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.

Existing facial image super-resolution methods have identified the capacity to transform low-resolution facial images into high-resolution ones. However, clearer high-resolution facial images increase the possibility of accurately extracting soft biometric features, such as gender, posing a significant risk of privacy leakage. To address this issue, we propose a gender-protected face super-resolution network, which can incorporate gender-identified privacy information by introducing fine image distortion during the super-resolution process. It progressively transforms low-resolution images into high-resolution ones while partially disturbing the face images. This procedure ensures that the generated super-resolution facial images can still be utilized by face matchers for matching purposes, but are less reliable for attribute classifiers that attempt to extract gender features. Furthermore, we introduce leaping adversarial learning to help the super-resolution network to generate gender-protected facial images and work on arbitrary gender classifiers. Extensive experiments have been conducted using multiple face matchers and gender classifiers to evaluate the effectiveness of the proposed network. The results also demonstrate that our proposed image super-resolution network is adaptable to arbitrary attribute classifiers for protecting gender privacy, while preserving facial image quality.

ACM TIOT launched a special issue on the theme of LLM Empowered Internet of Things, exploring the intersection of Large Language Models (LLMs) and the Internet of Things (IoT). As IoT continues to expand, advanced computational models are increasingly essential for processing and analyzing the massive data generated by interconnected devices. This special issue focuses on how LLMs can enhance IoT systems in several key areas. The second part of this special issue introduces the remaining six accepted papers that spans a board range of IoT scenarios from embedded and cyber-physical systems, human-centered applications, to IoT security.

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.

LoRaWANs are envisioned to connect billions of IoT devices through thousands of physically overlapping yet logically orthogonal channels (termed logical channels). These logical channels hold significant potential for enabling highly concurrent scalable IoT connectivity. Large-scale deployments however face strong interference between logical channels. This practical issue has been largely overlooked by existing works but becomes increasingly prominent as LoRaWAN scales up. To address this issue, we introduce Canas, an innovative gateway design that is poised to orthogonalize the logical channels by eliminating mutual interference. To this end, Canas develops a series of novel solutions to accurately extract the meta-information of individual ultra-weak LoRa signals from the received overlapping channels. The meta-information is then leveraged to accurately reconstruct and subtract the LoRa signals over thousands of logical channels iteratively. Real-world evaluations demonstrate that Canas can enhance concurrent transmissions across overlapping logical channels by 2.3× compared to the best known related works.

ACM TIOT launched a special issue on the theme of LLM Empowered Internet of Things, exploring the intersection of Large Language Models (LLMs) and the Internet of Things (IoT). As IoT continues to expand, advanced computational models are increasingly essential for processing and analyzing the massive data generated by interconnected devices. This special issue focuses on how LLMs can enhance IoT systems in several key areas. These include improving context-aware perception and retrieval in complex IoT environments, applications of LLMs in human–computer interaction as well as applications of AI agents for IoT. The issue also highlights emerging trends in low-code and zero-code development for IoT programming, and the deployment of AI models at the edge. These research directions reflect the diverse and evolving landscape of IoT and LLM integration, offering innovative solutions to real-world challenges.

Backscatter sensing has emerged as a significant technology within the Internet of Things (IoT), prompting extensive research interest. This paper presents LoMu, the first long-range multi-target backscatter sensing system designed for low-cost tags operating under ambient LoRa. LoMuintroduces an orthogonal sensing model that processes backscatter signals from multiple tags to extract motion information. The design addresses several practical challenges, including near-far interference among multiple tags, phase offsets from unsynchronized transceivers, and phase errors due to frequency drift in low-cost tags. To overcome these issues, we propose a conjugate-based energy concentration method to extract high-quality signals and a Hamming-window-based method to mitigate the near-far problem. Additionally, we exploit the relationship between excitation and backscatter signals to synchronize the transmitter (TX) and receiver (RX) and combine double sidebands of backscatter signals to eliminate tag frequency drift. Furthermore, a novel joint estimation algorithm is introduced to exploit both amplitude and phase information in target signals, enhancing frequency sensing results and robustness. Our implementation and extensive experiments demonstrate that LoMucan accurately sense up to 35 tags simultaneously and achieve an average frequency sensing error of 0.5% at a range of 400 meters, which is$4\times$the range of the state-of-the-art.

Low Power Wide Area Networks (LPWANs) have emerged as a promising technology for facilitating large-scale, cost-effective connections through low-power, long-range communications. Nevertheless, the deployment of existing LPWANs is impeded by severe packet collisions. In this paper, we present OrthoRa, an innovative technology that significantly enhances the concurrency of low-power, long-range LPWAN transmissions. The cornerstone of OrthoRa lies in a groundbreaking design named Orthogonal Scatter Chirp Spreading Spectrum (OSCSS), which facilitates orthogonal packet transmissions while ensuring low signal-to-noise ratio (SNR) communication within LPWANs. Utilizing OrthoRa, different nodes can transmit packets encoded with unique orthogonal scatter chirps, enabling the receiver to decode collided packets from various nodes. We provide a theoretical validation of OrthoRa, demonstrating its capacity for high concurrency in low SNR communication. To surmount practical challenges inherent in real network deployments, we address the detection of multiple packets in collisions, the identification of scatter chirps for each packet’s decoding, and the precise synchronization of packets under Carrier Frequency Offset. We implemented OrthoRa on the HackRF One platform and conducted extensive performance evaluations. The results corroborate that OrthoRa amplifies network throughput and concurrency by a factor of 50 compared to LoRa and significantly outstripping the state-of-the-art in terms of robustness against collision time offset.

Acoustic tracking and positioning technologies using microphones and speakers have gained significant interest for applications like virtual reality, augmented reality, and IoT devices. However, existing methods still face challenges in real-world deployment due to multipath interference, Doppler frequency shift, and sampling frequency offset between devices. We propose a versatile Acoustic Tracking and Positioning (ATP) method to address these challenges. First, we propose an iterative sampling frequency offset calibration method. Next, we propose a Doppler frequency shift estimation and compensation model. Finally, we propose a fast adaptive algorithm to reconstruct the line-of-sight (LOS) signal under multipath1. We implement ATP in Android and PC and compare it with eight different methods. Evaluation results show that ATP achieves mean accuracy of 0.66 cm, 0.56 cm, and 1.0 cm in tracking, ranging, and positioning tasks. It is 2×, 6×, and 5.8× better than the state-of-the-art methods. ATP advances acoustic sensing for practical applications by providing a robust solution for real-world environments.

Long-range(LoRa) backscatter has shown great potential in many applications. However, the narrow bandwidth of LoRa and the unstable backscatter tag make localization challenging in practice. This paper presents LoBaCa, the first super-resolution LoRa backscatter localization system for low-cost tags. To increase the overall bandwidth, LoBaCa utilizes the frequency hopping technique and exploits the phase slope to synchronize multiple frequency bands. We further show that the unstable low-cost backscatter tag causes additional phase error in the weak backscatter signal and thus introduces significant localization error. We use the upper and lower sideband signals to improve the SNR and correct the phase error. Finally, LoBaCa adopts a super-resolution ESPRIT algorithm to solve the complex multipath effect, estimate the angle of arrival (AoA), and localize the backscatter tag. We prototype LoBaCa and conduct extensive experiments to evaluate LoBaCa in both indoor and outdoor scenarios. Our results show that the localization error of LoBaCa is 5.0 cm and 71 cm when the LoBaCa tag is 5m and 40m away, which is 4.3× and 1.7× better than the state-of-the-art.

Backscatter sensing has shown great potential in the Internet of Things (IoT) and has attracted substantial research interest. We present LoMu, the first long-range multi-target backscatter sensing system for low-cost tags under ambient LoRa. LoMu analyzes the received low-SNR backscatter signals from different tags and calculates their phases to derive the motion information. The design of LoMu faces practical challenges including near-far interference between multiple tags, phase offsets induced by unsynchronized transceivers, and phase errors due to frequency drift in low-cost tags. We propose a conjugate-based energy concentration method to extract high-quality signals and a Hamming-window-based method to alleviate the near-far problem. We then leverage the relationship between the excitation signal and backscatter signals to synchronize TX and RX. Finally, we combine the double sidebands of backscatter signals to cancel the tag frequency drift. We implement LoMu and conduct extensive experiments to evaluate its performance. The results demonstrate that LoMu can accurately sense 35 tags at the same time. The average frequency sensing error is 0.7% at 400m, which is 4× distance of the state-of-the-art.

The rapid development of WiFi localization poses a serious privacy threat, as eavesdroppers can locate WiFi devices without their consent. In this paper, we present WiCloak, the first system that protects WiFi device location privacy while supporting normal WiFi communication simultaneously. The high-level idea of WiCloak is to inject a fake channel into WiFi CSI at the transmitter, which renders the CIR and time information obtained by eavesdroppers meaningless. We mathematically prove that the injected fake channel is effective in any wireless environment and can strictly protect the location privacy of WiFi devices. To simultaneously support communication for commercial WiFi receivers, we propose a method to cancel out the fake channel impacts in decoding and prove that the method should not impact communication performance. WiCloak can work on commercial WiFi devices without any hardware modification. We evaluate the communication performance of WiCloak on commercial WiFi receivers (e.g., MacBook and Mac Studio) and demonstrate that it achieves the same packet reception rate as normal WiFi. We show that WiCloak increases the localization error by 22× to normal WiFi.

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.

The ability of smart devices to determine their locations is the basis for many applications. We present LEAD, a system which can simultaneously Locate Everyday smArt Devices, such as smartphone, smartwatch, and headphone, with only one speaker. The principle of LEAD is leveraging the reflected path (e.g., by the wall) for single speaker based localization. Previous works cannot simultaneously locate multiple devices with unknown orientations. To overcome the challenges, we estimate the direction difference and distance difference between the LoS and Echo paths and combine them to derive the device location. Given limited sound bandwidth, we develop a high-resolution method to estimate the distance difference. To address the sparsity of microphones with large inter-distance, we generate virtual microphones on smart devices to estimate the direction difference. We reduce the computation overhead by searching the decomposed space for distance and direction. We extensively evaluate LEAD's performance in different scenarios. The results show a median relative distance error of 2.0 cm, relative direction error of 0.7°, and localization error of 0.29 m across various settings.

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 the development of the Internet of Things, location service plays a more important role in mobile computing. To provide location service for the already deployed devices and objects, we present LiTag, a visible light-based localization and posture estimation solution with commercial off-the-shelf (COTS) cameras. The core of LiTag is based on the design of a chip-less and battery-less optical tag which can show different color patterns from different observation directions. After capturing a photo containing the tag, LiTag can calculate the tag position and posture by combining the color pattern and the geometric relation in camera imaging. To solve the localization ambiguity, we propose an ambiguity-avoidance method based on a projection relationship. LiTag can work with a single camera without calibration, which significantly reduces the calibration overhead and deployment costs. We implement LiTag and evaluate its performance extensively. Results show that LiTag can provide the tag position with a median error of 1$cm$in the 2D plane, a median error of 5$cm$in the 3D space, and posture estimation with a median error of$0.8^{\circ }$. We believe that LiTag has high potential to provide a low-cost and easy-to-use solution for ubiquitous localization and posture estimation with widely deployed cameras.

UWB (Ultra-wideband) has been shown to be a promising technology to provide accurate positioning for the Internet of Things. However, its performance significantly degrades in practice due to Non-Line-Of-Sight (NLOS) issues. Various approaches have implicitly or explicitly explored the problem. In this article, we propose RefLoc , which leverages the unique benefits of UWB to address the NLOS problem. While we find that NLOS links can vary significantly in the same environment, LOS links possess similar features that can be captured by the high bandwidth of UWB. Specifically, the high-level idea of RefLoc is to first identify links among anchors with known positions and leverage those links as references for tag link identification. To achieve this, we address the practical challenges of deriving anchor link status, extracting qualified link features, and inferring tag links with anchor links. We implement RefLoc on commercial hardware and conduct extensive experiments in different environments. The evaluation results show that RefLoc achieves an average NLOS identification accuracy of 96% in various environments, improving the state-of-the-art by 10%, and reduces 80% localization error with little overhead.

