Recent research has advanced the transmission rate and quality of video streaming in backscatter communications. However, these studies often overlook the challenge of deploying such systems in environments without dedicated excitation signals. To address this, we propose VideoBack, a high-quality video backscatter system using ambient WiFi signals for easy monitoring. VideoBack employs JPEG compression on high-resolution images to reduce transmission payload, enabling adaptation to the lower rates of WiFi backscatter. We design a customized packet structure for tags to backscatter image data using multiple uncontrolled ambient WiFi packets. To ensure power efficiency, we incorporate envelope detection with energy harvesting. To enable single-receiver deployment, we design a pilot-subcarrier-based recovery method to reconstruct the original WiFi signal. Our system maintains video reliability through a low bit error rate (BER) decoding strategy that includes phase error tracking and self-correction with multi-subcarriers. We prototype VideoBack using a commercial WiFi adapter, achieving a transmission rate of nearly 250 kbps with a BER below 0.05% up to 9 meters. VideoBack can send one frame of an image after just 2 seconds of energy harvesting within 3 meters.

Currently, a major issue of WiFi-based sensing technologies is how to adapt to changes in the surrounding environment. The extreme sensitivity ofChannel State Information(CSI) makes many WiFi sensing arts frustrated when applied to the complex and unknown real world. To solve this problem, in this paper, we proposefreeEnvdesigned to automatically identify the micro-environmental changes (even tiny movements of the laptop) using WiFi devices, which can coexist with other WiFi sensing tasks with zero effort. To achieve automatic identification of micro-environmental changes, we quantify micro-environmental changes based on the physical propagation laws of WiFi signals and the main factors that affect CSI measurements. Then, we design a micro-environmental changes identification method, which determines whether the environment has changed by calculating theEarth Mover's Distance(EMD) of theProbability Density Function(PDF) of continuous CSI, without requiring training data. To remove the influence of dynamic human behaviors, we design a human dynamic detection scheme, which is achieved by obtaining the average inter-cluster distance of performingGaussian Mixture Model(GMM) clustering on CSI. We evaluatefreeEnvin real-world scenarios with six different hardware, four different scenarios, and twenty-four ways of micro-environmental changes. The results show that our method is robust to different devices and scenarios, and can achieve the average precision of 96.1% and 93.2% for micro-environmental changes identification and human dynamic behavior detection. By testing on a case study of threshold-based human presence detection,freeEnvcan effectively improve the detection performance.

Exercise speed is a crucial indicator in bodyweight exercise assessment, directly reflecting muscular strength, energy expenditure, and exercise power. Benefiting from ubiquitous infrastructure and non-intrusive properties, WiFi-based sensing has emerged as a promising technique for motion speed estimation. However, existing WiFi sensing systems are significantly influenced by user diversity, positional variations, and orientation changes, which limit their applicability in real-world scenarios. In this work, we propose a learning-based framework, named SpeedFi , to estimate exercise speed from WiFi Channel State Information (CSI). An encoder-decoder network is designed to infer body speed, while temporal–frequency constraints are introduced to model consistent speed dynamics. To enhance user generalization, a domain adversarial strategy is adopted to extract user-invariant features. Moreover, two orthogonal WiFi links are deployed to capture complementary spatial perspectives, and an attention-based fusion module is employed for effective feature integration. A physics-inspired simulation model is further developed to synthesize CSI frequency variations, guiding the design of a diversified data collection strategy covering multiple positions, orientations, and environmental configurations. We conduct extensive experiments involving 10 participants performing 3 types of bodyweight exercises across 5 distinct environments. In controlled settings, SpeedFi achieves an average speed estimation error of 0.113 m/s for peak speeds around 1 m/s. Real-time analysis indicates that the system runs at over 40 FPS on a mid-range GPU, supporting its potential for real-time deployment. Additional tests under multi-person scenarios, complex multipath conditions, and varying WiFi link configurations are conducted to assess the system’s operational boundaries and robustness, validating its applicability across diverse settings.

Human presence detection is crucial in various scenarios, from law enforcement surveillance to smart health-care systems. Traditional methods like cameras and acoustic signals face challenges such as privacy concerns, the need for line of sight (LoS), and susceptibility to environmental noise. This paper proposes MULiving, a real-time RFID-based system designed to monitor the survival status of multiple users simultaneously. Unlike previous RFID-based solutions, MULiving addresses two key challenges: achieving real-time detection and accurately identifying stationary users. By leveraging a signal threshold-based approach that adapts automatically to non-living states, MULiving can distinguish between living and non-living users, even during periods of minimal movement(e.g., sleep). Extensive experiments using Commercial off-the-shelf (COTS) RFID devices demonstrate that MULiving is not only highly accurate but also robust across different users, RFID tags, and behaviors. The results confirm MULiving as a promising solution for real-time multi-user survival monitoring, with significant potential for practical deployment in real-world applications.

Counterfeiting and product tampering pose significant challenges in high-value packaged goods, leading to brand value degradation, regulatory violations, and potential public health risks. Although radio-frequency identification (RFID)-based authentication technologies have been widely adopted for item-level verification, existing systems struggle to precisely locate anomalous items within densely packed packages, often necessitating manual inspection. This paper presents PackLoc, which achieves high-precision RFID-based item ordering entirely using commercial off-the-shelf (COTS) hardware. PackLoc recovers the ordering and spatial layout of tags in dense packaging through a fixed multi-antenna configuration. By combining RSSI clustering, phase recovery, and linear fitting, the system overcomes technical bottlenecks such as low-dimensional observations, hardware-induced phase offsets, and$\pi$-radians phase jump. Experimental results show that PackLoc achieves over 90% ordering accuracy in dense layouts without requiring mechanical motion or environment-specific calibration, enabling rapid, noninvasive detection of counterfeit or misplaced items.

Currently, a major challenge in WiFi-based respiration monitoring systems is overcoming issues such as hardware noise interference and sensitivity to the user's location and orientation. To address these challenges, we propose LOAR-Fi, a novel, low-cost, non-contact respiration monitoring system based on the commercial WiFi chip ESP32. LOAR-Fi utilizes a flexible, scalable one-transmitter, multiple- receivers ($1 ~\mathrm{T}-\text{nR}$) architecture to improve accuracy and stability while maintaining low power consumption and cost. To enhance the quality of respiration signal extraction, we introduce two key innovations. First, a subcarrier ratio-based method that selects the most sensitive subcarrier combinations for respiration signal detection, improving system robustness, especially under noisy conditions. Second, an optimal receiver selection method is proposed, quantifying each receiver's sensing capability and intelligently selecting the best combination to optimize signal fusion. We evaluate LOAR-Fi in real-world environments, demonstrating exceptional performance with a mean absolute error of 0.31 bpm in respiration rate measurement, and over 90 % of data errors remaining below 0.5 bpm. LOAR-Fi achieves a 96 % detection accuracy for respiration presence, even under different user locations and orientations. These results show that LOAR-Fi significantly enhances the performance, adaptability, and reliability of WiFi-based respiration monitoring, providing a promising solution for healthcare applications.

