Channel State Information Fingerprints Research Articles

Toward Practical Lightweight Passive Human Tracking Using WiFi Sensing

With the wide adoption of versatile IoT devices, device providers may desire to locate users around those devices to plan context-aware intelligence, which may improve the quality of daily life. As most IoT devices are Wi-Fi enabled, the Wi-Fi-based indoor positioning system is supposed to achieve this future scene. However, the state-of-art Wi-Fi indoor positioning systems face challenges when being practically deployed as they may have to trade-off between, say accuracy and computational overhead. In light of this, this article mainly introduces PLP-Track, a Practical Lightweight Passive indoor tracking system based on CSI (channel state information) fingerprints. To settle the low granularity of fingerprints in distinguishing different positions, we propose a fingerprint preprocessing algorithm based on unsupervised learning and incorporate this algorithm with a state-space model to enable lightweight real-time tracking. Our implementation and evaluation of commodity WiFi devices demonstrate that PLP-Track can achieve indoor localization with high accuracy and low computation cost.

Channel State Information Based Indoor Fingerprinting Localization.

Indoor localization is one of the key techniques for location-based services (LBSs), which play a significant role in applications in confined spaces, such as tunnels and mines. To achieve indoor localization in confined spaces, the channel state information (CSI) of WiFi can be selected as a feature to distinguish locations due to its fine-grained characteristics compared with the received signal strength (RSS). In this paper, two indoor localization approaches based on CSI fingerprinting were designed: amplitude-of-CSI-based indoor fingerprinting localization (AmpFi) and full-dimensional CSI-based indoor fingerprinting localization (FuFi). AmpFi adopts the amplitude of the CSI as the localization fingerprint in the offline phase, and in the online phase, the improved weighted K-nearest neighbor (IWKNN) is proposed to estimate the unknown locations. Based on AmpFi, FuFi is proposed, which considers all of the subcarriers in the MIMO system as the independent features and adopts the normalized amplitudes of the full-dimensional subcarriers as the fingerprint. AmpFi and FuFi were implemented on a commercial network interface card (NIC), where FuFi outperformed several other typical fingerprinting-based indoor localization approaches.

Over-the-Air Adversarial Attacks on Deep Learning Wi-Fi Fingerprinting

Empowered by deep neural networks (DNNs), Wi-Fi fingerprinting has recently achieved astonishing localization performance to facilitate many security-critical applications in wireless networks, but it is inevitably exposed to adversarial attacks, where subtle perturbations can mislead DNNs to wrong predictions. Such vulnerability provides new security breaches to malicious devices for hampering wireless network security, such as malfunctioning geofencing or asset management. The prior adversarial attack on localization DNNs uses additive perturbations on channel state information (CSI) measurements, which is impractical in Wi-Fi transmissions. To transcend this limitation, this paper presents FooLoc, which fools Wi-Fi CSI fingerprinting DNNs over the realistic wireless channel between the attacker and the victim access point (AP). We observe that though uplink CSIs are unknown to the attacker, the accessible downlink CSIs could be their reasonable substitutes at the same spot. We thoroughly investigate the multiplicative and repetitive properties of over-the-air perturbations and devise an efficient optimization problem to generate imperceptible yet robust adversarial perturbations. We implement FooLoc using commercial Wi-Fi APs and Wireless Open-Access Research Platform (WARP) v3 boards in offline and online experiments, respectively. The experimental results show that FooLoc achieves overall attack success rates of about 70% in targeted attacks and of above 90% in untargeted attacks with small perturbation-to-signal ratios of about -18 dB.