Acoustic motion tracking is rapidly evolving with various applications. However, existing approaches still have some limitations. Tracking based on single-frequency continuous wave (CW) faces cumulative errors in tracking and limited accuracy in tracking the absolute location of the target. Tracking based on frequency-modulated continuous wave (FMCW) faces errors introduced by the Doppler and multipath effects. To overcome these limitations, we propose FusionTrack, a novel device-free motion-tracking approach that leverages the fusion of CW and FMCW signals. We eliminate the absolute tracking errors of FMCW-based tracking by compensating for Doppler frequency offsets with the results of CW-based relative tracking. Furthermore, we address the static multipath with down-sampling and filtering and mitigate the dynamic multipath with chirp aggregation. We employ a Kalman filter-based fusion of relative and absolute tracking to enhance accuracy further. We implement FusionTrack on Android smartphones for real-time tracking and perform extensive experiments. The results show that FusionTrack achieves real-time 1D tracking with an accuracy of 1.5 mm, which is 46% better than the existing approaches and extends the 1D tracking range to 2.2 m, which is 3.1× of the existing approaches. FusionTrack also achieves a 2D tracking accuracy of 4.5 mm.

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.

Low Power Wide Area Networks (LPWANs) have been shown promising in connecting large-scale low-cost devices with low-power long-distance communication. However, existing LPWANs cannot work well for real deployments due to severe packet collisions. We propose OrthoRa, a new technology which significantly improves the concurrency for low-power long-distance LPWAN transmission. The key of OrthoRa is a novel design, Orthogonal Scatter Chirp Spreading Spectrum (OSCSS), which enables orthogonal packet transmissions while providing low SNR communication in LPWANs. Different nodes can send packets encoded with different orthogonal scatter chirps, and the receiver can decode collided packets from different nodes. We theoretically prove that OrthoRa provides very high concurrency for low SNR communication under different scenarios. For real networks, we address practical challenges of multiple-packet detection for collided packets, scatter chirp identification for decoding each packet and accurate packet synchronization with Carrier Frequency Offset. We implement OrthoRa on HackRF One and extensively evaluate its performance. The evaluation results show that OrthoRa improves the network throughput and concurrency by 50× compared with LoRa.

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.

Long-range backscatter localization is a promising technology for the Internet of Things. Existing works cannot work well for distributed base stations and low-cost tags. We present LocRa, which provides accurate localization for long-range backscatter with distributed base stations. We present a novel method to extract accurate channel information and synchronize the phase of different base stations. To compensate for the frequency and phase error on low-cost tags, we combine multiple channel measurements and eliminate the error by aligning different channels. Finally, we exploit frequency domain characteristics of the backscatter signal to extend its bandwidth and improve the SNR, thereby enhancing the localization accuracy. We prototype LocRa tags using custom low-cost hardware and implement LocRa base stations on USRP. Through extensive experiments, we show that the localization error of LocRa is 6.8 cm and 88 cm when the tag is 5m and 50m away from the base station, which is 3.1× and 2.3× better than the state-of-the-arts methods.

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.

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.

LoRa has been shown as a promising Low-Power Wide Area Network (LPWAN) technology to connect millions of devices for the Internet of Things by providing long-distance low-power communication when the SNR is very low. Real LoRa networks, however, suffer from severe packet collisions. Existing collision resolution approaches introduce a high SNR loss, i.e., require a much higher SNR than LoRa. To push the limit of LoRa collision decoding, we present AlignTrack, the first LoRa collision decoding approach that can work in the SNR limit of the original LoRa. Our key finding is that a LoRa chirp aligned with a decoding window should lead to the highest peak in the frequency domain and thus has the least SNR loss. By aligning a moving window with different packets, we separate packets by identifying the aligned chirp in each window. We theoretically prove this leads to the minimal SNR loss. In practical implementation, we address two key challenges: (1) accurately detecting the start of each packet, and (2) separating collided packets in each window in the presence of CFO and inter-packet interference. We implement AlignTrack on HackRF One and compare its performance with the state-of-the-arts. The evaluation results show that AlignTrack improves network throughput by 1.68$\times$compared with NScale and 3$\times$compared with CoLoRa.

Low Power Wide Area Networks (LPWAN) have become one of the key techniques to provide long-range, low-power communication for large-scale devices in the Internet of Things. However, LPWAN devices in real deployments (e.g., in buildings and basements) suffer from low-quality links due to signal attenuation, leading to coverage holes and significant deployment overhead. In this work, we propose Ostinato to enable communication for weak links and to enhance the coverage for real deployments of COTS LoRa. The key idea of Ostinato is to transform the original packet to a pseudo packet with repeated symbols and to concentrate the energy of multiple symbols to enhance the signal SNR. To address practical challenges, we reverse engineer the entire coding and modulation process of LoRa and propose a method to generate repeated symbols on COTS LoRa by manipulating input data bits. Thus, Ostinato can be directly used for widely deployed LoRa nodes without hardware modification. We achieve weak packet detection, synchronization, and effective decoding on the receiver side by concentrating energy from multiple symbols with phase offsets. We implement Ostinato on Software Defined Radio (SDR) platform and extensively evaluate its performance. The evaluation results show that Ostinato achieves an 8.5 dB gain on receiving sensitivity and 2.88× gain on the coverage compared with COTS LoRa.

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.

Mobile video streaming occupies three-quarters of today's cellular network traffic. The quality of mobile videos becomes increasingly important for video providers to attract more users. For example, they invest in network bandwidth resources and conduct adaptive bitrate techniques to improve video quality. Prior adaptive bitrate (ABR) algorithms perform well under given throughput traces on broadband and WiFi networks. They may perform poorly for mobile video streaming due to the high network dynamics of cellular networks. To study the properties of throughput traces under cellular networks, we collect 4G network throughput traces for over four months in two large cities, Beijing and Suzhou in China. We derive the environment-specific Markov property of throughputs in the dataset. Accordingly, we propose NEIVA, an environment identification based technique to adaptively predict future throughput for different types of environments. We also implement NEIVA and integrate it with the state-of-the-art ABR algorithm, model predictive control (MPC) approach in our testbed for experiments. By emulating mobile video streaming under throughput traces in our dataset, NEIVA achieves 20 - 25 percent improvement on throughput prediction accuracy comparing to baseline predictors. Meanwhile, NEIVA achieves 11 - 20 percent user QoE improvement over MPC with baseline predictors.

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.

No abstract available.

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.

Nowadays, video surveillance systems are widely deployed in various places, e.g., schools, parks, airports, roads, etc. However, existing video surveillance systems are far from full utilization due to high computation overhead in video processing. In this work, we present ViTrack, a framework for efficient multi-video tracking using computation resource on the edge for commodity video surveillance systems. In the heart of ViTrack lies a two layer spatial/temporal compressed target detection method to significantly reduce the computation overhead by combining videos from multiple cameras. Further, ViTrack derives the video relationship and camera information even in absence of camera location, direction, etc. To alleviate the impact of variant video quality and missing targets, ViTrack leverages a Markov Model based approach to efficiently recover missing information and finally derive the complete trajectory. We implement ViTrack on a real deployed video surveillance system with 110 cameras. The experiment results demonstrate that ViTrack can provide efficient trajectory tracking with processing time 45x less than the existing approach. For 110 video cameras, ViTrack can run on a Dell OptiPlex 390 computer to track given targets in almost real time. We believe ViTrack can enable practical video analysis for widely deployed commodity video surveillance systems.

LoRa has been shown as a promising Low-Power Wide Area Network (LPWAN) technology to connect millions of devices for the Internet of Things by providing long-distance low-power communication in a very low SNR. Real LoRa networks, however, suffer from severe packet collisions. Existing collision resolution approaches introduce a high SNR loss, i.e., require a much higher SNR than LoRa. To push the limit of LoRa collision decoding, we present AlignTrack, the first LoRa collision decoding approach that can work in the SNR limit of the original LoRa. Our key finding is that a LoRa chirp aligned with a decoding window should lead to the highest peak in the frequency domain and thus has the least SNR loss. By aligning a moving window with different packets, we separate packets by identifying the aligned chirp in each window. We theoretically prove this leads to the minimal SNR loss. In practical implementation, we address two key challenges: (1) accurately detecting the start of each packet, and (2) separating collided packets in each window in the presence of CFO and inter-packet interference. We implement AlignTrack on HackRF One and compare its performance with the state-of-the-arts. The evaluation results show that AlignTrack improves network throughput by 1.68× compared with NScale and 3× compared with CoLoRa.

Low-Power Wide-Area Networks (LPWANs) have been shown as a promising technique to provide long-range low-power communication for large-scale IoT devices. In this paper, however, we show the poor performance of LoRa network due to its link diversity in macro- and micro- scope through one-month measurements in an area of$2.2\ km\times 1.5\ km$. We present FerryLink, which exploits such link diversity and leverages peer nodes to ferry data of weak links, to combat performance degradation. Traditional arts (e.g., building multi-hop networks) are inefficient or too heavyweight for the current star-topology-based LoRa network. FerryLink thus proposes a novel ferry mechanism combining RSSI sampling and Channel Activity detection(CAD) to suit multiple orthogonal transmission parameters of LoRa. To reduce energy overhead, FerryLink leverages convention windows for coarse-grained transmission synchronization between two coupled nodes. Finally, FerryLink utilizes the orthogonality of uplink and downlink signals to avoid data redundancy due to the ferry mechanism, maintaining comparable capacity with original LPWANs. We build FerryLink on top of LoRaWANwith commercial off-the-shelf hardware. The extensive evaluation results show that FerryLink effectively improves the packet delivery rate (PDR) of LoRa nodes (to over 95%), achieves 2x less energy overhead, and increases communication range by 50% compared with the original LoRaWAN.

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 ×.

Today’s video service providers all desire to deeply understand the ever changing factors on user QoE to attract more users. In this paper, we study the user engagement with respect to video quality metrics and improve user engagement in adaptive video streaming systems. We conduct a comprehensive study of the real data from iQIYI, covering 700K users and 150K videos. We find bitrate switch becomes the new dominant factor on user engagement instead of rebuffering events. We also observe the impact of rate of rebuffering is more dominant than rebuffering time. We examine novel interdependencies between quality metrics in the system, e.g., the positive correlation between bitrate switch and average bitrate, which is due to the context system strategy, i.e., conservative bitrate enhancing strategy adopted by iQIYI. To improve user engagement, we propose a new engagement centric QoE function based on real data and design server side ABR algorithm which leverages our new QoE function. We evaluate our method for online test in iQIYI, with 490K real users viewing 666K streams. The results show our approach outperforms existing approaches by significantly improving the viewing time, i.e., 2.8 minutes longer viewing time per user.

LoRa backscatter is a promising technology to achieve low-power and long-distance communication for connecting millions of devices in the Internet of Things. We present P2LoRa, the first ambient LoRa backscatter system with parallel decoding and long-range communication. The high level idea of P2LoRa is to modulate data by shifting ambient LoRa packets with a small frequency. To achieve long distance communication, we enhance the SNR of the backscatter signal by concentrating leaked energy in both the frequency domain and time domain. We propose a method to accurately reconstruct and cancel the in-band excitation signal, which is orders of magnitude higher than the backscatter signal. For parallel decoding, we propose a method to cancel inter-tag interference with very low overhead and address the signal misalignment problem due to different time of flight. We prototype the P2LoRa tag with customized low-cost hardware and implement the P2LoRa gateway on USRP. Through extensive evaluations, we show that P2LoRa achieves a long communication distance of 2.2 km with ambient LoRa, and supports 101 parallel tag transmissions.

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.

