Liquid leakage detection is critical for product quality and user safety, yet existing methods require line-of-sight (LoS) or direct contact with the liquid, or could even introduce additional health or safety risks. In this paper, we propose SARLiquid, a novel mmWave-based through-package liquid leakage detection method. SARLiquid leverages mmWave signals to see through packaging and identify leakage by reconstructing mmWave images. We employ synthetic aperture radar (SAR) technique to enhance imaging resolution and tame multipath effects. We also develop a dedicated algorithm to calibrate the discontinuous phase in SAR imaging results, and propose a deep learning model for liquid leakage detection and liquid identification. We implement SARLiquid and evaluate it across a wide range of scenarios. Results show that SARLiquid achieves average accuracies above 93% for both liquid leakage detection and liquid identification, 14.70% and 9.98% higher than the baselines, respectively.

As an emerging technology, WiFi sensing has garnered widespread attention in recent years. However, due to limitations in WiFi bandwidth and hardware capacity, multi-target sensing with WiFi remains an unresolved issue. In this paper, we presentSlingShot, a first-of-its-kind approach for multi-target WiFi sensing enabled by metasurface.SlingShotexploits a metasurface's ability of beam scanning to periodically scan among targets in a high frequency, so their sensing signals can be separated in the time domain. However, the WiFi-initiated and the metasurface-initiated signals will interfere with each other, which could significantly degrade the sensing accuracy. Moreover, the WiFi devices and the metasurface operate in a distributed manner, so the inherent clock offset and clock drift among them make it difficult to accurately separate the sensing signals of the targets. To address the above challenges,SlingShotleverages the metasurface's ability of phase shifting to cancel interfering signals and designs a passive clock synchronization scheme to synchronize the metasurface and WiFi devices. We implementSlingShotand evaluate it under various settings. Results show thatSlingShotcan sense up to 8 targets. Respectively compared to the state-of-the-art methods,SlingShothas 6.75 % higher mean accuracy in activity recognition and 58.34 % lower mean error in respiration monitoring.

Carrier interference is a fundamental research challenge in backscatter systems. Existing solutions leverage frequency shifting or full duplex designs to mitigate carrier interference in the analog or digital domain. However, these solutions introduce extra spectrum usage, protocol overhead, and power consumption, all of which are undesirable in backscatter systems. In this paper, we revisit polarization mismatch and propose Lotus, a low-cost analog design to combat carrier interference for backscatter systems. Lotus comprises a novel antenna design and a backscatter tag design. At runtime, Lotus antenna replaces the receiver default antenna to cancel out the carrier interference, without any protocol or hardware overhead. Lotus tag mitigates the power loss caused by polarization mismatch and remains compatible with all existing backscatter radios. Experimental results show that Lotus achieves comparable cancellation gain (i.e., 42 dB) with the state-of-the-art frequency-shifting baseline. Meanwhile, Lotus outperforms the baseline by 2× and 6.5× in spectrum and power efficiency, respectively. Additionally, Lotus achieves comparable performance to the frequency shifting baseline regarding backscatter range and throughput across three backscatter technologies, including Wi-Fi, Bluetooth, and LoRa.

The swarm of Unmanned Aerial Vehicles (UAVs) has garnered considerable attention, particularly in scenarios with critical situations or limited communication infrastructure. In such cases, Mission UAVs (MUs) often be deployed in clusters to provide communication services. However, the high mobility of MUs always leads to frequent changes in swarm network topology, posing challenges for the network performance. To tackle this issue, we deploy additional Relay UAVs (RUs) with a two-stage adaptive learning-based approach. In the first stage, we employ a Delaunay triangulation-based algorithm to optimize RUs’ position and construct the initial topology. In the second stage, we implement a centralized learning and decentralized execution (CTDE) reinforcement learning framework to ensure continuous network connectivity and optimize performance throughout the task cycle. To further enhance cooperation among RUs, we introduce a sequential update technique coupled with an entropy regularization term during the policy network updates. Finally, extensive simulation results demonstrate the effectiveness of our proposed algorithms.

Large language models (LLMs) have emerged as a transformative advancement in artificial intelligence (AI). To fully leverage their potential, Docker containers, serving as a lightweight, portable, and isolated framework, facilitate the seamless deployment of LLM-based applications. However, the deployment of containerized AI microservices faces challenges such as heavy network loads, delayed image loading, and redundancy. In this paper, we introduce MetaPipe, an innovative incremental deployment approach for containerized AI microservices in edge cloud environments. MetaPipe aims to optimize startup times through a dynamic workflow that incorporates proactive layer pre-fetching and reinforcement layer re-scheduling. The proactive pre-fetching reduces service deployment time through layer caching prediction and pre-scheduling before requests arrive, while the reinforcement re-scheduling addresses inaccuracies by dynamically adjusting layer scheduling strategies after requests arrive. Extensive experiments on realworld datasets show that MetaPipe significantly outperforms traditional methods, achieving 83.58% reduction in initialization startup time and 85.58% reduction in cold startup time. These results highlight its effectiveness in enhancing the performance of AI microservices deployment within edge cloud environments.

Parallelizing passive Radio Frequency Identification (RFID) reading is an arguably crucial, yet unsolved challenge in modern IoT applications. Existing approaches remain limited to time-division operations and fail to read multiple tags simultaneously. In this paper, we introduce QuinID, the first frequency-division multiple access (FDMA) RFID system to achieve fully parallel reading. We innovatively exploit the frequency selectivity of the tag antenna rather than a conventional digital FDMA, bypassing the power and circuitry constraint of RFID tags. Specifically, we delicately design the frequency-selective antenna based on surface acoustic wave (SAW) components to achieve extreme narrow-band response, so that QuinID tags (i.e., QuinTags) operate exclusively within their designated frequency bands. By carefully designing the matching network and canceling various interference, a customized QuinReader communicates simultaneously with multiple QuinTags across distinct bands. QuinID maintains high compatibility with commercial RFID systems and presents a tag cost of less than 10 cents. We implement a 5-band QuinID system and evaluate its performance under various settings. The results demonstrate a fivefold increase in read rate, reaching up to 5000 reads per second.

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

We present a novel approach to monitor the Body in White (BiW), the fundamental metallic structure of a vehicle. Existing monitoring methods, including both wired and wireless sensor systems, face significant challenges due to integration complexity, weight considerations, material costs, and signal blockage within the metallic environment. To overcome these limitations, we introduce Arach-Net, an acoustic backscatter network that leverages the conductive properties of the BiW to propagate vibration signals for energy transfer and data communication. This system comprises battery-free tags that harvest energy from BiW vibrations and utilize a backscatter technique for efficient communication, thereby eliminating the need for external power sources and reducing the power consumption. We address key challenges such as power sufficiency for tag activation and sustained operation, and collision reduction in network communication, by designing an ultra-low power backscatter tag and a distributed slot allocation protocol. We implement ArachNet, and deploy 12 tags onto the BiW of an electric SUV car. The evaluation results show that the power consumption of the tag is 51.0 μW for uplink packet transmission, and 24.8 μW for downlink packet reception. With our network protocol, the slot utilization can be up to 81.2%.

Wi-Fi human sensing has attracted numerous research studies over the past decade. The rapid advancement of machine learning technology further boosts the development of Wi-Fi human sensing. However, current Wi-Fi human sensing suffers from the “data scarcity” problem: all the existing proposals require collecting a large amount of human-based datasets to train the sensing models, which is laborintensive and may raise ethical concerns in certain scenarios. This obstacle seriously restricts the size, quality, and diversity of available datasets, thereby affecting the sensing performance in terms of accuracy and cross-domain applicability. In order to solve this problem, we in this paper propose Metasurface-Assisted Sample Synthesis (MASS), a novel approach to synthesize high-fidelity Wi-Fi sensing samples that effectively capture both the essential features of human motion and environment-specific multipath characteristics without requiring human involvement. The evaluation results show that MASS is effective in boosting machine learning performance, improving classification accuracy by 18%, and enhancing the cross-domain sensing accuracy by 22%. We further analyze inherent synthesis distortions stemming from hardware limitations and introduce a mitigation technique, which significantly enhances data fidelity, achieving 91.8% accuracy even when training exclusively on synthesized samples. These findings underscore the potential of MASS to facilitate the creation of high-quality, diverse datasets with minimal human involvement and associated labor costs.

The rapid development of wireless sensing technology has opened new possibilities for non-contact health monitoring. Among them, respiration is crucial information for assessing vital signs. However, traditional methods face challenges of signal interference and overlap in multi-person environments. In this work, we propose a novel method for multi-person respiration detection using a digital programmable metasurface (DPM). This method takes advantage of the modulation characteristics of the DPM in the time and space domain. It divides the Channel State Information (CSI) from the Wi-Fi transmitter into multiple sub-signals in the time domain and modulates the radiation directions of the sub-signals for space redistribution. These signals are received by the Wi-Fi receiver and recombined to restore the CSI in each direction, thus enabling the accurate extraction of respiration signals from targets in different directions. Experimental results show that this method can directionally sense the human respiration information in a specific direction under the static working mode. Under dynamic scanning mode, it can effectively separate and detect the respiration information of four people from different directions. This system has great potential for applications in wireless communication, healthcare, and smart home environments.

The emerging integrated sensing and communication (ISAC) technologies show great potential for 5G NR, offering a new wireless-sensing infrastructure paradigm. Users can benefit from pervasive sensing applications in various scenarios without communication penalties. Given the diverse demands for sensing resources across different sensing tasks, elastic resource scheduling becomes crucial, particularly when resources are constrained. However, existing approaches often treat users equally, limiting the applicability in dealing with diverse sensing tasks in the real world. In this article, we introduce ElaSe , a pioneering sensing technique that enables elastic and prompt scheduling of sensing resources. At the core of ElaSe is the exploration of the user’s state to precisely determine the sensing resource requirements and schedule resources accordingly. We build the first model for matching sensing resources with sensing demands, and further propose a predictive scheduling scheme to eliminate delays by leveraging the 5G virtualized radio access network (vRAN). ElaSe has been implemented on a CPU-based 5G vRAN and commercial 5G user equipments. We conduct experiments to evaluate the performance of ElaSe under different settings. The results demonstrate that ElaSe outperforms the non-scheduling scheme, with a 34% reduction in trajectory tracking error and a 92% decrease in resource allocation error.

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

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

Backscatter is an enabling technology for battery-free sensing in today’s Artificial Intelligence of Things (AIOT). Building a backscatter sensing system, however, is a daunting task, due to two obstacles: the unaffordable power consumption of the microprocessor and the coexistence with the ambient carrier’s traffic. In order to address the issues, we present Leggiero, the first-of-its-kind analog WiFi backscatter with payload transparency, and its enhanced version, Leggiero+. A specially designed circuit based on the varactor diode directly converts fast-varying analog sensor signals into the RF (radio frequency) signal phase, eliminating the need for a microprocessor to interface between the radio and the sensor. By precisely locating the WiFi packet’s extra long training field (LTF) section and carefully designing the reference circuit, Leggiero embeds the analog phase into the channel state information (CSI). A commodity WiFi receiver without hardware modification can simultaneously decode the WiFi and the sensor data. We implement and evaluate Leggiero and Leggiero+ under varied settings. Results show the tag’s power consumption (excluding the power of the peripheral sensor module) is$30\mu $W at a 400Hz sampling rate,$4.8\times $and$4\times $lower than the state-of-the-art WiFi backscatter schemes. Leggiero+ demonstrates enhanced throughput, communication range, and analog signal reproduction accuracy. Our design supports a variety of sensing applications, while maintaining the WiFi carrier’s throughput performance.

Backscatter is a key technology for battery-free sensing in industrial IoT applications. To fully cover numerous tags in the deployment area, one often needs to deploy multiple readers, each of which communicates with tags within its communication range. However, the actual backscattered signals from a tag are likely to reach a reader outside its communication range and cause interference. Conventional TDMA or CSMA based approaches for interference avoidance separate readers’ media access in time, leading to limited network throughput. In this paper, we propose Trident, a novel backscatter design that enables interference avoidance via frequency-space division. By incorporating a tunable bandpass filter and multiple terminal loads, a Trident tag can detect its channel condition and adaptively adjust the frequency and the power of its backscattered signals. We further propose a frequency assignment algorithm for the readers. With these designs, all the readers in the network can operate concurrently without being interfered. We implement Trident and evaluate its performance under various settings. The results demonstrate that Trident enhances the network throughput by$3.18\times $, compared to the TDMA-based scheme.

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

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

Heart rate recovery (HRR) within the initial minute following exercise is a widely utilized metric for assessing cardiac autonomic function in individuals and predicting mortality risk in patients with cardiovascular disease. However, prevailing solutions for HRR monitoring typically involve the use of specialized medical equipment or contact wearable sensors, resulting in high costs and poor user experience. In this paper, we propose a contactless HRR monitoring technique, mmHRR, which achieves accurate heart rate (HR) estimation with a commercial mmWave radar. Unlike HR estimation at rest, the HR varies quickly after exercise and the heartbeat signal entangles with the respiration harmonics. To overcome these hurdles and effectively estimate the HR from the weak and non-stationary heartbeat signal, we propose a novel signal processing pipeline, including dynamic target tracking, adaptive heartbeat signal extraction, and accurate HR estimation with composite sliding windows. Real-world experiments demonstrate that mmHRR exhibits exceptional robustness across diverse environmental conditions, and achieves an average HR estimation error of 3.31 bpm (beats per minute), 71% lower than that of the state-of-the-art method.

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

Backscatter is an enabling technology for battery-free sensing in industrial IoT applications. For the purpose of full coverage of numerous tags in the deployment area, one often needs to deploy multiple readers, each of which is to communicate with tags within its communication range. But the actual backscattered signals from a tag are likely to reach a reader outside its communication range, causing undesired interference. Conventional approaches for interference avoidance, either TDMA or CSMA based, separate the readers’ media accesses in the time dimension and suffer from limited network throughput. In this paper, we propose Trident, a novel backscatter tag design that enables interference avoidance with frequency-space division. By incorporating a tunable bandpass filter and multiple terminal loads, a Trident tag is able to detect its channel condition and adaptively adjust the frequency band and the power of its backscattered signals, so that all the readers in the network can operate concurrently without being interfered. We implement Trident and evaluate its performance under various settings. The results demonstrate that Trident enhances the network throughput by 3.18×, compared to the TDMA based scheme.

Integrated Sensing and Communication (ISAC) has been witnessed to be a new paradigm of wireless sensing in 5G networks. Users can benefit from pervasive sensing applications in various scenarios with no communication penalty. Given the diverse demands for sensing resources across different sensing tasks, elastic resource scheduling becomes crucial, particularly when resources are constrained. However, existing approaches often treat users equally, limiting their applicability in dealing with diverse sensing tasks in the real world. In this paper, we introduce ElaSe, a pioneering sensing technique that enables real-time elastic scheduling of sensing resources. At the core of ElaSa is the exploration of the user's state to precisely determine the sensing resource requirements and schedule resources accordingly. We build the first model for matching sensing resources with sensing demands, and further propose a predictive scheduling scheme to eliminate delays by leveraging the 5G virtualized radio access network (vRAN). We conduct experiments to evaluate the performance of ElaSe under different settings. The results demonstrate that ElaSe outperforms the non-scheduling scheme, with a 34% reduction in trajectory tracking error and a 92% decrease in resource allocation error.