IPos-5G: Indoor Positioning via Commercial 5G NR CSI

The fifth-generation (5G) networks have been massively deployed in commerce. The new features introduced by 5G networks are beneficial to wireless positioning. In this study, the performance of indoor positioning with commercial 5G new radio (NR) signals is investigated, and the channel state information (CSI) extracted from the downlink synchronization signal block is utilized. Considering the limited 5G NR base station (known as gNodeB) is hearable indoors, the fingerprint method is used, and an indoor positioning system termed iPos-5G is developed. The system consists of four components. Firstly, a module of quality control is applied for CSI preprocessing. Secondly, an unsupervised deep-autoencoder network is utilized to reconstruct CSI features. Thirdly, by supervised learning, a radial basis function is improved to optimize the probability model for similarity calculations. Finally, an amplitude-phase probability fusion function is proposed for positioning by weighting the coordinates of reference points. To verify the effectiveness of iPos-5G, indoor field tests are carried out in the scenarios of an office and a corridor. The test results show that iPos-5G achieves mean absolute errors of 2.14 and 2.81 m and standard deviation of the errors of 1.07 and 1.66 m, which outperforms the compared CSI fingerprint methods in terms of positioning accuracy and stability.

D-Fi: Domain adversarial neural network based CSI fingerprint indoor localization

Deep learning based channel state information (CSI) fingerprint indoor localization schemes need to collect massive labeled data samples for training, and the parameters of the deep neural network are used as the fingerprints. However, the indoor environment may change, and the previously constructed fingerprint may not be valid for the changed environment. In order to adapt to the changed environment, it requires to recollect massive amount of labeled data samples and perform the training again, which is labor-intensive and time-consuming. In order to overcome this drawback, in this paper, we propose one novel domain adversarial neural network (DANN) based CSI Fingerprint Indoor Localization (D-Fi) scheme, which only needs the unlabeled data samples from the changed environment to update the fingerprint to adapt to the changed environment. Specifically, the previous environment and changed environment are treated as the source domain and the target domain, respectively. The DANN consists of the classification path and the domain-adversarial path, which share the same feature extractor. In the offline phase, the labeled CSI samples are collected as source domain samples to train the neural network of the classification path, while in the online phase, for the changed environment, only the unlabeled CSI samples are collected as target domain samples to train the neural network of the domain-adversarial path to update parameters of the feature extractor. In this case, the feature extractor extracts the common features from both the source domain samples corresponding to the previous environment and the target domain samples corresponding to the changed environment. Experiment results show that for the changed localization environment, the proposed D-Fi scheme significantly outperforms the existing convolutional neural network (CNN) based scheme.

Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting.

Indoor signals are susceptible to NLOS propagation effects, multipath effects, and a dynamic environment, posing more challenges than outdoor signals despite decades of advancements in location services. In modern Wi-Fi networks that support both MIMO and OFDM techniques, Channel State Information (CSI) is now used as an enhanced wireless channel metric replacing the Wi-Fi received signal strength (RSS) fingerprinting method. The indoor multipath effects, however, make it less robust and stable. This study proposes a positive knowledge transfer-based heterogeneous data fusion method for representing the different scenarios of temporal variations in CSI-based fingerprint measurements generated in a complex indoor environment targeting indoor parking lots, while reducing the training calibration overhead. Extensive experiments were performed with real-world scenarios of the indoor parking phenomenon. Results revealed that the proposed algorithm proved to be an efficient algorithm with consistent positioning accuracy across all potential variations. In addition to improving indoor parking location accuracy, the proposed algorithm provides computationally robust and efficient location estimates in dynamic environments. A Cramer-Rao lower bound (CRLB) analysis was also used to estimate the lower bound of the parking lot location error variance under various temporal variation scenarios. Based on analytical derivations, we prove that the lower bound of the variance of the location estimator depends on the (i) angle of the base stations, (ii) number of base stations, (iii) distance between the target and the base station, djr (iv) correlation of the measurements, ρrjai and (v) signal propagation parameters σC and γ.

SASMOTE: A Self-Attention Oversampling Method for Imbalanced CSI Fingerprints in Indoor Positioning Systems.

