ECG monitoring during human activities is crucial since many heart attacks occur when people are exercising, driving a car, operating a machine, etc. Unfortunately, existing ECG monitoring devices fail to timely detect abnormal ECG signals during activities due to the need for many cables or a sustained press on devices (e.g., smartwatches). This paper introduces ZeroEcg, a wireless, battery-free, lightweight, electronic-skin-like tag integrated with commodity RFIDs, which can continuously track a user's ECG during activities. By exploring and leveraging the RFID MOSFET switch, which is traditionally used for backscatter modulation, we map the ECG signal to the RFID RSS and phase measurement. It opens a new RFID sensing approach for sensing any physical world variable that can be translated into voltage signals. We model and analyze the RFID MOSFET-based backscatter modulation principle, providing design guidance for other sensing tasks. Real-world results illustrate the effectiveness of ZeroEcg on ECG sensing.

Intelligent irrigation based on measurements of soil moisture levels in every pot in a greenhouse can not only improve plant productivity and quality but also save water. However, existing soil moisture sensors are too expensive to deploy in every pot. We therefore introduce GreenTag, a low-cost RFID-based soil moisture sensing system whose accuracy is comparable to that of an expensive soil moisture sensor. Our key idea is to attach two RFID tags to a plant’s container so that changes in soil moisture content are reflected in their Differential Minimum Response Threshold (DMRT) metric at the reader. We show that a low-pass filtered DMRT metric is robust to changes both in the RF environment (e.g., from human movement) and in pot locations. In addition, we propose a fast DMRT acquisition algorithm and a time-efficient tag query protocol, which can reduce the sensing latency by 90%. In a realistic setting, GreenTag achieves a 90-percentile moisture estimation errors of 5%, which is comparable to the 4% errors using expensive soil moisture sensors. Moreover, this accuracy is maintained despite changes in the RF environment and container locations. We also show the effectiveness of GreenTag in a real greenhouse.

Soil moisture sensing plays an important role in agriculture, especially in greenhouses or vertical farms. However, existing soil moisture sensing systems are either expensive and require batteries or suffer from low accuracy, preventing their real-world applications. This paper introduces SoilTAG, a battery-free, chipless tag-based high accuracy soil moisture sensing system. The key insight is that the tag's resonator can convert changes in soil moisture levels into changes in the tag's frequency response. However, two challenges need to be addressed before applying the system to the real world. First, how to design a resonator whose frequency response is sensitive to even small moisture changes, which is the basis for high-precision moisture sensing. To solve the challenge, we design a special structure (i.e., a defected ground structure) as the tag's resonator and optimize its key parameters to increase the frequency response sensitivity for different soil moisture levels. Second, how to design a robust soil moisture feature that is independent of the tag's location changes, since the frequency response varies by both the tag location and soil moisture. To deal with this challenge, we introduce a relative frequency response feature whose amplitude ratio is only related to soil moisture levels and independent of the tag location changes. Extensive experiments show that SoilTAG achieves$90th$percentile moisture sensing error of$2\%$,$3.64\%$, and$8\%$when the distance between transmitter and tag is 6 m, 10 m, and 13.9 m. Compared to current commodity sensors, SoilTAG saves the cost per sensor by more than 70%.

Backscatter tag based sensing has received a lot of attention recently due to the battery-free, low-cost, and widespread use of backscatter tags, e.g., RFIDs. Despite that, they suffer from an extensive, costly, and time-consuming redesign effort when there are changes in application requirements, such as changes in sensing targets or working frequency bands. This paper introduces a reconfigurable sensing framework, which enables us to easily reconfigure the design parameters of chipless backscatter tags for sensing different targets or working with different frequency bands, without the need for onerous design effort. To realize this vision, we capture the relationship between the application requirements and the sensing tag’s design parameters. This relationship enables us to fast and efficiently reconfigure/change an existing sensing tag design to meet new application requirements. Real-world experiments show that, by using our reconfigurable framework to flexibly redesign a tag’s parameters, the sensing tag achieves more than 92.1% accuracy for sensing four different applications and working on four different frequency bands.

To deal with notorious delays in communication systems, it is crucial to forecast key system characteristics, such as the communication load. Most existing studies aggregate data from multiple edge nodes for improving the forecasting accuracy. However, the bandwidth cost of such data aggregation could be unacceptably high from the perspective of system operators. To achieve both the high forecasting accuracy and bandwidth efficiency, this paper proposes an Adaptive Multi-Teacher Weighting in Teacher-Student Learning approach, namely AdaTeacher, for communication load forecasting of multiple edge nodes. Each edge node trains a local model on its own data. A target node collects multiple models from its neighbor nodes and treats these models as teachers. Then, the target node trains a student model from teachers via Teacher-Student (T-S) learning. Unlike most existing T-S learning approaches that treat teachers evenly, resulting in a limited performance, AdaTeacher introduces a bilevel optimization algorithm to dynamically learn an importance weight for each teacher toward a more effective and accurate T-S learning process. Compared to the state-of-the-art methods, Ada Teacher not only reduces the bandwidth cost by 53.85%, but also improves the load forecasting accuracy by 21.56% and 24.24% on two real-world datasets.