This paper presents the design, implementation, and evaluation of MagWear, a novel biomagnetism-based system that can accurately and inclusively monitor the heart rate and respiration rate of mobile users with diverse skin tones. MagWear's contributions are twofold. Firstly, we build a mathematical model that characterizes the magnetic coupling effect of blood flow under the influence of an external magnetic field. This model uncovers the variations in accuracy when monitoring vital signs among individuals. Secondly, leveraging insights derived from this mathematical model, we present a softwarehardware co-design that effectively handles the impact of human diversity on the performance of vital sign monitoring, pushing this generic solution one big step closer to real adoptions. We have implemented a prototype of MagWear on a two-layer PCB board and followed IRB protocols to conduct system evaluations. Our extensive experiments involving 30 volunteers demonstrate that MagWear achieves high monitoring accuracy with a mean percentage error (MPE) of 1.55% for heart rate and 1.79% for respiration rate. The head-to-head comparison with Apple Watch 8 further demonstrates MagWear's consistently high performance in different user conditions.

We presentMicNest: an acoustic localization system enabling precise drone landing. InMicNest, multiple microphones are deployed on a landing platform in carefully devised configurations. The drone carries a speaker transmitting purposefully-designed acoustic pulses. The drone may be localized as long as the pulses are correctly detected. Doing so is challenging:i)because of limited transmission power, propagation attenuation, background noise, and propeller interference, the Signal-to-Noise Ratio (SNR) of received pulses is intrinsically low;ii)the pulses experience non-linear Doppler distortion due to the physical drone dynamics;iii)as location information is used during landing, the processing latency must be reduced to effectively feed the flight control loop. To tackle these issues, we design a novel pulse detector, Matched Filter Tree (MFT), whose idea is to convert pulse detection to a tree search problem. We further present three practical methods to accelerate tree search jointly. Our experiments show thatMicNestcan localize a drone 120 m away with 0.53% relative localization error at 20 Hz location update frequency. For navigating drone landing,MicNestcan achieve a success rate of 94%. The average landing error (distance between landing point and target point) is only 4.3 cm.

We present Acoustic Inertial Measurement (AIM), a one-of-a-kind technique for indoor drone localization and tracking. Indoor drone localization and tracking are arguably a crucial, yet unsolved challenge: in GPS-denied environments, existing approaches enjoy limited applicability, especially in Non-Line of Sight (NLoS), require extensive environment instrumentation, or demand considerable hardware/software changes on drones. In contrast, AIM exploits the acoustic characteristics of the drones to estimate their location and derive their motion, even in NLoS settings. We tame location estimation errors using a dedicated Kalman filter and the Interquartile Range rule (IQR) and demonstrate that AIM can support indoor spaces with arbitrary ranges and layouts. We implement AIM using an off-the-shelf microphone array and evaluate its performance with a commercial drone under varied settings. Results indicate that the mean localization error of AIM is 46% lower than that of commercial UWB-based systems in a complex 10m×10m indoor scenario, where state-of-the-art infrared systems would not even work because of NLoS situations. When distributed microphone arrays are deployed, the mean error can be reduced to less than 0.5m in a 20m range, and even support spaces with arbitrary ranges and layouts.

The radio range of backscatter systems continues growing as new wireless communication primitives are continuously invented. Nevertheless, both the bit error rate and the packet loss rate of backscatter signals increase rapidly with the radio range, thereby necessitating the cooperation between the access point and the backscatter tags through a feedback loop. Unfortunately, the low-power nature of backscatter tags limits their ability to demodulate feedback signals from a remote access point and scales down to such circumstances. This paper presents, an ultra-low-power demodulator for long-range LoRa backscatter systems. is based on an observation that a frequency-modulated chirp signal can be transformed into an amplitude-modulated signal using a differential circuit. Moreover, we redesign a LoRa backscatter tag which integrates and a ring oscillator-based modulator. The LoRa backscatter tag enables re-transmission, rate adaption and channel hopping – three PHY-layer operations that are important to channel efficiency yet unavailable on existing long-range backscatter systems. We prototype and the LoRa backscatter tag on two PCB boards and evaluate their performance in different environments. Results show that achieves 3.5–5$\times$gain on the demodulation range, compared with state-of-the-art systems. Our ASIC simulation shows that the power consumption of and the LoRa backscatter tag are around 93.2$\mu W$and 94.7$\mu W$. Code and hardware schematics can be found at: https://github.com/ZangJac/Saiyan.

This paper presents RF-Transformer, a unified backscatter radio hardware abstraction that allows a low-power IoT device to directly communicate with heterogeneous wireless receivers. Unlike existing backscatter systems that are tailored to a specific wireless communication protocol, RF-Transformer provides a programmable interface to the micro-controller, allowing IoT devices to synthesize different types of protocol-compliant backscatter signals in the PHY layer. By leveraging the nonlinear characteristics of the negative impedance, RF-Transformer also achieves a cross-frequency backscatter design that enables IoT devices in harmonic frequency bands to communicate with each other. We implement a PCB prototype of RF-Transformer on 2.4 GHz ISM band and conduct extensive experiments. We leverage the software defined platform USRP to transmit the carrier signal and receive the backscatter signal to verify the efficacy of our design. Our extensive field studies show that RF-Transformer achieves 23.8 Mbps, 247.1 Kbps, 986.5 Kbps, and 27.3 Kbps throughput when generating standard Wi-Fi, ZigBee, Bluetooth, and LoRa signals.

Along with the proliferation of IoT devices, people have a lot of concerns on the privacy issues brought by them. Existing solutions, employing encryption or trying to hide the communication in PHY layer, often suffer from the limited capability of IoT devices. To address this issue, we propose Rustle, an acoustic communication design which builds covert connection among IoT devices using random noise. Noise signal can be easily generated and exchanged by the widely used speakers and microphones on IoT devices. Based on a fine-grained control of signal’s wave shape, Rustle generates a series of mutually uncorrelated random signals that contain “hidden patterns” to embed information. Extensive evaluations demonstrate that Rustle can achieve a lower than 1% BER while the eavesdropper’s error rate on detecting the signal is higher than 80%.

Small Unmanned Aerial Vehicles (UAVs) are becoming potential threats to security-sensitive areas and personal privacy. A UAV can shoot photos at height, but how to detect such an uninvited intruder is an open problem. This article presents mmHawkeye, a passive approach for non-cooperative UAV detection and identification with a commercial off-the-shelf millimeter wave (mmWave) radar. mmHawkeye does not require prior knowledge of the type, motions, and flight trajectory of the UAV, while exploiting the signal feature induced by the UAV’s periodic micro-motion (PMM) for long-range accurate detection. The design is therefore effective in dealing with low signal-to-noise ratio and uncertain reflected signals from the UAV. After analyzing the theoretical model of the PMM feature, mmHawkeye can further track the UAV’s position containing range, azimuth and altitude angle with dynamic programming and particle filtering and then identify it with a Long Short-Term Memory–based detector. We implement mmHawkeye on a commercial mmWave radar and evaluate its performance under varied settings. The experimental results show that mmHawkeye has a detection accuracy of 95.8% and can realize detection at a range up to 80 m.

This paper proposes Meta-Speaker, an innovative speaker capable of projecting audible sources into the air with a high level of manipulability. Unlike traditional speakers that emit sound waves in all directions, Meta-Speaker can manipulate the granularity of the audible region, down to a single point, and can manipulate the location of the source. Additionally, the source projected by Meta-Speaker is a physical presence in space, allowing both humans and machines to perceive it with spatial awareness. Meta-Speaker achieves this by leveraging the fact that air is a nonlinear medium, which enables the reproduction of audible sources from ultrasounds. Meta-Speaker comprises two distributed ultrasonic arrays, each transmitting a narrow ultrasonic beam. The audible source can be reproduced at the intersection of the beams. We present a comprehensive profiling of Meta-Speaker to validate the high manipulability it offers. We prototype Meta-Speaker and demonstrate its potential through three applications: anchor-free localization with a median error of 0.13 m, location-aware communication with a throughput of 1.28 Kbps, and acoustic augmented reality where users can perceive source direction with a mean error of 9.8 degrees.

Backscatter is an enabling technology for battery-free sensing in today's Artificial Intelligence of Things (AIOT). Building a backscatter-based sensing system, however, is a daunting task, due to two obstacles: the unaffordable power consumption of the microprocessor and the coexistence with the ambient carrier's traffic. In order to address the above issues, in this paper, we present Leggiero, the first-of-its-kind analog WiFi backscatter with payload transparency. Leveraging a specially designed circuit with a varactor diode, this design avoids using a microprocessor to interface between the radio and the sensor, and directly converts the analog sensor signal into the phase of RF (radio frequency) signal. By carefully designing the reference circuit on the tag and precisely locating the extra long training field (LTF) section of a WiFi packet, Leggiero embeds the analog phase value into the channel state information (CSI). A commodity WiFi receiver without hardware modification can simultaneously decode the WiFi and the sensor data. We implement Leggiero design and evaluate its performance under varied settings. The results show that the power consumption of the Leggiero tag (excluding the power of the peripheral sensor module) is 30μW at a sampling rate of 400Hz, which is 4.8× and 4× lower than the state-of-the-art WiFi backscatter schemes. The uplink throughput of Leggiero is suficient to support a variety of sensing applications, while keeping the WiFi carrier's throughput performance unaffected.

Small Unmanned Aerial Vehicles (UAVs) are becoming potential threats to security-sensitive areas and personal privacy. A UAV can shoot photos at height, but how to detect such an uninvited intruder is an open problem. This paper presents mmHawkeye, a passive approach for UAV detection with a COTS millimeter wave (mmWave) radar. mmHawkeye doesn’t require prior knowledge of the type, motions, and flight trajectory of the UAV, while exploiting the signal feature induced by the UAV’s periodic micro-motion (PMM) for long-range accurate detection. The design is therefore effective in dealing with low-SNR and uncertain reflected signals from the UAV. mmHawkeye can further track the UAV’s position with dynamic programming and particle filtering, and identify it with a Long Short-Term Memory (LSTM) based detector. We implement mmHawkeye on a commercial mmWave radar and evaluate its performance under varied settings. The experimental results show that mmHawkeye has a detection accuracy of 95.8% and can realize detection at a range up to 80m.

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

ACM TIOT launched its first special issue on the theme of wireless sensing for IoT. As an important component of the special issue and a novel practice of the journal, an online virtual workshop will be held, with presentations for each of the accepted articles. Welcome to join us for online discussion! Free registration is required for an attendee of the workshop. The zoom link will be shared to registered attendees before the workshop.

Vibration measurement is a crucial task in industrial systems, where vibration characteristics reflect health conditions and indicate anomalies of the devices. Previous approaches either work in an intrusive manner or fail to capture the micrometer-level vibrations. In this work, we propose mmVib, a practical approach to measure micrometer-level vibrations with mmWave radar. First, we derive a metric calledVibration Signal-to-Noise Ratio(VSNR) that highlights the directions of reducing measurement errors of tiny vibrations. Then, we introduce the design of mmVib based on the concept ofMulti-Signal Consolidation(MSC) for the error reduction and multi-object measurement. We implement a prototype of mmVib, and the experiments show that it achieves$3.946\%$relative amplitude error and$0.02487\%$relative frequency error in median. Typically, the average amplitude error is only$3.174um$when measuring the$100um$-amplitude vibration at around 5 meters. Compared to two existing mmWave-based approaches, mmVib reduces the 80th-percentile amplitude error by$69.21\%$and$97.99\%$respectively.

The signal phase is one of the most important metrics in RFID-based sensing, which is a useful technique enabling many significant applications. However, existing approaches of RFID-based sensing are often restricted in terms of the sensing capability or accuracy, due to the phase entanglement problem: the phase of the RFID signal is jointly affected by multiple factors, and the change in the signal phase cannot be directly attributed to any one of them. In order to tackle this problem, we propose RF-Prism, a versatile sensing approach that can simultaneously infer multiple physical factors (i.e., location, orientation, and material of targets), purely based on the phase readings. RF-Prism includes a comprehensive model to describe how different physical factors affect the phase of the received signal, and a complete design to disentangle the phase in the multi-frequency and multi-antenna scenario. We implement RF-Prism and evaluate its performance with extensive experiments. The results show that RF-Prism simultaneously achieves a mean localization error of$\text{7.61}~cm$, a mean orientation error of 9.83 degrees, a mean tracking error of$\text{6.12}~cm$, and 87.9% material identification accuracy, which outperforms state-of-the-art approaches.

The ability to localize acoustic sources can greatly improve the perception of smart devices (e.g., a smart speaker like Amazon Alexa). In this work, we study the problem of concurrently localizing multiple acoustic sources with a single smart device. Our proposal calledSymphonyis the first complete solution to tackle the above problem, including method, theory, and practice. The method stems from the insight that the geometric layout of microphones on the array determines the unique relationship among signals from the same source along the same arriving path. We also establish the theoretical model ofSymphony, which reveals the relation between localization performance (resolution and coverage) and impacting factors (sampling rate, array aperture, and array-wall distance). Moreover, the ability to separate and localize multiple sources is also studied theoretically and numerically. We implementSymphonywith different types of commercial off-the-shelf microphone arrays and evaluate its performance under different settings. The results show thatSymphonyhas a median localization error of 0.662 m.

Gaze tracking is a useful human-to-computer interface, which plays an increasingly important role in a range of mobile applications. Gaze calibration is an indispensable component of gaze tracking, which transforms the eye coordinates to the screen coordinates. The existing approaches of gaze tracking either have limited accuracy or require the user's cooperation in calibration and in turn hurt the quality of experience. We in this paper propose vGaze, continuous gaze tracking with implicit saliency-aware calibration on mobile devices. The design of vGaze stems from our insight on the temporal and spatial dependent relation between the visual saliency and the user's gaze. vGaze is implemented as a light-weight software that identifies video frames with “useful” saliency information, sensing the user's head movement, performs opportunistic calibration using only those “useful” frames, and leverages historical information for accelerating saliency detection. We implement vGaze on a commercial mobile device and evaluate its performance in various scenarios. The results show that vGaze can work at real time with video playback applications. The average error of gaze tracking is 1.51cm (2.884$^{\circ }$) which decreases to 0.99cm (1.891$^{\circ }$) with historical information and 0.57cm (1.089$^{\circ }$) with an indicator.