WiFi localization based on channel state information (CSI) fingerprints has become the mainstream method for indoor positioning due to the widespread deployment of WiFi networks, in which fingerprint database building is critical. However, issues, such as insufficient samples or missing data in the collection fingerprint database, result in unbalanced training data for the localization system during the construction of the CSI fingerprint database. To address the above issue, we propose a deep learning-based oversampling method, called Self-Attention Synthetic Minority Oversampling Technique (SASMOTE), for complementing the fingerprint database to improve localization accuracy. Specifically, a novel self-attention encoder-decoder is firstly designed to compress the original data dimensionality and extract rich features. The synthetic minority oversampling technique (SMOTE) is adopted to oversample minority class data to achieve data balance. In addition, we also construct the corresponding CSI fingerprinting dataset to train the model. Finally, extensive experiments are performed on different data to verify the performance of the proposed method. The results show that our SASMOTE method can effectively solve the data imbalance problem. Meanwhile, the improved location model, 1D-MobileNet, is tested on the balanced fingerprint database to further verify the excellent performance of our proposed methods.

CSI Fingerprinting for Device-Free Localization: Phase Calibration and SSIM-Based Augmentation

Fingerprint-based methods have become the mainstream for Device-Free Localization (DFL), and their performances depend heavily on the labeled dataset. In this letter, a novel Channel State Information (CSI) fingerprinting scheme is proposed for fully-exploiting the power of a given dataset. First, a phase calibration approach is applied to eliminate the phase offset due to the imperfection of synchronization and further make the collected phases from the different reference points distinguishable. Then, a Structural Similarity-based (SSIM-based) augmentation method is used to generate artificial samples for expanding the given dataset. Compared to existing GAN-based methods, it is very simple and computationally more efficient. Extensive experiments demonstrate the effectiveness of the proposed method. In comparison with the initial dataset and the AC-GAN based method, using the proposed method can gain approximately 13% and 8% Root-Mean-Square-Error (RMSE) enhancement, respectively.

Toward Long-Term Effective and Robust Device-Free Indoor Localization via Channel State Information

With the rapid development of location-based IoT applications in recent years, indoor device-free passive localization based on Wi-Fi channel state information (CSI) has attracted considerable attention. In this article, we propose a long-term effective, robust, and accurate device-free passive fingerprinting localization scheme LTLoc, which only requires a single communication link. It takes the amplitudes extracted from the CSI along with the calibrated phases as fingerprints and trains a deep neural network (DNN) regression model to estimate the target location. Since Wi-Fi signals are susceptible to various environmental factors, CSI fingerprints also change over time, making the performance of the localization model built with the fingerprint database drop dramatically over a long period, and recalibrating the entire positioning area is laborious and time-consuming. To address this problem, we design an adaptive DNN (AdaptDNN) based on meta-networks by combining deep learning and domain adaptive methods. It can use meta-network learning to determine which layers and features of the DNN need to be transferred to automatically adapt to CSI fingerprints change. Extensive evaluations in an indoor environment with significantly different CSI fingerprints over six days have shown that LTLoc’s effectiveness in coping with changing CSI fingerprints over a long period is significantly superior to existing work in terms of localization and adaptability.

Enhancing Packet-Level Wi-Fi Device Authentication Protocol Leveraging Channel State Information

Wi-Fi device authentication is crucial for defending against impersonation attacks and information forgery attacks. Most of the existing authentication technologies rely on complex cryptographic algorithms. However, they cannot be supported well on the devices with limited hardware resources. A fine-grained device authentication technology based on channel state information (CSI) provides a noncryptographic method, which uses the CSI fingerprints for authentication since CSI can uniquely identify the devices. But long-term authentication based on CSI fingerprints is a challenging work. First, the CSI fingerprints are environment-sensitive, which means that the local authenticator should be updated to adapt to the changing channel state. Second, the local authenticator trained with old CSI fingerprints is outdated when users reconnect to the network after being offline for a long time, thus, it needs to be retrained in the access phase with new fingerprints. To tackle these challenges, we propose a CSI-based enhancing Wi-Fi device authentication protocol and an authentication framework. The protocol helps to collect new CSI fingerprints for authenticator’s training in access phase and performs the fingerprints’ dispersion analysis for authentication. In the association phase, it provides packet-level authentication and updates the authenticator with valid CSI fingerprints. The authenticator consists of an ensemble of small-scale autoencoders, which has high enough time efficiency for packet-level authentication and authenticator’s update. Experiments show that the accuracy of the framework is up to 98.7%, and the authenticator updating method can help the framework maintains high accuracy.