Visible Light Positioning (VLP) has attracted much research effort recently. Most existing VLP approaches require special designed light or receiver, collecting light information or strict user operation (e.g., horizontally holding the mobile phone). This incurs a high deployment, maintenance and usage cost. We present RainbowLight, a low-cost ambient light 3D localization approach that is easy to deploy in today's buildings. Our key finding is that light through a chip of polarizer and birefringence material produces specific interference and light spectrum at different directions to the chip. We derive a model to characterize the relation for direction, light interference, and spectrum. Exploiting the model, RainbowLight calculates the direction to a chip after taking a photo containing the chip. With multiple chips, RainbowLight designs a direction intersection based method to derive the location. We implement RainbowLight and extensively evaluate its performance in various environments. The evaluation results show that RainbowLight achieves an average localization error of 3.3 cm in 2D and 9.6 cm in 3D for light on, and an error of 7.4 cm in 2D and 20.5 cm in 3D for light off scenario in the daytime.

Acoustic motion tracking has been viewed as a promising user interaction technique in many scenarios such as Virtual Reality (VR), Smart Appliance, video gaming, etc. Existing acoustic motion tracking approaches, however, suffer from long window of accumulated signal and time-consuming signal processing. They are inherently difficult to achieve both high accuracy and low delay. In this paper, we present Vernier, an efficient and accurate acoustic tracking method based on commodity mobile devices. We design a new approach to efficiently and accurately derive phase change and thus moving distance. Vernier significantly reduces the tracking delay/overhead by removing the complicated frequency analysis and long window of signal accumulation, while keeping a high tracking accuracy. We implement Vernier on Android, and evaluate its performance with COTS mobile devices including Samsung Galaxy S7 and Sony L50t. Experimental results show that Vernier outperforms previous approaches with a tracking error less than 4 mm. The tracking speed achieves 3× improvement to the previous phase based approaches and 10× to Doppler Effect based approaches. Vernier is also validated in applications like controlling and drawing, and we believe it is generally applicable in many real applications.

Due to limited energy supply on many Internet of Things (IoT) devices, asynchronous duty cycle radio management is widely adopted to save energy. Flooding is a critical way to disseminate messages through the whole network. Capture effect enabled concurrent broadcast is appealing to accelerate network flooding in asynchronous duty cycle networks. However, when the flooding payload's size is large, the concurrent broadcast performance is far from efficient due to the frequently unsatisfied capture effect. Intuitively, senders can send a short packet containing partial flooding payload to keep concurrent broadcast efficiency. In practice, we still face two challenges. Considering packet loss, a receiver needs an effective way to recover the entire flooding payload from several received packets as soon as possible. Moreover, considering different channel states of different senders, how a sender chooses the optimal packet length to guarantee high channel utilization is not easy. In this paper, we propose Chase++ a Fountain-code based concurrent broadcast control layer to enable fast flooding in asynchronous duty cycle networks. Chase++ uses Fountain code to alleviate the negative influence of a certain part of the flooding payload's continuous loss. Moreover, Chase++ adaptively selects packet length with the local estimation of channel utilization. Specifically, Chase++ partitions long payload into several short payload blocks, further encoded into many encoded payload blocks by Fountain-code. Then, with temporal and spatial features of the sampled RSS (received signal strength) sequence, a sender estimates the number of concurrent senders. Finally, according to the estimated number of concurrent senders, the sender determines the optimal number of encoded payload blocks in a packet and assembles the encoded payload blocks as lots of packets. Then, the concurrent broadcast layer continuously transmits these packets. Receivers can recover the original flooding payload after several independent encoded payload blocks are collected. We implement Chase++ in TinyOS with TelosB nodes. We further evaluate Chase++ on Local testbed with 50 nodes and Indriya testbed with 95 nodes. The improvement of network flooding speed can reach 23.6% and 13.4%, respectively.

Adaptive bitrate (ABR) algorithms are critical techniques for high quality-of-experience (QoE) Internet video delivery. Early ABR algorithms conducting the overall QoE function of fixed parameters are limited by the fact that the QoE of end-users are diverse such that the video bitrate is often chosen in a misleading way. State-of-the-art ABR algorithms like MPC and Pensieve utilize offline modeling techniques and result in performance degradation for online QoE diversity adaptation. To address this issue, we propose Elephanta, an online ABR algorithm for edge users, which incorporates user QoE perception interface and adaptation algorithm with flexible parameters. In order to avoid overhead from updating parameters online, we model video streaming as a renewal system and formulate the specific QoE function into flexible formats by setting constraints on corresponding QoE metrics. To validate parameter settings, we emulate Elephanta under 1500 throughput traces, including FCC broadband, $3G$ HSDPA data set from the Internet, as well as the $4G$ /LTE data set we collect. Evaluation results show that Elephanta achieves QoE improvement of 7% over MPC and 3% over Pensieve under QoE diversity in part because of its superior adaptability to QoE diversity. We implemented Elephanta in dash.js at the client side for subjective experiments. We observed the diverse QoE preferences across users and 19/21 users (strongly) agree that Elephanta is responsive to parameter changes while watching videos.

Acoustic based tracking has been shown promising in many applications like Virtual Reality, smart home, video gaming, etc. Its real life deployments, however, face fundamental limitations. Existing approaches generally need three sound sources, while most COTS devices (e.g., TVs) and speakers have only two sound sources. We present AcouRadar, an acoustic-based localization system with single sound source. In the heart of AcouRadar we adopt a general new model which quantifies signal properties of different frequencies, distances and angles to the source. We verify the model and show that signal from a single source can provide features for localization. To address practical challenges, (1) we design an online model adaption method to address model deviation from real signal, (2) we design pulse modulated signals to alleviate the impact of environment such as multipath effect, and (3) to address signal dynamics over time, we derive relatively stable amplitude ratio between different frequencies, and thus provide a spectrum based localization method. We implement AcouRadar on Android and evaluate its performance for different COTS speakers in different environments. The results show that the model for localization can be generalized to different speakers. AcouRadar can achieve single source localization with average error less than 5 cm and average angle error of 1.76°.

LoRa has been shown as a promising platform for connecting large scale of Internet of Things (IoT) devices, by providing low-power long-range communication with a low data rate. LoRa has different transmission parameters (e.g., transmission power and spreading factor) to tradeoff noise resilience, transmission range and energy consumption for different environments. Thus, adjusting those parameters is essential for LoRa performance. Existing approaches are mainly threshold based and fail to achieve optimal energy efficiency. We propose DyLoRa, a dynamic LoRa transmission control system to improve energy efficiency. The high level idea of DyLoRa is to adjust parameters to different environments. The main challenge is that LoRa has very limited data rate and sparse data, making it very time- and energy-consuming to obtain physical link properties. We show that symbol error rate is highly related to the Signal- Noise Ratio (SNR) and derive the model to characterize this. We further derive energy efficiency model based on the symbol error model. DyLoRa can adjust parameters for optimal energy efficiency from sparse LoRa packets. We implement DyLoRa based on LoRaWAN 1.0.2 with SX1276 LoRa node and SX1301 LoRa gateway and evaluate its performance in real networks. The evaluation results show that DyLoRa improves the energy efficiency by 41.2% on average compared with the state-of-the- art LoRaWAN ADR.

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.

Today's smart devices like fitness tracker, smartwatch, etc., often employ Bluetooth Low Energy (BLE) for data transmission. Such devices thus become our information portal, e.g., SMS message and notifications are delivered to those devices through BLE. In this study, we present BlueDoor, which can obtain unauthorized information from smart devices via BLE vulnerability. We thoroughly examine the BLE protocol, and leverage its intrinsic properties designed for low-cost embedded and wearable devices to bypass the encryption and authentication in BLE. By mimicking a low capacity device to downgrade the process of encryption key negotiation and authentication, BlueDoor can enforce a new key with the peripheral BLE device and pass the authentication without user participation. As a result, BlueDoor can extract BLE packets as well as read/write stored data on BLE devices. We show that BlueDoor works well on the fundamental design tradeoff of using BLE on diverse embedded and wearable devices, and thus can be generalized to various BLE devices. We implement the BlueDoor design and examine its performance on 15 COTS BLE enabled smart devices, including fitness trackers, smartwatch, smart bulb, etc. The results show that BlueDoor can break the information flow and obtain different types of information (e.g., SMS message, notifications) delivered to BLE devices. In addition to privacy threats, this further means traditional operations such as using SMS for verification in widely adopted authentication, are insecure.

We propose Magic Wand which automatically recognizes 2D gestures (e.g., symbol, circle, polygon, letter) performed by users wearing a smartwatch in real-time manner. Meanwhile, users can freely choose their convenient way to perform those gestures in 3D space. In comparison with existing motion sensor based methods, Magic Wand develops a white-box model which adaptively copes with diverse hardware noises and user habits with almost zero overhead. The key principle behind Magic Wand is to utilize 2D stroke sequence for gesture recognition. Magic Wand defines 8 strokes in a unified 2D plane to represent various gestures. While a user is freely performing gestures in 3D space, Magic Wand collects motion data from accelerometer and gyroscope. Meanwhile, Magic Wand removes various acceleration noises and reduces the dimension of 3D acceleration sequences of user gestures. Moreover, Magic Wand develops stroke sequence extraction and matching methods to timely and accurately recognize gestures. We implement Magic Wand and evaluate its performance with 4 smartwatches and 6 users. The evaluation results show that the median recognition accuracy is 94.0% for a set of 20 gestures. For each gesture, the processing overhead is tens of milliseconds.

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.

The development of Internet of Things calls for ubiquitous and low-cost localization and posture estimation. We present LiTag, a visible light based localization and posture estimation solution with COTS cameras. The core of LiTag is based on the design of a chip-less and battery-less optical tag which can show different color patterns from different observation directions. After capturing a photo containing the tag, LiTag can calculate the tag position and posture by combining the color pattern and the geometry relation between the camera image plane and the real world. Unlike existing marker-based visible localization and posture estimation approaches, LiTag can work with a single camera without calibration, which significantly reduces the calibration overhead and deployment costs. We implement LiTag and evaluate its performance extensively. Results show that LiTag can provide the tag position with a median error of 1.6 cm in the 2D plane, a median error of 12 cm in the 3D space, and posture estimation with a median error of 1°. We believe that LiTag has a high potential to provide a low-cost and easy-to-use solution for ubiquitous localization and posture estimation with existing widely deployed cameras.

Adaptive bitrate (ABR) algorithms have been critical techniques for high quality-of-experience (QoE) Internet video delivery. Prior work designs ABR algorithms by conducting the overall QoE function of fixed parameters. However, the QoE of end users are diverse and video bitrate may be chosen in a misleading way when leaving out the diversity. State-of-the-art ABR algorithms like MPC, Pensieve utilize off-line modeling techniques and result in performance degradation for online QoE diversity adaption. To address this issue, we propose Elephanta, an online flexible ABR algorithm for edge users which incorporates (1) user QoE perception interface and (2) adaption algorithm with flexible parameters. To avoid overheads for updating parameters online, we model video streaming as a renewal system and formulate specific QoE function into flexible formats by setting constraints on corresponding QoE metrics. To validate parameter setting, we emulate Elephanta under 5 thousand throughput traces including FCC broadband, 3G HSDPA data set from the Internet and 4G/LTE data set collected by ourselves. Accordingly, we implement Elephanta in dash.js at client side for user test. Evaluation results show that Elephanta achieves QoE improvement by 21.1% over MPC, in part for its superior adaptability to QoE diversity.