In cellular networks, User Equipment (UE) handoff from one Base Station (BS) to another, giving rise to the load balancing problem among the BSs. To address this problem, BSs can work collaboratively to deliver a smooth migration (or handoff) and satisfy the UEs' service requirements. This paper formulates the load balancing problem as a Markov game and proposes a Robust Multi-agent Attention Actor-Critic (Robust-MA3C) algorithm that can facilitate collaboration among the BSs (i.e., agents). In particular, to solve the Markov game and find a Nash equilibrium policy, we embrace the idea of adopting a nature agent to model the system uncertainty. Moreover, we utilize the self-attention mechanism, which encourages high-performance BSs to assist low-performance BSs. In addition, we consider two types of schemes, which can facilitate load balancing for both active UEs and idle UEs. We carry out extensive evaluations by simulations, and simulation results illustrate that, compared to the state-of-the-art MARL methods, Robust-MA3C scheme can improve the overall performance by up to 45%.

Sweat contains a wealth of physiologically relevant information and has been used to detect underlying diseases or the sub-health state. However, existing sweat sensors suffer from high energy consumption due to the need for energy-hungry components (i.e., ADC and DAC) and active radio front-ends, making them unable to support continuous and long-term monitoring.

Due to their large bandwidth and impressive data speed, millimeter-wave (mmWave) radios are expected to play a key role in the 5G and beyond (e.g., 6G) communication networks. Yet, to release mmWave’s true power, the highly directional mmWave beams need to be aligned perfectly. Most existing beam alignment methods adopt an exhaustive or semi-exhaustive space scanning, which introduces up to seconds of delays. To eliminate the need for complex space scanning, this article presents an Ultra-wideband (UWB)-assisted mmWave communication framework, which leverages the co-located UWB antennas to estimate the best angles for mmWave beam alignment. One major challenge of applying this idea in the real world is the barrier of limited antenna numbers. Commercial-Off-The-Shelf (COTS) devices are usually equipped with only a small number of UWB antennas, which are not enough for the existing algorithms to provide an accurate angle estimation. To solve this challenge, we design a novel Multi-Frequency MUltiple SIgnal Classification (MF-MUSIC) algorithm, which extends the classic MUltiple SIgnal Classification (MUSIC) algorithm to the frequency domain and overcomes the antenna limitation barrier in the spatial domain. Extensive real-world experiments and numerical simulations illustrate the advantage of the proposed MF-MUSIC algorithm. MF-MUSIC uses only three antennas to achieve an accurate angle estimation, which is a mere 0.15° (or a relative difference of 3.6%) different from the state-of-the-art 16-antenna-based angle estimation method.

Advanced communication network functions, such as resource allocation and dynamic spectrum management, heavily rely on the accurate forecasting of traffic. Data-driven solutions, e.g., Neural Network (NN) based forecasting methods, have been proven to be effective only when sufficient data is available. However, Base Stations (BSs) have limited data in the real world, since big data for communication networks could be extremely expensive to collect, store, and migrate. Therefore, most existing traffic forecasting methods have limited accuracy in reality due to the lack of big data. To tackle this problem, our key observation is that, despite the data “amount” in a BS is limited, the data “source” is rich and diverse, i.e., in addition to Internet traffic logs, there are logs of Call and SMS. More importantly, our analysis shows a high correlation between different sources, which can be utilized to improve the forecasting accuracy. Motivated by this, we introduce AdaSource, a Multi-Source Adaptive Feature Boosting approach, which utilizes data source correlations for accurate traffic forecasting even on data-limited BSs. The core idea of AdaSource is a novel two-branch NN structure that adaptively trains multiple Encoder-Decoders for refining different data sources and multiple Encoder-Predictors for utilizing data source correlations to improve the accuracy. The experiments on a real-world dataset show that AdaSource improves the forecasting accuracy by up to 30.14%, compared to the state-of-the-art methods.