Along with the proliferation of Internet of Things (IoT) devices, people have a lot of concerns on the privacy issues brought by those devices. Existing solutions, employing strong encryption al-gorithms or trying to hide the communication in PHY layer, often suffer from the limited capability of IoT devices. In order to address the issue, we propose Rustle, an acoustic communication design which builds covert connection among IoT devices using random noise signal. Noise signal can be easily generated and exchanged by the widely used speakers and microphones on IoT devices. Based on the weak transitivity of signal's correlation and a fine-grained control of signal's wave shape, Rustle generates a series of mutu-ally uncorrelated random signals that contain “hidden patterns” to embed information. Extensive evaluations demonstrate that Rustle can achieve a lower than 1% BER while the eavesdropper's error rate on detecting the signal is higher than 80%.

Time-Slotted Channel Hopping (TSCH) is a popular link-layer pro-tocol defined in the IEEE 802.15.4e standard that improves the reli-ability and throughput of wireless sensor networks by exploiting diversity in both time and frequency. Despite the body of literature proposing several scheduling schemes for TSCH, a gap yet to be filled is the design of an effective way to deal with internal and external interference, which are both known to strongly affect communication performance. In fact, existing works either make use of a fixed schedule (and hence cannot cope with interference), or re-quire extra control traffic (and hence increase energy consumption). In this paper, we present SmarTiSCH, an interference-aware en-gine for IEEE 802.15.4e-based networks that retains the simplicity and energy-efficiency of autonomous scheduling, while increasing the awareness as well as robustness to both internal and external interference. With SmarTiSCH, the nodes in the network infer the presence of interference and react to it without the need of extra control traffic. Specifically, SmarTiSCH enables each node to infer the interference by passively observing existing data exchanges. It then lets a pair of nodes exchange information and mutually agree on a proper strategy to mitigate interference without the need of extra transmissions. We implement SmarTiSCH in Contiki-NG and evaluate its performance on a testbed of 20 off-the-shelf IEEE 802.15.4 devices based on the nRF52840. Our results show that SmarTiSCH increases the reliability of transmissions by up to 2.9 times compared to state-of-the-art approaches in the presence of interference, while even lowering the duty cycle by 54.3%.

This paper presents RF-Transformer, a unified backscatter radio hardware abstraction that allows a low-power IoT device to directly communicate with heterogeneous wireless receivers at the minimum power consumption. Unlike existing backscatter systems that are tailored to a specific wireless communication protocol, RF-Transformer provides a programmable interface to the micro-controller, allowing IoT devices to synthesize different types of protocol-compliant backscatter signals sharing radically different PHY-layer designs. To show the efficacy of our design, we implement a PCB prototype of RF-Transformer on 2.4 GHz ISM band and showcase its capability on generating standard ZigBee, Bluetooth, LoRa, and Wi-Fi 802.11b/g/n/ac packets. Our extensive field studies show that RF-Transformer achieves 23.8 Mbps, 247.1 Kbps, 986.5 Kbps, and 27.3 Kbps throughput when generating standard Wi-Fi, ZigBee, Bluetooth, and LoRa signals while consuming 7.6--74.2X less power than their active counterparts. Our ASIC simulation based on the 65-nm CMOS process shows that the power gain of RF-Transformer can further grow to 92--678X. We further integrate RF-Transformer with pressure sensors and present a case study on detecting foot traffic density in hallways. Our 7-day case studies demonstrate RF-Transformer can reliably transmit sensor data to a commodity gateway by synthesizing LoRa packets on top of Wi-Fi signals. Our experimental results also verify the compatibility of RF-Transformer with commodity receivers. Code and hardware schematics can be found at: https://github.com/LeFsCC/RF-Transformer.

We present Acoustic Inertial Measurement (AIM), a one-of-a-kind technique for indoor drone localization and tracking. Indoor drone localization and tracking are arguably a crucial, yet unsolved challenge: in GPS-denied environments, existing approaches enjoy limited applicability, especially in Non-Line of Sight (NLoS), require extensive environment instrumentation, or demand considerable hardware/software changes on drones. In contrast, AIM exploits the acoustic characteristics of the drones to estimate their location and derive their motion, even in NLoS settings. We tame location estimation errors using a dedicated Kalman filter and the Interquartile Range rule (IQR). We implement AIM using an off-the-shelf microphone array and evaluate its performance with a commercial drone under varied settings. Results indicate that the mean localization error of AIM is 46% lower than commercial UWB-based systems in complex indoor scenarios, where state-of-the-art infrared systems would not even work because of NLoS settings. We further demonstrate that AIM can be extended to support indoor spaces with arbitrary ranges and layouts without loss of accuracy by deploying distributed microphone arrays.

We present MicNest: an acoustic localization system enabling precise landing of aerial drones. Drone landing is a crucial step in a drone's operation, especially as high-bandwidth wireless networks, such as 5G, enable beyond-line-of-sight operation in a shared airspace and applications such as instant asset delivery with drones gain traction. In MicNest, multiple microphones are deployed on a landing platform in carefully devised configurations. The drone carries a speaker transmitting purposefully-designed acoustic pulses. The drone may be localized as long as the pulses are correctly detected. Doing so is challenging: i) because of limited transmission power, propagation attenuation, background noise, and propeller interference, the Signal-to-Noise Ratio (SNR) of received pulses is intrinsically low; ii) the pulses experience non-linear Doppler distortion due to the physical drone dynamics while airborne; iii) as location information is to be used during landing, the processing latency must be reduced to effectively feed the flight control loop. To tackle these issues, we design a novel pulse detector, Matched Filter Tree (MFT), whose idea is to convert pulse detection to a tree search problem. We further present three practical methods to accelerate tree search jointly. Our real-world experiments show that MicNest is able to localize a drone 120 m away with 0.53% relative localization error at 20 Hz location update frequency.

Backscatter communication holds potential for ubiquitous and low-cost connectivity among low-power IoT devices. To avoid interference between the carrier signal and the backscatter signal, recent works propose a frequency-shifting technique to separate these two signals in the frequency domain. Such proposals, however, have to occupy the precious wireless spectrum that is already overcrowded, and increase the power, cost, and complexity of the backscatter tag. In this paper, we revisit the classic ON-OFF Keying (OOK) modulation and propose Aloba, a backscatter system that takes the ambient LoRa transmissions as the excitation and piggybacks the in- band OOK modulated signals over the LoRa transmissions. Our design enables the backscatter signal to work in the same frequency band of the carrier signal, meanwhile achieving flexible data rate at different transmission range. The key contributions of Aloba include: i) the design of a low-power backscatter tag that can pick up the ambient LoRa signals from other signals; ii) a novel decoding algorithm to demodulate both the carrier signal and the backscatter signal from their superposition. We further adopt link coding mechanism and interleave operation to enhance the reliability of backscatter signal decoding. We implement Aloba and conduct head-to-head comparison with the state-of-the-art LoRa backscatter system PLoRa in various settings. The experiment results show Aloba can achieve 39.5–199.4 Kbps data rate at various distances, 10.4–$52.4\times $higher than PLoRa.

Cross-Technology Communication (CTC) emerges as a technology to enable direct communication across different wireless technologies. The state of the art on CTC employs physical-level emulation. Due to the protocol incompatibility and the hardware restriction, there are intrinsic emulation errors between the emulated signals and the legitimate signals. Unresolved emulation errors hurt the reliability of CTC and the achievable throughput, but how to improve the reliability of CTC remains a challenging problem. Taking the CTC from WiFi to BLE as an example, this work first presents a comprehensive understanding of the emulation errors. We then propose WEB, a practical CTC approach that can be implemented with commercial devices. The core design of WEB is split encoding: based on the probabilistic distribution of emulation errors, the WiFi sender manipulates its payload to maximize the successful decoding rate at the BLE receiver. We implement WEB and evaluate its performance with extensive experiments. Compared to two existing approaches, WEBee and WIDE, WEB reduces the SER (Symbol Error Rate) by 54.6% and 42.2%, respectively. For the first time in the community, WEB achieves practically effective CTC from WiFi to BLE, with an average throughput of 522.2 Kbps.

Research on cross-technology communication ( CTC ) has made rapid progress in recent years. While the CTC links are complex and dynamic, how to estimate the quality of a CTC link remains an open and challenging problem. Through our observation and study, we find that none of the existing approaches can be applied to estimate the link quality of CTC. Built upon the physical-level emulation, transmission over a CTC link is jointly affected by two factors: the emulation error and the channel distortion. Furthermore, the channel distortion can be modeled and observed through the signal strength and the noise strength. We, in this article, propose a new link metric called C-LQI and a joint link model that simultaneously takes into account the emulation error and the channel distortion in the In-phase and Quadrature ( IQ ) domain. We accurately describe the superimposed impact on the received signal. We further design a lightweight link estimation approach including two different methods to estimate C-LQI and in turn the packet reception rate ( PRR ) over the CTC link. We implement C-LQI and compare it with two representative link estimation approaches. The results demonstrate that C-LQI reduces the relative estimation error by 49.8% and 51.5% compared with s-PRR and EWMA, respectively.

The signal phase is one of the most important metrics in RFID-based sensing, which is a useful technique enabling many significant applications. However, existing approaches of RFID-based sensing are often restricted in terms of the sensing capability or accuracy, due to the phase entanglement problem: the phase of the RFID signal is jointly affected by multiple factors, and the change in the signal phase cannot be directly attributed to anyone of them. In order to tackle this problem, we propose RF-Prism, a versatile sensing approach that can simultaneously infer multiple physical factors (i.e., location, orientation, and material of targets), purely based on the phase readings. RF-Prism includes a comprehensive model to describe how different physical factors affect the phase of the received signal, and a complete design to disentangle the phase in the multi-frequency and multi-antenna scenario. We implement RF-Prism and evaluate its performance with extensive experiments. The results show that RF-Prism simultaneously achieves a mean localization error of 7.61 cm, a mean orientation error of 9.83 degrees, and 87.9% material identification accuracy, which outperforms state-of-the-art approaches.

Cross-technology interference is a major threat to the dependability of low-power wireless communications. Due to power and bandwidth asymmetries, technologies such as Wi-Fi tend to dominate the RF channel and unintentionally destroy low-power wireless communications from resource-constrained technologies such as ZigBee, leading to severe coexistence issues. To address these issues, existing schemes make ZigBee nodes individually assess the RF channel's availability or let Wi-Fi appliances blindly reserve the medium for the transmissions of low-power devices. Without a two-way interaction between devices making use of different wireless technologies, these approaches have limited scenarios or achieve inefficient network performance. This paper presents BiCord, a bidirectional coordination scheme in which resource-constrained wireless devices such as ZigBee nodes and powerful Wi-Fi appliances coordinate their activities to increase coexistence and enhance network performance. Specifically, in BiCord, ZigBee nodes directly request channel resources from Wi-Fi devices, who then reserve the channel for ZigBee transmissions on-demand. This interaction continues until the transmission requirement of ZigBee nodes is both fulfilled and understood by Wi-Fi devices. This way, BiCord avoids unnecessary channel allocations, maximizes the availability of the spectrum, and minimizes transmission delays. We evaluate BiCord on off-the-shelf Wi-Fi and ZigBee devices, demonstrating its effectiveness experimentally. Among others, our results show that BiCord increases channel utilization by up to 50.6% and reduces the average transmission delay of ZigBee nodes by 84.2% compared to state-of-the-art approaches.

Gaze tracking is a useful human-to-computer interface, which plays an increasingly important role in a range of mobile applications. Gaze calibration is an indispensable component of gaze tracking, which transforms the eye coordinates to the screen coordinates. The existing approaches of gaze tracking either have limited accuracy or require the user's cooperation in calibration and in turn hurt the quality of experience. We in this paper propose vGaze, implicit saliency-aware calibration for continuous gaze tracking on mobile devices. The design of vGaze stems from our insight on the temporal and spatial dependent relation between the visual saliency and the user's gaze. vGaze is implemented as a light-weight software that identifies video frames with "useful" saliency information, sensing the user's head movement, and performs opportunistic calibration using only those "useful" frames. We implement vGaze on a commercial mobile device and evaluate its performance in various scenarios. The results show that vGaze can work at real time with video playback applications. The average error of gaze tracking is 1.51cm.

Recently, mmWave has been widely used in fine-grained sensing applications due to its short wavelength and large bandwidth. One mmWave device usually can measure the target's 1D micro-displacement along the line-of-sight (LOS) direction. In this work, we try to empower mmWave with the capability of measuring 2D micro-displacements. Our insight is that although the mmWave reflection from one path contains only 1D observation, the spatial separability of mmWave offers an opportunity to separate multipath reflections from the received signal. Combining the coherent observations from multipath reflections can restore the 2D orbit of the target. Based on this insight, we present GWaltz, a mmWave sensing system that manages to measure sub-mm-level 2D orbits of rotating machinery. In GWaltz, we first reveal the relationship between the rotor's movement and the observed ghost multipath reflections (GMRs) and then design a set of novel signal processing techniques to restore the rotor orbit from the poor-quality GMR signals. We implement GWaltz with a commercial mmWave radar, and our evaluation results show that it achieves an absolute error of about 8.42um when measuring 100um-diameter rotor orbits.

iBeacon protocol is widely deployed to provide location-based services. By receiving its BLE advertisements, nearby devices can estimate the proximity to the iBeacon or calculate indoor positions. However, the open nature of these advertisements brings vulnerability to impersonation attacks. Such attacks could lead to spam, unreliable positioning, and even security breaches. In this paper, we propose Wi-attack, revealing the feasibility of using WiFi devices to conduct impersonation attacks on iBeacon services. Different from impersonation attacks using BLE compatible hardware, Wi-attack is not restricted by broadcasting intervals and is able to impersonate multiple iBeacons at the same time. Effective attacks can be launched on iBeacon services without modifications to WiFi hardware or firmware. To enable direct communication from WiFi to BLE, we use the digital emulation technique of cross technology communication. To enhance the packet reception along with its stability, we add redundant packets to eliminate cyclic prefix error entirely. The emulation provides an iBeacon packet reception rate up to 66.2%. We conduct attacks on three iBeacon services scenarios, point deployment, multilateration, and fingerprint-based localization. The evaluation results show that Wi-attack can bring an average distance error of more than 20 meters on fingerprint-based localization using only 3 APs.

Wireless sensing has great significance in Internet of Things (IoT) applications and has attracted substantial research interests in academia. In this study, we propose Palantir, a first-of-its-kind long-range sensing system based on the LoRa backscatter technology. By utilizing the ON-OFF-Keying modulated backscatter signals, Palantir can perform fine-grained long-range cyclist sensing. Our findings show that sensing is more susceptible to channel quality than communication. Hence, the design of Palantir particularly addresses the critical challenges of signal processing, such as amplitude instability, frequency offset, clock drift, spectrum leakage, and multiplicative noise. We implement Palantir and evaluate its performance by conducting comprehensive benchmark experiments. A prototype is also built and a case study of respiration monitoring in the real world is implemented. Results demonstrate that Palantir can perform accurate sensing at a range up to 100 m, which is twice that of state-of-the-art approaches. The median deviation of the detected motion period is as low as 0.2%.