Indoor Localization Based on CSI Fingerprint by Siamese Convolution Neural Network

In this paper, a novel indoor localization system with channel state information (CSI) fingerprints is proposed, which learns the spatial and frequency features of CSI in the fifth-generation (5G) cellular network by a Siamese convolution neural network. In particular, considering that the CSI continuously collected by a moving target possesses the implicit spatial association, we locate the target by the successive CSI data gathered within a time interval which can be regarded as an information subspace of the fingerprint database. Therefore, the fingerprint localization can be modeled as a subspace matching problem and solved by the Siamese network-based similarity learning. In the proposed system, we design a structure of CSI fingerprint which includes the information from multiple base stations in spatial and frequency domains. Then, the proposed Siamese architecture extracts the CSI feature and estimates the location of the target by feature similarity comparison. Compared with the existing algorithms, it can increase the positioning accuracy significantly by the feature relevance among the CSI data collected at different positions. The field tests indicate that compared to other CSI fingerprint-based positioning methods, our proposed algorithm can effectively reduce the localization error.

A Low-Overhead Indoor Positioning System Using CSI Fingerprint Based on Transfer Learning

With the rapidly growing demand for Location-Based Services in indoor environments, fingerprint-based indoor positioning has caused great interest due to its high positioning accuracy and low equipment cost. However, the standard signal radio map cannot provide consistent high positioning accuracy under environmental changes and new scenarios. To address this problem, we present a novel indoor positioning Transfer Learning(TL) system based on improved TrAdaBoost. First perform phase correction on the raw CSI(Channel State Information) phase, then use One-vs-Rest algorithm and One-Hot coding, which can realize the multi-classification ability of the TrAdaBoost algorithm. Meanwhile, optimize the iterative process of the algorithm by using a correction factor. In addition, Confidence regression is used to obtain the final estimated position. Experimental results show that the positioning accuracy can be improved by 35% in dynamic environment conditions. The proposed method can improve the positioning accuracy by an average of 30% in new scenes and the Site Survey Overhead(SSO) is reduced by 40%. Compared to other position b algorithm, our proposed method has more robustness in time and space.

CSI Fingerprinting Localization With Low Human Efforts

Fingerprinting indoor localization systems exploit wireless signal propagation features to estimate the location of wireless devices, where the major challenge in practice is the all-consuming training process: it requires site survey to establish the mapping between the signal feature and the location where the feature is observed. In this paper, we present a Wi-Fi localization scheme based on channel state information (CSI) of wireless signals, which manages to relieve time-consuming site survey. In particular, we first propose how to automatically generate the theoretical fingerprints database based on the signal propagation model and geometric methods. Localization with the theoretical fingerprints database yields accuracy close to existing methods. Second, we improve localization accuracy by parsing the user's trajectory instead of restricting to the single spot, where human movement features introduce more information for localization. Third, we present an automatic update scheme for the theoretical fingerprints database to improve time efficiency for localization, which can save 94 - 98% processing time for utilizing the CSI fingerprints database. We implement a prototype with COTS devices and conduct comprehensive experiments to verify proposed mechanisms. Results show that our design achieves 80% localization errors within 0.3m, which is 3× accuracy compared with the state-of-the-art design leveraging CSI.