With the rapid development of wireless sensing, trajectory-tracking methods based on Wi-Fi channel state information (CSI) have broad applicability in intelligent security systems, indoor navigation, and related scenarios owing to their low cost, contactless operation, and ease of deployment. Nevertheless, the high variability and noise of CSI limit the robustness and accuracy of existing methods, particularly in complex environments with dynamic pedestrian behavior. This paper proposes an indoor pedestrian trajectory-tracking framework that integrates deep learning, diffusion-based data augmentation, and particle filtering. First, we collect CSI in real environments and employ a diffusion model to augment the dataset, thereby improving the model's generalization. Next, a CNN-LSTM architecture extracts spatiotemporal features from CSI and learns a mapping from CSI to pedestrian positions. Finally, particle filtering is applied to smooth and reconstruct continuous position states over time. The proposed system achieves high-accuracy trajectory reconstruction in dynamic environments, with localization errors of approximately 0.5 m. It generalizes across diverse settings and demonstrates strong practical value and scalability.

Bluetooth-low-energy (BLE)-based low-power cameras have expanded the applications of battery-powered low-power Internet of Things (IoT) vision systems. The adaptive tuning of connection and image encoding settings plays a vital role in optimizing the efficiency of wireless vision systems. This article introduces a framework for adaptive adjustment in BLE-based low-power vision systems. To reduce power consumption, we model BLE power usage and design a dynamic BLE parameters and resolution adjustment method for low-power IoT vision system. Additionally, we design an edge-end combined approach that delegates the task of key region prediction to the receiving device to achieve adaptive intraframe JPEG encoding. Our approach leverages commercially available BLE devices without requiring physical layer modifications, ensuring compatibility with standard devices. We design a wireless hardware prototype using off-the-shelf CMOS sensors and BLE Systems on a Chip to evaluate our framework. Extensive experiments demonstrate a 66.9% increase in energy efficiency compared to traditional fixed parameter methods.

Nowadays, voice input on earphones has become one of the most paramount human-computer interaction approaches. Traditional voice interaction is built on the air channel, which is highly noise-susceptible and suffers from being falsely triggered by nearby competing users. In our work, we design a noise-resistant voice interaction interface on earphones, namedEarOE, which takes advantage of the narrow-bandwidth body channel to reconstruct high-fidelity audible speech. Although promising, directly taking the body channel as a voice interaction interface is nontrivial since the limited bandwidth of body-channel speech causes the original timbre information and linguistic content to be lost. To address these issues, we employ an electro-acoustic (EA) model for occlusion effect-based cross-channel correlation analysis. Based on that, we carefully design an attention-based encoder-decoder network to embrace cross-channel correlation for high-quality wide-bandwidth spectrum synthesis. To accommodate individual differences and improve model generalization, we implement a physics-based data augmentation strategy to expand the scale of the training dataset. Through extensive real-world experiments with 28 participants,EarOE can achieve an average Mel-cepstral distance of 8.08, an average modulation spectra distance of 0.82, and an average log-spectral distance of 11.16, outperforming existing solutions.

The effectiveness of WiFi-based localization systems heavily relies on the spatial accuracy of WiFi AP. In real-world scenarios, factors such as AP rotation and irregular antenna tilt contribute significantly to inaccuracies, surpassing the impact of imprecise AP location and antenna separation. In this paper, we proposeAnteumbler, a non-invasive, accurate, and efficient system for measuring the orientation of each antenna in physical space. By leveraging the fact that maximum received power occurs when a Tx-Rx antenna pair is perfectly aligned, we build a spatial angle model capable of determining antennas’ orientations without prior knowledge. However, achieving comprehensive coverage across the spatial angle necessitates extensive sampling points. To enhance efficiency, we exploit the orthogonality of antenna directivity and polarization, and adopt an iterative algorithm, thereby reducing the number of sampling points by several orders of magnitude. Additionally, to attain the required antenna orientation accuracy, we mitigate the influence of propagation distance using a dual plane intersection model while filtering out ambient noise. Our real-world experiments, covering six antenna types, two antenna layouts, two antenna separations ($\lambda /2$and$\lambda$), and three AP heights, demonstrate thatAnteumblerachieves median errors below$\text{6}^\circ$for both elevation and azimuth angles, and exhibits robustness in NLoS and dynamic environments. Moreover, when integrated into the reverse localization system,Anteumblerdeployed over LocAP reduces antenna separation error by$10 \,\mathrm{mm}$, while for user localization system, its integration over SpotFi reduces user localization error by more than$1 \,\mathrm{m}$.

WiFi-based gait recognition technologies have seen significant advancements in recent years. However, most existing approaches rely on a critical assumption: users must walk continuously and maintain a consistent body posture. This poses a substantial challenge when users engage in non-periodic or discontinuous behaviors (e.g., stopping, starting, or turning mid-walk), which can disrupt the extraction of gait-related features and degrade recognition performance. To address this issue, we proposefreeGait, a novel approach designed to mitigate the impact of non-gait behaviors in WiFi-based gait recognition systems. Our solution models this problem as domain adaptation, where we learn domain-independent representations to isolate gait features from behavior-dependent noise. We treat human behaviors with labeled user data as source domains and behaviors without user labels as target domains. However, applying domain adaptation directly is challenging due to the ambiguous classification boundaries in the target domains for WiFi signals. To overcome this, we align the posterior distributions between the source and target domains and constrain the conditional distribution within the target domains to enhance gait classification accuracy. Additionally, we implement a data augmentation module to generate data resembling the labeled data, while supervised learning ensures distinctiveness between users. Our experiments, conducted with 20 participants across 3 different scenarios, demonstrate thatfreeGaitcan accurately predict data across 15 domains by labeling only a small subset from 6 source domains, achieving up to a 45% improvement in user classification accuracy compared to existing methods.

Accurately estimating the velocity (including speed and direction) of moving targets has recently attracted widespread attention in augmented reality, security monitoring and sports health. In particular, the Doppler Frequency Shift (DFS)-based velocity estimation schemes using WiFi devices have shown great potential and have been widely studied. However, previous Fast Fourier Transform (FFT)-based and path-parameter-based arts have inherent limitations in DFS estimation and, worse still, ignore the nonlinear measurement errors caused by the relative orientation between the moving target and the WiFi transceiver. The above limitations make it difficult to meet the requirements for fine-grained velocity estimation in practical applications. To cope with these limitations, in this article, we propose a learning-based velocity estimation framework, named freeDoppler , to achieve fine-grained, multi-target and orientation-independent velocity estimation. Specifically, we construct a WiFi-based Velocity Estimation Network (VEN), which leverages continuous complex-valued Channel State Information (CSI) sequences as input, to fully learn the inherent information of the Doppler effect and accurately predict velocity series. In addition, we adopt the electric field scattering model of Maxwell’s equations to construct a physics-informed CSI Generation Model (CGM), thereby generating large-scale and high-quality simulated CSI samples to improve the generalization of the VEN model. Throughout extensive real-world experiments, freeDoppler can achieve median errors of 7.98 cm/s for speed estimation, 28° for direction estimation and 35 cm for human tracking in one or two moving targets, significantly outperforming the state-of-the-art methods.