Cross-Technology Communication (CTC) is an emerging technique that enables direct communication across different wireless technologies. The state-of-the-art works in this area propose physical-level CTC, in which the transmitters emulate signals that follow the receiver's standard. Physical-level CTC means considerable processing complexity at the transmitter, which does not apply to the communication from a low-end transmitter to a high-end receiver. This article proposes LEGO-Fi, which supports the CTC from ZigBee to WiFi and Bluetooth to WiFi. LEGO-Fi is a transmitter-transparent CTC technique, which leaves the processing complexity solely at the receiver side and therefore, makes a critical advance toward bidirectional high-throughput CTC. The key technique inside is cross-demapping, which stems from two key technical insights: 1) a ZigBee/Bluetooth packet leaves distinguishable features when passing the WiFi modules and 2) compared to ZigBee's/Bluetooth's simple encoding and modulation schemes, the rich processing capacity of WiFi offers extra flexibility to process a ZigBee/Bluetooth packet. The evaluation results show that LEGO-Fi achieves a throughput of 213.6 Kb/s in practice, which is, respectively, 13000×, 1200×, and 7.5× faster than FreeBee, ZigFi, and SymBee, the three existing ZigBee-to-WiFi CTC approaches. For the CTC from Bluetooth to WiFi, LEGO-Fi achieves a throughput of 904.1 Kb/s.

Eccentricity detection is a crucial issue for high-speed rotating machinery, which concerns the stability and safety of the machinery. Conventional techniques in industry for eccentricity detection are mainly based on measuring certain physical indicators, which are costly and hard to deploy. In this paper, we propose RED, a non-intrusive, low-cost, and real-time RFID-based eccentricity detection approach. Differing from the existing RFID-based sensing approaches, RED utilizes the temporal and phase distributions of tag readings as effective features for eccentricity detection. RED includes a Markov chain based model called RUM, which only needs a few sample readings from the tag to make a highly accurate and precise judgement. The design of RED further addresses practical issues, such as parameterizing the RUM model, making it robust to dynamic and noisy environments, and considering how the doppler shift may affect our system. We implement RED with COTS RFID reader and tags, and evaluate its performance across various scenarios. The overall accuracy is 93.6 percent and the detection latency is 0.68 seconds in average.

RFID tracking attracts a lot of research efforts in recent years. Most of the existing approaches, however, adopt an orientation-oblivious model. When tracking a target whose orientation changes, those approaches suffer from serious accuracy degradation. In order to achieve target tracking with pervasive applicability in various scenarios, in this article, we propose OmniTrack, an orientation-aware RFID tracking approach. Our study presents a generic model that discribes the linear relationship between the tag orientation and the phase change of the backscattered signals. Based on this finding, we propose an orientation-aware phase model to explicitly quantify the respective impact of the read-tag distance and the tag's orientation. OmniTrack addresses practical challenges in tracking the location and orientation of a mobile tag. Our experimental results demonstrate that OmniTrack achieves centimeter-level location accuracy and has significant advantages in tracking targets with varing orientations, compared to the state-of-the-art approaches.

Operating in unlicensed ISM bands, ZigBee devices often yield poor performance due to the interference from ever increasing wireless devices in the 2.4 GHz band. Our empirical results show that, a specific interference is likely to have different influence on different outbound links of a ZigBee sender, which indicates the chance of concurrent transmissions. Based on this insight, we propose Smoggy-Link, a practical protocol to exploit the potential concurrency for adaptive ZigBee transmissions under harsh interference. Smoggy-Link maintains an accurate link model to quantify and trace the relationship between interference and link qualities of the sender's outbound links. With such a link model, Smoggy-Link can translate low-cost interference information to the fine-grained spatiotemporal link state. The link information is further utilized for adaptive link selection and intelligent transmission schedule. We implement and evaluate a prototype of our approach with TinyOS and TelosB motes. The evaluation results show that Smoggy-Link has consistent improvements in both throughput and packet reception ratio under interference from various interferers.

Cross-Technology Communication (CTC) is an emerging technique that enables direct communication across different wireless technologies. Recent works achieve physical-level CTC by emulating the standard time-domain waveform of the receiver. This method faces the challenges of inherent unreliability due to the imperfect emulation. Different from analog emulation, we propose a novel concept named digital emulation, which stems from the following insight: The receiver relies on phase shift rather than the phase itself to decode signals. Instead of emulating the original time-domain waveform, the sender emulates the phase shift associated with the desired signals. Clearly there are multiple different phase sequences that correspond to the same signs of phase shifts. Digital emulation has flexibility in setting the phase values in the emulated signals, which is effective in reducing emulation errors and enhancing the reliability of CTC. The key point of digital emulation is generic and applicable to a set of CTCs, where the transmitter has a wider bandwidth for emulation and the receiver decoding is based on the phase shift. In this paper, we implement our proposal as WIDE, a physical-level CTC via digital emulation from WiFi to ZigBee. We conduct extensive experiments to evaluate the performance of WIDE. The results show that WIDE significantly improves the Packet Reception Ratio (PRR) from 41.7% to 86.2%, which is 2× of WEBee's, an existing representative physical-level CTC.

As backscatter-based IoT applications get proliferated, how to exploit backscattered signals for efficient sensing becomes a significant issue. Backscatter-based sensing requires accurate estimation of a backscatter channel (phase and amplitude), which is distorted when multiple signals collide with each other. As a result, the state of the arts is limited to either parallel decoding of collided signal or channel estimation with clean signal. Motivated by the need of high sensing capacity, we in this article present Fireworks, the first approach for channel estimation of parallel backscattered signals. The insight of Fireworks is that although the channel is distorted due to collision, the movements of the ON-OFF Keying modulated signal still preserve the channel properties of the respective tags. By modeling the relationship between the channels and the signal's moving trajectory in the IQ domain, one can make accurate estimation of the channels directly from the collision. We address practical problems of Fireworks, such as the high computing complexity and the compatibility with the commercial MAC protocol, and implement Fireworks. The results show that Fireworks is able to estimate the channels of up to five tags in parallel. When applied to the tracking application, Fireworks achieves 2 ~ 4× improvement in the tracking accuracy, compared with the state-of-the-art approach.

Parallel backscatter is a promising technique for high throughput, low power communications. The existing approaches of parallel backscatter are based on a common assumption, i.e. the states of the collided signals are distinguishable from each other in either the time domain or the IQ (the In-phase and Quadrature) domain. We in this paper disclose the superclustering phenomenon, which invalidates that assumption and seriously affects the decoding performance. Then we propose an interstellar travelling model to capture the bursty Gaussian process of a collided signal. Based on this model, we design Hubble, a reliable signal processing approach to support parallel backscatter in the wild. Hubble addresses several technical challenges: (i) a novel scheme based on Pearson's Chi-Square test to extract the collided signals' combined states, (ii) a Markov driven method to capture the law of signal state transitions, and (iii) error correction schemes to guarantee the reliability of parallel decoding. Theoretically, Hubble is able to decode all the backscattered data, as long as the signals are detectable by the receiver. The experiment results demonstrate that the median throughput of Hubble is 11.7× higher than that of the state-of-the-art approach.

Acoustic signal acts as an essential input to many systems. However, the pure acoustic signal is very difficult to extract, especially in noisy environments. Existing beamforming systems are able to extract the signal transmitted from certain directions. However, since microphones are centrally deployed, these systems have limited coverage and low spatial resolution. We overcome the above limitations and present ChordMics, a distributed beamforming system. By leveraging the spatial diversity of the distributed microphones, ChordMics is able to extract the acoustic signal from arbitrary points. To realize such a system, we further address the fundamental challenge in distributed beamforming: aligning the signals captured by distributed and unsynchronized microphones. We implement ChordMics and evaluate its performance under both LOS and NLOS scenarios. The evaluation results tell that ChordMics can deliver higher SINR than the centralized microphone array. The average performance gain is up to 15dB.

Research on Cross-technology communication (CTC) has made rapid progress in recent years, but how to estimate the quality of a CTC link remains an open and challenging problem. Through our observation and study, we find that none of the existing approaches can be applied to estimate the link quality of CTC. Built upon the physical-level emulation, transmission over a CTC link is jointly affected by two factors: the emulation error and the channel distortion. We in this paper propose a new link metric called C-LQI and a joint link model that simultaneously takes into account the emulation error and the channel distortion in the process of CTC. We further design a light-weight link estimation approach to estimate C-LQI and in turn the PRR over the CTC link. We implement C-LQI and compare it with two representative link estimation approaches. The results demonstrate that C-LQI reduces the relative error of link estimation respectively by 46% and 53% and saves the communication cost by 90%.

As the proliferation of backscatter-based applications, exploiting backscatter-based sensing becomes more important. Due to the requirement of accurate estimation of backscatter channels (phase and amplitude), which is often distorted when multiple signals collide with each other, existing works are generally limited to either parallel decoding of collided signals or with non-collided signals only. Motivated by our observation that a channel can be distorted during collisions, the movements of the ON-OFF Keying modulated signal still preserve channel properties of the respective tags, we propose the first approach to channel estimation of parallel 2backscattered signals, called Fireworks. We model the relationship between the channel and the signal moving trajectory in the In-phase and Quadrature (IQ) domain and implement this design in our lab. The results show that Fireworks is able to estimate up to five channels in parallel. When applied to the tracking application, Fireworks achieves 2~4× improvement in the tracking accuracy, compared with the state-of-the-art approach.

Vibration measurement is a crucial task in industrial systems, where vibration characteristics reflect the health and indicate anomalies of the objects. Previous approaches either work in an intrusive manner or fail to capture the micrometer-level vibrations. In this work, we propose mmVib, a practical approach to measure micrometer-level vibrations with mmWave radar. By introducing a Multi-Signal Consolidation (MSC) model to describe the properties of the reflected signals, we exploit the inherent consistency among those signals to accurately recover the vibration characteristics. We implement a prototype of mmVib, and the experiments show that this design achieves 8.2% relative amplitude error and 0.5% relative frequency error in median. Typically, the median amplitude error is 3.4um for the 100um-amplitude vibration. Compared to two existing approaches, mmVib reduces the 80th-percentile amplitude error by 62.9% and 68.9% respectively.

Opportunistic forwarding seizes early forwarding opportunities in duty-cycled networks to reduce delay and energy consumption. But increasingly serious Cross-Technology Interference (CTI) significantly counteracts the benefits of opportunistic forwarding. Existing solutions try to reserve the channel for low-power networks by implicit avoidance or explicit coordination but ignore the potential of high-power CTI's superior capability. In this paper, we propose a new paradigm for low-power opportunistic forwarding in CTI environments. Instead of keeping high-power CTI devices silent, we directly involve them into the forwarding, as cross-technology forwarders. We design Portal to solve the challenges of realizing cross-technology opportunistic forwarding. To be transparent to the low-power networks, Portal adopts cross-technology rebroadcasting to enable the fast overhearing and forwarding of cross-technology data. To maximize the performance gain of using heterogeneous forwarders while minimizing the influence on legacy high-power traffic, we propose a post-forwarding forwarder selection and a traffic scheduling method. We also propose a feature-based ACK recognition method and a jamming-based ACK replying mechanism to forward the unreliable ACKs from asymmetric regions. Extensive experiments demonstrate that Portal not only avoids the CTI but also breaks through the existing performance limit.

Backscatter communication holds potential for ubiquitous and low-cost connectivity among low-power IoT devices. To avoid interference between the carrier signal and the backscatter signal, recent works propose a frequency-shifting technique to separate these two signals in the frequency domain. Such proposals, however, have to occupy the precious wireless spectrum that is already overcrowded, and increase the power, cost, and complexity of the backscatter tag. In this paper, we revisit the classic ON-OFF Keying (OOK) modulation and propose Aloba, a backscatter system that takes the ambient LoRa transmissions as the excitation and piggybacks the in-band OOK modulated signals over the LoRa transmissions. Our design enables the backsactter signal to work in the same frequency band of the carrier signal, meanwhile achieving good tradeoff between transmission range and link throughput. The key contributions of Aloba include: i) the design of a low-power backscatter tag that can pick up the ambient LoRa signals from other signals; ii) a novel decoding algorithm to demodulate both the carrier signal and the backscatter signal from their superposition. The design of Aloba completely unleashes the backscatter tag's ability in OOK modulation and achieves flexible data rate at different transmission range. We implement Aloba and conduct head-to-head comparison with the state-of-the-art LoRa backscatter system PLoRa in various settings. The experiment results show Aloba can achieve 39.5--199.4 Kbps data rate at various distances, 10.4--52.4X higher than PLoRa.

Sound recognition is an important and popular function of smart devices. The location of sound is basic information associated with the acoustic source. Apart from sound recognition, whether the acoustic sources can be localized largely affects the capability and quality of the smart device's interactive functions. In this work, we study the problem of concurrently localizing multiple acoustic sources with a smart device (e.g., a smart speaker like Amazon Alexa). The existing approaches either can only localize a single source, or require deploying a distributed network of microphone arrays to function. Our proposal called Symphony is the first approach to tackle the above problem with a single microphone array. The insight behind Symphony is that the geometric layout of microphones on the array determines the unique relationship among signals from the same source along the same arriving path, while the source's location determines the DoAs (direction-of-arrival) of signals along different arriving paths. Symphony therefore includes a geometry-based filtering module to distinguish signals from different sources along different paths and a coherence-based module to identify signals from the same source. We implement Symphony with different types of commercial off-the-shelf microphone arrays and evaluate its performance under different settings. The results show that Symphony has a median localization error of 0.694m, which is 68% less than that of the state-of-the-art approach.

The proliferation of IoT applications brings the demand of ubiquitous connections among heterogeneous wireless devices. Cross-Technology Communication (CTC) is a significant technique to directly exchange data among heterogeneous devices that follow different standards. By exploiting a side-channel like frequency, amplitude, or temporal modulation, the existing works enable CTC but have limited performance under channel noise. In this article, we propose WiZig, a novel CTC technique from WiFi to ZigBee that employs modulations in both the amplitude and temporal dimensions to optimize the throughput over a noisy channel. We establish a theoretical model of the energy communication channel to clearly understand the channel capacity. We then devise an online rate adaptation algorithm to adjust the modulation strategy according to the channel condition. Based on the theoretical model, WiZig controls the number of encoded energy amplitudes and the length of a receiving window, so as to optimize the CTC throughput. We implement a prototype of WiZig on a software radio platform and a commercial ZigBee device. The evaluation shows that WiZig achieves a throughput of 153.85bps with less than 1% symbol error rate in a real environment.

Cross-technology communication (CTC) is a technique that enables direct communication among different wireless technologies. Recent works in this area have made substantial progress, but CTC from ZigBee to WiFi remains an open problem. In this paper, we propose ZigFi, a novel CTC framework that enables communication from ZigBee to WiFi. ZigFi carefully overlaps ZigBee packets with WiFi packets. Through experiments we show that Channel State Information (CSI) of the overlapped packets can be used to convey data from ZigBee to WiFi. Based on this finding, we propose a receiver-initiated protocol and translate the decoding problem into a problem of CSI classification with Support Vector Machine. We further build a generic model through experiments, which describes the relationship between the Signal to Interference and Noise Ratio (SINR) and the symbol error rate (SER). Moreover, we extend ZigFi to multiple-to-one concurrent transmissions. We implement ZigFi on commercial-off-the-shelf WiFi and ZigBee devices. We evaluate the performance of ZigFi under different experimental settings. The results demonstrate that ZigFi achieves a throughput of 215.9bps, which is 18X faster than the state of the arts.

