cold event data classification and dynamic migration, and compress cold data to save space on a hybrid storage architecture. The experimental results show that the clustering purity can reach more than 93% and the query time can be reduced by more than 70% using clustering and hybrid storage policy.

LoRa, as a representative Low-Power Wide Area Network (LPWAN) technology, has emerged as a promising platform for connecting the Internet of Things (IoTs). It enables low-rate communications over upto tens of kilometers with a 10-year battery lifetime. However, practical LoRa deployments suffer from collisions, given the dense deployment of devices and the wide coverage area. We propose CoLoRa, an approach to decompose large numbers of concurrent transmissions from one collision and enable multi-packet reception in LoRa networks. At the heart of CoLoRa, we utilize the packet time offset to disentangle collided packets. CoLoRa incorporates several novel techniques to address practical challenges. (1) We translate time offset, which is difficult to measure, to frequency features that can be reliably measured. (2) We propose a method to extract peak features from low-SNR LoRa signals iteratively. (3) We address frequency shift incurred by carrier frequency offset and time offset for LoRa decoding. We implement CoLoRa on USRP N210 and evaluate its performance in both indoor and outdoor networks. CoLoRa is implemented in software at the base station, and it can work for COTS LoRa nodes. The evaluations show that CoLoRa improves the network throughput by 3.4× compared with Choir and 14× compared with LoRaWAN.

Unlicensed LPWANs on ISM bands share the spectrum with various wireless techniques, such as Wi-Fi, Bluetooth, and ZigBee. The explosion of IoT deployments calls for an increasing need for long-range cross-technology communication (CTC) between LPWANs and other techniques. Yet, existing technologies cannot achieve real long-range CTC for commodity wireless. We propose L2X, which provides long-range CTC to diverse receivers with LoRa transmitters. At the heart of L2X, we design an energy-concentrating demodulation mechanism that de-spreads LoRa chirps over the air. Therefore, L2X enables non-LoRa receivers to detect and demodulate LoRa signals even under extremely low SNR. We address practical challenges in L2X design. We propose a packet detection method to detect low-SNR LoRa transmissions at non-LoRa receivers. To decode LoRa transmissions, we accurately synchronize the demodulation window with incoming packets and propose a cross-domain demodulation approach to enhance the demodulation SNR. We implement L2X, all using commodity devices, and extensively evaluate its performance. The results show that L2X achieves 1.2 km CTC with the signal -9 dB below the noise floor, improving the distance by 30X compared with state-of-the-arts.

LoRa, a representative Low-Power Wide Area Network (LPWAN) technology, has been shown as a promising platform to connect Internet of Things. Practical LoRa deployments, however, suffer from collisions, especially in dense networks and wide coverage areas expected by LoRa applications. Existing collision resolving approaches do not exploit the modulation properties of LoRa and thus cannot work well for low-SNR LoRa signals. We proposeNScaleto decompose concurrent transmissions by leveraging subtle inter-packet time offsets for low SNR LoRa collisions. NScale (1) translates subtle time offsets, which are vulnerable to noise, to robust frequency features, and (2) further amplifies the time offsets by non-stationary signal scaling, i.e., scaling the amplitude of a symbol differently at different positions. In practical implementation, we propose a noise resistant iterative symbol recovery method to combat symbol distortion in low SNR, and address frequency shifts incurred by CFO and packet time offsets in decoding. We propose optimized designs for diminishing the time costs of computation-intensive tasks and meeting the real-time requirements of LoRa collision resolving. We theoretically show that NScale introduces$3.3\times $for low SNR collided signals compared with other state-of-the-art methods.

LoRa, as a representative of Low Power Wide Area Network technology, has attracted significant attention from both academia and industry. However, the current understanding of LoRa is far from complete, and implementations have a large performance gap in SNR and packet reception rate. This article presents a comprehensive understanding of LoRa physical layer protocol (PHY) and reveals the fundamental reasons for the performance gap. We present the first full-stack LoRa PHY implementation with a provable performance guarantee. We enhance the demodulation to work under extremely low SNR (-20 dB) and analytically validate the performance, where many existing works require SNR > 0. We derive the order and parameters of decoding operations, including dewhitening, error correction, deinterleaving, and so on, by leveraging LoRa features and packet manipulation. We implement a complete real-time LoRa on the GNU Radio platform and conduct extensive experiments. Our method can achieve (1) a 100% decoding success rate while existing methods can support at most 66.7%, (2) -142 dBm sensitivity, which is the limiting sensitivity of the commodity LoRa, and (3) a 3,600-m communication range in the urban area, even better than commodity LoRa under the same setting.

LoRa, as a representative Low-Power Wide-Area Network (LPWAN) technology, can provide long-range communication for battery-powered IoT devices with a 10-year lifetime. LoRa links in practice, however, experience high dynamics in various environments. When the SNR falls below the threshold (e.g., in the building), a LoRa device disconnects from the network. We propose Falcon, which addresses the link dynamics by enabling data transmission for very low SNR or even disconnected LoRa links. At the heart of Falcon, we reveal that low SNR LoRa links that cannot deliver packets can still introduce interference to other LoRa transmissions. Therefore, Falcon transmits data bits on the low SNR link by selectively interfering with other LoRa transmissions. We address practical challenges in Falcon design. We propose a low-power channel activity detection method to detect other LoRa transmissions for selective interference. To interfere with the so-called interference-resilient LoRa, we accurately estimate the time and frequency offsets on LoRa packets and propose an adaptive frequency adjusting strategy to maximize the interference. We implement Falcon, all using commercial off-the-shelf LoRa devices, and extensively evaluate its performance. The results show that Falcon can provide reliable communication links for disconnected LoRa devices and achieves the SNR boundary upto 7.5 dB lower than that of standard LoRa.