Wi-Fi backscatter communication is a passive wireless technology that relies on modulating Wi-Fi signals through reflection. However, due to the limited availability of interference-free channels, interference and collisions between tags can occur, especially as the number of tags increases. Existing solutions employ static channel allocation methods, which restrict scalability and result in higher data packet loss rates. To address these challenges, we propose WiB-MAC. WiB-MAC utilizes a request-and-allocation mechanism to prevent collisions, supports dynamic tag networks, and is scalable for large-scale backscatter communication. Our protocol efficiently utilizes the exciter channel for tag channel requests and employs a Bloom filter-inspired approach to resolve conflicts when multiple tags apply for channels simultaneously. Using this approach, we ensure that only one tag uses a channel at a time, thereby avoiding collisions between tags. We further enhance network throughput by implementing a priority-based scheduling algorithm that reduces the impact of errors caused by tags. Finally, we designed our tag and tested the scenario where three tags simultaneously apply for the channel, demonstrating the feasibility of the protocol. We also compare our protocol to current backscatter communication networks as a baseline. In a network with 64 tags, WiB-MAC achieves 2.75 times the throughput of the baseline. In a network with 500 tags, the baseline’s throughput drops to 0, while WiB-MAC’s throughput only decreases by 5%.

WiFi backscatter communication demonstrates significant potential for the upcoming era of low-power wireless networks. Nevertheless, due to the low-power requirement of backscatter tags, there are limitations in their capacity to eliminate conflicts, posing a significant challenge for WiFi backscatter communication in environments with ambient interference. To address that, we introduce SlickScatter, an interference-insensitive WiFi backscatter system that can retrieve WiFi backscatter signals even in the presence of unknown ambient interference. The core strategy of SlickScatter involves designating a portion of the tag data symbols as pilot symbols. This approach enables the detection of uninterfered subcarriers and the estimation of channel state information and phase errors for all symbols within a packet, facilitating the demodulation of packets affected by interference. We have prototyped and evaluated SlickScatter with 802.11g OFDM WiFi signals, demonstrating its robustness and effectiveness against unknown ambient interference. Compared with a state-of-the-art solution, SlickScatter significantly decreases the frame error rate by 50% and improves the throughput by 1.96× at a distance of 12 m.

The performance of WiFi-based localization systems is affected by the spatial accuracy of WiFi AP. Compared with the imprecision of AP location and antenna separation, the imprecision of AP’s or antenna’s orientation is more important in real scenarios, including AP rotation and antenna irregular tilt. In this paper, we propose Anteumbler that non-invasively, accurately and efficiently measures the orientation of each antenna in physical space. Based on the fact that the received power is maximized when a Tx-Rx antenna pair is perfectly aligned, we construct a spatial angle model that can obtain the antennas’ orientations without prior knowledge. However, the sampling points of traversing the spatial angle need to cover the entire space. We use the orthogonality of antenna directivity and polarization and adopt an iterative algorithm to reduce the sampling points by hundreds of times, which greatly improves the efficiency. To achieve the required antenna orientation accuracy, we eliminate the influence of propagation distance using a dual plane intersection model and filter out ambient noise. Our real-world experiments with six antenna types, two antenna layouts and two antenna separations show that Anteumbler achieves median errors below 6 ° for both elevation and azimuth angles, and is robust to NLoS and dynamic environments. Last but not least, for the reverse localization system, we deploy Anteumbler over LocAP and reduce the antenna separation error by 10 mm, while for the user localization system, we deploy Anteumbler over SpotFi and reduce the user localization error by more than 1 m.

A crucial task in autonomous driving is to continuously detect nearby vehicles. Problems thus arise when a vehicle is occluded and becomes "unseeable", which may lead to accidents. In this study, we develop mmOVD, a system that can detect fully occluded vehicles by involving millimeter-wave radars to capture the ground-reflected signals passing beneath the blocking vehicle's chassis. The foremost challenge here is coping with ghost points caused by frequent multi-path reflections, which highly resemble the true points. We devise a set of features that can efficiently distinguish the ghost points by exploiting the neighbor points' spatial and velocity distributions. We also design a cumulative clustering algorithm to effectively aggregate the unstable ground-reflected radar points over consecutive frames to derive the bounding boxes of the vehicles.

Recently, WiFi-based gait recognition technologies have been widely studied. However, most of them work on a strong assumption that users need to walk continuously and periodically under a constant body posture. Thus, a significant challenge arises when users engage in non-periodic or discontinuous behaviors (e.g., stopping and going, turning around during walking). This is because variations of non-gait behaviors interfere with the extraction of gait-related features, resulting in recognition performance degradation. To solve this problem, we propose freeGait, which aims to mitigate the user's non-gait behaviors of WiFi-based gait recognition system. Specifically, we model this problem as domain adaptation, by learning domain-independent representations to extract behavior-independent gait features. We consider human behaviors with labels of users as source domains, and human behaviors without labels of users as target domains. However, directly applying domain adaptation to our specific problem is challenging, because the classification boundaries of the unknown target domains are unclear for WiFi signals. We align the posterior distributions of the source and target domains, and constrain the conditional distribution of the target domains to optimize the gait classification accuracy. To obtain enough source domains data, we build a data augmentation module to generate data similar to the labeled data, and use supervised learning to make the data different between users. We conduct experiments with 20 people and 3 different scenarios, and the results show that accurate predictions of a total of 15 domains data can be achieved by only collecting and labeling a small amount of data from 6 source domains, and user classification accuracy can be improved by up to 45% compared to other existing techniques.

Despite the potential for throughput enhancement with multiple tags, existing WiFi backscatter systems have been limited by inband interference among various tags. In response, we propose MultiRider, the first WiFi backscatter system that can tame in-band interference and support multi-tag parallel communication on commercial OFDM protocol. The principle behind MultiRider lies in its ability to demodulate and reconstruct tag data using just one uncorrupted subcarrier in the spectrum domain. To address the inherent challenges of preamble corruption and data collision due to in-band interference, we design three modules: 1) preamble recovery based on a concurrency-driven backscatter packet structure; 2) subcarrier-level demodulation using uncorrupted subcarriers; and 3) iterative interference cancellation for multiple tags. We prototype and evaluate MultiRider under 802.11g OFDM WiFi signals with commercial adapters and software-defined radios. Comprehensive evaluations illustrate that MultiRider can efficiently solve in-band interference. Notably, it can expand the network capacity of WiFi backscatter by 4× and use 8 channels in the 2.4GHz WiFi band for concurrent communication. Further results reveal that MultiRider can gain 10× network capacity in 35MHz bandwidth and reach 2.29 Mbps system throughput.