Cross-Technology Communication (CTC) is an emerging technique that enables direct communication across different wireless technologies. The state-of-the-art works in this area propose physical-level CTC, in which the transmitters emulate signals that follow the receiver's standard. Physical-level CTC means considerable processing complexity at the transmitter, which doesn't apply to the communication from a low-end transmitter to a high-end receiver, e.g. from ZigBee to WiFi. This paper presents transmitter-transparent cross-technology communication, which leaves the processing complexity solely at the receiver side and therefore makes a critical advance toward bidirectional high-throughput CTC. We implement our proposal as LEGO-Fi, the communication from ZigBee to WiFi. The key technique inside is cross-demapping, which stems from two key technical insights: (1) A ZigBee packet leaves distinguishable features when passing the WiFi modules. (2) Compared to ZigBee's simple encoding and modulation schemes, the rich processing capacity of WiFi offers extra flexibility to process a ZigBee packet. The evaluation results show that LEGO-Fi achieves a throughput of 213.6Kbps, which is respectively 13000× and 1200× faster than FreeBee and ZigFi, the two existing ZigBee-to-WiFi CTC approaches.

Liquid leakage detection is a crucial issue in modern industry, which concerns industrial safety. Traditional solutions, which generally rely on specialized sensors, suffer from intrusive deployment, high cost, and high power consumption. Such problems prohibit applying those solutions for large-scale and continuously industrial monitoring. In this work, we present a RFID-based solution, TwinLeak, to detect liquid leakage using COTS RFID devices. Detecting the leakage accurately with coarse-grained RSSI and phase readings of tags has been a daunting task, which is especially challenging when low detection delay is required. Our system achieves these goals based on the fact that the inductive coupling between two adjacent tags is highly sensitive to the liquid leaked between them. Therefore, instead of judging according to the signals of each individual tag, TwinLeak utilizes the relationship between the signals of two tags as an effective feature for leakage detection. Specifically, Twin-Leak extracts discriminative signal features from short segments of signals and instantly identifies leakage using a light-weight classifier. A model-guided method for leakage progress tracking is further devised to simultaneously estimate the leakage volume and rate. We implement TwinLeak, evaluate its performance across various scenarios, and deploy it in a real-world industrial IoT system. In average, TwinLeak achieves a TPR higher than 97.2%, a FPR lower than 0.5%, and a relative property estimation error around 10%, while triggering early alarms after only about 4.6mL liquid leaks.

Cross-Technology Communication (CTC) is an emerging technique that enables direct communication across different wireless technologies. Recent works achieve physical-level CTC by emulating the standard time-domain waveform of the receiver. This method faces the challenges of inherent unreliability due to the imperfect emulation. Different from analog emulation, we propose a novel concept named digital emulation, which stems from the following insight: The receiver relies on the phase shift to decode symbols rather than the shape of analog time-domain waveform. There are lots of phase sequences which satisfy the requirement of phase shift. The distortions of these phase sequences after WiFi emulation are different. We have the opportunity to select an appropriate phase sequence with the relatively small emulation errors to achieve a reliable CTC. The key point of digital emulation is generic and applicable to a set of CTCs, where the transmitter has a wider bandwidth for emulation and the receiver decoding is based on the phase shift. In this paper, we implement our proposal as WIDE, a physical-level CTC via digital emulation from WiFi to ZigBee. We conduct extensive experiments to evaluate the performance of WIDE. The results show that WIDE significantly improves the Packet Reception Ratio (PRR) from 41.7% to 86.2%, which is 2X of WEBee's, an existing representative physical-level CTC.

RFID tracking has attracted significant interest from both academia and industry due to its low cost and ease of deployment. Previous works focus more on tracking in 2D space or separately consider tracking of the location and the orientation. They especially struggle in 3D situations due to the increase in the degree of freedom and the limited information conveyed by the RFID tags. In this paper, we propose 3D-OmniTrack, an approach that can accurately track the 3D location and orientation of an object. We introduce a polarization-sensitive phase model in an RFID system, which takes into consideration both the distance and the 3D posture of an object. Based on this model, we design an algorithm to accurately track the object in 3D space. We conduct real-world experiments and present results that show 3D-OmniTrack can achieve centimeter-level location accuracy with the average orientation error of 5°. 3D-OmniTrack has significant advantages in both the accuracy and the efficiency, compared with state-of-the-art approaches.

Cross-Technology Communication (CTC) is an emerging technology to support direct communication between wireless devices that follow different standards. In spite of the many different proposals from the community to enable CTC, the performance aspect of CTC is an equally important problem but has seldom been studied before. We find this problem is extremely challenging, due to the following reasons: on one hand, a link for CTC is essentially different from a conventional wireless link. The conventional link indicators like RSSI (received signal strength indicator) and SNR (signal to noise ratio) cannot be used to directly characterize a CTC link. On the other hand, the indirect indicators like PER (packet error rate), which is adopted by many existing CTC proposals, cannot capture the short-term link behavior. As a result, the existing CTC proposals fail to keep reliable performance under dynamic channel conditions. In order to address the above challenge, we in this paper propose AdaComm, a generic framework to achieve self-adaptive CTC in dynamic channels. Instead of reactively adjusting the CTC sender, AdaComm adopts online learning mechanism to adaptively adjust the decoding model at the CTC receiver. The self-adaptive decoding model automatically learns the effective features directly from the raw received signals that are embedded with the current channel state. With the lossless channel information, AdaComm further adopts the fine tuning and full training modes to cope with the continuous and abrupt channel dynamics. We implement AdaComm and integrate it with two existing CTC approaches that respectively employ CSI (channel state information) and RSSI as the information carrier. The evaluation results demonstrate that AdaComm can significantly reduce the SER (symbol error rate) by 72.9% and 49.2%, respectively, compared with the existing approaches.

Consumer Virtual Reality (VR) has been widely used in various application areas, such as entertainment and medicine. In spite of the superb immersion experience, to enable high-quality VR on untethered mobile devices remains an extremely challenging task. The high bandwidth demands of VR streaming generally overburden a conventional wireless connection, which affects the user experience and in turn limits the usability of VR in practice. In this paper, we propose FoVR, attention-based hierarchical VR streaming through bandwidth-limited wireless networks. The design of FoVR stems from the insight that human's vision is hierarchical, so that different areas in the field of view (FoV) can be served with VR content of different qualities. By exploiting the gaze tracking capacity of the VR devices, FoVR is able to accurately predict the user's attention so that the streaming of hierarchical VR can be appropriately scheduled. In this way, FoVR significantly reduces the bandwidth cost and computing cost while keeping high quality of user experience. We implement FoVR on a commercial VR device and evaluate its performance in various scenarios. The experiment results show that FoVR reduces the bandwidth cost by 88.9% and 76.2%, respectively compared to the original VR streaming and the state-of-the-art approach.

With parallel decoding for backscatter communication, tags are allowed to transmit concurrently and more efficiently. Existing parallel decoding mechanisms, however, assume that signals of the tags are highly stable and, hence, may not perform optimally in the naturally dynamic backscatter systems. This paper introduces FlipTracer, a practical system that achieves highly reliable parallel decoding even in hostile channel conditions. FlipTracer is designed with a key insight; although the collided signal is time-varying and irregular, transitions between signals' combined states follow highly stable probabilities, which offers important clues for identifying the collided signals and provides us with an opportunity to decode the collided signals without relying on stable signals. Motivated by this observation, we propose a graphical model, called one-flip-graph (OFG), to capture the transition pattern of collided signals, and design a reliable approach to construct the OFG in a manner robust to the diversity in backscatter systems. Then, FlipTracer can resolve the collided signals by tracking the OFG. We have implemented FlipTracer and evaluated its performance with extensive experiments across a wide variety of scenarios. Our experimental results have shown that FlipTracer achieves a maximum aggregated throughput that approaches 2 Mb/s, which is 6× higher than the state of the art.

Due to limited energy supply of Internet of Things (Zhao et al. 2018) (IoT) devices, asynchronous duty cycle radio management is widely adopted to save energy. Since the sleep schedules of nodes are unsynchronized, a sender has to repeatedly send frames to coordinate with its receiver or keep sleeping until the receiver’s wake-up time will come according to receiver’s sleep-wake schedule. In such contexts, opportunistic forwarding, which takes the earliest forwarding opportunity instead of a deterministic forwarder, shows great advantage in utilizing channel resource for duty-cycled IoT networks. The multiple forwarding choices with temporal and spatial diversity increase the chance of collision tolerance in opportunistic forwarding, potentially enhancing the overall performance of duty-cycled multi-hop networks. However, since the current channel contention mechanisms mainly focus on collision avoidance, it is too conservative to exploit concurrency. To address this problem, in this article, we propose COF to fully exploit the potential Concurrency for Opportunistic Forwarding in duty-cycled IoT networks. COF achieves concurrent transmission by: (i) measuring conditional link quality under the interference of on-going transmissions, and then (ii) further modeling the benefit of potential concurrency opportunities. According to the expected benefit of concurrency, COF decides whether or not to transmit in concurrent way. COF also adopts concurrency flag and signal features to avoid data collision caused by disordered concurrent transmissions and enhance the accuracy of conditional link quality estimation. COF can be easily integrated into the conventional unsynchronized and duty-cycled protocols. We have implemented COF and evaluated its performance on a 40-node testbed. The results show that COF can effectively exploit potential concurrency in opportunistic forwarding and COF outperforms the state-of-art protocols under diverse traffic load and network density.

Backscatter communication, due to its low energy consumption, attract a broad range of applications. The throughput of such low-power communication is however limited. Parallel backscatter is deemed as a promising technique for improving the overall throughput by enabling concurrent transmissions of the backscattering tags. The state-of-the-art approaches for parallel backscatter assume that all the states of the collided signals are distinguishable in the In-phase and Quadrature (IQ) signal plane. In this paper, we disclose the superclustering phenomenon that makes the assumption untenable and significantly degrades the overall performance. Moreover, we observe that the indistinguishable states at different channels are not the same due to the intrinsic channel diversity. Motivated by the observation, we propose Canon, an approach that exploits the channel diversity of the backscatter tags for reliable parallel decoding. In Canon, we address two critical challenges: (i) designing the Multi-Carrier Backscatter (MCB) module to extract the collided signals simultaneously from multiple channels, (ii) designing the Multi-Channel Cluster Union (MCCU) algorithm to distinguish each state of the collided signals. The experiments demonstrate that Canon can achieve over 10 times higher throughput than the state-of-the-art approaches.

Cross- Technology Communication (CTC) is an emerging technique to enable the direct communication among different wireless technologies. A main category of the existing proposals on CTC propose to modulate packets at the sender side, and demodulate them into 1 and 0 bits at the receiver side. The performance of those proposals is likely to degrade in a densely coexisting environment. Solely judged according to the received signal strength, a symbol 0 that is modulated as packet absence is generally indistinguishable from dynamic interference. In this paper, we propose StripComm, interference-resilient CTC in coexisting environments. A sender in StripComm adopts an interference-resilient coding scheme that contains both presence and absence of packets in one symbol. The receiver strips the interference from the interested signal by exploiting the self-similarity of StripComm signals. We prototype StripComm with commercial WiFi, ZigBee devices and a software radio platform. The theoretical and experimental evaluation demonstrate that StripComm offers a data rate up to 1.1K bps with a SER (Symbol Error Rate) lower than 0.01 and a data rate of 0.89K bps even against strong interference.

Cross-technology communication (CTC) is a technique that enables direct communication among different wireless technologies. Recent works in this area have made positive progress, but high-throughput CTC from ZigBee to WiFi remains an open problem. In this paper, we propose ZigFi, a novel CTC framework that enables direct communication from ZigBee to WiFi. Without impacting the ongoing WiFi transmissions, ZigFi carefully overlaps ZigBee packets with WiFi packets. Through experiments we show that Channel State Information (CSI) of the overlapped packets can be used to convey data from ZigBee to WiFi. Based on this finding, we propose a receiver-initiated protocol and translate the decoding problem into a problem of CSI classification with Support Vector Machine. We further build a generic model through experiments, which describes the relationship between the Signal to Interference and Noise Ratio (SINR) and the symbol error rate (SER). We implement ZigFi on commercial-off-the-shelf WiFi and ZigBee devices. We evaluate the performance of ZigFi under different experimental settings. The results demonstrate that ZigFi achieves a throughput of 215.9bps, which is 18X faster than the state-of-the-art.

Eccentricity detection is a crucial issue for highspeed rotating machinery, which concerns the stability and safety of the machinery. Conventional techniques in industry for eccentricity detection are mainly based on measuring certain physical indicators, which are costly and hard to deploy. In this paper, we propose RED, a non-intrusive, low-cost, and realtime RFID-based eccentricity detection approach. Differing from the existing RFID-based sensing approaches, RED utilizes the temporal and phase distributions of tag readings as effective features for eccentricity detection. RED includes a Markov chain based model called RUM, which only needs a few sample readings from the tag to make a highly accurate and precise judgement. We implement RED with commercial-of-the-shelf RFID reader and tags, and evaluate its performance across various scenarios. The overall accuracy is 93.59% and the detection latency is 0.68 seconds in average.

RFID tracking attracts a lot of research efforts in recent years. Most of the existing approaches, however, adopt an orientation-oblivious model. When tracking a target whose orientation changes, those approaches suffer from serious accuracy degradation. In order to achieve target tracking with pervasive applicability in various scenarios, we in this paper propose OmniTrack, an orientation-aware RFID tracking approach. Our study discovers the linear relationship between the tag orientation and the phase change of the backscattered signals. Based on this finding, we propose an orientation-aware phase model to explicitly quantify the respective impact of the read-tag distance and the tags orientation. OmniTrack addresses practical challenges in tracking the location and orientation of a mobile tag. Our experimental results demonstrate that OmniTrack achieves centimeterlevel location accuracy and has significant advantages in tracking targets with varing orientations, compared to the state-of-the-art approaches.

Parallel backscatter is a promising technique for high throughput, low power communications. The existing approaches of parallel backscatter are based on a common assumption, i.e. the states of the collided signals are distinguishable from each other in either the time domain or the IQ (the In-phase and Quadrature) domain. We in this paper disclose the superclustering phenomenon, which invalidates that assumption and seriously affects the decoding performance. Then we propose an interstellar travelling model to capture the bursty Gaussian process of a collided signal. Based on this model, we design Hubble, a reliable signal processing approach to support parallel backscatter in the wild. Hubble addresses several technical challenges: (i) a novel scheme based on Pearson's Chi-Square test to extract the collided signals' combined states, (ii) a Markov driven method to capture the law of signal state transitions, and (iii) error correction schemes to guarantee the reliability of parallel decoding. Theoretically, Hubble is able to decode all the backscattered data, as long as the signals are detectable by the receiver. The experiment results demonstrate that the median throughput of Hubble is $11.7\times$ higher than that of the state-of-the-art approach.