With the popularization of advanced cellular networks, mobile video occupies nearly three quarters of cellular network traffic. While previous adaptive bitrate (ABR) algorithms perform well under broadband network, their performance degrades in cellular networks due to throughput fluctuation. Through real world 4G/LTE network measurement, we find that throughput in cellular networks exhibits high fluctuation. It follows Markov behaviors with different states and different transition probability among states. We further find that the transition probability is stable along time but varies significantly under different environments. This inspires us to design ABR algorithms by improving throughput prediction in cellular networks. We propose NEIVA, a network environment identification based video bitrate adaption method in cellular networks. NEIVA trains a network environment identifier based on throughput data and trains a hidden Markov model (HMM) based throughput predictor for different environments. In online video bitrate selection, NEIVA utilizes the environment identifier to select the model for corresponding environment. Then NEIVA predicts future network performance by combining offline model and online throughput data. We implement NEIVA with MPC and evaluate it in real environment. The evaluation results show that with manually identifying environment, NEIVA improves 20% -- 25% bandwidth prediction accuracy and 11% -- 20% QoE improvement over the baseline predictors. With online environment identification, online NEIVA achieves 3.8% and 11.1% average QoE improvement over MPC and HMM, respectively.

The rate capacity region of an uplink Gaussian channel is a generalized symmetric polymatroid. Practical applications impose additional lower and upper bounds on the rate allocations, which are represented by box constraints. A fundamental scheduling problem over an uplink Gaussian channel is to seek a rate allocation maximizing the weighted sum-rate (MWSR) subject to the box constraints. The best-known algorithm for this problem has time complexity O (n5 lnO(1) n). In this paper, we take a polymatroidal approach to developing a quadratic-time greedy algorithm and a linearithmic-time divide-and-conquer algorithm. A key ingredient of these two algorithms is a linear-time algorithm for minimizing the difference between a generalized symmetric rank function and a modular function after a linearithmic-time ordering.

With the development and popularization of WiFi, surfing on the Internet with mobile devices has become an indispensable part of people's daily life. However, as an infrastructure, WiFi access points (APs) are easily connected by some undesired users nearby. In this paper, we propose NiFi, a non-intrusive WiFi user-identification system based on WiFi signals that enable AP to automatically identify legitimate users in indoor environments, such as home, office, and hotel. The core idea is that legitimate and undesired users may have different physical constraints, e.g., moving area, walking path, and so on, leading to different signal sequences. NiFi analyzes and exploits the characteristics of signal sequences generated by mobile devices. NiFi proposes a practical and effective method to extract useful features and measures similarity for signal sequences while not relying on precise user location information. We implement NiFi on Commercial Off-The-Shelf APs, and the implementation does not require any modification to user devices. The experiment results demonstrate that NiFi is able to achieve an average identification accuracy at 90.83% with true positive rate at 98.89%.

Video streaming takes up an increasing proportion of network traffic nowadays. Dynamic adaptive streaming over HTTP (DASH) becomes the de facto standard of video streaming and it is adopted by Youtube, Netflix, and so on. Despite of the popularity, network traffic during video streaming shows an identifiable pattern which brings threat to user privacy. In this paper, we propose a video identification method using network traffic while streaming. Though there is bitrate adaptation in DASH streaming, we observe that the video bitrate trend remains relatively stable because of the widely used variable bit-rate (VBR) encoding. Accordingly, we design a robust video feature extraction method for eavesdropped video streaming traffic. Meanwhile, we design a VBR-based video fingerprinting method for candidate video set which can be built using downloaded video files. Finally, we propose an efficient partial matching method for computing similarities between video fingerprints and streaming traces to derive video identities. We evaluate our attack method in different scenarios for various video content, segment lengths, and quality levels. The experimental results show that the identification accuracy can reach up to 90% using only three-minute continuous network traffic eavesdropping.

Motivated by the requirement of heterogeneity in the Internet of Things, we initiate the joint study of capacity and energy efficiency scaling laws in heterogeneous wireless sensor networks, and so on. The whole network is composed of n nodes scattered in a square region with side length L = n α , and there are m = n ν home points { c j } j=1 m , where a generic home point c j generates q j nodes independently according to a stationary and rotationally invariant kernel k ( c j , ⋅). Among the n nodes, we schedule n s independent multicast sessions each consisting of k − 1 destination nodes and one source node. According to the heterogeneity of nodes’ distribution, we classify the network into two regimes: a cluster-dense regime and a cluster-sparse regime. For the cluster-dense regime, we construct single layer highway system using percolation theory and then build the multicast spanning tree for each multicast session. This scheme yields the Ω( n ½+(α − ½)γ / n s √ k ) per-session multicast capacity. For the cluster-sparse regime, we partition the whole network plane into several layers and construct nested highway systems. The similar multicast spanning tree yields the Ω( n ½−(1− ν)γ/2 / n s √ k ) per-session multicast capacity, where γ is the power attenuation factor. Interestingly, we find that the bottleneck of multicast capacity attributes to the network region with largest node density, which provides a guideline for the deployment of sensor nodes in large-scale sensor networks. We further analyze the upper bound of multicast capacity and the per-session multicast energy efficiency. Using both synthetic networks and real-world networks (i.e., Greenorbs), we evaluate the asymptotic capacity and energy efficiency and find that the theoretical scaling laws are gracefully supported by the simulation results. To our best knowledge, this is the first work verifying the scaling laws using real-world large-scale sensor network data.

An adaptive backstepping controller based on fuzzy logic is designed for an underwater vehicle manipulator system (UVMS) to track desired joint trajectories. Firstly, the dynamic model of a UVMS with hydrodynamic effects is developed. Then, the adaptive backstepping control method is introduced and the stability is analyzed in detail. A fuzzy logic controller is utilized to estimate system parameters. Simulation results show that this adaptive backstepping controller is able to follow desired joint trajectory and has better performance than the classical proportional-differential (PD) controller.

Due to limited energy supply on many Internet of Things (IoT) devices, asynchronous duty cycle radio management is widely adopted to save energy. Flooding is a critical way to quickly disseminate system parameters to adapt diverse network requirements. Capture effect enabled concurrent broadcast is appealing to accelerate network flooding in asynchronous duty cycle networks. However, when the length of flooding payload is long, due to frequently unsatisfied capture effect construction, the performance of concurrent broadcast is far from efficient. Intuitively, senders can send short packet that contains partial flooding payload to keep the efficiency of concurrent broadcast. In practice, we still face two challenges. Considering packet loss, a receiver needs an effective way to recover entire flooding payload from several received packets as soon as possible. Moreover, considering diverse channel state of different senders, how a sender chooses the optimal packet length to guarantee high channel utilization in a light-weight way is not easy. In this paper, we propose Chase++ a Fountain code based concurrent broadcast control layer to enable fast flooding in asynchronous duty cycle networks. Chase++ uses Fountain code to alleviate the negative influence of the continuous loss of a certain part of flooding payload. Moreover, Chase++ adaptively selects packet length with the local estimation of channel utilization. Specifically, Chase++ partitions long payload into several short payload blocks, which are further encoded into many encoded payload blocks by Fountain code. Then, with temporal and spatial features of the sampled RSS (received signal strength) sequence, a sender estimates the number of concurrent senders. Finally, according to the estimated number of concurrent senders, the sender determines the optimal number of encoded payload blocks in a packet and assembles the encoded payload blocks as lots of packets. Then, concurrent broadcast layer continuously transmits these packets. Receivers can recover original flooding payload after several independent encoded payload blocks are collected. We implement Chase++ in TinyOS with TelosB nodes. We further evaluate Chase++ on local testbed with 50 nodes and Indriya testbed with 95 nodes. The improvement of network flooding speed can reach 23.6% and 13.4%, respectively.

Nowadays, video surveillance systems are widely deployed in various places, e.g., schools, parks, airports, roads, etc. However, existing video surveillance systems are far from full utilization due to high computation overhead in video processing. In this work, we present ViTrack, a framework for efficient multi-video tracking using computation resource on the edge for commodity video surveillance systems. In the heart of ViTrack lies a two layer spatial/temporal compressive target detection method to significantly reduce the computation overhead by combining videos from multiple cameras. Further, ViTrack derives the video relationship and camera information even in absence of camera location, direction, etc. To address variant video quality and missing targets, ViTrack leverages a Markov Model based approach to efficiently recover missing information and finally derive the complete trajectory. We implement ViTrack on a real deployed video surveillance system with 110 cameras. The experiment results demonstrate that ViTrack can provide efficient trajectory tracking with processing time 45x less than the existing approach. For 110 video cameras, ViTrack can run on a Dell OptiPlex 390 computer to track given targets in almost real time. We believe ViTrack can enable practical video analysis for widely deployed commodity video surveillance systems.

Video streaming takes up an increasing proportion of network traffic nowadays. Dynamic Adaptive Streaming over HTTP (DASH) becomes the de facto standard of video streaming and it is adopted by Youtube, Netflix, etc. Despite of the popularity, network traffic during video streaming shows identifiable pattern which brings threat to user privacy. In this paper, we propose a video identification method using network traffic while streaming. Though there is bitrate adaptation in DASH streaming, we observe that the video bitrate trend remains relatively stable because of the widely used Variable Bit-Rate (VBR) encoding. Accordingly, we design a robust video feature extraction method for eavesdropped video streaming traffic. Meanwhile, we design a VBR based video fingerprinting method for candidate video set which can be built using downloaded video files. Finally, we propose an efficient partial matching method for computing similarities between video fingerprints and streaming traces to derive video identities. We evaluate our attack method in different scenarios for various video content, segment lengths and quality levels. The experimental results show that the identification accuracy can reach up to 90 % using only three-minute continuous network traffic eavesdropping.

Acoustic motion tracking has been viewed as a promising user interaction technique in many scenarios such as Virtual Reality (VR), Smart Appliance, video gaming, etc. Existing acoustic motion tracking approaches, however, suffer from long window of accumulated signal and time-consuming signal processing. Consequently, they are inherently difficult to achieve both high accuracy and low delay. We propose Vernier, an efficient and accurate acoustic tracking method on commodity mobile devices. In the heart of Vernier lies a novel method to efficiently and accurately derive phase change and thus moving distance. Vernier significantly reduces the tracking delay/overhead by removing the complicated frequency analysis and long window of signal accumulation, while keeping a high tracking accuracy. We implement Vernier on Android, and evaluate its performance on COTS mobile devices including Samsung Galaxy S7 and Sony L50t. Evaluation results show that Vernier outperforms previous approaches with a tracking error less than 4 mm. The tracking speed achieves 3×improvement to existing phase based approaches and 10×to Doppler Effect based approaches. Vernier is also validated in applications like controlling and drawing, and we believe it is generally applicable in many real applications.

Visible Light Positioning (VLP) has attracted much research effort recently. Most existing VLP approaches require special designed light or receiver, collecting light information or strict user operation (e.g., horizontally holding mobile phone). This incurs a high deployment, maintenance and usage overhead. We present RainbowLight, a low cost ambient light 3D localization approach easy to deploy in today's buildings. Our key finding is that light through a chip of polarizer and birefringence material produces specific interference and light spectrum at different directions to the chip. We derive a model to characterize the relation for direction, light interference and spectrum. Exploiting the model, RainbowLight calculates the direction to a chip after taking a photo containing the chip. With multiple chips, RainbowLight designs a direction intersection based method to derive the location. We implement RainbowLight and extensively evaluate its performance in various environments. The evaluation results show that RainbowLight achieves an average localization error of 3.3 cm in 2D and 9.6 cm in 3D for light on, and an error of 7.4 cm in 2D and 20.5 cm in 3D for light off scenario in daytime.

Most existing VLP approaches require special designed light or receiver, collecting light information or strict user operation (e.g., horizontally holding mobile phone). This incurs a high deployment, maintenance and usage overhead. In this demo, we present RainbowLight, a low cost ambient light 3D localization approach easy to deploy in today's buildings. Our key finding is that light through a chip of polarizer and birefringence material produces specific interference and light spectrum at different directions to the chip. We derive a model to characterize the relation for direction, light interference and spectrum. Exploiting the model, RainbowLight calculates the direction to a chip after taking a photo containing the chip. With multiple chips, RainbowLight designs a direction intersection based method to derive the location. This demo shows our prototype of implementation, with simple photo capturing and deriving location of camera on mobile phone. The evaluation results show that RainbowLight achieves an average localization error of 3.3 cm in 2D and 9.6 cm in 3D for light on, and an error of 7.4 cm in 2D and 20.5 cm in 3D for light off scenario in daytime.