Recently, Wi-Fi based sensing technology has been widely studied to provide more convenient services for humans. Although previous arts claim to achieve diverse fine-grained sensing using Wi-Fi signals, most of them assume that the data such as Channel State Information (CSI) used to achieve the sensing tasks can be sufficiently collected. However, in practical, due to the competitive nature of Wi-Fi and the frequent intermittent traffic, Wi-Fi based sensing applications often encounter problems of irregular intervals and insufficient sampling. Therefore, in this paper, we propose a Wi-Fi signal sparse recovery system (WiSR) that aims to recover sufficient and uniform sensing data from unevenly spaced and under-sampled CSI. Inspired by the success of image and audio restoration, we improve the Generative Adversarial Network (GAN) to recover Wi-Fi CSI. However, the direct application of GAN technologies for image and audio to CSI is not effective due to the difference in data representation. First, to avoid spectral impairments after conversion from time domain to frequency domain, we directly operate on the original time series CSI waveforms, thus being able to recover continuous channel variations from intermittent sparse samples. Second, to enhance the above recovery process, we utilize two novel denoising methods to obtain clean CSI, and introduce restrictions in the time and frequency domains to optimize low-level features and high-frequency information, respectively. Real-world experiments show that WiSR can accurately recover CSI, even at a rate of 10 packets per second. Through practical applications of gait recognition and gesture recognition, WiSR significantly improves accuracy compared to traditional linear interpolation and cubic interpolation.

Due to contactless and convenient experiences, WiFi-based device-free fingerprints localization technologies have extensively attracted research attention. However, they are studied based on an assumption that the user is stationary and face a major challenge in the presence of users motions. That is because users motions induced CSIWiFi variations results in inconsistent location fingerprints during training and prediction, leading to system ineffective. To solve this problem, in this paper, we propose freeLoc, which aims to free users motions (even unseen) while maintaining accurate localization. Specifically, we construct a domain adaptation network that defines different users and motions as different domains, and learns domain-independent representations to extract location fingerprints independent of users motions. Unfortunately, collecting sufficient amounts of WiFi data is difficult. To reduce the cost of labeling data and ensure the performance of domain adaptation network, we utilize adversarial autoencoder to build a data augmentation module to introduce data diversity. We deploy experiments in a real scenario, and the results show that only by labeling three motions of three users, we can achieve accurate localization (the nearest locations are about one meter away) for a total of 36 domains including 6 users and 6 motions. Compared to other existing technologies, freeLoc can improve location prediction accuracy by up to 35%.

The existing RF-based liquid identification systems usually rely on prior knowledge, such as pre-build database or the material and width information of the vessel. Furthermore, existing methods may not work in scenarios where the height of liquid is smaller than that of antenna. In this paper, we proposesLiqRay$^+$, a contactless system which can identify liquids in a fine-grained level without prior knowledge. To remove the effect of vessel, we build a dual-antenna model and craft a relative frequency response factor, exploring diversity of the permittivity in frequency domain. To eliminate the effect of different height, we devise the electric field distribution model at the receiving antenna, solving the unknown heights via spatio-differential model. Among eight different solvents,LiqRay$^+$can identify alcohol solutions with a concentration difference of 1% with 92.9% accuracy. Even if the liquid height is about 4 cm, which is fairly lower than that of most antennas’ heights, the accuracy is more than 85%.

Accurate and high-speed object movement tracking systems often face significant challenges due to signal instability caused by the object’s movement and rotation. To address these issues, we present STABack, a novel object tracking system using backscatter tags and accelerometer sensors, designed for high-accuracy, high-speed movement tracking. We developed an amplitude stabilization algorithm which uses envelope detection to reduce the impact of high-frequency movement on the signal, and uses dynamic threshold output to lower the BER in backscatter demodulation. Our method reduces the BER by 0.3757 compared to the regular demodulation method, resulting in a final BER of 0.07. Our evaluation of the STABack prototype shows that it achieves a median distance measurement accuracy of 6.45 cm with a standard deviation of 6.95 cm, under the condition of a speed of 120 cm. The attitude angle estimation’s mean error is under 7 degrees. The system’s accuracy in detecting the target object’s trajectory is as high as 99%, and it can still decode with a bit error rate of no more than 0.034 at a speed of 166 cm/s. The power consumption of our system prototype is only 38.54 μW based on our experimental results. Overall, our results demonstrate that STABack can accurately estimate the movement and rotation of target objects in unstable backscatter channels.

Recent works have achieved considerable success in improving the concurrency of backscatter network. However, they do not optimize the balance between throughput and spectrum occupancy, both of which serve as pivotal parameters in concurrent transmissions. Moreover, these works also introduce complex components on tag thereby increasing both power consumption and deployment costs. In this article, we proposeSpray, a tag-lightweight system to achieve high throughput and narrow-band occupancy with low power. The key idea is to incorporate an agile channel allocating and scheduling mechanism into the backscatter network. This approach allows for efficient spectrum utilization and concurrency without the need for energy-intensive components. To optimize throughput in the presence of the challenge of harmonic interference, we introduce a novel algorithm that determines the channels with an optimal combination of central frequencies and bandwidths. Additionally, we propose a fair scheduling strategy to ensure equitable transmission opportunities for all tags. We prototype theSpraytag using commercial off-the-shelf components and implement the excitation and receiver with software-defined radio platform. Our evaluation shows that the system supports 30 parallel tags transmitting in the bandwidth of 600 kHz and the throughput can reach more than 280 kbps.

Recent years have witnessed the emerging development of single-antenna wireless respiration detection that can be integrated into IoT devices with a single transceiver chain. However, existing single-antenna-based solutions are all limited by the short sensing range within 2-4 m due to noise interference, which makes them difficult to be adopted in most room-scale scenarios. To deal with this dilemma, we propose a room-scale, noise-resistance, and accurate respiration monitoring system, named Wi-Cyclops , 1 which captures CSI changes induced by respiratory movements only via one antenna on commercial WiFi devices. To push the limits of effective sensing distance, we innovatively supply a new perspective to review the CSI samples along the sub-carrier dimension. From this dimension, we find that the interrelationship between sub-carriers with different timestamps still shows a high correlation even though the SNR decreases. Based on that, we analyze the noise characteristics along the sub-carrier dimension and correspondingly design a series of denoising schemes. Specifically, we carefully design a PCA-based denoising method to filter out ambient noises. After that, considering the low distribution densities of the AGC-induced noise, we then remove it by optimizing the DBSCAN denoising method with the K-Means-based adaptive radius search. Extensive experiments demonstrate that our system can work effectively in three typical family scenarios. Wi-Cyclops can achieve 98% accuracy even when the person is 7 m away from the transceiver pair. Compared with the start-of-art single-antenna-based approaches in real scenarios, Wi-Cyclops can improve the sensing range from 3 m to 7 m, which can meet the requirements of room-scale respiration monitoring. Additionally, to show the high compatibility with smart home devices, Wi-Cyclops is deployed on seven commercial IoT devices and still achieves a low average absolute error with 0.41 bpm.

Mechanical vibration sensing is one of the most critical issues for advanced industrial IoT applications, such as troubleshooting and working condition analysis. Different from status quo solutions, the approaches based on wireless sensing have the advantages of non-invasive and easy to deploy. However, existing wireless works such as RFID and radar are limited by deployment distance or NLoS working scenarios. In this work, we propose a wireless vibration sensing system using COTS WiFi named Wi-Ear, which could monitor multiple devices in LoS or NLoS scenarios. Moreover, Wi-Ear can distinguish the vibration frequency belonging to which device without utilizing any sensor. Especially, in a weak signal scenario where the vibration signal is buried by noise in the raw CSI, Wi-Ear can also detect the vibration frequency effectively. Finally, we implement Wi-Ear with COTS WiFi devices and evaluate it with commercially available motors. For vibration sensing, Wi-Ear can sense weak vibration with an error of 0.1Hz. For device identification, Wi-Ear can identify 10 vibrating devices of 3 different types with an accuracy of 92%. Comprehensive experiments in various scenarios are conducted to show great robustness and stability.