The development of Internet-of-Things (IoT) technologies in recent years brings us unprecedented opportunities for innovations in the power industry. This demo abstract introduces our research and practice with Pavatar - IoT for the power industry. Pavatar includes a series of system deployments in the core sections of Global Energy Internet (GEI), for the purposes of automatic surveillance and remote diagnosis of ultra-high-voltage converter stations (UHVCSs). Pavatar incorporates technologies like lower-power or battery-free sensing, cross-technology communication, edge computing, machine learning, and enhances the user experience with 3D virtual reality. The deployed system significantly reduces the manpower cost and enhances the operational efficiency of the UHVCS.

Smartphone theft is a non-negligible problem that causes serious concerns on personal property and privacy. The existing solutions to this problem either provide only functions like retrieving a phone, or require dedicated hardware to detect thefts. How to protect smartphones from being stolen at all times is still an open problem. In this paper, we propose iGuard, a real-time anti-theft system for smartphones. iGuard utilizes only the inertial sensing data from the smartphone. The basic idea behind iGuard is to distinguish different people holding a smartphone, by identifying the order of the motions during the `take-out' behavior andhoweach motion is performed. For this purpose, we design a motion segmentation algorithm to detect the transition between two motions from the noisy sensing data. We then leverage the distinct feature contained in each sub-segment of a motion to estimate the probability that the motion is performed by the smartphone owner himself/herself. Based on such pre-processed data, we propose a Markov based model to track the behavior of a smartphone user. According to this model, iGuard instantly alarms once the tracked data deviate from the smartphone owner's usual habit. We implement iGuard on Android and evaluate its performance in real environments. The experimental results show that iGuard is accurate and robust in various scenarios.

Software-defined networking (SDN) is deemed as a promising direction to offer generalizability of wireless sensor networks (WSN). To introduce SDN into WSNs, however, means a series of non-trivial challenges due to the wireless and ad-hoc nature of WSNs. In this paper, we present our study towards a software-defined architecture for multi-function wireless sensor networks. Our proposal called Pangu is built upon the opportunistic routing protocol stack and introduces the concept of modality properties of sensor nodes. It enables centralized network control over a WSN while preserving the flexibility of underlying ad-hoc routing. We tackle the critical problems of the architecture design by presenting three essential components of Pangu. Moreover, we implement Pangu on a real-world testbed and evaluate it with various experiments.

The proliferation of loT applications drives the need of ubiquitous connections among heterogeneous wireless devices. Cross-Technology Communication (CTC) is a significant technique to directly exchange information among heterogeneous devices that follow different standards. By exploiting a side-channel like frequency, amplitude or temporal modulation, the existing works enable CTC but have limited performance under channel noise. In this paper, we propose WiZig, a novel CTC technique that employs modulation techniques in both the amplitude and temporal dimensions to optimize the throughput over a noisy channel. We establish a theoretical model of the energy communication channel to clearly understand the channel capacity. We then devise an online rate adaptation algorithm to adjust the modulation strategy according to the channel condition. Based on the theoretical model, WiZig can accurately control the number of encoded energy amplitudes and the length of a receiving window, so as to optimize the CTC throughput. We implement a prototype of WiZig on a software radio platform and a commercial ZigBee device. The evaluation show that WiZig achieves a throughput of 153.85 bps with less than 1 % symbol error rate in a real environment. The results demonstrate that WiZig realizes efficient and reliable CTC under varied channel conditions.

Smartphone theft is a non-negligible problem that causes serious concerns on personal property, privacy, and public security. The existing solutions to this problem either provide only functions like retrieving a phone, or require dedicated hardware to detect thefts. How to protect smartphones from being stolen at all times is still an open problem. In this paper, we propose iGuard, a real-time anti-theft system for smartphones. iGuard utilizes only the inertial sensing data from the smartphone. The basic idea behind iGuard is to distinguish different people holding a smartphone, by identifying the order of the motions during the `take-out' behavior and how each motion is performed. For this purpose, we design a motion segmentation algorithm to detect the transition between two motions from the noisy sensing data. We then leverage the distinct feature contained in each sub-segment of a motion, instead of the entire motion, to estimate the probability that the motion is performed by the smartphone owner himself/herself. Based on such pre-processed data, we propose a Markov Chain based model to track the behavior of a smartphone user. According to this model, iGuard instantly alarms once the tracked data deviate from the smartphone owner's usual habit. We implement iGuard on Android and evaluate its performance in real environments. The experimental results show that iGuard is accurate and robust in various scenarios.

With parallel decoding for backscatter communication, tags are allowed to transmit concurrently and more efficiently. Existing parallel decoding mechanisms, however, assume that signals of the tags are highly stable, and hence may not perform optimally in the naturally dynamic backscatter systems. This paper introduces FlipTracer, a practical system that achieves highly reliable parallel decoding even in hostile channel conditions. FlipTracer is designed with a key insight: although the collided signal is time-varying and irregular, transitions between signals' combined states follow highly stable probabilities, which offers important clues for identifying the collided signals, and provides us with an opportunity to decode the collided signals without relying on stable signals. Motivated by this observation, we propose a graphical model, called one-flip-graph (OFG), to capture the transition pattern of collided signals, and design a reliable approach to construct the OFG in a manner robust to the diversity in backscatter systems. Then FlipTracer can resolve the collided signals by tracking the OFG. We have implemented FlipTracer and evaluated its performance with extensive experiments across a wide variety of scenarios. Our experimental results have shown that FlipTracer achieves a maximum aggregated throughput that approaches 2 Mbps, which is 6x higher than the state-of-the-art.

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.

Mobile crowdsourcing is deemed as a powerful technique to solve traditional problems. But the crowdsourced data from smartphones are generally low quality, which can induce crucial challenges and hurt the applicability of crowdsourcing applications. This paper presents our study to address such challenges in a concrete application, namely, floorplan generation. Existing proposals mostly rely on infrastructural references or accurate data sources, which are restricted in terms of applicability and pervasiveness. Our proposal called SenseWit is motivated by the observation that people's behavior offers meaningful clues for location inference. The noise, ambiguity, and behavior diversity contained in the crowdsourced data, however, mean non-trivial challenges in generating high-quality floorplans. We propose: 1) a novel concept called Nail to identify featured locations in indoor space and 2) a heuristic pathlet bundling algorithm to progressively discover the internal layouts of a floorplan. We implement SenseWit and conduct real-world experiments in different spaces to demonstrate its efficacy. This paper offers an efficient technique to obtain high-quality structures (either logical or physical) from low-quality data. We believe it can be generalized to other crowdsourcing applications.

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.

Mobile crowdsourcing is deemed as a powerful technique to solve traditional problems. But the crowdsourced data from smartphones are generally with low quality, which induce crucial challenges and hurt the applicability of crowdsourcing applications. This paper presents our study to address such challenges in a concrete application, namely floorplan generation. In order to utilize pedestrians' traces for indoor location inference, existing proposals mostly rely on infrastructural references or accurate data sources, which are by nature restricted in terms of applicability and pervasiveness. Our proposal called SenseWit is motivated by the observation that people's behavior offers meaningful clues for location inference. The noise, ambiguity, and behavior diversity contained in the crowdsourced data, however, mean non-trivial challenges in generating high-quality floorplans. We propose 1) a novel concept called Nail to identify featured locations in indoor space and 2) a heuristic pathlet bundling algorithm to progressively discover the internal layouts. We implement SenseWit and conduct real-world experiments in different spaces. Our work offers an efficient technique to obtain high-quality structures (either logical or physical) from low-quality data. We believe it can be generalized to other crowdsourcing applications.

Operating in unlicensed ISM bands, ZigBee devices often yield poor throughput and packet reception ratio due to the interference from ever increasing wireless devices in 2.4 GHz band. Although there have been many efforts made for interference avoidance, they come at the cost of miscellaneous overhead, which oppositely hurts channel utilization. Our empirical results show that, a specific interference is likely to have different influence on different outbound links of a ZigBee sender, which indicates the chance of concurrent transmissions. Based on this insight, we propose Smoggy-Link, a practical protocol to exploit the potential concurrency for adaptive ZigBee transmissions under harsh interference. Smoggy-Link maintains an accurate link model to describe and trace the relationship between interference and link quality of the sender's outbound links. With such a link model, Smoggy-Link can obtain fine-grained spatiotemporal link information through a low-cost interference identification method. The link information is further utilized for adaptive link selection and intelligent transmission schedule. We implement and evaluate a prototype of our approach with TinyOS and TelosB motes. The evaluation results show that Smoggy-Link has consistent improvements in both throughput and packet reception ratio under interference from various interferer.

RFID systems nowadays are operated at large-scale in terms of both occupied space and tag quantity. One may have prior knowledge of the complete set of tags (denoted by N) and any set of wanted tags (denoted by M) within the complete set, i.e., M ⊆ N. Then here comes an open problem: when one is particularly interested in a subarea of the system, how to collect information (not simply tagIDs) from a wanted subset (denoted by dM) of the interrogated tags (denoted by dN) in that subarea? This issue has great significance in many practical applications but appears to be challenging when there is a stringent time constraint. In this work, we first establish the lower-bound of this problem, and show a straightforward polling solution. Then, we propose a novel polling protocol called LocP, which consists of two phases: the Tags-Filtering phase and the Ordering-and-Reporting phase. LocP employs Bloom Filter twice to significantly reduce the scale of candidate tags in the Tags-Filtering phase. In the Ordering-and-Reporting phase, tags determine their own transmission time-slots according to the allocation vectors iteratively broadcasted by the reader. LocP thus achieves a delicate tradeoff between time and polling accuracy. We conduct extensive simulations to evaluate the performance of LocP. The results demonstrate that LocP is highly efficient in terms of information collection time, leading to convincing applicability and scalability of large-scale RFID systems.

Providing personalized content on smartphones is significant in ensuring user experience and making mobile applications profitable. The existing approaches mostly ignore the rich personalized information from user interaction with smartphones. In this paper, we address the issue of recommending personalized music to smartphone users and propose Smart-DJ. Smart-DJ incorporates an evolutionary model called Incremental Regression Tree, which incrementally collects contextual data, music data and user feedback to characterize his/her personal taste of music. An efficient recommending algorithm is designed to make accurate recommendations within bounded latency. We implement Smart-DJ and evaluate its performance through analysis and real-world experiments. The results demonstrate that Smart-DJ outperforms the state-of-arts approaches in terms of recommendation accuracy and overhead.

The low-cost crystal oscillators embedded in wireless sensor nodes are prone to be affected by their working condition, leading to undesired variation of clock skew. To preserve synchronized clocks, nodes have to undergo frequent re-synchronization to cope with the time-varying clock skew, which in turn means excessive energy consumption. In this paper, we propose DualSync, a synchronization approach for low-power wireless networks under dynamic working condition. By utilizing time-stamp exchanges and local measurement of temperature and voltage, DualSync maintains an accurate clock model to closely trace the relationship between clock skew and the influencing factors. We further incorporate an error-driven mechanism to facilitate interplay between Inter-Sync and Self-Sync, so as to preserve high synchronization accuracy while minimizing communication cost. We evaluate the performance of DualSync across various scenarios and compare it with state-of-art approaches. The experimental results illustrate the superior performance of DualSync in terms of both accuracy and energy efficiency.

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.

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.

Accurate maps are increasingly important with the growth of smart phones and the development of location-based services. Several crowdsourcing based map generation protocols that rely on users to provide their traces have been proposed. Being creative, however, those methods pose a significant threat to user privacy as the traces can easily imply user behavior patterns. On the flip side, crowdsourcing-based map generation method does need individual locations. To address the issue, we present a systematic participatory-sensing-based high-quality map generation scheme, PMG, that meets the privacy demand of individual users. To be specific, the individual users merely need to upload unorganized sparse location points to reduce the risk of exposing users’ traces and utilize theCrust, a technique from computational geometry for curve reconstruction, to estimate the unobserved map as well as evaluate the degree of privacy leakage. Experiments show that our solution is able to generate high-quality maps for a real environment that is robust to noisy data. The difference between the ground-truth map and the produced map is less than 10 m, even when the collected locations are about 32 m apart after clustering for the purpose of removing noise.

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.

On-air access of individual sensor node (called remote control) is an indispensable function in operational wireless sensor networks, for purposes like network management and real-time information delivery. To realize reliable and efficient remote control in a wireless sensor network (WSN), however, is extremely challenging, due to the stringent resource constraints and intrinsically unrealizable wireless communication. In this paper, we propose TeleAdjusting, a ready-to-use protocol to remotely control any individual node in a WSN. We develop a coding scheme for addressing on the cost-optimal reverse routing tree. In the address of each node, all its upstream relaying nodes are implicitly encoded. Then through a distributed prefix matching process between the local address and the destination address, a packet used for remote control is forwarded along a cost-optimal path. Moreover, TeleAdjusting incorporates opportunistic forwarding into the addressing process, so as to improve the network performance in terms of reliability and energy efficiency. We implement TeleAdjusting with TinyOS and evaluate its performance through extensive simulations and experiments. The results demonstrate that compared to the existing protocols, TeleAdjusting can provide high performance of remote control, which is as reliable as network-wide flooding and much more efficient than remote control through a pre-determined path.

Due to the constraint of energy resource, the radio of sensor nodes usually works in a duty-cycled mode. Since the sleep schedules of nodes are unsynchronized, a sender has to send preambles to coordinate with its receiver(s). In such contexts, opportunistic forwarding, which takes the earliest forwarding opportunity instead of a deterministic forwarder, shows great advantage in utilizing channel resource. The multiple forwarding choices with temporal and spatial diversity increase the chance of collision tolerance in concurrent transmissions, potentially enhancing end-to-end network performance. However, the current channel contention mechanism based on collision avoidance is too conservative to exploit concurrency. To address this problem, we propose COF, a practical protocol to exploit the potential Concurrency for low power Opportunistic Forwarding. COF determines whether a node should concurrently transmit or not, by incorporating: (1) a distributed and light-weight link quality measurement scheme for concurrent transmission and (2) a synthetic method to estimate the benefit of potential concurrency opportunity. COF can be easily integrated into the conventional unsynchronized sender-initiated protocols. We evaluate COF on a 40-node testbed. The results show that COF can reduce the end-to-end delay by up to 41% and energy consumption by 18.9%, compared with the state-of-the-art opportunistic forwarding protocol.