Device-to-device (D2D) communication is widely used for mobile devices and Internet of Things (IoT). Authentication and key agreement are critical to build a secure channel between two devices. However, existing approaches often rely on a pre-built fingerprint database and suffer from low key generation rate. We present GeneWave, a fast device authentication and key agreement protocol for commodity mobile devices. GeneWave first achieves bidirectional initial authentication based on the physical response interval between two devices. To keep the accuracy of interval estimation, we eliminate time uncertainty on commodity devices through fast signal detection and redundancy time cancellation. Then we derive the initial acoustic channel response (ACR) for device authentication. We design a novel coding scheme for efficient key agreement while ensuring security. Therefore, two devices can authenticate each other and securely agree on a symmetric key. GeneWave requires neither special hardware nor pre-built fingerprint database, and thus it is easy-to-use on commercial mobile devices. We implement GeneWave on mobile devices (i.e., Nexus 5X and Nexus 6P) and evaluate its performance through extensive experiments. Experimental results show that GeneWave efficiently accomplish secure key agreement on commodity smartphones with a key generation rate 10x faster than the state-of-the-art approach.

Ultrasound based tracking and localization are more and more popular in recent years. However, to achieve 3D tracking or localization, at least three sound sources are needed. Unfortunately, there are only two sound sources available in most scenarios in daily life. To address this problem, we propose SOLO (Single sound sOurce LOcalization with single microphone), a novel approach to infer 2D information from single sound source. We found that sound with different frequencies from a signal source may have different strength distribution in different areas. Each area has a unique group of sound strengths of different frequencies. So we can take this question as a classify question. Based on this, we first obtain the signal strength in different frequencies by using STFT (Short-Time Fourier Transform). Next we measure 1D distance with traditional phase-based distance measuring approach. Then we take 1D distance and the sound strengths as features to a neural network to classify the sample into different classes, each representing a region in 2D space. With our system, we can distinguish 3 × 3 regions (5 cm × 5 cm for each region) with only single sound source and single microphone. SOLO is the first work to achieve 2D localization and tracking with only single sound source and single microphone as far as we know. This work make it possible to realize 3D tracking or localization with only two sound sources.

Consider a set of point-to-point communication requests in a multi-channel multihop wireless network, each of which is associated with a traffic demand of at most one unit of transmission time, and a weight representing the utility if its demand is fully met. A subset of requests is said to be schedulable without spectral splitting if they can be scheduled within one unit of time subject to the constraint each request is assigned with a unique channel throughout its transmission. This paper develops efficient and provably good approximation algorithms for finding a maximum-weighted subset of communication requests schedulable without spectral splitting.

Crowdsourced mobile network access (CMNA), in which mobile users can share their Internet access with others, is a promising paradigm for addressing users' increasing needs for ubiquitous connectivity and alleviating cellular network congestion. In this paper, we study the operator-assisted CMNA model, in which a mobile virtual network operator (MVNO) incentivizes its subscribers to operate as mobile WiFi hotspots (hosts) through reimbursement and gets revenue from the relayed traffic. Despite of the promising performance, practical strategies for MVNO and hosts have not been studied yet. Existing works usually assume both MVNO and hosts can obtain complete information, and ignore the accompanied overhead in backhaul and privacy threats to users. Such assumptions are unrealistic in practice. To address this issue, we first systematically characterize the revenue loss for both MVNO and hosts with incomplete market information. Based on the analysis, we propose a novel partial cooperation strategy (PCS) to enable appropriate information exchange between MVNO and hosts with little overhead. With adaptive reimbursement and subtle information control, our PCS efficiently improves MVNO's revenue at equilibrium, and also satisfies the hosts' rationality. Through extensive evaluation on data from the real world, we demonstrate our PCS can improve MVNO's revenue by 23% at equilibrium, compared with the results without PCS.

To save energy, wireless sensor networks often run in a low-duty-cycle mode, where the radios of sensor nodes are scheduled between ON and OFF states. For nodes to communicate with each other, low power listening (LPL) and low power probing (LPP) are two types of rendezvous mechanisms. Nodes with LPL or LPP rely on signal strength or probe packets to detect potential transmissions, and then keep the radio-ON for communications. Unfortunately, in co-existing environments, signal strength and probe packets are susceptible to interference, resulting in undesirable radio ON time when the signal strength of interference is above a threshold or a probe packet is interfered. To address the issue, we propose ZiSense, a low duty cycling mechanism resilient to interference. Instead of checking the signal strength or decoding the probe packets, ZiSense detects the ZigBee signals and wakes up nodes accordingly. On sensor nodes with limited information and resource, we carefully study and extract short-term features purely from the time-domain RSSI sequence, and design a rule-based approach to efficiently identify the existence of ZigBee. We theoretically analyze the benefit of ZiSense in different environments and implement a prototype in TinyOS with TelosB motes. We examine ZiSense performance under controlled interference and office environments. The evaluation results show that, compared with the state-of-the-art rendezvous mechanisms, ZiSense significantly reduces the energy consumption.

Packet corruption caused by collision is a critical problem that hurts the performance of wireless networks. Conventional medium access control (MAC) protocols resort to collision avoidance to maintain acceptable efficiency of channel utilization. According to our investigation and observation, however, collision avoidance comes at the cost of miscellaneous overhead, which oppositely hurts channel utilization, not to mention the poor resiliency and performance of those protocols in face of dense networks or intensive traffic. Discovering the ability to tolerate collisions at the physical layer implementations of wireless networks, we in this paper propose Coco, a protocol that advocates simultaneous accesses from multiple senders to a shared channel, i.e., optimistically allowing collisions instead of simply avoiding them. With a simple but effective design, Coco addresses the key challenges in achieving collision tolerance, such as precise sender alignment and the control of transmission concurrency. We implement Coco in 802.15.4 networks and evaluate its performance through extensive experiments with 21 TelosB nodes. The results demonstrate that Coco is light-weight and enhances channel utilization by at least 20 percent in general cases, compared with state-of-the-arts protocols.

Asynchronous duty cycle is widely used for energy constraint wireless nodes to save energy. The basic flooding service in asynchronous duty cycle networks, however, is still far from efficient due to severe packet collisions and contentions. We present Chase, an efficient and fully distributed concurrent broadcast layer for flooding in asynchronous duty cycle networks. The main idea of Chase is to meet the strict signal time and strength requirements (e.g., Capture Effect) for concurrent broadcast while reducing contentions and collisions. We propose a distributed random inter-preamble packet interval adjustment approach to constructively satisfy the requirements. Even when requirements cannot be satisfied due to physical constraints (e.g., the difference of signal strength is less than a 3 dB), we propose a lightweight signal pattern recognition-based approach to identify such a circumstance and extend radio-on time for packet delivery. We implement Chase in TinyOS with TelosB nodes and extensively evaluate its performance. The implementation does not have any specific requirement on the hardware and can be easily extended to other platforms. The evaluation results also show that Chase can significantly improve flooding efficiency in asynchronous duty cycle networks.

Even though indoor smoking ban is being put into practice in civilized countries, existing vision or sensor-based smoking detection methods cannot provide ubiquitous detection service. In this paper, we take the first attempt to build a ubiquitous passive smoking detection system, Smokey, which leverages the patterns smoking leaves on WiFi signal to identify the smoking activity even in the non-line-of-sight and through-wall environments. We study the behaviors of smokers and leverage the common features to recognize the series of motions during smoking, avoiding the target-dependent training set to achieve the high accuracy. We design a foreground detection-based motion acquisition method to extract the meaningful information from multiple noisy subcarriers even influenced by posture changes. Without the requirement of target's compliance, we leverage the rhythmical patterns of smoking to detect the smoking activities. We also leverage the diversity of multiple antennas to enhance the robustness of Smokey. Due to the convenience of integrating new antennas, Smokey is scalable in practice for ubiquitous smoking detection. We prototype Smokey with the commodity WiFi infrastructure and evaluate its performance in real environments. Experimental results show Smokey is accurate and robust in various scenarios.

Asynchronous duty cycle is widely used for energy constraint wireless nodes to save energy. The basic flooding service in asynchronous duty cycle networks, however, is still far from efficient due to severe packet collisions and contentions. We present Chase, an efficient and fully distributed concurrent broadcast layer for flooding in asynchronous duty cycle networks. The main idea of Chase is to meet the strict signal timing and strength requirements (e.g., Capture Effect) for concurrent transmission while reducing contentions and collisions. We propose a distributed random inter-preamble packet interval adjustment approach to constructively satisfy the requirements. Even when requirements cannot be satisfied due to physical constraints (e.g., the difference of signal strength is less than a 3 dB), we propose a light-weight signal pattern recognition based approach to identify such a circumstance and extend radio-on time for packet delivery. We implement Chase in TinyOS and TelosB platform and extensively evaluate its performance. The implementation does not have any specific requirement on the hardware and can be easily extended to other platforms. The evaluation results also show that Chase can significantly improve flooding efficiency in asynchronous duty cycle networks.

Even though indoor smoking ban is being put into practice in civilized countries, existing vision or sensor-based smoking detection methods cannot provide ubiquitous smoking detection. In this paper, we take the first attempt to build a ubiquitous passive smoking detection system, which leverages the patterns smoking leaves on WiFi signals to identify the smoking activity even in the non-line-of-sight and through-wall environments. We study the behaviors of smokers and leverage the common features to recognize the series of motions during smoking, avoiding the target-dependent training set to achieve the high accuracy. We design a foreground detection based motion acquisition method to extract the meaningful information from multiple noisy subcarriers even influenced by posture changes. Without requirements of target's compliance, we leverage the rhythmical patterns of smoking to reduce the detection false positives. We prototype Smokey with the commodity WiFi infrastructure and evaluate its performance in real environments. Experimental results show Smokey is accurate and robust in various scenarios.

With the development and popularization of WiFi, surfing on the Internet with mobile devices has become an indispensable part of people's daily life. However, as an infrastructure, WiFi APs are easily connected by some undesired users nearby. In this paper, we propose NiFi, a non-intrusive WiFi user identification system based on WiFi signals that enables AP to automatically identify legitimate users in indoor environment such as home, office and hotel. The core idea is that legitimate and undesired users may have different physical constraints, e.g., moving area, walking path, etc, leading to different signal sequences. NiFi analyzes and exploits the characteristics of signal sequences generated by mobile devices. NiFi proposes a practical and effective method to extract useful features and measure similarity for signal sequences, while not relying on precise user location information. We implement NiFi on Commercial Off-The-Shelf (COTS) APs, and the implementation does not require any modification to user devices. The experiment results demonstrate that NiFi is able to achieve an average identification accuracy at 90.83% with true positive rate at 98.89%.

We present the first comprehensive link-level measurements in an operational large-scale urban sensor network. By carefully analyzing the performance metrics, we seek to answer several fundamental questions: what are the characteristics of links in a real large-scale network, and what causes link performance degradation? The key findings of this study are that (1) the performance of intermediate links is the most unpredictable and some links exhibit highly periodic patterns, (2) the width of the reception "transitional region" is much larger than those reported in previous experiments, indicating that an outdoor environment might have a larger impact on the link performance and current protocol parameters should be carefully designed, and (3) different from previously reported results, link performance degradation has a relatively weak correlation with the corresponding RSSI (Received Signal Strength Indicator) values fluctuating near the noise floor.