Recent works have achieved considerable success in increasing the transmission rate of video streaming backscatter communications. However, they do not consider the ease of deployment in real-world environments where these dedicated excitations are not readily available. In addition, these works are transmitted with lower image resolution, and the images received by users are not eye-friendly. In this paper, we propose VideoBack, a high quality video backscatter system with commercial WiFi excitation for easy-to-use monitoring. The key idea is to perform JPEG compression on high-pixel images to adapt to intermittent ambient WiFi transmission. This approach allows ambient commercial routers to be used for excitation. Furthermore, our work enables high-quality video transmissions by devising a low bit error rate decoding scheme. We build a prototype of VideoBack and use a commercial WiFi adapter to excite the tag to realize the transmission and reception of video. The prototype uses RF signals to charge and store energy. Our evaluations show that VideoBack can transmit images at a throughput of nearly 250 kbps with bit error rate below 0.0005 within 9 meters, while the images only have minor distinctions. VideoBack can support sending one frame of image after just 2 seconds of power acquisition within 3m.

In this paper, we present a novel object tracking system, named STABack, that utilizes backscatter tags and accelerometer sensors. The system is designed to support high-speed movement tracking with high accuracy. One of the challenges faced in this system is the instability of the received signal due to the motion and rotation of the backscatter tag. To address this issue, we propose an amplitude stabilization algorithm to eliminate the interference caused by the tag's motion. The algorithm uses envelope detection to remove the impact of high-frequency motion on the signal, and a dynamic threshold output to further reduce the bit error rate of backscatter demodulation. Additionally, we perform outliers removal and interpolation on the three-axis accelerometer data and compute the attitude angle using 3D geometric quadrants. Finally, we implement the prototype of STABack and evaluate its performance. Our method improves BER up to 0.3757 compared with the regular demodulation method. Our evaluation of the STABack prototype shows that it achieves a median distance measurement accuracy of 6.45 cm with a standard deviation of 6.95 cm. The mean error of the attitude angle estimation is less than 7 degrees, and the average relative error of three-axis acceleration tracking is only 0.097. The system's accuracy in detecting the target object's trajectory is as high as 98%, and it can still decode with a bit error rate of no more than 0.034 at a speed of 166 cm/s. The power consumption of backscatter communication is$38.54\ \mu\mathrm{W}$. Based on our experimental results, Overall, our results demonstrate that STABack can accurately estimate the movement and rotation of target objects in unstable backscatter channels.

In this paper, we present PAssTrack, a Wi-Fi based passive human tracking system which is adapted to the practical antenna spacing of most commodity Wi-Fi access points (APs) and achieves accurate tracking results. We mainly enhance our system in the following four aspects. Firstly, we modify 2D MUSIC algorithm for estimating parameters including angle of arrival (AoA) and relative time of flight (rToF) of dynamic human reflection path. And we analyze the superiority of our algorithm compared with the state-of-the-art solutions. Secondly, we leverage the estimated rToF and the continuity of AoA to resolve the angle ambiguity caused by antenna spacing larger than half of the wavelength. Thirdly, we optimize the mesh model based on Fresnel zone theory to a dual antenna version for fine-grained velocity estimation. Lastly, we introduce hologram for target localization in order to compensate the errors of estimated path parameters using velocity estimates, and reduce the impact of outliers through kernel density estimation (KDE). The experiments are conducted in two real-world indoor environments, and the results demonstrate the advantages of PAssTrack in aspects of better tracking accuracy than the state-of-the-arts, easy and effective calibration, robustness under the case of blocked transceivers and adaptiveness to different antenna spacing.

WiFi connections are vulnerable to simulated attacks from rogue access points (APs) or devices whose SSID and/or MAC/IP address are the same as legitimate devices. This kind of attack is difficult to counter with traditional network security mechanisms. In this article, we propose a new security mechanism that uses environment-independent features extracted from channel state information (CSI) to detect and identify rogue WiFi devices or APs, and reject their connections. We find that due to the$I/Q$imbalance and imperfect oscillator of each WiFi network card (NIC), the nonlinear phase error of different subcarriers will vary with the NIC. Through our experimental verification, this cross-subcarrier phase feature is invariant to the location and the environment. We deploy systems on two platforms that can extract constant phase errors from the constantly changing CSI in less than 1 s, which is at least$8 \times $faster than that of the state-of-the-art solution. Extensive experiments on commercial routers and end devices in different scenarios show that based on the Industrial Platform Computer (IPC) platform, where only nonencrypted rogue connections can be detected, the detection accuracy rate reaches 96%, and the false alarm rate is less than 2%. Based on the ASUS router platform (a commercial WiFi router), WiFi channels and smart device types are not restricted, which greatly improved universality, and the accuracy of device connection detection can even reach more than 99%. We improve a device-type identification method based on the communication traffic features of the device when connected to WiFi. Experiments show that even if there are multiple similar devices from the same manufacturer, the accuracy of device-type detection exceeds 99%.

The existing RF-based liquid identification methods commonly require a training network of liquid or the container information, such as material and width. Moreover, status quo methods are inapplicable when the solution height is lower than that of the antenna, which is generally unknown either. This paper proposes LiqRay, an RF-based solution, retaining non-invasive and fine-grained liquid recognition abilities, thus can recognize unknown solutions without prior knowledge. In dealing with the unknown container material and width, we utilize a dual-antenna model and craft a relative frequency response factor, exploring diversity of the permittivity in frequency domain. In tackling the unknown heights of solution and antenna, we devise the electric field distribution model at the receiving antenna, solving the unknown heights via spatio-differential model. Among eight different solvents, LiqRay can identify alcohol solutions with a concentration difference of 1% with 94.92% accuracy. Nevertheless, LiqRay can obtain the relative frequency response factor with a relative error of 6.7% without being affected by the height of the solution. Even if it is merely 4 cm, this is fairly lower than that of most antennas' heights, since the operating frequency is around 2 GHz.

The global IoT market is experiencing a fast growth with a massive number of IoT/wearable devices deployed around us and even on our bodies. This trend incorporates more users to upload data frequently and timely to the APs. Previous work mainly focus on improving the up-link throughput. However, incorporating more users to transmit concurrently is actually more important than improving the throughout for each individual user, as the IoT devices may not require very high transmission rates but the number of devices is usually large. In the current state-of-the-arts (up-link MU-MIMO), the number of transmissions is either confined to no more than the number of antennas (node-degree-of-freedom, node-DoF) at an AP or clock synchronized with cables between APs to support more concurrent transmissions. However, synchronized APs still incur a very high collaboration overhead, prohibiting its real-life adoption. We thus propose novel schemes to remove the cable-synchronization constraint while still being able to support more concurrent users than the node-DoF limit, and at the same time minimize the collaboration overhead. In this paper, we design, implement, and experimentally evaluate OpenCarrier, the first distributed system to break the user limitation for up-link MU-MIMO networks with coordinated APs. Our experiments demonstrate that OpenCarrier is able to support up to five up-link high-throughput transmissions for MU-MIMO network with 2-antenna APs.