Although reinforcement learning (RL) shows advantages in cellular network load balancing, it suffers from a low generalization ability, preventing it from real-world applications. Specifically, if network traffic pattern changes, the learned RL policy cannot adapt accordingly, resulting in system performance degradation. To address this issue, we propose a Multi-teacher MOdel BAsed Reinforcement Learning algorithm (MOBA), which leverages multi-teacher knowledge distillation theory to learn a generalized load balancing policy for adapting the real-world traffic pattern changes. The key is that different teachers represent different traffic patterns, and can learn various system models. By distilling and transferring the teacher knowledge, the student network is able to learn a generalized system model that covers different traffic patterns and unseen situations. Moreover, to improve the robustness of multi-teacher knowledge transfer, we learn a set of student models and use an ensemble method to jointly predict system dynamics. Results show that, compared with state-of-the-art RL methods, MOBA improves the minimal throughput and total throughput of a cellular network by up to 28.6% and 23.2%. Results also show that MOBA improves the training efficiency by up to 64%.

Prediction of future traffic load is a crucial task to support the automatic Operations, Administration, and Management (OAM) of communication networks. Existing Machine Learning (ML) models require big data to accomplish this task. However, large data sets are not always available, due to the limited storage capacity and the high storage cost at Base Stations (BSs). To solve the problem, we leverage the spatial-temporal correlation among different BSs, which allows other BSs’ data to be used for the prediction of the target BS. One major challenge in realizing this idea is the imbalance of data amounts between neighbor BSs and a prediction target BS. If one simply aggregates the data from both neighbors and target, the target’s traffic features would be overwhelmed by the neighbors’ data. To address this challenge, we propose a Spatial-Temporal Transfer (STT) framework, which trains a base model with an aggregated data set from multiple BSs, and then carefully refines the base model to serve a target BS. To strike a perfect balance between general tendency and individual features, STT adopts an advanced transfer learning technique that exploits regularization on model parameters. Experiments show the efficiency of the proposed STT framework.

Accurate traffic volume estimation and prediction are essential for advanced communication network functions, such as automatic operations and predictive resource allocation. Although machine learning (ML)-based approaches achieve great success in accomplishing this goal, existing approaches suffer from two drawbacks that limit their real-world applications. First, the ML-based prediction models developed in the past might be obsolete now, since the communication traffic patterns and volumes keep changing in the real world, leading to prediction errors. Second, most Base Stations (BSs) can only save a small amount of data due to the limited storage capacity and high storage costs, which prevents from training an accurate prediction model. In this paper, we propose a novel framework that adapts the prediction model to the constantly changing traffic with only a few current traffic data. Specifically, the framework first learns the knowledge of historical traffic data as much as possible by using a proposed two-branch neural network design, which includes a prediction and a reconstruction module. Then, the framework transfers the knowledge from an old (past) prediction model to a new (current) model for the model update by using a proposed continual knowledge distillation technique. Evaluations on a real-world dataset show that the proposed framework reduces the Mean Absolute Error (MAE) of traffic prediction by up to 9.62% compared to the state-of-the-art prediction methods.

With the increasing popularity of electric vehicles and the growing trend of working from home, electricity consumption in the residential sector is expected to continue to grow rapidly over the next few years. As a consequence, short-term residential load forecasting is becoming even more vital for the reliability and sustainability of the smart grid. Although deep learning models have shown impressive success in different areas including short-term electric load forecasting, such models require a large amount of training data. For many real-world load forecasting cases, we may not have enough training data to learn a reliable forecasting model. In this paper, we address this challenge through the use of boosting-based transfer learning with multiple sources. We first train a set of deep regression models on source houses that can provide relatively abundant data. We then transfer these learned models via the boosting framework to support data-scarce target houses. The transfer process is selective and customized for each target house to minimize the potential for negative transfer. Experimental results, based on real-world residential data sets, show that the proposed method can significantly improve forecasting accuracy.

Backscatter tag based sensing has received a lot of attention recently due to the battery-free, low-cost and widespread use of backscatter tags, e.g., RFIDs. Despite that, they suffer from an ex-tensive, costly, and time-consuming redesign effort when there are changes in application requirements, such as changes in sensing targets or working frequency bands. This paper introduces a reconfigurable sensing framework, which enables us to easily reconfigure the design parameters of chipless backscatter tags for sensing different targets or working with differ-ent frequency bands, without the need of onerous design effort. To realize this vision, we capture the relationship between the application requirements and the sensing tag's design parameters. This relationship enables us to fast and efficiently reconfigure/change an existing sensing tag design for meeting new application requirements. Real-world experiments show that, by using our reconfig-urable framework to flexibly redesign a tag's parameters, the sensing tag achieves more than 92.1 % accuracy for sensing four different applications and working on four different frequency bands.