Offloading network traffic from cellular to WiFi is widely used to reduce energy consumption since WiFi is assumed to have lower power consumption than cellular. However, we find that WiFi link quality may vary significantly under user mobility. Consequently, the energy efficiency of WiFi varies and sometimes becomes even worse than that of cellular. Therefore, widely used WiFi offloading may not be beneficial or even incurs more energy consumption. To address this issue, we propose Furion, an energy efficient WiFi offloading scheme that exploits beneficial WiFi links on smartphones. Towards such a goal, we investigate the relationship between energy efficiency and link quality. Accordingly, we propose a practical probabilistic model to predict WiFi energy efficiency based on the dynamics of link quality. We further extend the method to different environments by exploiting contextual factors in the prediction model to improve the accuracy. Based on the model, we design an adaptive offloading scheme to optimize the energy efficiency of WiFi offloading, while also guaranteeing user experience. We have implemented Furion on the Android platform and conduct extensive real-world experiments. The results demonstrate that Furion achieves 34.13% improvement in energy efficiency compared with the state-of-the- arts.

Duty cycling mode is widely adopted in wireless sensor networks to save energy. Existing duty-cycling protocols cannot well adapt to different data rates and dynamics, resulting in a high energy consumption in real networks. Improving those protocols may require global information or heavy computation and thus may not be practical, leading to many empirical parameters in real protocols. To fill the gap between the application requirement and protocol performance, in this paper, we analyze the energy consumption for duty cycled sensor networks with different data rates. Our analysis shows that existing protocols cannot lead to an efficient energy consumption in various scenarios. Based on the analysis, we design a light-weight adaptive duty-cycling protocol (LAD), which reduces the energy consumption under different data rates and protocol dynamics. LAD can adaptively adjust the protocol parameters according to network conditions such as data rate and achieve an optimal energy efficiency. To make LAD practical in real network, we further pre-calculate optimal parameters offline and store them on sensor nodes, which significantly reduces the computation time. We theoretically validate the performance improvement of the protocol. We implement the protocol in TinyOS and extensively evaluate it on 40 TelosB nodes. The evaluation results show the energy consumption can be reduced by 28.2-40.1 percent compared with state-of-the-art protocols. Results based on data from a 1,200-node operational network further show the effectiveness and scalability of the design.

To guarantee reliability, bulk data dissemination relies on the negotiation scheme in which senders and receivers negotiate transmission schedule through a three-way handshake procedure. However, we find negotiation incurs a long dissemination time and seriously defers the network-wide convergence. On the other hand, the flooding approach, which is conventionally considered inefficient and energy-consuming, can facilitate bulk data dissemination if appropriately incorporated. This motivates us to pursue a delicate tradeoff between negotiation and flooding in the bulk data dissemination. We propose SurF (Survival of the Fittest), a bulk data dissemination protocol which adaptively adopts negotiation and leverages flooding opportunistically. SurF incorporates a time-reliability model to estimate the time efficiencies (flooding versus negotiation) and dynamically selects the fittest one to facilitate the dissemination process. We implement SurF in TinyOS 2.1.1 and evaluate its performance with 40 TelosB nodes. The results show that SurF, while retaining the dissemination reliability, reduces the dissemination time by 40 percent in average, compared with the state-of-the-art protocols.

The notion of global time is of great importance for many sensor network applications. Time reconstruction methods aim to reconstruct the global time with respect to a reference clock. To achieve microsecond accuracy, MAC-layer timestamping is required for recording packet transmission and reception times. The timestamps, however, can be invalid due to multiple reasons, such as imperfect system designs, wireless corruptions, or timing attacks, etc. In this paper, we propose ART, an accurate and robust time reconstruction approach to detecting invalid timestamps and recovering the needed information. ART is much more accurate and robust than threshold-based approach, especially in dynamic networks with inherently varying propagation delays. We evaluate our approach in both testbed and a real-world deployment. Results show that: 1) ART achieves a high detection accuracy with low false-positive rate and low false-negative rate; 2) ART achieves a high recovery accuracy of less than 2 ms on average, much more accurate than previously reported results.

Opportunistic routing, offering relatively efficient and adaptive forwarding in low-duty-cycled sensor networks, generally allows multiple nodes to forward the same packet simultaneously, especially in networks with intensive traffic. Uncoordinated transmissions often incur a number of duplicate packets, which are further forwarded in the network, occupy the limited network resource, and hinder the packet delivery performance. Existing solutions to this issue, e.g., overhearing or coordination based approaches, either cannot scale up with the system size, or suffer high control overhead. We present Duplicate-Detectable Opportunistic Forwarding (DOF), a duplicate-free opportunistic forwarding protocol for low-duty-cycled wireless sensor networks. DOF enables senders to obtain the information of all potential forwarders via a slotted acknowledgment scheme, so the data packets can be sent to the deterministic next-hop forwarder. Based on light-weight coordination, DOF explores the opportunities as many as possible and removes duplicate packets from the forwarding process. We implement DOF and evaluate its performance on an indoor testbed with 20 TelosB nodes. The experimental results show that DOF reduces the average duplicate ratio by 90%, compared to state-of-the-art opportunistic protocols, and achieves 61.5% enhancement in network yield and 51.4% saving in energy consumption.

Delay is an important metric to understand and improve system performance. While existing approaches focus on aggregated delay statistics in pre-programmed granularity and provide results such as average and deviation, those approaches may not provide fine-grained delay measurement and thus may miss important delay characteristics. For example, delay anomaly, which is a critical system performance indicator, may not be captured by coarse-grained approaches. We propose a new measurement structure design called order preserving aggregator (OPA). Based on OPA, we can efficiently encode and recover the ordering and loss information by exploiting inherent data characteristics. We then propose a two-layer design to convey both ordering and time stamp, and efficiently derive per-packet delay/loss measurement. We evaluate our approach both analytically and experimentally. The results show that our approach can achieve per-packet delay measurement with an average of per-packet relative error at 2%, and an average of aggregated relative error at 10-5, while introducing additional communication overhead in the order of 10-4in terms of number of packets. While at a low data rate, the computation overhead of OPA is acceptable. Reducing the computation and communication overhead under high data rate, to make OPA more practical in real applications, will be our future direction.

Recently, carrying control signals on passing data packets has emerged as a promising direction for efficient control information transmission. With control messages carried on data payload, the extra air time needed for control packets like RTS/CTS is eliminated and thus channel utilization is improved. However, carrying control signals on the data payload of a packet requires the data packet to have a sufficiently large SNR, otherwise both the data packet and the control messages are lost. In this paper, we propose Hitchhike, a technique that utilizes the preamble field to carry control messages. Hitchhike completely decouples the control messages from the payload and therefore the superposition of (multiple) control messages has little adverse effect on the operation of the payload decoding. We implement and evaluate Hitchhike in the USRP2 platform with five nodes. Evaluation results demonstrate the feasibility and effectiveness of Hitchhike. Compared with the state-of-the-art, e.g., side-channel in 802.15.4, Hitchhike improves the detection accuracy of control messages by 40% and reduces the data loss caused by control messages by 15%.

In distributed networks such as wireless ad hoc networks, local and lossy logs are often available on individual nodes. We propose REFILL, which analyzes lossy and unsynchronized logs collected from individual nodes and reconstructs the network behaviors. We design an inference engine based on protocol semantics to abstract states on each node. Further we leverage inherent and implicit event correlations in and between nodes to connect interference engines and analyze logs from different nodes. Based on unsynchronized and incomplete logs, REFILL can reconstruct network behavior, recover the network scenario and understand what has happened in the network. We show that the result of REFILL can be used to guide protocol design, network management, diagnosis, etc. We implement REFILL and apply it to a large-scale wireless sensor network project. REFILL provides a detailed per-packet tracing information based on event flows. We show that REFILL can reveal and verify fundamental issues, like locating packet loss positions and root causes. Further, we present implications and demonstrate how to leverage REFILL to enhance network performance.

Driven by the proliferation of mobile applications, the conflict between data communication requirement and limited battery capacity is becoming sharp on modern smartphones. Offloading mobile traffic from cellular to WiFi is widely recognized as a viable solution to improve the energy efficiency. However, through extensive field experiments, we find WiFi offloading is not always energy efficient and even consumes more energy than cellular network due to link quality variation. In addition, we also observe that practical data transmission deadline requirement and link utilization allows scheduling of data traffic to time periods with good link quality. Accordingly, we propose Q-offload, the first attempt towards energy efficient WiFi offloading with link dynamics. In Q-offload, we propose an iterative framework to achieve energy efficient WiFi offloading by exploiting good link quality while not affecting user experience. We evaluate the performance of Q-offload through both trace-driven analysis and real-world experiments. The results show that it can achieve 33.5%~55.7% energy efficiency improvement, compared with state-of-the-arts under different conditions.

Emergency navigation is an emerging application of wireless sensor networks with significant research and social value. In order to ensure the safe and timely navigation of the evacuees, most of the existing works model navigation as a path-planning problem or movement decision support problem and adopt different metrics, such as the shortest route, the minimum exposure path, and the maximum safe distance. Without sufficient consideration of the dynamics of danger, the existing approaches are likely to cause users to move back and forth during navigation, known as oscillation. Frequent oscillations inevitably result in the user remaining in danger for a longer period of time, amplification of the user's panic, and eventual decrease in the chances of survival. In this paper we take users' oscillations in the dynamic environments into account and quantify the local success rate of navigation using a metric called ENO (Expected Number of Oscillations). We then propose OPEN, an oscillation-free navigation approach that minimizes the probability of oscillation and guarantees the success rate of emergency navigation. We implement OPEN and evaluate its performance through the trace from our system and extensive simulations. The results demonstrate that OPEN outperforms the current state-of-the-art approaches with respect to user safety and navigation efficiency.

Coexistence of Wi-Fi and ZigBee in 2.4 GHz ISM band is a long standing and challenging problem. Previous solutions either require modifications of current ZigBee protocols or Wi-Fi re-configurations, which is not feasible in large-scale wireless sensor networks. In this paper, we present WizBee, a coexistence system using single-antenna sink without changing current Wi-Fi and ZigBee design. WizBee is based on an observation that Wi-Fi signal is about 5 to 20 dB stronger than ZigBee signal in symmetric area, which leaves much room for applying interference cancelation technique to mitigate Wi-Fi interference, and extract ZigBee signals. However, we need to cancel the Wi-Fi interference perfectly for residual ZigBee signal decoding, which needs more accurate channel coefficient across data transmissions in spite of cross technology interference. For robust and accurate Wi-Fi decoding, we use soft Viterbi decoding with weighted confidence value over interfered subcarriers. Consequently, our solution uses decoded data for channel coefficient estimation instead of conventional training symbol based methods. The key insight is that, the signal recovery opportunity for cross technology coexistence, lies in multi-domain information, such as power, frequency and coding discrepancies. Using these information properly will improve the coexistence network throughput effectively. We implemented WizBee in USRP/GNURadio software radio platform, and studied the decoding performance of interference cancelation technique. Our extensive evaluations under real wireless conditions show that WizBee improves ZigBee throughput up to 1.9x, with median throughput gain of 1.2x.

We present a comprehensive delay performance measurement and analysis in a large-scale wireless sensor network. We build a lightweight delay measurement system and present a robust method to calculate the per-packet delay. We show that the method can identify incorrect delays and recover them with a bounded error. Through analysis of delay and other system metrics, we seek to answer the following fundamental questions: What are the spatial and temporal characteristics of delay performance in a real network? What are the most important impacting factors, and is there any practical model to capture those factors? What are the implications to protocol designs? In this paper, we identify important factors from the data trace and show that the important factors are not necessarily the same with those in the Internet. Furthermore, we propose a delay model to capture those factors. We revisit several prevalent protocol designs such as Collection Tree Protocol, opportunistic routing, and Dynamic Switching-based Forwarding and show that our model and analysis are useful to practical protocol designs.

Delay is an important metric to understand and improve system performance. While existing approaches focus on aggregate delay statistics in pre-programmed granularity, providing only statistical results such as averages and deviations, those approaches fail to provide fine-grained delay measurement at a flexible level and thus may miss important delay characteristics. For example, delay anomalies, which are critical system performance indicators, may not be captured by existing coarse grained approaches. In this work, we propose a fine-grained delay measurement approach based on a new measurement structure design called order preserving aggregator (OPA). OPA can efficiently encode the ordering and loss information by exploiting inherent data characteristics. Based on OPA, we propose a two layer design to convey both ordering and time stamp information, and then derive per-packet delay/loss measurement with a small overhead. We evaluate our approach both analytically and experimentally with widely used real-world data sets. The results show that our approach can achieve accurate per-packet delay measurement with an average of per-packet relative error at 2%, and an average of aggregated relative error at 10-5, while introducing less than 4 × 10-4additional overhead.