Low-Power Wide-Area Networks (LPWANs) are an emerging Internet-of-Things (IoT) paradigm marked by low-power and long-distance communication. Among them, LoRa is widely deployed for its unique characteristics and open-source technology. By adopting the Chirp Spread Spectrum (CSS) modulation, LoRa enables low signal-to-noise ratio (SNR) communication. However, the standard demodulation method does not fully exploit the properties of chirp signals, thus yields a sub-optimal SNR threshold under which the decoding fails. Consequently, the communication range and energy consumption have to be compromised for robust transmission. This paper presents NELoRa, a neural-enhanced LoRa demodulation method, exploiting the feature abstraction ability of deep learning to support ultra-low SNR LoRa communication. Taking the spectrogram of both amplitude and phase as input, we first design a mask-enabled Deep Neural Network (DNN) filter that extracts multi-dimension features to capture clean chirp symbols. Second, we develop a spectrogram-based DNN decoder to decode these chirp symbols accurately. Finally, we propose a generic packet demodulation system by incorporating a method that generates high-quality chirp symbols from received signals. We implement and evaluate NELoRa on both indoor and campus-scale outdoor testbeds. The results show that NELoRa achieves 1.84-2.35 dB SNR gains and extends the battery life up to 272% (~0.38-1.51 years) in average for various LoRa configurations.

LoRa, more generically Low-Power Wide Area Network (LPWAN), is a promising platform to connect Internet of Things. It enables low-cost low-power communication at a few kbps over upto tens of kilometers with a 10-year battery lifetime. However, practical LPWAN deployments suffer from collisions, given the dense deployment of devices and wide coverage area. We propose CoLoRa, a protocol to decompose large numbers of concurrent transmissions from one collision in LoRa networks. At the heart of CoLoRa, we utilize packet time offset to disentangle collided packets. CoLoRa incorporates several novel techniques to address practical challenges. (1) We translate time offset, which is difficult to measure, to frequency features that can be reliably measured. (2) We propose a method to cancel inter-packet interference and extract accurate feature from low SNR LoRa signal. (3) We address frequency shift incurred by CFO and time offset for LoRa decoding. We implement CoLoRa on USRP N210 and evaluate its performance in both indoor and outdoor networks. CoLoRa is implemented in software at the base station and it can work for COTS LoRa nodes. The evaluation results show that CoLoRa improves the network throughput by 3.4× compared with Choir and by 14× compared with LoRaWAN.

LoRa, a representative Low-Power Wide Area Network (LPWAN) technology, has been shown as a promising platform to connect Internet of Things. Practical LoRa deployments, however, suffer from collisions, especially in dense networks and wide coverage areas expected by LoRa applications. Existing collision resolution approaches do not exploit the coding properties of LoRa and thus cannot work well for low SNR LoRa signals. We propose NScale to decompose concurrent transmissions by leveraging subtle inter-packet time offsets for low SNR LoRa collisions. NScale (1) translates subtle time offsets, which are vulnerable to noise, to robust frequency features, and (2) further amplifies the time offsets by non-stationary signal scaling, i.e., scaling the amplitude of a symbol differently at different positions. In practical implementation, we propose a noise resistant iterative symbol recovery method to combat symbol distortion in low SNR, and address frequency shifts incurred by CFO and packet time offsets in decoding. We theoretically show that NScale introduces < 1.7 dB SNR loss compared with the original LoRa. We implement NScale on USRP N210 and evaluate its performance in both indoor and outdoor networks. NScale is implemented in software at the gateway and can work for COTS LoRa nodes without any modification. The evaluation results show that NScale improves the network throughput by 3.3x for low SNR collided signals compared with other state-of-the-art methods.

LoRa is recently a rising star in Low Power Wide Area Network (LPWAN) family to provide low power and long range communication for large number of devices in Internet of Things. LoRa is based on Chirp Spread Spectrum (CSS) and uses chirp frequency shift to encode data. It has been shown that collision significantly degrades LoRa performance in practice. We propose FlipLoRa, a new mechanism to disentangle LoRa collisions, which allows concurrent transmission of multiple packets. The key idea of FlipLoRa is to utilize the quasi-orthogonality between upchirp and downchirp. FlipLoRa encodes packets with interleaved upchirps and downchirps instead of only using upchirps as in LoRa. We then propose a novel method to disentangle chirps and decode multiple collided packets. To evaluate the performance, we formally prove the quasi-orthogonality and analyze its applicable conditions. We validate the performance improvement by theoretical analysis. Further, we implement FlipLoRa on software-defined radio and extensively evaluate its performance for real LoRa networks. The evaluation results show that FlipLoRa can improve the throughput by 3.84x over LoRa physical layer.