Radio-Frequency IDentification (RFID) technology permits a reader to wirelessly query a tag for its embedded globally unique identifier. Passive RFID tags, which are small, low-cost (a few cents each), and batteryless, can be reliably read only when they are within a few meters of the reader since the tag must power up itself by harvesting energy from the reader. Past work attempts to increase the RFID range by providing them with more energy, such as by synchronizing multiple custom design RFID readers and performing beamforming. However, we demonstrate that a passive tag's range is limited not only by the need for the tag to harvest energy but also by the need for the tag to decode the reader's transmission, and vice versa. Thus, instead of modifying readers, we ask if a tag's manufacturer can increase passive RFIDs' range by lowering the data rate. Our results show that the working range can be increased by a factor of about 10 by simply using a low data rate. Our real-world experiments using customized tag prototypes have a range of ~40 m, with an SNR exceeding 12 dB.

By placing the computing, storage and networking resources close to the end users, distributed edge computing greatly benefits the performance of 5G communication systems. However, as a tradeoff, resources on the edge are usually limited and imbalanced among the heterogeneous edge nodes. To overcome this drawback, this paper proposes a Transfer Learning based Prediction (TLP) framework that allows the edge nodes to share their resources and data in an efficient manner. In particular, the TLP framework focuses on the prediction of the future traffic load, which is a key reference for many automated network functions. To enhance the efficiency of data and bandwidth, TLP first learns a base model on a data-abundant edge node (the source), and then transfers this model (instead of data) to other data-limited nodes (the targets). To achieve a delicate balance between maintaining common features and learning target-specific features, we develop a new transfer learning technique named Similarity-based Elastic Weight Con-solidation (SEWC), and integrate it into TLP. Experiments on real-world data illustrate that, compared to the state-of-the-art methods, TLP-SEWC reduces the Mean Absolute Error (MAE) of traffic prediction by up to 57.9%.

Prediction of key system characteristics, such as the communication load, is required to overcome the delays in wireless communication systems. State-of-The-Art (SOTA) approaches mostly apply existing Neural Network (NN) structures, and extract latent features purely based on their sensitivity to the forecasting accuracy. This way of feature extraction may neglect some non-obvious yet informative dimensions in the model input, leading to inaccurate forecasting results. In this paper, we present an Adaptive Feature Boosting (AFB) approach, which integrates multiple AutoEncoders (AEs) to automatically extract robust and comprehensive latent features for communication load forecasting. The recurrent and residual connections among the AEs make sure that the extracted latent features are representative for all input dimensions. With more comprehensive information extracted from the history, the forecasting accuracy is thus improved. We evaluate AFB against existing approaches on a real-world dataset that contains Call Detail Records (CDRs) of the Milan city over a period of two months. The evaluation shows that our AFB-based approach achieves 35.2% more accurate load forecasting results than the SOTA deep approaches.

Due to their large bandwidth and impressive data speed, millimeter-wave (mmWave) radios are expected to play a key role in the 5G and beyond (e.g., 6G) communication networks. Yet, to release mmWave’s true power, the highly directional mmWave beams need to be aligned perfectly. Most existing beam alignment methods adopt an exhaustive or semi-exhaustive space scanning, which introduces up to seconds of delays.To eliminate the need of a complex space scanning, this paper presents an Ultra-wideband (UWB)-assisted mmWave communication framework, which leverages the co-located UWB antennas to estimate the best angles for mmWave beam alignment. One major challenge to apply this idea in real-world is the barrier of limited antenna numbers. Commercial-Off-The-Shelf (COTS) devices are usually equipped with only a few number of UWB antennas, which are not enough for the existing algorithms to provide an accurate angle estimation. To solve this challenge, we design a novel Multi-Frequency MUSIC (MF-MUSIC) algorithm, which extends the classic MUSIC algorithm to the frequency domain and overcomes the antenna limitation barrier in the spatial domain. By doing this, our framework uses only 3 antennas to achieve an accurate angle estimation, which is merely 0.15° different from the state-of-the-art 16-antenna method.

Material identification and target imaging play an important role in many applications. This paper introduces TagScan, a system that can identify the material type and image the horizontal cut of a target simultaneously with cheap commodity Radio-Frequency IDentification (RFID) devices. The key intuition is that different materials and/or target sizes cause different amounts of phase and RSS (Received Signal Strength) changes, when radio frequency (RF) signal penetrates through the target. Multiple challenges need to be addressed before we can turn the idea into a functional system, including (i) indoor environments exhibit rich multipath which breaks the linear relationship between the phase change and the propagation distance inside a target; (ii) without knowing either material type or target size, trying to obtain these two information simultaneously is challenging; and (iii) stitching pieces of the propagation distances inside a target for an image estimate is non-trivial. We propose solutions to all the challenges and evaluate the system's performance in three different environments. TagScan is able to achieve higher than 94 percent material identification accuracies for 10 liquids and differentiates even very similar objects such as Coke and Pepsi. TagScan can accurately estimate the horizontal cut images of more than one target behind a wall.