For large-scale Internet of Things (IoT), backscatter communication is a promising technology to reduce power consumption and simplify deployment. However, due to the variable excitation source (ES) signal strength and time-varying channel condition, backscatter communication lacks stability, along with limited communication range as a few meters. Adaptive date rate (ADR) is beneficial to solve such issues, but is burdensome when implement on the capability limited tags. In this paper, we design a system named FreeBack with rate adaptation in backscatter communication. Our modulation approach is denoted as Adaptive Chirp-OOK where the ES recursively generates chirp signal, and the tags reflect the chirp signal with the On-Off Key modulation. According to channel symmetry, the tags perform rate adaption only based on the received ES signal strength instead of feedback from receiver. Such adaptation method enables the receiver to successfully decode signal through the time-varying channel, even for signal under the noise floor. We have implemented the prototype system based on the USRP platform. Extensive experiment results demonstrate the effectiveness of the proposed system. Our system provides valid ES-tag distance up to 27m, which is 7× as compared with normal backscatter system. FreeBack significantly increases the backscatter communication stability, by supporting data rate adaptation ranges from 0. 33kbps to 1. 2Mbps, and guaranteeing the bit error rate (BER) below 1%.

Driven by a wide range of real-world applications, several ground-breaking RF-based motion-recognition systems were proposed to detect and/or recognize macro/micro human movements. These systems often suffer from various interferences caused by multiple-users moving simultaneously, resulting in extremely low recognition accuracy. Even if the repetitive motions are fairly well detectable through the wireless signals in theory, in reality they get blended into various other system noises during the motion. Moreover, irregular motion patterns among users will lead to expensive computation cost for motion recognition. To tackle these challenges, we propose a novel wireless sensing system, calledMotion-Fi$\ ^+$+, which marries battery-free wireless backscattering and device-free sensing in one clean sheet.Motion-Fi$\ ^+$+is an accurate, interference tolerable motion-recognition system, which counts repetitive motions without using scenario-dependent templates or profiles and enables multi-user performing certain motions simultaneously because of the relatively short transmission range of backscattered signals and dedicated signal separation method. We implement a backscattering wireless platform to validate our design in various scenarios for over 6 months when different persons, distances and orientations are incorporated. In our experiments, the periodicity in motions could be recognized without any learning or training process, and the accuracy of counting such motions can be achieved within 5 percent count error. With little efforts in learning the patterns, our method could achieve 95.2 percent motion-recognition accuracy for a variety of 7 typical motions. Moreover, by leveraging the periodicity of motions, the recognition accuracy could be further improved to nearly 100 percent with only three repetitions. Our experiments also show that the motions of multiple persons separating by around$ 2$meters cause little accuracy reduction in the counting process.

Acoustic motion tracking is an exciting new research area with promising progress in the last few years. Due to the inherent low propagation speed in the air, acoustic signals have the unique advantage of fine sensing granularity compared to RF signals. Speakers and microphones nowadays are pervasively available in devices surrounding us, such as smartphones and voice-controlled smart speakers. Though promising, one fundamental issue hindering the adoption of acoustic-based motion tracking is that the positions of microphones and speakers inside a device are fixed, which greatly limits the flexibility of acoustic motion tracking. In this work, we propose a new modality of acoustic motion tracking using earphones. Earphone-based tracking mitigates the constraints associated with traditional smartphone-based tracking. With novel designs and comprehensive experiments, we show earphone-based motion tracking can achieve a great flexibility and a high accuracy at the same time. We believe this is an important step towards "earable" sensing.

WiFi has become a pervasive communication medium in connecting various devices of WLAN and IoT. However, WiFi connections are vulnerable to the impersonation attack from rogue access points (AP) or devices, whose SSID and/or MAC/IP address are identical to the legitimate devices. This kind of attack is difficult to countermeasure with traditional network security mechanisms. In this paper, we present a novel security mechanism to detect and identify rogue WiFi devices or AP using environment-independent characteristics extracted from channel state information (CSI), and refuse their connections. We find that nonlinear phase errors of different subcarriers change with WiFi network interface cards (NIC), due to the I/Q imbalance and imperfect oscillator of each WiFi NIC. Validated by our experiments, this phase feature across subcarriers is consistent and invariant to location and external environment, and can be extracted to build an essential signature of the NIC itself. Such signature of the transmitter can be calculated in real-time by the receiver and cannot be forged by rogue devices. Extensive experiments with dozens of WiFi devices demonstrate that the proposed mechanism can reliably detect the rogue WiFi connections and prevent impersonation in various scenarios. The speed of identification is 8× faster than that of the state-of-the-art solution. Moreover, the accuracy of rogue connection detection is up to 96% and false alarm rate is shown below 2%.

We propose a novel motion sensing/recognition system, called DF-Mose, which marries low-power wireless backscattering and device-free sensing in one clean sheet. DF-Mose is an accurate, interference tolerable motion-recognition system that counts repetitive motions without using scenario-dependent templates or profiles within 5% count error and enables multiuser to perform certain motions simultaneously based on the nature of backscattered signals and dedicated signal separation method. With little efforts in learning the patterns, our method could achieve 95.2% motion-recognition accuracy for a variety of 7 typical motions.

In this paper, we propose a system called "RFRecorder" based on COTS RFID system which can inspect the string vibration and recognize the tone and tempo accurately. Our system can recover the music score played by some string instruments such as ukulele. Specifically, the string vibration influences the reflection of RF signal and causes a phase change. This change can be captured and analyzed to recover the frequency of the string vibration. In addition, the RFID tag is attached on the body of instrument but not on the string, which can not affect the music playing. Compared to the recorder, our system is immune to the environmental noise. Furthermore, it also can work on NLoS scenario. For single-string vibration, we use compressive sensing to recover the frequency duo to the low sampling rate of the COTS RFID. For multiple-string vibration, we recognize the music tone using machine learning method. We build a prototype and test the performance using guitar, ukulele and zither, which achieves 90%, 89%, 85% accuracy respectively.

Recently several ground-breaking RF-based motion-recognition systems were proposed to detect and/or recognize macro/micro human movements. These systems often suffer from various interferences caused by multiple-users moving simultaneously, resulting in extremely low recognition accuracy. To tackle this challenge, we propose a novel system, called Motion-Fi, which marries battery-free wireless backscattering and device-free sensing. Motion-Fi is an accurate, interference tolerable motion-recognition system, which counts repetitive motions without using scenario-dependent templates or profiles and enables multi-users performing certain motions simultaneously because of the relatively short transmission range of backscattered signals. Although the repetitive motions are fairly well detectable through the backscattering signals in theory, in reality they get blended into various other system noises during the motion. Moreover, irregular motion patterns among users will lead to expensive computation cost for motion recognition. We build a backscattering wireless platform to validate our design in various scenarios for over 6 months when different persons, distances and orientations are incorporated. In our experiments, the periodicity in motions could be recognized without any learning or training process, and the accuracy of counting such motions can be achieved within 5% count error. With little efforts in learning the patterns, our method could achieve 93.1% motion-recognition accuracy for a variety of motions. Moreover, by leveraging the periodicity of motions, the recognition accuracy could be further improved to nearly 100% with only 3 repetitions. Our experiments also show that the motions of multiple persons separated by around 2 meters cause little accuracy reduction in the counting process.