Collision resolution is a crucial issue in wireless sensor networks. The existing approaches of collision resolution have drawbacks with respect to energy efficiency and processing latency. In this paper, we propose ST AIRS, a time and energy efficient collision resolution mechanism for wireless sensor networks. STAIRS incorporates the constructive interference technique in its design and explicitly forms superimposed colliding signals. Through extensive observations and theoretical analysis, we show that the RSSI of the superimposed signals exhibit stairs-like phenomenon with different number of contenders. That principle offers an attractive feature to efficiently distinguish multiple contenders and in turn makes collision-free schedules for channel access. In the design and implementation of STAIRS, we address practical challenges such as contenders alignment, online detection of RSSI change points, and fast channel assignment. The experiments on real testbed show that STARIS realizes fast and effective collision resolution, which significantly improves the network performance in terms of both latency and throughput.

Recently, carrying control signals on passing data packets has emerged as a promising direction for efficient control information transmission. With control messages carried on data payload, the extra air time needed for control packets like RTS/CTS is eliminated and thus channel utilization is improved. However, carrying control signals on the data payload of a packet requires the data packet to have a sufficiently large SNR, otherwise both the data packet and the control messages are lost. In this paper, we proposeHitchhike, a technique that utilizes the preamble field to carry control messages. Hitchhike completely decouples the control messages from the payload and therefore the superposition of (multiple) control messages has little adverse effect on the operation of the payload decoding. We implement and evaluate Hitchhike in the USRP2 platform with 5 nodes. Evaluation results demonstrate the feasibility and effectiveness of Hitchhike. Compared with the state-of-the-art, e.g., Side-channel in 802.15.4, Hitchhike improves the detection accuracy of control messages by 40% and reduces the data loss caused by control messages by 15%.

Duty cycling mode is widely adopted in wireless sensor networks to save energy. Existing duty-cycling protocols cannot well adapt to different data rates and dynamics, resulting in a high energy consumption in real networks. Improving those protocols may require global information or heavy computation and thus may not be practical, leading to empirical parameters in real protocols. To fill the gap between the application requirement and protocol performance, we design a light-weight adaptive duty-cycling protocol (LAD), which reduces the energy consumption under different data rates and protocol dynamics. We theoretically validate the performance improvement of the protocol. We implement the protocol in TinyOS and extensively evaluate it on 40 TelosB nodes. The evaluation results show the energy consumption can be reduced by 28.2%~40.1% compared with state-of-the-art protocols. Results based on data from a 1200-node operational network further show the effectiveness and scalability of the design.

In large scale wireless sensor networks, retransmission strategies are widely adopted to guarantee the reliability of multi-hop forwarding. However, keeping retransmission over a bursty link may fail consecutively. Moreover, the retransmission will also be useless over those back-up links which are spatial correlated with the failed link. Thus, it is necessary to design an unified retransmission strategy, which considers both temporal and spacial link properties, to further improve network reliability and efficiency. In this paper, we propose RxLayer, a practical and general supporting layer of data retransmission. Without inducing noticeable overhead, RxLayer captures the temporal and spatial link properties by conditional probability models. A sender will retransmit data over the candidate link with the highest delivery probability while failures occur. RxLayer can be transparently integrated with most of the existing forwarding protocols. We implement RxLayer and evaluate it on both indoor and outdoor testbeds. The results show that RxLayer improves networks reliability and energy efficiency in various scenarios. The network reliability is improved by up to 7.82%, and the total number of transmissions is reduced by up to 36.3%.

We present Dylog, a dynamic logging facility for networked embedded systems. Dylog employs several techniques to enable lightweight and interactive logging. First, Dylog uses binary instrumentation for dynamically inserting or removing logging statements, enabling interactive debugging at the runtime. Second, Dylog incorporates an efficient storage system and log collection protocol for recording and transferring the logging messages. In particular, Dylog significantly reduces the communication cost by storing string identifiers and restoring them back to corresponding strings at the PC. Third, Dylog employs MAC layer timestamping and a linear clock model for reconstructing the synchronized time of the logging messages with a very high precision. We implement and evaluate Dylog on TinyOS 2.1.1/TelosB. Results show that Dylog incurs a reasonable overhead. Dylog can help gain great visibility into the system behaviors, and diagnose performance issues at the source code level.

To save energy, wireless sensor networks often run in a low duty cycle mode, where the radios of sensor nodes are scheduled between ON and OFF states. For nodes to communicate with each other, Low Power Listening (LPL) and Low Power Probing (LPP) are two types of rendezvous mechanisms. Nodes with LPL or LPP rely on signal strength or probe packets to detect potential transmissions, and then keep the radio-on for communications. Unfortunately, in many cases, signal strength and probe packets are susceptible to interference, resulting in undesirable radio on time when the signal strength of interference is above a threshold or a probe packet is interfered. To address the issue, we propose ZiSense, an energy efficient rendezvous mechanism which is resilient to interference. Instead of checking the signal strength or decoding the probe packets, ZiSense detects the existence of ZigBee transmissions and wakes up nodes accordingly. On sensor nodes with limited information and resource, we carefully study and extract short-term features purely from the time-domain RSSI sequence, and design a rule-based approach to efficiently identify the existence of ZigBee. We theoretically analyze the benefit of ZiSense in different environments and implement a prototype in TinyOS with TelosB motes. We examine ZiSense performance under controlled interference and office environments. The evaluation results show that, compared with state-of-the-art rendezvous mechanisms, ZiSense significantly reduces the energy consumption.

The notion of global time is of great importance for many sensor network applications. To achieve microsecond accuracy, MAC-level timestamping is required for recording packet transmission and reception times. The MAC-level timestamps, however, are known to be error-prone, especially with low power listening techniques. In this paper, we propose ART, an accurate and robust time reconstruction approach to detecting invalid timestamps and recovering the needed information. We evaluate our approach in both testbed and a real-world deployment. Results show ART is accurate and robust for deployed sensor networks.

Due to its large scale and constrained communication radius, a wireless sensor network mostly relies on multi-hop transmissions to deliver a data packet along a sequence of nodes. It is of essential importance to measure the forwarding quality of multi-hop paths and such information shall be utilized in designing efficient routing strategies. Existing metrics like ETX, ETF mainly focus on quantifying the link performance in between the nodes while overlooking the forwarding capabilities inside the sensor nodes. The experience on manipulating GreenOrbs, a large-scale sensor network with 330 nodes, reveals that the quality of forwarding inside each sensor node is at the least an equally important factor that contributes to the path quality in data delivery. In this paper we propose QoF, Quality of Forwarding, a new metric which explores the performance in the gray zone inside a node left unattended in previous studies. By combining the QoF measurements within a node and over a link, we are able to comprehensively measure the intact path quality in designing efficient multi-hop routing protocols. We implement QoF and build a modified Collection Tree Protocol (CTP). We evaluate the data collection performance in a testbed consisting of 50 TelosB nodes, and compare it with the original CTP protocol. The experimental results show that our approach takes both transmission cost and forwarding reliability into consideration, thus achieving a high throughput for data collection.

Packet corruption caused by collision is a critical problem that hurts the performance of wireless networks. Conventional medium access control (MAC) protocols resort to collision avoidance to maintain acceptable efficiency of channel utilization. According to our investigation and observation, however, collision avoidance comes at the cost of miscellaneous overhead, which oppositely hurts channel utilization, not to mention the poor resiliency and performance of those protocols in face of dense networks or intensive traffic. Discovering the ability to tolerate collisions at the physical layer implementations of wireless networks, we in this paper propose Coco, a MAC protocol that advocates simultaneous accesses from multiple senders to a shared channel, i.e., optimistically allowing collisions instead of simply avoiding them. With a simple but effective design, Coco addresses the key challenges in achieving collision tolerance, such as precise sender alignment and fine control of the transmission concurrency. We implement Coco in 802.15.4 networks and evaluate its performance through extensive experiments with 21 TelosB nodes. The results demonstrate that Coco is light-weight and enhances channel utilization by at least 20% in general cases, compared with state-of-the-arts protocols.

Opportunistic routing, offering relatively efficient and adaptive forwarding in low-duty-cycled sensor networks, generally allows multiple nodes to forward the same packet simultaneously, especially in networks with intensive traffic. Uncoordinated transmissions often incur a number of duplicate packets, which are further forwarded in the network, occupy the limited network resource, and hinder the packet delivery performance. Existing solutions to this issue, e.g. overhearing or coordination based approaches, either cannot scale up with the system size, or suffers high control overhead. We present Duplicate-Detectable Opportunistic Forwarding (DOF), a duplicate free opportunistic forwarding protocol for low-duty-cycled wireless sensor networks. DOF enables senders to obtain the information of all potential forwarders via a slotted acknowledgement scheme, so the data packets can be sent to the deterministic next-hop forwarder. Based on light-weight coordination, DOF explores the opportunities as many as possible and removes duplicate packets from the forwarding process. We implement DOF and evaluate its performance on an indoor test-bed with 20 TelosB nodes. The experimental results show that DOF reduces the average duplicate ratio by 90%, compared to state-of-the-art opportunistic protocols, and achieves 61.5% enhancement in network yield and 51.4% saving in energy consumption.

Channel utilization for wireless sensor networks is far from efficient, especially for convergecast in which multiple nodes are sending packets to a receiver. In this paper, we analyze the channel utilization when multiple nodes contend for the channel in convergecast and show that channel utilization can be improved by accumulating packets on each node. However, the number of accumulated packets should be carefully determined. Otherwise, the system performance may not be improved or even be degraded, e.g., incurring additional packet delay. Based on the analysis result, we present STAGGER to achieve channel utilization improvement while guarantee the worst case performance. We implement STAGGER in TinyOS 2.1 and evaluate its performance on TelosB nodes. STAGGER only uses local information to determine the number of accumulated packets without incurring additional overhead. It adopts CSMA at the low level and preserves its nice properties, e.g., fairness. The experimental results show that the design can significantly improve the per-hop throughput and reduce packet loss ratio under high traffic rate.

Data dissemination is a building block of wireless sensor networks (WSNs). In order to guarantee the reliability, many existing works rely on a negotiation scheme, making senders and receivers negotiate the schedule of transmissions through a three-way handshake procedure. According to our observation, however, negotiation incurs long dissemination time and seriously defers the network wide convergence. On the other hand, the flooding approach, which is conventionally considered to be inefficient and energy-consuming, may facilitate data dissemination if appropriately designed. This motivates us to pursue a delicate tradeoff between negotiation and flooding in the data dissemination process. In this paper, we propose SurF (Survival of the Fittest), a data dissemination protocol which selectively adopts negotiation and leverages flooding opportunistically. How to capture and utilize the opportunities when negotiation should be used is a challenging issue. SurF incorporates a time-reliability model to estimate the time efficiencies of the two schemes (flooding vs. negotiation) and dynamically selects the fittest one to facilitate the dissemination process. We implement SurF based on TinyOS 2.1.1 and evaluate its performance with 40 TelosB nodes. The results show that SurF, while retaining the dissemination reliability, reduces the dissemination time by 40% in average, compared with the state-of-the-art protocols.

We study the ubiquitous data collection for mobile users in wireless sensor networks. People with handheld devices can easily interact with the network and collect data. We propose a novel approach for mobile users to collect the network-wide data. The routing structure of data collection is additively updated with the movement of the mobile user. With this approach, we only perform a limited modification to update the routing structure while the routing performance is bounded and controlled compared to the optimal performance. The proposed protocol is easy to implement. Our analysis shows that the proposed approach is scalable in maintenance overheads, performs efficiently in the routing performance, and provides continuous data delivery during the user movement. We implement the proposed protocol in a prototype system and test its feasibility and applicability by a 49-node testbed. We further conduct extensive simulations to examine the efficiency and scalability of our protocol with varied network settings.