Intelligent irrigation based on measurements of soil moisture levels in every pot in a greenhouse can not only improve plant productivity and quality but also save water. However, existing soil moisture sensors are too expensive to deploy in every pot. We therefore introduce GreenTag, a low-cost RFID-based soil moisture sensing system whose accuracy is comparable to that of an expensive soil moisture sensor. Our key idea is to attach two RFID tags to a plant's container so that changes in soil moisture content are reflected in their Differential Minimum Response Threshold (DMRT) metric at the reader. We show that a low-pass filtered DMRT metric is robust to changes both in the RF environment (e.g., from human movement) and in pot locations. In a realistic setting, GreenTag achieves a 90-percentile moisture estimation errors of 5%, which is comparable to the 4% errors using expensive soil moisture sensors. Moreover, this accuracy is maintained despite changes in the RF environment and container locations. We also show the effectiveness of GreenTag in a real greenhouse.

We introduce a passive Radio Frequency IDentification (RFID) based system to detect finger gesture input for Human-Computer Interaction applications. The device is simple, inexpensive and does not require calibration to accommodate changes in the device location or the Radio Frequency (RF) environment. This is achieved by connecting the chips of two RFID tags together using a strip transmission line. The key observation is that touching different positions along the transmission line changes the impedance matching between each chip and its antenna, changing Received Signal Strength (RSS) values for each tag. When a finger slides in different directions between key positions along the transmission line, there are relative RSS patterns and trends that are robust to changes in the device location and the RF environment. We implemented and evaluated an detection algorithm and system using a commercial RFID reader and two commercial RFID chips. Results show that precision and recall are greater than 95% and 94% when detecting 10 finger gesture inputs across 48 different device locations.

Target material identification is playing an important role in our everyday life. Traditional camera and video-based methods bring in severe privacy concerns. In the last few years, while RF signals have been exploited for indoor localization, gesture recognition and motion tracking, very little attention has been paid in material identification. This paper introduces WiMi, a device-free target material identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate material sensing. We also design a new material feature which is only related to the material type and is independent of the target size. Comprehensive real-life experiments demonstrate that WiMi can achieve fine-grained material identification with cheap commodity Wi-Fi devices. WiMi can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors. Even for very similar items such as Pepsi and Coke, WiMi can still differentiate them at a high accuracy.

Passive Radio Frequency IDentification (RFID) tags are commonly used to provide Radio Frequency (RF) accessible unique identifiers for physical objects due to their low-cost, lack of battery, and small size. Besides this basic function, many novel RFID-based sensing applications have been proposed in the last decade, including localization, gesture sensing, and touch sensing, among others. Nevertheless, none of these systems are in widespread use today. We hypothesize that this is because the accuracy of these systems does not meet application requirements when there are even minor changes in the RF environment or in tag geometry, i.e., changes in a tag's orientation or flexing. This paper uses both theoretical analysis and real-world experiments to test this hypothesis. Our theoretical analysis shows that even a small phase or RSS noise level can result in significant estimation errors. Our extensive real-world experiments find that both the absolute and differential values of phase and RSS readings of an RFID tag's signal can vary as much as by π radians and 10 dB, respectively, due to small changes in the tag's orientation or flexing. Because of these large variations, RFID-based application systems relying on the signal phase or RSS cannot meet application requirements, confirming our hypothesis. In addition to this strong negative result, we also present some insights into designing robust RFID systems that are suitable for use in the real world.

We describe Tip-Tap, a wearable input technique that can be implemented without batteries using a custom RFID tag. It recognizes 2-dimensional discrete touch events by sensing the intersection between two arrays of contact points: one array along the index fingertip and the other along the thumb tip. A formative study identifies locations on the index finger that are reachable by different parts of the thumb tip, and the results determine the pattern of contacts points used for the technique. Using a reconfigurable 3x3 evaluation device, a second study shows eyes-free accuracy is 86% after a very short period, and adding bumpy or magnetic passive haptic feedback to contacts is not necessary. Finally, two battery-free prototypes using a new RFID tag design demonstrates how Tip-Tap can be implemented in a glove or tattoo form factor.