In this work, we leverage the reflection property explored in our experimental results and propose an efficient and effective algorithm for the reconstruction of the layout. The proposed algorithm identifies the reflective path, which is caused by obstacle, and evaluated by the difference of Angle of Arrival (AoA) at each transceiver deployed at the room. Then with the knowledge of AoA corresponding to the affected reflective path, the reflection points can be calculated. These reflection points could be used to outline the surface of obstacle. Moreover, we can estimate the shape, size, and location of the obstacle by using AlphaShape algorithm. The simulation results prove the effectiveness of our algorithm.

Recent years have seen major innovations in cooperative wireless networks. Despite the fact that throughput gains have been achieved in packet recovery, hardly any of these technologies could effectively decode signals when the number of signal sources is greater than available antennas in each AP. Thus, conventional cooperative methods rely on adaptive scheduling for interference-free data packets. Deploying scheduling-free cooperative signal recovery requires prompt processing with low overheads. Yet potentially, the concurrent client transmissions could overwhelm any single AP, i.e., the number of concurrent transmissions are more than that of antennas. This paper presents the first step towards breaking this stalemate, by enabling symbol alignment and constellation reinforcement instead of relying on scheduling. We present POLYPHONY, a scheduling-free cooperative design, where decoding process could be coordinated without over-the-airs-cheduling, and coupled signals are decoded promptly after deep cooperations. We implement POLYPHONY prototype with GNURadio/USRP platform, and deploy it witha16-node enterprise network. Particularly, we demonstrate how it manages the complex interactions with scheduling-free signal enforcement, and enables a beyond node-DoF (Degree of Freedom) decoding with AP coordinations. Furthermore, we show how the cooperative decoding process improves radio access among clients. Our results demonstrate a gain of nearly 200 percent for network throughput, which is a significant improvement for heavy contending networks.

In this paper, we consider typical wireless energy harvesting network, where a mobile charging vehicle is scheduled to serve a wireless sensor network. Specifically, for practical considerations such as deployment restrictions, the charging vehicle could not provide full efficiency energy supply to sensor nodes. For wireless energy harvesting, there is an inevitable tradeoff between the charging distance and the angle. We focus on optimizing the charging efficiency when the distance and angle factors are concerned. Scheduling charging vehicle for rechargeable nodes in previous studies has been proved to be NP hard. Even worse, the non-linear property between the charging distance and angle should be carefully considered, which makes the problem even harder. We investigate how to minimize the recharging cycle for all the deployed sensors in network, which contains the traveling time and recharging time. With this problem, we show the charging vehicle is required to move at the shortest Hamiltonian cycle. And we present optimal charging location for each wireless charging incident. Experimental results demonstrate that, our proposed solution could enhance the charging efficiency up to 2 times comparing with the baseline scheme without optimization for angle.

In this work, we address the spectrum utilization problem in cognitive radio (CR) networks, in which a CR can only utilize spectrum opportunities when the channel is idle. One challenge for a CR is to balance exploring new channels and exploiting existing channel, due to the fact that the channel availability and channel quality, potentially heterogeneous and time-dependent, are often unknown in advance due to the large number of channels, and the limited hardware capability of single CR. In this work, we propose joint channel sensing, probing, and accessing schemes for secondary users in cognitive radio networks. Our method has time and space complexity O(N · u) for a network with N channels and u secondary users, while applying classic methods requires exponential time complexity. We prove that, even when channel states are selected by adversary (thus nonstochastic), it results in a total regret uniformly upper bounded by Θ(√TN log N), w.h.p, for communication lasts for T timeslots. Our protocol can be implemented in a distributed manner due to the nonstochastic channel assumption. Our experiments show that our schemes achieve almost optimal throughput compared with an optimal static strategy, and perform significantly better than previous methods in many settings.

Recent studies show that Wi-Fi interference has been a major problem for low power urban sensing technology ZigBee networks. Existing approaches for dealing with such interferences often modify either the ZigBee nodes or Wi-Fi nodes. However, massive deployment of ZigBee nodes and uncooperative Wi-Fi users call for innovative cross-technology coexistence without intervening legacy systems. In this work, we investigate the Wi-Fi and ZigBee coexistence when ZigBee is the interested signal. Typically, the duration of transmitting a ZigBee data packet is longer than that of a Wi-Fi packet. Mitigating short duration Wi-Fi interference (calledflash) in long duration ZigBee data (calledsmog) is challenging. To address these challenges, we propose ZIMO: a sink-based MIMO design for harmony coexistence of ZigBee and Wi-Fi networks with the goal of protecting the ZigBee data packets from being interfered by high-power cross-technology signals. The key insight is to properly exploit opportunities resulted from differences between Wi-Fi and ZigBee, and bridge the gap between interested data and cross technology signals. Also, extracting the channel coefficient of Wi-Fi and ZigBee will enhance other coexistence technologies such as TIMO[1]. We implement a prototype in GNURadio-USRP N200, and our extensive evaluations under real wireless conditions show that ZIMO can improve ZigBee network throughput up to 1.9$\times$, with 1.5$\times$in media, and 1.1$\times$to 1.9$\times$for Wi-Fi network as byproduct in ZigBee signal recovery.

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

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

In opportunistic channel access, the user needs to make real time decisions on when and which channel to access with uncertainty. Assuming perfect channel statistics, several studies have applied optimal stopping theory to derive control strategy for sequential sensing/probing based opportunistically accessing (s-SPA), exploiting temporary opportunities among multiple channels. Meanwhile, numerous multi-arm bandit (MAB)-based approaches have been proposed for online learning of channel selection in periodical sensing/accessing system, however, these schemes fail to exploit the opportunistic diversity in short term. In this paper, we investigate online learning of optimal control in s-SPA systems, where both statistics learning and temporary opportunity utilization are jointly considered. An effective and efficient online policy, so called IE-OSP, is proposed, which theoretically guarantees system converges to the optimal s -SPA strategy with bounded probability. Experimental results further show that, the regret of IE-OSP is almost in optimal logarithmic increasing rate over time, and is sub-linear with the increasing number of channels. Compared with existing solutions, our proposed algorithm achieves 25 ~ 30% throughput gain in typical scenarios.

In this work, we investigate the distributed and balanced task reassignment in mobile social networks. Previous studies have shown the power of random choice in load balancing with random walking model. Inspired by the `2-choice' paradigm in `ball and bin' theory, we evaluate this simple but effective scheme with real trace data `MobiClique'. According to the preliminary evaluation results, we find that, social relationship significantly differs from pure random walk model, and will bring challenges in task reassignment in the followings: First, friendships are relatively stable, which will lead to imbalanced task assignment. Second, some users meet quite infrequently, which will lead to intolerable time delay and uneven task distribution. In tackling with these challenges, we propose `iTop-K', leveraging the basic concept, i.e., your friends are more powerful than you, which encourages mobile users to assign tasks among intimate friends instead of pure random assignment. With the selection of `top-K' friends, we can achieve load balancing and guaranteed network performance at the same time. Experimental studies verify our scheme and show the effectiveness. In typical working scenario, where real-trace driven simulation is applied, ours outperforms the conventional random choice up to 15×, and the social relationship assignment without priority method up to 9×.