Cardinality estimation is the process to survey the quantity of tags in a RFID system. Generally, the cardinality is estimated by exchanging information between reader(s) and tags. To ensure the time efficiency and accuracy of estimation, numerous probability-based approaches have been proposed, most of which follow a similar way of minimizing the number of required time slots from tags to reader. The overall execution time of the estimator, however, is not necessarily minimized. The estimation accuracy of those approaches also largely depends on the repeated rounds, leading to a dilemma of choosing efficiency or accuracy. In this paper, we propose BFCE, a Bloom Filter based Cardinality Estimator, which only needs a constant number of time slots to meet desired estimation accuracy, regardless of the actual tag cardinality. The overall communication overhead is also significantly cut down, as the reader only needs to broadcast a constant number of messages for parameter setting. Results from extensive simulations under various tag IDs distributions shows that BFCE is accurate and highly efficient. In terms of the overall execution time, BFCE is 30 times faster than ZOE and 2 times faster than SRC in average, the two state-of-the-arts estimation approaches.

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.

In order to simultaneously achieve good energy efficiency and high packet delivery performance, a multihop forwarding scheme should generally involve three design elements: media access mechanism, link estimation scheme, and routing strategy. Disregarding the low-duty-cycle nature of media access often leads to overestimation of link quality. Neglecting the bursty loss characteristic of wireless links inevitably consumes much more energy than necessary and underutilizes wireless channels. The routing strategy, if not well tailored to the above two factors, results in poor packet delivery performance. In this paper, we propose L2, a practical design of data forwarding in low-duty-cycle wireless sensor networks. L2addresses link burstiness by employing multivariate Bernoulli link model. Further incorporated with synchronized rendezvous, L2enables sensor nodes to work in a lazy mode, keep their radios off most of the time, and realize highly reliable forwarding by scheduling very limited packet transmissions. We implement L2on a real sensor network testbed. The results demonstrate that L2outperforms state-of-the-art approaches in terms of energy efficiency and network yield.

Constructive interference (CI) enables concurrent transmissions to interfere non-destructively, so as to enhance network concurrency. In this paper, we propose deliberate synchronized constructive interference (Disco), which ensures concurrent transmissions of an identical packet to synchronize more precisely than traditional CI. Disco envisions concurrent transmissions to positively interfere at the receiver, and potentially allows orders of magnitude reductions in energy consumption and improvements in link quality. We also theoretically introduce a sufficient condition to construct Disco with IEEE 802.15.4 radio for the first time. Moreover, we propose Triggercast, a distributed middleware service, and show it is feasible to generate Disco on real sensor network platforms like TMote Sky. To synchronize transmissions of multiple senders at the chip level, Triggercast effectively compensates propagation and radio processing delays, and has 95th percentile synchronization errors of at most 250 ns. Triggercast also intelligently decides which co-senders to participate in simultaneous transmissions, and aligns their transmission time to maximize the overall link Packet Reception Ratio (PRR), under the condition of maximal system robustness. Extensive experiments in real testbeds demonstrate that Triggercast significantly improves PRR from 5 to 70 percent with seven concurrent senders. We also demonstrate that Triggercast provides 1.3χ PRR performance gains in average, when it is integrated with existing data forwarding protocols.

Smartphones nowadays are installed with diverse applications, each of which consumes energy and bandwidth. As more and more applications are crowded into a smart- phone, they cause serious problems with regard to battery life and bandwidth utilization. Existing proposals to tackle such challenges usually resort to two ways: avoiding energy- consuming network activities or improving communication efficiency in terms of power consumption. Those approaches either affect the smartphone users' experience, or offer little benefit in prolonging the battery life. Motivated by insightful understanding of users' habit, we in this paper propose a novel approach to orchestrate network activities of smartphone applications, based on user's habit. We implement our approach on smartphones as a middleware service called NetMaster. The performance evaluation with real traces shows that NetMaster reduces energy consumption of network activities by 77.8% in average and increases network bandwidth utilization by over 200%. The user experience is surprisingly well preserved. The chance of undesired interrupt during normal usage is less than 1%.

Accurate maps are increasingly important with the growth of smart phones and the development of location-based services. Several crowdsourcing based map generation protocols have been proposed that rely on volunteers to provide their traces. Being creative, however, those methods pose a significant threat to user privacy as the traces can easily imply user behavior patterns. On the flip side, crowdsourcing-based map generation method does need individual locations. To address the issue, we present a systematic participatory-sensing-based high-quality map generation scheme, PMG, that meets the privacy demand of individual users. In this approach, individual users merely need to upload unorganized sparse location points so as to reduce the risk of exposing privacy, while the server generates accurate maps with unorganized points, instead of user traces. Experiments show that our solution is able to generate high-quality maps for a real environment that is robust to noisy data. The difference between the ground-truth map and the produced map is <; 10m, even when the collected locations are about 32m apart after clustering for the purpose of removing noise.

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.

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.

Understanding the packet delivery performance of a wireless sensor network (WSN) is critical for improving system performance and exploring future developments and applications of WSN techniques. In spite of many empirical measurements in the literature, we still lack in-depth understanding on how and to what extent different factors contribute to the overall packet losses for a complete stack of protocols at large scale. Specifically, very little is known about: 1) when, where, and under what kind of circumstances packet losses occur; 2) why packets are lost. As a step toward addressing those issues, we deploy a large-scale WSN and design a measurement system for retrieving important system metrics. We propose MAP, a step-by-step methodology to identify the losses, extract system events, and perform spatial-temporal correlation analysis by employing a carefully examined causal graph. MAP enables us to get a closer look at the root causes of packet losses in a low-power ad hoc network. This study validates some earlier conjectures on WSNs and reveals some new findings. The quantitative results also shed lights for future large-scale WSN deployments .

Gradient-based routing using Bloom filters is an effective mechanism to enable data-centric queries in multihop networks. A node compressively describes its data items as a Bloom filter, which is then diffused away to the other nodes with information decay. The Bloom filters form an information potential that eventually navigates queries to the source node by ascending the potential field. The existing designs of Bloom filters, however, have critical limitations with respect to the feasibility of gradient-based routing. The compressed routing entries appear to be noisy. Noise in unrelated routing entries is very likely to equal to even outweigh information in right routing entries, thus blinding a query to its desired destination. This work addresses the root cause of the mismatch between the ideal and the practical performance of gradient-based routing using Bloom filters. We first investigate the impact of decaying model on the effectiveness of routing entries, and then evaluate the negative impact of noise on routing decisions. Based on such analytical results, we derive the necessary and sufficient condition of feasible gradient-based routing using Bloom filters. Accordingly, we propose a receiver-oriented design of Bloom filters, called Wader, which satisfies the necessary and sufficient condition. The evaluation results demonstrate that Wader guarantees the correctness and efficiency of gradient-based routing with high probability.

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.

Previous packet length optimizations for sensor networks often employ a fixed optimal length scheme, while in this study we present DPLC, a Dynamic Packet Length Control scheme. To make DPLC more efficient in terms of channel utilization, we incorporate a lightweight and accurate link estimation method. We further provide two easy-to-use services, i.e., small message aggregation and large message fragmentation, to facilitate upper-layer application programming. The implementation of DPLC based on TinyOS 2.1 is lightweight, with respect to computation, memory, and header overhead. Our experiments using a real indoor testbed running CTP show that DPLC achieves the best performance compared with previous works.

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.

Understanding the packet delivery performance of a wireless sensor network (WSN) is critical for improving system performance and exploring further development and applications of WSN techniques. In spite of many empirical measurements in the literature, we still lack in-depth understanding on how and to what extent different factors contribute to the overall packet losses with respect to a complete stack of protocols at large scales. Specifically, very little is known about (1) When, where, and under what kind of circumstances packet losses occur. (2) Why packets are lost. As a step towards addressing those issues, we deploy a large-scale WSN and design a measurement system for retrieving important system metrics. We propose MAP, a step-by-step methodology to identify the losses, extract system events, and perform spatial-temporal correlation analysis by employing a carefully examined casual graph. MAP enables us to get a closer look at the root causes of packet losses in a low-power adhoc network. This study validates some earlier conjectures on WSNs and reveals some new findings. The quantitative results also shed lights for future large-scale WSN deployments.

Constructive Interference (CI) proposed in the existing work (e.g., A-MAC [1], Glossy [2]) may degrade the packet reception performance in terms of Packet Reception Ratio (PRR) and Received Signal Strength Indication (RSSI). The packet reception performance of a set of nodes transmitting simultaneously might be no better than that of any single node transmitting individually. In this paper, we redefine CI and propose TriggerCast, a practical wireless architecture which ensures concurrent transmissions of an identical packet to interfere constructively rather than to interfere non-destructively. CI potentially allows orders of magnitude reductions in energy consumption and improvements in link quality. Moreover, we for the first time present a theoretical sufficient condition for generating CI with IEEE 802.15.4 radio: concurrent transmissions with an identical packet should be synchronized at chip level. Meanwhile, co-senders participating in concurrent transmissions should be carefully selected, and the starting instants for the concurrent transmissions should be aligned. Based on the sufficient condition, we propose practical techniques to effectively compensate propagation and radio processing delays. TriggerCast has 95thpercentile synchronization errors of at most 250ns. Extensive experiments in practical testbeds reveal that TriggerCast significantly improves PRR (from 5% to 70% with 7 concurrent senders, from 50% to 98.3% with 6 senders) and RSSI (about 6dB with 5 senders).

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.

Routing dynamics are intrinsic characteristics of operational wireless sensor networks (WSNs). We present the measurement and analysis results for routing dynamics in a large-scale WSN. We seek to answer several fundamental questions: How dynamically are current routing protocols performing? What causes routing dynamics? What is the impact of routing dynamics? Answers to the above questions are critical to understanding the interactions among multiple network elements, evaluating protocol design strategies, and improving system performances. However, measurements in large-scale WSNs are challenging due to the lack of dedicated log information (be analogous to configuration files, syslog messages used in Internet). We propose an approach to identify the routing dynamics based on limited information and correlate them with system events to find out the root causes. The key findings of our study include: parent change events mainly affect local nodes, i.e. They do not cause routing instability on far-away nodes, environment dynamics and routing loops have large impact on routing, small portion of parent changes might not be necessary, while a large portion of parent changes are effective in improving network performance.

In this paper, we investigate the predictability of packet arrivals in asynchronous duty-cycling wireless sensor networks (WSNs). We conduct statistical analysis on data traces collected in both outdoor large-scale and indoor testbed WSN to show that traditional well-known traffic models, e.g., Poisson process and Self-similarity process, do not fit well for modeling packet arrivals in both networks. According to our observations, some key characteristics such as sleeping interval and sampling rate have significant impact on the traffic patterns under Low power listening (LPL) models. Hence, we raise a question: could we achieve accurate prediction on the packet arrivals in asynchronous duty-cycling WSNs? To answer this question, we design a novel data-driven predictor P2IT focusing on two prediction goals: (i) the arrival time of the next packet (ii) the number of arrival packets within a short time interval. We conduct extensive trace-driven experiments to demonstrate that our predictor achieves high acuracies on both prediction goals under various experimental settings.

Recent advances in the application field increasingly demand the use of wireless camera sensor networks (WCSNs), for which localization is a crucial task to enable various location-based services. Most of the existing localization approaches for WCSNs are essentially interactive, i.e., require the interaction among the nodes throughout the localization process. As a result, they are costly to realize in practice, vulnerable to sniffer attacks, inefficient in energy consumption and computation. In this paper, we propose LISTEN, a noninteractive localization approach. Using LISTEN, every camera sensor node only needs to silently listen to the beacon signals from a mobile beacon node and capture a few images until determining its own location. We design the movement trajectory of the mobile beacon node, which guarantees to locate all the nodes successfully. We have implemented LISTEN and evaluated it through extensive experiments. Both the analytical and experimental results demonstrate that it is accurate, cost-efficient, and especially suitable for WCSNs that consist of low-end camera sensors.

Constructive interference-based flooding (CIBF) is a latency-optimal flooding protocol, which can realize millisecond network flooding latency and submicrosecond time synchronization accuracy, require no network state information, and be adapted to topology changes. However, constructive interference (CI) has a precondition to function, i.e., the maximum temporal displacement Δ of concurrent packet transmissions should be less than a given hardware constrained threshold (e.g., 0.5 μs, for the IEEE 802.15.4 radio). In this paper, we derive the closed-form packet reception ratio (PRR) formula for CIBF and theoretically disclose that CIBF suffers the scalability problem. The packet reception performance of intermediate nodes degrades significantly as the density or the size of the network increases. We analytically show that CIBF has a PRR lower bound (94.5%) in the grid topology. Based on this observation, we propose the spine constructive interference-based flooding (SCIF) protocol for an arbitrary uniformly distributed topology. Extensive simulations show that SCIF floods the entire network much more reliably than the state-of- the-art Glossy protocol does in high-density or large-scale networks. We further explain the root cause of CI with waveform analysis, which is mainly examined in simulations and experiments.

Localization is a fundamental issue of wireless sensor networks that has been extensively studied in the literature. Our real-world experience from GreenOrbs, a sensor network system deployed in a 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 for localization that exploits the strength of range-free approaches and range-based approaches using received signal strength indicator (RSSI). A critical observation is that ranging quality greatly impacts the overall localization accuracy. To achieve a better ranging quality, our method CDL incorporates virtual-hop localization, local filtration, and ranging-quality aware calibration. We have implemented and evaluated CDL by extensive real-world experiments in GreenOrbs and large-scale simulations. Our experimental and simulation results demonstrate that CDL outperforms current state-of-art localization approaches with a more accurate and consistent performance. For example, the average location error using CDL in GreenOrbs system is 2.9 m, while the previous best method SISR has an average error of 4.6 m.

Multiple-Input-Multiple-Output (MIMO) has great potential for enhancing the throughput of multihop wireless networks via spatial multiplexing or spatial reuse. Spatial reuse with Stream Control (SC) provides a considerable improvement of the network throughput over spatial multiplexing. The gain of spatial reuse, however, is still not fully exploited. There exist large numbers of additional data streams, which could be transmitted concurrently with those data streams scheduled by stream control at certain time slots and vicinities. In this paper, we address the issue of MIMO link scheduling to maximize the gain of spatial reuse and thus network throughput. We propose a Receiver-Oriented Interference Suppression model (ROIS), based on which we design both centralized and distributed link scheduling algorithms to fully exploit the gain of spatial reuse in multihop MIMO networks. Further, we address the traffic-aware link scheduling problem by injecting nonuniform traffic load into the network. Through theoretical analysis and comprehensive performance evaluation, we achieve the following results: 1) link scheduling based on ROIS achieves significant higher network throughput than that based on stream control, with any interference range, number of antennas, and average hop length of data flows. 2) The traffic-aware scheduling is enticingly complementary to the link scheduling based on ROIS model. Accordingly, the two scheduling schemes can be combined to further enhance the network throughput.

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.