Passive radio frequency identification (RFID) tags are ubiquitous today due to their low cost (a few cents), relatively long communication range ($\sim$7-11~m), ease of deployment, lack of battery, and small form factor. Hence, they are an attractive foundation for environmental sensing. Although RFID-based sensors have been studied in the research literature and are also available commercially, manufacturing them has been a technically-challenging task that is typically undertaken only by experienced researchers. In this paper, we show how even hobbyists can transform commodity RFID tags into sensors by physically altering (`hacking') them using COTS sensors, a pair of scissors, and clear adhesive tape. Importantly, this requires no change to commercial RFID readers. We also propose a new legacy-compatible tag reading protocol called Differential Minimum Response Threshold (DMRT) that is robust to the changes in an RF environment. To validate our vision, we develop RFID-based sensors for illuminance, temperature, touch, and gestures. We believe that our approach has the potential to open up the field of batteryless backscatter-based RFID sensing to the research community, making it an exciting area for future work.

RFID-based positioning is emerging as a promising solution for inventory management in places like warehouses and libraries. However, existing solutions either are too sensitive to the environmental noise, or require deploying a large number of reference tags which incur expensive deployment cost and increase the chance of data collisions. This paper presents CSRP, a novel RFID based positioning system, which is highly accurate and robust to environmental noise, but relies on much less reference tags compared with the state-of-the-art. CSRP achieves this by employing an noise-resilient RFID fingerprint scheme and a compressive sensing based algorithm that can recover the target tag's position using a small number of signal measurements. This work provides a set of new analysis, algorithms and heuristics to guide the deployment of reference tags and to optimize the computational overhead. We evaluate CSRP in a deployment site with 270 commercial RFID tags. Experimental results show that CSRP can correctly identify 84.7% of the test items, achieving an accuracy that is comparable to the state-of-the-art, using an order of magnitude less reference tags.

Device-free localization of objects not equipped with RF radios is playing a critical role in many applications. This paper presents LIFS, a Low human-effort, device-free localization system with fine-grained subcarrier information, which can localize a target accurately without offline training. The basic idea is simple: channel state information (CSI) is sensitive to a target's location and thus the target can be localized by modelling the CSI measurements of multiple wireless links. However, due to rich multipath indoors, CSI can not be easily modelled. To deal with this challenge, our key observation is that even in a rich multipath environment, not all subcarriers are affected equally by multipath reflections. Our CSI pre-processing scheme tries to identify the subcarriers not affected by multipath. Thus, CSI on the “clean” subcarriers can still be utilized for accurate localization. Without the need of knowing the majority transceivers' locations, LiFS achieves a median accuracy of 0.5 m and 1.1 m in line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios, respectively, outperforming the state-of-the-art systems.

Target imaging and material identification play an important role in many real-life applications. This paper introduces TagScan, a system that can identify the material type and image the horizontal cut of a target simultaneously with cheap commercial off the-shelf (COTS) RFID devices. The key intuition is that different materials and target sizes cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. Multiple challenges need to be addressed before we can turn the idea into a functional system including (i) indoor environments exhibit rich multipath which breaks the linear relationship between the phase change and the propagation distance inside a target; (ii) without knowing either material type or target size, trying to obtain these two information simultaneously is challenging; and (iii) stitching pieces of the propagation distances inside a target for an image estimate is non-trivial. We propose solutions to all the challenges and evaluate the system's performance in three different environments. TagScan is able to achieve higher than 94% material identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi. TagScan can accurately estimate the horizontal cut images of more than one target behind a wall.

Device-free localization (DFL), which does not require any devices to be attached to target(s), has become an appealing technology for many applications, such as intrusion detection and elderly monitoring. To achieve high localization accuracy, most recent DFL methods rely on collecting a large number of received signal strength (RSS) changes distorted by target(s). Consequently, the incurred high energy consumption renders them infeasible for resource-constraint networks, such as wireless sensor networks. This paper introduces an energy-efficient framework for high-precision multi-target-adaptive device-free localization (E-HIPA). Compared with the existing methods, E-HIPA demands fewer transceivers, applies the compressive sensing (CS) theory to guarantee high localization accuracy with less RSS change measurements. The motivation behind the proposed E-HIPA is the sparse nature of multi-target locations in the spatial domain. Before taking advantage of this intrinsic sparseness, we theoretically prove the validity of the proposed CS-based framework problem formulation. Based on the formulation, the proposed E-HIPA primarily includes an adaptive orthogonal matching pursuit (AOMP) algorithm, by which it is capable of recovering the precise location vector with high probability, even for a more practical scenario with unknown target number. Experimental results via real testbed demonstrate that, compared with the previous state-of-the-art solutions, i.e., RTI, SCPL, and RASS approaches, E-HIPA reduces the energy consumption by up to 69 percent with meter-level localization accuracy.