CRISLoc: Reconstructable CSI Fingerprinting for Indoor Smartphone Localization

Channel-state information (CSI)-based fingerprinting for WIFI indoor localization has attracted lots of attention very recently. The frequency diverse and temporally stable CSI better represents the location-dependent channel characteristics than the coarse received signal strength (RSS). However, the acquisition of CSI requires the cooperation of access points (APs) and involves only data frames, which imposes restrictions on real-world deployment. In this article, we present CRISLoc, the first CSI fingerprinting-based localization prototype system using ubiquitous smartphones. CRISLoc operates in a completely passive mode, overhearing the packets on-the-fly for his own CSI acquisition. The smartphone CSI is sanitized via calibrating the distortion enforced by WiFi amplifier circuits. CRISLoc tackles the challenge of altered APs with a joint clustering and outlier detection method to find them. A novel transfer learning approach is proposed to reconstruct the high-dimensional CSI fingerprint database on the basis of the outdated fingerprints and a few fresh measurements, and an enhanced KNN approach is proposed to pinpoint the location of a smartphone. Our study reveals important properties about the stability and sensitivity of smartphone CSI that has not been reported previously. Experimental results show that CRISLoc can achieve a mean error of around 0.29 m in a 6 m $\times$ 8 m research laboratory. The mean error increases by 5.4 and 8.6 cm upon the movement of one and two APs, which validates the robustness of CRISLoc against environmental changes.

Adaptive Device-Free Localization in Dynamic Environments Through Adaptive Neural Networks

Device-free localization based on WiFi Channel State Information (CSI) has attracted considerable attention in recent years. Many of the schemes are learning-based, among which CSI fingerprinting based on deep learning is the most promising. Learning-based schemes assume the distributions of CSI fingerprints keep relatively stable over time. However, this assumption often does not hold. As WiFi signals are prone to be influenced by various environmental factors, CSI fingerprints vary significantly with environmental changes. Consequently, the localization model built for the original environment degrades dramatically in the changed environment. Recalibrating the whole area of interest is labor-intensive and time-consuming. This is a major challenge of device-free WiFi localization. To mitigate this issue, we propose the method AdapLoc, aiming to adapt the original localization model to the changed environment with significantly reduced recalibration effort. AdapLoc is based on one-dimensional Convolutional Neural Network (1D-CNN) and exploits Domain Adaptation (DA) with Semantic Alignment (SA) to achieve adaptation. Extensive evaluations in four real-world single-link testbeds with multiple environmental changes demonstrate the effectiveness of AdapLoc in coping with localization in dynamic environments, outperforming the existing work with respect to localization and adaptation.

Transformable Fingerprinting with Deep Metric Learning Approach for Indoor Localization

Within state-of-the-art indoor localization approaches, fingerprinting based method is more applicable and easier to integrate into most of today’s commodity Wi-Fi devices such as mobile phones and IOT devices which require low cost and computation burden. However, most fingerprinting systems intrinsically depend on fixed channel propagation environment and thus suffers huge reconstruction cost and high localization error when environment changes. In this paper, we propose a novel transformable fingerprinting localization method based on deep metric learning approaches. Our fingerprinting reconstruction method only requires some fresh measurements of CSI (Channel State Information) on a few reference points (RPs) with all the outdated CSI fingerprinting. Extensive system level simulations on Quadriga show that an average of 0.2m error reduction is achieved when our reconstruction method is applied.

Discrete Hopfield neural network based indoor Wi-Fi localization using CSI

The fingerprint indoor localization method based on channel state information (CSI) has gained widespread attention. However, this method fails to provide a better localization effect and higher localization accuracy due to poor fingerprint accuracy, unsatisfactory classification and matching effect, and vulnerability to environmental impacts. In order to solve the problem, this paper proposes a CSI fingerprint indoor localization method based on the Discrete Hopfield Neural Network (DHNN). The method mainly consists of off-line and on-line phases. At the off-line phase, a low-pass filter is applied to conduct a preliminary processing on the fingerprint information of each reference point, and then, phase difference is adopted to correct the fingerprint data of all reference points. In this way, the quality of fingerprint data is improved, hence avoiding problems such as indoor environmental changes and multipath effect of signals, etc. in which impact the fingerprint data. Finally, the characteristic fingerprint database is established after acquiring relatively accurate fingerprint data. At the on-line phase, to maintain the consistency of data, the data of each reference point in the fingerprint database is set as an attractor. Meanwhile, the localization information of the test point is processed to make convergence judgment through DHNN. Eventually, the localization result is obtained. The experimental results show that the localization accuracy with a median error of 1.6 m can be achieved through the proposed method in the experimental environment. Compared with similar methods, it has a higher stability which can significantly reduce the cost of manpower and time.