In wireless sensor networks (WSNs), diagnosis is a crucial and challenging task due to the distributed nature and stringent resources. Most previous approaches are supervised, relying on a-priori knowledge of network faults. Our experience with GreenOrbs, a long-term large-scale WSN system, reveals the need of diagnosis in an agnostic manner. Specifically, in addition to predefined faults (i.e., with known types and symptoms), silent failures that are unknown beforehand, account for a large fraction of network performance degradation. Currently, there is no effective solution for silent failures because they are often diverse and highly system-related. In this paper, we propose Agnostic Diagnosis (AD), an online lightweight failure detection approach. AD is motivated by the fact that the system metrics (e.g., radio-on time, number of packets transmitted) of sensor nodes usually exhibit certain correlation patterns. Violations of such patterns indicate potential silent failures. We implement AD on a working WSN consisting of 330 nodes. Our experimental results demonstrate the advantages of AD to discover silent failures, effectively expanding the capacity and scope of WSN diagnosis.

Energy-constrained wireless sensor networks are duty-cycled, relaying on multi-hop forwarding to collect data packets. A forwarding scheme generally involves three design elements: media access, link estimation, and routing strategy. Most existing studies, however, focus only on a subset of those three. Disregarding the low duty cycle nature of media access often leads to overestimate of link quality. Neglecting the characteristic of bursty loss over wireless links inevitably consumes much more energy than necessary and underutilizes wireless channels. The routing strategy, if not well tailored to the above factors, results in poor packet delivery performance. In this paper, we propose L2, a practical design of data forwarding in low duty cycle wireless sensor networks. L2addresses link burstiness using multivariate Bernoulli link model. Further incorporated with synchronized rendezvous, L2enables sensor nodes to work in a lazy mode, keep their radios off as long as they can, and allocates the precious energy for only a limited number of promising transmissions. We implement L2on real sensor network testbeds. The results show that L2outperforms state-of-the-art approaches in terms of energy efficiency and network yield.

Motivated by the needs of precise carbon emission measurement and real-time surveillance for CO2management in cities, we present CitySee, a real-time CO2-monitoring system using sensor networks for an urban area (around 100 square kilometers). In order to conduct environment monitoring in a real-time and long-term manner, CitySee has to address the following challenges, including sensor deployment, data collection, data processing, and network management. In this discussion, we mainly focus on the sensor deployment problem so that necessary requirements like connectivity, coverage, data representability are satisfied. We also briefly go through the solutions for the remaining challenges. In CitySee, the sensor deployment problem can be abstracted as a relay node placement problem under hole-constraint. By carefully taking all constraints and real deployment situations into account, we propose efficient and effective approaches and prove that our scheme uses additional relay nodes at most twice of the minimum. We evaluate the performance of our approach through extensive simulations resembling realistic deployment. The results show that our approach outperforms previous strategies. We successfully apply this design into CitySee, a large-scale wireless sensor network consisting of 1096 relay nodes and 100 sensor nodes in Wuxi City, China.

Exploiting constructive interference in wireless networks is an emerging trend for it allows multiple senders transmit an identical packet simultaneously. Constructive interference based flooding can realize millisecond network flooding latency and sub-microsecond time synchronization accuracy, require no network state information and adapt to topology changes. However, constructive interference has a precondition to function, namely, the maximum temporal displacement Δ of concurrent packet transmissions should be less than a given hardware constrained threshold. We disclose that constructive interference based flooding suffers the scalability problem. The packet reception performances of intermediate nodes degrade significantly as the density or the size of the network increases. We theoretically show that constructive interference based flooding has a packet reception ratio (PRR) lower bound (95:4%) in the grid topology. For a general topology, we propose the spine constructive interference based flooding (SCIF) protocol. With little overhead, SCIF floods the entire network much more reliably than Glossy [1] in high density or large-scale networks. Extensive simulations illustrate that the PRR of SCIF keeps stable above 96% as the network size grows from 400 to 4000 while the PRR of Glossy is only 26% when the size of the network is 4000. We also propose to use waveform analysis to explain the root cause of constructive interference, which is mainly examined in simulations and experiments. We further derive the closed-form PRR formula and define interference gain factor (IGF) to quantitatively measure constructive interference.

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 asymptotic networking-theoretic multicast capacity bounds for random extended networks (REN) under Gaussian channel model, in which all wireless nodes are individually power-constrained. During the transmission, the power decays along path with attenuation exponent \alpha > 2. In REN, n nodes are randomly distributed in the square region of side length \sqrt{n}. There are n_s randomly and independently chosen multicast sessions. Each multicast session has n_d+1 randomly chosen terminals, including one source and n_d destinations. By effectively combining two types of routing and scheduling strategies, we analyze the asymptotic achievable throughput for all n_s=\omega (1) and n_d. As a special case of our results, we show that for n_s=\Theta (n), the per-session multicast capacity for REN is of order \Theta ({1\over \sqrt{n_d n}}) when n_d=O({n\over ({\log n})^{\alpha +1}} ) and is of order \Theta ({1\over n_d} \cdot (\log n)^{-{\alpha \over 2} }) when n_d=\Omega ({n\over \log n} ).

Demands on better interacting with physical world require an effective and efficient collaboration mechanism of multiple heterogeneous sensor networks. Previous works mainly focus on each single and specific sensor network, thus failing to address issues in the newly emerging scenario. In this paper, we propose COSE, a query-centric framework of collaborative heterogeneous sensor networks, where sensor networks collaborate with each other for effective and efficient processing of queries. Finding an optimal strategy of query processing with respect to energy efficiency is a crucial issue in COSE, which we formulate into an optimization problem, called EE-QPS. We prove the NP-hardness of EE-QPS, and then design a heuristic approach named IAP by utilizing the correlation (called implication in this paper) among different sensor networks. The experimental results demonstrate that in the context of COSE, IAP achieves optimized energy efficiency under various settings.

Data collection is a fundamental task in wireless sensor networks. In many applications of wireless sensor networks, approximate data collection is a wise choice due to the constraints in communication bandwidth and energy budget. In this paper, we focus on efficient approximate data collection with prespecified error bounds in wireless sensor networks. The key idea of our data collection approach ADC (Approximate Data Collection) is to divide a sensor network into clusters, discover local data correlations on each cluster head, and perform global approximate data collection on the sink node according to model parameters uploaded by cluster heads. Specifically, we propose a local estimation model to approximate the readings of sensor nodes in subsets, and prove rated error-bounds of data collection using this model. In the process of model-based data collection, we formulate the problem of selecting the minimum subset of sensor nodes into a minimum dominating set problem which is known to be NP-hard, and propose a greedy heuristic algorithm to find an approximate solution. We further propose a monitoring algorithm to adaptively adjust the composition of node subsets according to changes of sensor readings. Our trace-driven simulations demonstrate that ADC remarkably reduces communication cost of data collection with guaranteed error bounds.

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.

In wireless sensor networks (WSNs), diagnosis is a crucial and challenging task due to the distributed nature and stringent resources. Most previous approaches are supervised, relying on a-priori knowledge of network faults. On the other hand, our experience with GreenOrbs, a long-term large-scale WSN system, reveals the need of diagnosis in an agnostic manner. Specifically, in addition to predefined faults (i.e., with known types and symptoms), silent failures that are unknown beforehand, account for a large fraction of network performance degradation. Currently, there is no effective solution for silent failures because they are often diverse and highly system-related. In this paper, we propose Agnostic Diagnosis (AD), an online lightweight failure detection approach. AD is motivated by the fact that the system metrics (e.g., radio-on time, number of packets transmitted) of GreenOrbs sensors usually exhibit certain correlation patterns. Violations of such patterns indicate potential silent failures. We accordingly design a correlation graph, which systematically characterizes internal correlations inside a node. Silent failures are discovered by tracking the changes and anomalies of correlation graphs. We implement AD on a working WSN consisting of 330 nodes. Our experimental results demonstrate the advantages of AD to discover silent failures, effectively expanding the capacity and scope of WSN diagnosis.

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.

Although GPS is deemed as ubiquitous outdoor localization technology, we are still far from a similar technology for indoor environments. Though a number of techniques are proposed for indoor localization, they are separated efforts that are way from a real ubiquitous localization system. Our real-world experience from InSpace, a pervasive computing system with wireless devices to provide intelligent services to users, shows that locating mobile users remains very challenging due to various interfering factors. We analyze real traces of mobile phones carried by users and find that mobile users exhibit temporal-spatial stability and neighborhood relativity. Motivated by this observation, we develop a Mobile Boundary Localization approach, MBL, to exploit the associated information to locate mobile users. This localization approach uses different treatment in different conditions and lets each mobile phone try to estimate its possible location range. We have implemented and evaluated MBL by extensive real-world experiments in InSpace and simulations. The results demonstrate that MBL significantly outperforms state-of-the-art localization approaches with more accurate, efficient, and consistent performance.

Diagnosing a large-scale sensor network is a crucial but challenging task due to the spatiotemporally dynamic network behaviors of sensor nodes. In this demo, we present Sensor Anomaly Visualization Engine (SAVE), an integrated system that tackles the sensor network diagnosis problem using both visualization and anomaly detection analytics to guide the user quickly and accurately diagnose sensor network failures. Temporal expansion model, correlation graphs and dynamic projection views are proposed to effectively interpret the topological, correlational and dimensional sensor data dynamics and their anomalies. Through a real-world large-scale wireless sensor network deployment (GreenOrbs), we demonstrate that SAVE is able to help better locate the problem and further identify the root cause of major sensor network failures.

Data collection is a fundamental issue in wireless sensor networks. In many application scenarios for sensor networks, approximate data collection is a wise choice due to the constraints in communication bandwidth and energy budget. In this paper, we focus on efficient approximate data collection with given error bounds in wireless sensor networks. The key idea of our data collection approach ADC (Approximate Data Collection) is to divide a sensor network into clusters, discover local data correlations on each cluster head, and perform a global approximate data collection on the sink according to model parameters uploaded by cluster heads. Specifically, we propose a local estimation model to approximate the readings of several subsets of sensor nodes, and prove rated error-bounds of data collection using this model. In the process of model-based data collection, we formulate the problem of selecting the minimum subset of sensor nodes into a minimum dominating set problem which is known to be NP-hard, and use a greedy heuristic algorithm to find an approximate solution. We also propose a monitoring algorithm to adjust these subsets according to the changes of sensor readings. Our trace-driving simulation results show that our data collection approach ADC can notably reduce the communication cost with given error bounds.

Previous packet length optimizations for sensor networks often employ a fixed optimal length scheme, while in this study we present DPLC, a Dynamic Packet Length Control scheme. To make DPLC more efficient in terms of channel utilization, we incorporate a lightweight and accurate link estimation method that captures both physical channel conditions and interferences. We further provide two easy-to-use services, i.e., small message aggregation and large message fragmentation, to facilitate upper-layer application programming. The implementation of DPLC based on TinyOS 2.1 is lightweight, with respect to computation, memory, and header overhead. Our experiments using a real indoor testbed running CTP show that DPLC results in a 13% reduction in transmission overhead and a 41.8% reduction in energy consumption compared with the original protocol, and a 21% reduction in transmission overhead and a 15.1% reduction in energy consumption compared with simple aggregation schemes.

Recent advances in the application field increasingly demand the use of wireless camera sensor networks (WCSNs), for which localization is a crucial task to enable various location-based services. Most of the existing localization approaches for WCSNs are essentially interactive, i.e. require the interaction among the nodes throughout the localization process. As a result, they are costly to realize in practice, vulnerable to sniffer attacks, inefficient in energy consumption and computation. In this paper we propose LISTEN, a non-interactive localization approach. Using LISTEN, every camera sensor node only needs to silently listen to the beacon signals from a mobile beacon node and capture a few images until determining its own location. We design the movement trajectory of the mobile beacon node, which guarantees to locate all the nodes successfully. We have implemented LISTEN and evaluated it through extensive experiments. The experimental results demonstrate that it is accurate, efficient, and suitable for WCSNs that consist of low-end camera sensors.

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.

Motivated by the needs of precise forest inventory and real-time surveillance for ecosystem management, in this paper we present GreenOrbs [2], a wireless sensor network system and its application for canopy closure estimates. Both the hardware and software designs of GreenOrbs are tailored for sensing in wild environments without human supervision, including a firm weatherproof enclosure of sensor motes and a light-weight mechanism for node state monitoring and data collection. By incorporating a pre-deployment training process as well as a distributed calibration method, the estimates of canopy closure stay accurate and consistent against uncertain sensory data and dynamic environments. We have implemented a prototype system of GreenOrbs and carried out multiple rounds of deployments. The evaluation results demonstrate that GreenOrbs outperforms the conventional approaches for canopy closure estimates. Some early experiences are reported in this paper.

Most P2P Video-On-Demand (VOD) schemes mainly focus more on mending service architectures and optimizing overlays but do not carefully consider the user behavior and the benefit of prefetching strategies. As a result, they cannot better support VCR-oriented services in terms of substantive asynchronous clients, and free VCR controls for P2P VODs. In this paper, we propose VOVO, VCR-oriented VOD for large-scale P2P networks. By mining associations inside a video, the segments requested in VCR interactivities are accurately predicted based on the information collected through gossips. Together with a hybrid caching strategy, a collaborative prefetching scheme is proposed to optimize resource distribution among neighboring peers. We evaluate VOVO through extensive experiments. Results show that VOVO is scalable and effective, providing short startup latencies and good performance in VCR interactivities.

In large-scale, self-organized and distributed systems, such as peer-to-peer (P2P) overlays and wireless sensor networks (WSN), a small proportion of nodes are likely to be more critical to the system's reliability than the others. This paper focuses on detecting cut vertices so that we can either neutralize or protect these critical nodes. Detection of cut vertices is trivial if the global knowledge of the whole system is known but it is very challenging when the global knowledge is missing. In this paper, we propose a completely distributed scheme where every single node can determine whether it is a cut vertex or not. In addition, our design can also confine the detection overhead to a constant instead of being proportional to the size of a network. The correctness of this algorithm is theoretically proved and a number of performance measures are verified through trace driven simulations.

Existing localization approaches are divided into two groups: range-based and range-free. The range-free schemes often suffer from poor accuracy and low scalability, while the range-based localization approaches heavily depend on extra hardware capabilities or rely on the absolute RSSI (received signal strength indicator) values, far from practical. In this work, we propose a mobile-assisted localization scheme called perpendicular intersection (PI), setting a dedicate tradeoff between range-free and range-based approaches. Instead of directly mapping RSSI values into physical distances, by contrasting RSSI values from the mobile beacon to a sensor node, PI utilizes the geometric relationship of perpendicular intersection to compute node positions. We have implemented the prototype of PI with 100 TelosBmotes. Through comprehensive experiments, we show that PI achieves high accuracy and low overhead, significantly outperforming the existing range-based and the mobile-assisted localization schemes.

No abstract available.

Video-on-demand (VOD) is an interactive multimedia service, which delivers video content to subscribers (users). We propose a scheme of P2P VOD supporting VCR interactivities. By mining associations inside a video, the segments requested in VCR interactivities are accurately predicted based on the information collected through gossips. Together with a hybrid caching strategy, a collaborative prefetching scheme is proposed to optimize resource distribution among neighboring peers. We evaluate our scheme through extensive experiments. Results show that it is scalable and effective, providing short startup latencies and good performance in VCR interactivities.