Many emerging applications driven the fast development of the device-free localization (DfL) technique, which does not require the target to carry any wireless devices. Most current DfL approaches have two main drawbacks in practical applications. First, as the pre-calibrated received signal strength (RSS) in each location (i.e., radio-map) of a specific area cannot be directly applied to the new areas, the manual calibration for different areas will lead to a high human effort cost. Second, a large number of RSS are needed to accurately localize the targets, thus causes a high communication cost and the areas variety will further exacerbate this problem. This paper proposes FitLoc, a fine-grained and low cost DfL approach that can localize multiple targets over various areas, especially in the outdoor environment and similar furnitured indoor environment. FitLoc unifies the radio-map over various areas through a rigorously designed transfer scheme, thus greatly reduces the human effort cost. Furthermore, benefiting from the compressive sensing theory, FitLoc collects a few RSS and performs a fine-grained localization, thus reduces the communication cost. Theoretical analyses validate the effectivity of the problem formulation and the bound of localization error is provided. Extensive experimental results illustrate the effectiveness and robustness of FitLoc.

Device-free localization, which does not require any device attached to the target, is playing a critical role in many applications, such as intrusion detection, elderly monitoring and so on. This paper introduces D-Watch, a device-free system built on the top of low cost commodity-off-the-shelf RFID hardware. Unlike previous works which consider multipaths detrimental, D-Watch leverages the “bad” multipaths to provide a decimeter-level localization accuracy without offline training. D-Watch harnesses the angle-of-arrival information from the RFID tags' backscatter signals. The key intuition is that whenever a target blocks a signal's propagation path, the signal power experiences a drop which can be accurately detected by the proposed novel P-MUSIC algorithm. The proposed wireless phase calibration scheme does not interrupt the ongoing data communication and thus reduces the deployment burden. We implement and evaluate D-Watch with extensive experiments in three different environments. D-Watch achieves a median accuracy of 16.5 cm for library, 25.5 cm for laboratory, and 31.2 cm for hall environment, outperforming the state-of-the-art systems. In a table area of 2 $\text{m}\times 2$ m, D-Watch can track a user's fist at a median accuracy of 5.8 cm. D-Watch is also capable of localizing multiple targets which is well known to be challenging in passive localization.

Device-free localization, which does not require any device attached to the target is playing a critical role in many applications such as intrusion detection, elderly monitoring, etc. This paper introduces D-Watch, a device-free system built on top of low cost commodity-off-the-shelf (COTS) RFID hardware. Unlike previous works which consider multipaths detrimental, D-Watch leverages the "bad" multipaths to provide a decimeter level localization accuracy without offline training. D-Watch harnesses the angle-of-arrival (AoA) information from the RFID tags' backscatter signals. The key intuition is that whenever a target blocks a signal's propagation path, the signal power experiences a drop which can be accurately captured by the proposed novel P-MUSIC algorithm. The wireless phase calibration scheme proposed does not interrupt the ongoing communication. Real-world experiments demonstrate the effectiveness of D-Watch. In a rich-multipath library environment, D-Watch can localize a human target at a median accuracy of 16.5 cm. In a table area of 2 m×2 m, D-Watch can track a user's fist at a median accuracy of 5.8 cm. D-Watch is capable of localizing multiple targets which is well known to be challenging in passive localization

Device-free localization (DfL) techniques, which can localize targets without carrying any wireless devices, have attracting an increasing attentions. Most current DfL approaches, however, have two main drawbacks hindering their practical applications. First, one needs to collect large number of measurements to achieve a high localization accuracy, inevitably causing a high deployment cost, and the areas variety will further exacerbate this problem. Second, as the pre-obtained Received Signal Strength (RSS) from each location (i.e., radio-map) in a specific area cannot be directly applied to new areas for localization, the calibration process of different areas will lead to the high human effort cost. In this paper, we propose, FitLoc, a fine-grained and low cost DfL approach that can localize multiple targets in various areas. By taking advantage of the compressive sensing (CS) theory, FitLoc decreases the deployment cost by collecting only a few of RSS measurements and performs a fine-grained localization. Further, FitLoc employs a rigorously designed transfer scheme to unify the radio-map over various areas, thus greatly reduces the human effort cost. Theoretical analysis about the effectivity of the problem formulation is provided. Extensive experimental results illustrate the effectiveness of FitLoc.

This paper introduces a wireless phase calibration algorithm that can be applied on cheap commercial off-the-shelf (COTS) radio frequency identification (RFID) system and auto acquire an accurate radio frequency (RF) phase information without any offline training. The key observation is that the raw phase measurements even measured form different RFID tags contain a same set of unknown phase errors. With enough tags' phase measurements, we can determine all the unknown phase errors, since the number of known phase measurements is much larger than the number of unknown phase errors. Real-world experimental results demonstrate the effectiveness of the proposed method.