Sensor networks are deemed suitable for large-scale deployments in the wild for a variety of applications. In spite of the remarkable efforts the community put to build the sensor systems, an essential question still remains unclear at the system level, motivating us to explore the answer from a point of real-world deployment view. Does the wireless sensor network really scale? We present findings from a large-scale operating sensor network system, GreenOrbs, with up to 330 nodes deployed in the forest. We instrument such an operating network throughout the protocol stack and present observations across layers in the network. Based on our findings from the system measurement, we propose and make initial efforts to validate three conjectures that give potential guidelines for future designs of large-scale sensor networks. 1) A small portion of nodes bottlenecks the entire network, and most of the existing network indicators may not accurately capture them. 2) The network dynamics mainly come from the inherent concurrency of network operations instead of environment changes. 3) The environment, although the dynamics are not as significant as we assumed, has an unpredictable impact on the sensor network. We suggest that an event-based routing structure can be trained and thus better adapted to the wild environment when building a large-scale sensor network.

We propose a pervasive usage of the sensor network infrastructure as a cyber-physical system for navigating internal users in locations of potential danger. Our proposed application differs from previous work in that they typically treat the sensor network as a media of data acquisition while in our navigation application, in-situ interactions between users and sensors become ubiquitous. In addition, human safety and time factors are critical to the success of our objective. Without any preknowledge of user and sensor locations, the design of an effective and efficient navigation protocol faces nontrivial challenges. We propose to embed a road map system in the sensor network without location information so as to provide users navigating routes with guaranteed safety. We accordingly design efficient road map updating mechanisms to rebuild the road map in the event of changes in dangerous areas. In this navigation system, each user only issues local queries to obtain their navigation route. The system is highly scalable for supporting multiple users simultaneously. We implement a prototype system with 36 TelosB motes to validate the effectiveness of this design. We further conduct comprehensive and large-scale simulations to examine the efficiency and scalability of the proposed approach under various environmental dynamics.

We present a comprehensive delay performance measurement and analysis in an operational large-scale urban wireless sensor network. We build a light-weight delay measurement system in such a network and present a robust method to calculate per-packet delay. Through analysis of delay and system metrics, we seek to answer the following fundamental questions: what are the spatial and temporal characteristics of delay performance in a real network? what are the most important impacting factors and is there any practical model to capture those factors? what are the implications to protocol design? In this paper, we explore the important factors from the data in presence of various metrics and randomness, and show that the important factors are not necessarily the same with that in Internet. Further, we propose a delay model to capture those factors and validate it in the network. We revisit several prevalent protocol designs such as Collection Tree Protocol, opportunistic routing and Dynamic Switching based Forwarding, and show the implications to protocol designs.

Emergency navigation is an emerging application of wireless sensor networks with significant research and social values. In order to ensure the safety and timeliness of navigation for the users, most of the existing works model navigation as a path-planning problem and adopt different metrics, such as the shortest route, the minimum exposure path, and the maximum safe distance. Without sufficient consideration of the dynamics of danger, the existing approaches are likely to cause users to move back and forth during navigation, known as oscillation. Frequent oscillations inevitably result in the user remaining in danger for a longer period of time, amplification the user's panic, and eventual decrease in the chances of survival. In this paper we take users' oscillations in the dynamic environments into account and quantify the local success rate of navigation using a metric called ENO (Expected Number of Oscillations). We then propose OPEN, an oscillation-free navigation approach that minimizes the probability of oscillation and guarantees the success rate of emergency navigation. We implement OPEN and evaluate its performance through test-bed experiments and extensive simulations. The results demonstrate that OPEN outperforms the current state-of-the-arts approaches with respect to user safety and navigation efficiency.

We study the ubiquitous data collection for mobile users in wireless sensor networks. People with handheld devices can easily interact with the network and collect data. We propose a novel approach for mobile users to collect the network-wide data. The routing structure of data collection is additively updated with the movement of the mobile user. With this approach, we only perform a local modification to update the routing structure while the routing performance is bounded and controlled compared to the optimal performance. The proposed protocol is easy to implement. Our analysis shows that the proposed approach is scalable in maintenance overheads, performs efficiently in the routing performance, and provides continuous data delivery during the user movement. We implement the proposed protocol in a prototype system and test its feasibility and applicability by a 49-node testbed. We further conduct extensive simulations to examine the efficiency and scalability of our protocol with varied network settings.

In spite of the remarkable efforts the community put to build the sensor systems, an essential question still remains unclear at the system level, motivating us to explore the answer from a point of real-world deployment view. Does the wireless sensor network really scale? We present findings from a large scale operating sensor network system, GreenOrbs, with up to 330 nodes deployed in the forest. We instrument such an operating network throughout the protocol stack and present observations across layers in the network. Based on our findings from the system measurement, we propose and make initial efforts to validate three conjectures that give potential guidelines for future designs of large scale sensor networks. (1) A small portion of nodes bottlenecks the entire network, and most of the existing network indicators may not accurately capture them. (2) The network dynamics mainly come from the inherent concurrency of network operations instead of environment changes. (3) The environment, although the dynamics are not as significant as we assumed, has an unpredictable impact on the sensor network. We suggest that an event-based routing structure can be trained optimal and thus better adapt to the wild environment when building a large scale sensor network.

Due to its large scale and constrained communication radius, a wireless sensor network mostly relies on multi-hop transmissions to deliver a data packet along a sequence of nodes. It is of essential importance to measure the forwarding quality of multi-hop paths and such information shall be utilized in designing efficient routing strategies. Existing metrics like ETX, ETF mainly focus on quantifying the link performance in between the nodes while overlooking the forwarding capabilities inside the sensor nodes. The experience on manipulating GreenOrbs, a large-scale sensor network with 330 nodes, reveals that the quality of forwarding inside each sensor node is at least an equally important factor that contributes to the path quality in data delivery. In this paper we propose QoF, Quality of Forwarding, a new metric which explores the performance in the gray zone inside a node left unattended in previous studies. By combining the QoF measurements within a node and over a link, we are able to comprehensively measure the intact path quality in designing efficient multi-hop routing protocols. We implement QoF and build a modified Collection Tree Protocol (CTP). We evaluate the data collection performance in a test-bed consisting of 50 TelosB nodes, and compare it with the original CTP protocol. The experimental results show that our approach takes both transmission cost and forwarding reliability into consideration, thus achieving a high throughput for data collection.

For energy conservation, a wireless sensor network is usually designed to work in a low-duty-cycle mode, in which a sensor node keeps active for a small percentage of time during its working period. In applications where there are multiple data delivery tasks with high data rates and time constraints, low-duty-cycle working mode may cause severe transmission congestion and data loss. In order to alleviate congestion and reduce data loss, the tasks need to be carefully scheduled to balance the workloads among the sensor nodes in both spatial and temporal dimensions. This paper studies the load balancing problem, and proves it is NP-Complete in general network graphs. Two efficient scheduling algorithms to achieve load balance are proposed and analyzed. Furthermore, a task scheduling protocol is designed relying on the proposed algorithms. To the best of our knowledge, this paper is the first one to tackle multiple task scheduling for low-duty-cycled sensor networks. The simulation results show that the proposed algorithms greatly improve the network performance in most scenarios.

Wireless sensor networks are deployed in various territories executing different tasks. In many applications, it is very useful to understand their topological characteristics. This paper studies the problem of discovering the topological properties of a sensor network such as boundaries and holes. Previous works have revealed that, such a problem could be addressed with knowledge of node locations, measures of interdistances, or ideal assumptions of particular communication models, e.g., unit disk graph model. In this work, however, we explore the possibility of discovering sensor network topology merely with connectivity information. We propose a virtual voronoi diagram approach to detect both the inner and outer boundaries of a sensor network. We do not rely on any communication models, yet any geometric knowledge of the network. Compared with previous connectivity based approaches, we further release the assumption of regular wireless signals. Our approach works even for anisotropic network with irregular wireless links. We design our approach to be light-weight, preventing frequent global operations that have been intensively used in previous designs. We conduct intensive simulations in networks of different topologies with different node degrees and densities, and containing various signal irregularities. The results validate the effectiveness and efficiency of our approach.

Localization is a fundamental issue of wireless sensor networks that has been extensively studied in the literature. The real-world experience from GreenOrbs, a sensor network system in the forest, shows that localization in the wild remains very challenging due to various interfering factors. In this paper we propose CDL, a Combined and Differentiated Localization approach. The central idea is that ranging quality is the key that determines the overall localization accuracy. In its unremitting pursuit of better ranging quality, CDL incorporates virtual-hop localization, local filtration, and ranging-quality aware calibration. We have implemented CDL and evaluated it by extensive experiments and simulations. The results demonstrate that CDL outperforms current state-of-art approaches with better accuracy, efficiency and consistent performance.

We propose a pervasive usage of the sensor network infrastructure as a cyber-physical system for navigating internal users in locations of potential danger. Our proposed application differs from previous work in that they typically treat the sensor network as a media of data acquisition while in our navigation application, in-situ interactions between users and sensors become ubiquitous. In addition, human safety and time factors are critical to the success of our objective. Without any pre-knowledge of user and sensor locations, the design of an effective and efficient navigation protocol faces non-trivial challenges. We propose to embed a road map system in the sensor network without location information so as to provide users navigating routes with guaranteed safety. We accordingly design efficient road map updating mechanisms to rebuild the road map in the event of changes in dangerous areas. In this navigation system, each user only issues local queries to obtain their navigation route. The system is highly scalable for supporting multiple users simultaneously. We implement a prototype system with 36 TelosB motes to validate the effectiveness of this design. We further conduct comprehensive and large-scale simulations to examine the efficiency and scalability of the proposed approach under various environmental dynamics.

Abstract—We propose a pervasive usage of the sensor network infrastructure as a cyber-physical system for navigating internal users in locations of potential danger. Our proposed application differs from previous work in that they typically treat the sensor network as a media of data acquisition while in our navigation application, in-situ interactions between users and sensors become ubiquitous. In addition, human safety and time factors are critical to the success of our objective. Without any preknowledge of user and sensor locations, the design of an effective and efficient navigation protocol faces non-trivial challenges. We propose to embed a road map system in the sensor network without location information so as to provide users navigating routes with guaranteed safety. We accordingly design efficient road map updating mechanisms to rebuild the road map in the event of changes in dangerous areas. In this navigation system, each user only issues local queries to obtain their navigation route. The system is highly scalable for supporting multiple users simultaneously. We implement a prototype system with 36 TelosB motes to validate the effectiveness of this design. We further conduct comprehensive and large-scale simulations to examine the efficiency and scalability of the proposed approach under various environmental dynamics. Keywords—navigation; sensor networks; cyber-physical system I.

Current search mechanisms of DHT-based P2P systems can well handle a single keyword search problem. Other than single keyword search, multi-keyword search is quite popular and useful in many real applications. Simply using the solution for single keyword search will require distributed intersection/union operations in wide area networks, leading to unacceptable traffic cost. As it is well known that Bloom Filter (BF) is effective in reducing traffic, we would like to use BF encoding to handle multi-keyword search. Applying BF is not difficult, but how to get optimal results is not trivial. In this study we show, through mathematical proof, that the optimal setting of BF in terms of traffic cost is determined by the global statistical information of keywords, not the minimized false positive rate as claimed by previous methods. Through extensive experiments, we demonstrate how to obtain optimal settings. We further argue that the intersection order between sets is important for multi-keyword search. Thus, we design optimal order strategies based on BF for both "and" and "or" queries. To better evaluate the performance of this design, we conduct extensive simulations on TREC WT10G test collection and the query log of a commercial search engine. Results show that our design significantly reduces the search traffic of existing approach by 73%.