Device-free passive wireless localization system with transfer deep learning method

Recently, device-free passive wireless indoor localization (DFPWL) has attracted great interest due to the widespread deployment of Wi-Fi devices and the rapid growth in demand for location-based services (LBS). The DFPWL fingerprinting approach based on channel state information (CSI) has become the mainstream method since its simple deployment and localization accuracy. It determines the location of the target from the new measurement CSI by collecting a training database that measures the CSI and using a machine learning classifier. However, we have found through experiments that even if the indoor environment does not change, the CSI fingerprint will be different from the CSI fingerprint in the database over time, and most of the CSI-based DFPWL fingerprinting method ignores this. To cope with the reduction in localization accuracy caused by the time-varying characteristic of CSI, we propose a novel transfer deep learning-based DFPWL system in this paper. It uses the CSI extracted from a single link to estimate the location of the target, neither requiring the target to wear any electronic equipment nor deploying a large number of APs and Monitor Devices. Unlike the other traditional CSI-based DFPWL fingerprinting approaches using the pre-processed CSI samples as fingerprints, our system utilizes the transfer deep learning (TDL) method to learn new feature representations from the CSI samples as fingerprints, which can simultaneously minimize the intra-class differences, maximize inter-class differences, and minimize the distribution differences between fingerprint database and test samples. Finally, the KNN algorithm is utilized to compare the test samples and the fingerprint database under the new feature representation to obtain the estimation location of the target. Experiment results show that our system can effectively improve localization accuracy compared to the other state-of-art, and can maintain stable localization accuracy for a long time without reacquiring the CSI fingerprint database.

An Indoor Passive Positioning Method Using CSI Fingerprint Based on Adaboost

Wi-Fi fingerprint-based indoor positioning systems have extensive application prospects. Indoor positioning based on channel state information (CSI) becomes a research hotspot because it can provide fine-grained information. This paper proposes the Adaboost positioning system (ABPS) that uses the phase information in CSI and the ensemble learning (EL) method to train the fingerprint map. In this system, the abnormal phase data is eliminated by density-based clustering and the remainder is linearly transformed to build the fingerprint map. Through continuous iteration with the Adaboost algorithm, the sample weights of the training sets are continuously adjusted to prepare for classification. Finally, it can achieve position coordinates regression by means of confidence level. A series of experiments have been conducted to illustrate the effects of EL parameters, such as the number of iterations, the maximum depth of decision tree, the maximum number of features, and the minimum number of samples required for node subdivision. Meanwhile, the influence of input data sets size is investigated in an open environment and a complex laboratory environment. The experimental results of various classification and regression methods are also discussed, which validates the effectiveness of the proposed method.

FreeTrack: Device-Free Human Tracking With Deep Neural Networks and Particle Filtering

Human tracking is of key importance to a variety of applications. Different from the previous approaches requiring the targets to carry electronic devices, this paper proposes a device-free tracking method based on WiFi channel state information (CSI) through deep neural networks (DNN) and particle filtering (PF). In the area covered with WiFi, human movements may cause observable variations of WiFi signals. By analyzing the CSI fingerprint patterns and modeling the dependency between CSI fingerprints and locations through DNN, the proposed method is able to localize the targets according to the measured CSI fingerprints through DNN regression. Localization with DNN is accurate for static targets, yet unable to form reasonable trajectories of moving targets. This paper proposes to apply PF on DNN localization results and form the walking trajectories of the targets. To avoid the trajectories to cross obstacles, map matching is integrated to constrain the unreasonable transfer of particles, which further improves the precision of trajectories. To combat with environmental variants, fine tuning on DNN is proposed to enhance the adaptivity of the method. Extensive evaluations in two representative scenarios with only one pair of WiFi transmitter and receiver achieve the high accuracy of around half a meter and precise trajectories as well as adaptation to environmental variants.