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

Recent studies show that WiFi interference has been a major problem for low power urban sensing technology ZigBee networks. Existing approaches for dealing with such interferences often modify either the ZigBee nodes or WiFi nodes. However, massive deployment of ZigBee nodes and uncooperative WiFi users call for innovative cross-technology coexistence without intervening legacy systems. In this work we investigate the WiFi and ZigBee coexistence when ZigBee is the interested signal.Mitigating short duration WiFi interference (called flash) in long duration ZigBee data (called smog) is challenging, especially when we cannot modify the WiFi APs and the massively deployed sensor nodes. To address these challenges, we propose ZIMO, a sink-based MIMO design for harmony coexistence of ZigBee and WiFi networks with the goal of protecting the ZigBee data packets.The key insight of ZIMO is to properly exploit opportunities resulted from differences between WiFi and ZigBee, and bridge the gap between interested data and cross technology signals. Also, extracting the channel coefficient of WiFi and ZigBee will enhance other coexistence technologies such as TIMO [1]. We implement a prototype for ZIMO in GNURadio-USRP N200, and our extensive evaluations under real wireless conditions show that ZIMO can improve up to 1.9x throughput for ZigBee network, with median gain of 1.5x, and 1.1x to 1.9x for WiFi network as byproduct in ZigBee signal recovery.

Neighbor discovery is the very first step for many mobile applications. Previous neighbor discovery protocols that use single channel may be failed when faced with unreliable channel environment, whereas most of multi-channel schemes do not focus on neighbor discovery in low duty cycle wireless networks. The key challenge for multi-channel slotted neighbor discovery is how to effectively assign the channel during each active slot. We propose McDisc, an energy-efficient and reliable duty-cycle-based neighbor discovery protocol that is built on two basic idea. First, it leverages randomized approach to establish the multi-channel discovery schedule, where the node is assigned to the chosen channel during each active slot randomly. Randomized approach may suffer from extremely low discovery probability in worst case. In tackling this problem, we then employ deterministic channel assignment that can ensure bounded discovery latency. The theoretical analysis and simulation results confirm that McDisc can cope with unreliable channel environment ensuring reliable and low latency discovery efficiently. In terms of 60% packet loss ratio, McDisc outperforms U-Connect [1] by approximate 100% in the worst-case discovery latency.

We propose joint channel sensing, probing, and accessing schemes for secondary users in cognitive radio networks. Our method has time and space complexity O(N·k) for a network with N channels and k secondary users, while applying classic methods requires exponential time complexity. We prove that, even when channel states are selected by adversary (thus non-stochastic), it results in a total regret uniformly upper bounded by Θ(√TN logN), w.h.p, for communication lasts for T timeslots. Our protocol can be implemented in a distributed manner due to the nonstochastic channel assumption. Our experiments show that our schemes achieve almost optimal throughput compared with an optimal static strategy, and perform significantly better than previous methods in many settings.

Multichannel accessing is a basic and important function in wireless sensor networks, where intelligent and low-cost methods are needed for energy-efficient spectrum utilization. Unfortunately, for energy constrained nodes, methods from cognitive radio technologies are not applicable in WSN multichannel systems due to computation and communication overhead, as well as extremely large energy consumptions. In this work, we propose a spectrum learning and utilizing paradigm, where sensor nodes can sense, probe, access and switch among channels automatically without prior knowledge. Under this basic and important learning method, we present a two-stage learning and utilization algorithm, which constitutes of two interleaving parts: `transmitting while learning' and `learning while transmitting'. Building these two parts is a non-trivial work, which need efficient channel learning and utilizing scheme working together seamlessly. In the first `transmitting while learning' stage, we propose the `non-call-back' policy and leverage the Lagrangian method for the additional energy cost considerations. Under this heuristic policy, an improved version for the traditional Gittins Index model is provided. After that, the heuristic algorithm is proved `2-approximate' to the optimal result. Furthermore, leveraging the channel correlations, the learning complexity is reduced from O(T) stages to O(log T) stages, where T is the number of time slots for channel learning. Secondly, we make a structural analysis on the `learning while transmitting' stage, leveraging the 2-dimensional optimal stopping theory. Notably, we use the a 16-node TelosB sensor network and an 8-node USRP network for performance evaluation. The USRP nodes are playing as interfering and monitoring nodes for controllable network states design, making network performance evaluation convincible. The proposed algorithm is designed in real implementations with experimental results, showing the efficiency of the proposed scheme.

The main challenge in DTNs is how to deal with path uncertainty in achieving a reliable routing scheme. All Erasure coding based routing algorithms make the assumption that the underlying path probabilities are known previously and remain constant, which is unpractical. On the other hand, the overall behavior of path probability tends to be stable with the increasing number of paths, which can be used to increase the stability of Erasure coding based schemes. Bearing this in mind, we present Inter-Coding: Inter-Coding is designed to fully combine the reliability of erasure coding, and the stability of interleaving to cope with uncertainties. We evaluate our approach in terms of delivery ratio under different level of uncertainty as well as different interleaving policy, and validate that Inter-Coding offers reliable and stable performance even the path uncertainty and dynamic is high.

In extremely low-duty-cycle wireless sensor networks, a sender has to wait for a certain period of time to forward a packet until its receiver becomes active, which will result in longer end-to-end delay than ever. Many works have been done to improve delivery ratio but lack of the consideration on energy efficient delivery delay. In addition, unreliable links is another challenge in wireless sensor networks. Redundancy and multiple paths can be used to cope with unreliability, but neither of them is energy efficient. Even worse, both of them have poor performance on delivery delay. In this work, we introduce a novel way of allocating erasure coded blocks over multiple paths to improve energy efficient delivery delay while achieving comparably high delivery ratio. We evaluate our algorithm with extensive simulations. Evaluations show that our design decreases delivery delay greatly with slight decrease in delivery ratio.

In wireless sensor networks, there are many link quality measurement metrics such as RSSI (Received Signal Strength Indicator), LQI (Link Quality Indicator) and PRR (Packet Reception Rate), which can be used under different channel quality and application scenarios. As in time synchronization, channel quality would be essential to the clock synchronization. The broadcast nature of time synchronization algorithm makes channel quality measurement complex. Moreover, the measurement on channel quality would be costly. We make an experimental study on time synchronization in wireless sensor network. Firstly, we use the RSSI and PRR with different packet lengths, secondly, we find that, the PRR would be accurate but costly, and the RSSI is not accurate enough for time synchronization algorithm evaluation. In the end, we propose a channel clustering and categorization mechanism in dealing with the channel measurement difficulties. And we find that, in dealing with the lossy links, the compensation model for synchronization errors is more important than reliable transmissions. Also, the proposed measurement would be helpful to the time synchronization algorithm, especially the FTSP (Flooding Time Synchronize Protocol).