Device-free localization of people and objects indoors not equipped with radios is playing a critical role in many emerging applications. This paper presents an accurate model-based device-free localization system LiFS, implemented on cheap commercial off-the-shelf (COTS) Wi-Fi devices. Unlike previous COTS device-based work, LiFS is able to localize a target accurately without offline training. The basic idea is simple: channel state information (CSI) is sensitive to a target's location and by modelling the CSI measurements of multiple wireless links as a set of power fading based equations, the target location can be determined. However, due to rich multipath propagation indoors, the received signal strength (RSS) or even the fine-grained CSI can not be easily modelled. We observe that even in a rich multipath environment, not all subcarriers are affected equally by multipath reflections. Our pre-processing scheme tries to identify the subcarriers not affected by multipath. Thus, CSIs on the "clean" subcarriers can be utilized for accurate localization.

Many emerging applications drive the needs of device-free localization (DfL), in which the target can be localized without any device attached. Because of the ubiquitousness of WiFi infrastructures nowadays, the widely available Received Signal Strength (RSS) information at the WiFi Access points are commonly employed for localization purposes. However, current RSS based DfL systems have one main drawback hindering their real-life applications. That is, the RSS measurements (fingerprints) vary slowly in time even without any change in the environment and frequent updates of RSS at each location lead to a high human labor cost. In this paper, we propose an RSS based low cost DfL system named TafLoc which is able to accurately localize the target over a long time scale. To reduce the amount of human labor cost in updating the RSS fingerprints, TafLoc represents the RSS fingerprints as a matrix which has several unique properties. Based on these properties, we propose a novel fingerprint matrix reconstruction scheme to update the whole fingerprint database with just a few RSS measurements, thus the labor cost is greatly reduced. Extensive experiments illustrate the effectiveness of TafLoc, outperforming the state-of-the-art RSS based DfL systems.

This poster introduces JRD, a novel device-free localization system which can achieve high accuracy with low cost and little human effort, and is even robust to different scenarios. Unlike the previous Radio Signal Strength (RSS)-based systems which depend on the dense deployment to provide high accuracy, JRD extracts the fine-grained RSS distributions of a single link and presents a voting algorithm based on multi-link to identify the object location accurately while maintaining a low-cost deployment. Furthermore, JRD is flexible to different scenarios by using the transferring technique with less time-consuming and human effort. Experimental results show that JRD can improve the localization accuracy by up to 50% with less cost as compared with the existing RSS approaches.

For a long history, stampede is one of the high potential disaster when thousands of people gathered. Current monitoring systems, however, can only detect the presence of a small number of sparsely located targets, rather than to monitor the change of people flow where there are large number of dense crowd in the environment. This paper presents DanSen, a low-cost people flow monitoring system for sensing the potential danger using the existing wifi infrastructures. Inspired by the dynamic light scattering (DLS) theory, the designed DanSen calculates the correlations between the initial channel state information (CSI) data and all the history CSI data to monitor the changes of people flow and also estimates the sharpness of the changes. By doing so, DanSen can be utilised to perceive the potential danger. Real-world experimental results illustrate the advantage and effectiveness of DanSen.

Many emerging applications and the ubiquitous wireless signals have accelerated the development of Device Free localization (DFL) techniques, which can localize objects without the need to carry any wireless devices. Most traditional DFL methods have a main drawback that as the pre-obtained Received Signal Strength (RSS) measurements (i.e., fingerprint) in one area cannot be directly applied to the new area for localization, and the calibration process of each area will result in the human effort exhausting problem. In this paper, we propose FALE, a fine-grained transferring DFL method that can adaptively work in different areas with little human effort and low energy consumption. FALE employs a rigorously designed transferring function to transfer the fingerprint into a projected space, and reuse it across different areas, thus greatly reduce the human effort. On the other hand, FALE can reduce the data volume and energy consumption by taking advantage of the compressive sensing (CS) theory. Extensive real-word experimental results also illustrate the effectiveness of FALE.

Without relying on devices carried by the target, device-free localization (DFL) is attractive for many applications, such as wildlife monitoring. There still exist many challenges for DFL for multiple targets without dense deployment of sensor nodes. To fit the gap, in this paper, we propose a multi-target localization method based on compressive sensing, named LCS. The key observation is that given a pair of nodes, the received signal strength (RSS) will be different when a target locates at different locations. Taking advantage of compressive sensing in sparse recovery to handle the sparse property of the localization problem, (i.e., the vector which contains the number and location information of k targets is an ideal k-sparse signal), we presented a scalable compressive sensing based multiple target counting and localization method i.e., LCS, and rigorously justify the validity of the problem formulation. The results from our realistic deployment in a 12m×12m open space are promising. For 12 people with 24 nodes, the worst localization error ratio and counting error ratio of our LCS is no more than 8.3% and 33.3% respectively.