WiFi Fingerprint-based Localization Research Articles

Train Once, Locate Anytime for Anyone: Adversarial Learning-based Wireless Localization

Among numerous indoor localization systems, WiFi fingerprint-based localization has been one of the most attractive solutions, which is known to be free of extra infrastructure and specialized hardware. To push forward this approach for wide deployment, three crucial goals on high deployment ubiquity, high localization accuracy, and low maintenance cost are desirable. However, due to severe challenges about signal variation, device heterogeneity, and database degradation root in environmental dynamics, pioneer works usually make a trade-off among them. In this article, we propose iToLoc, a deep learning-based localization system that achieves all three goals simultaneously. Once trained, iToLoc will provide accurate localization service for everyone using different devices and under diverse network conditions, and automatically update itself to maintain reliable performance anytime. iToLoc is purely based on WiFi fingerprints without relying on specific infrastructures. The core components of iToLoc are a domain adversarial neural network and a co-training-based semi-supervised learning framework. Extensive experiments across 7 months with eight different devices demonstrate that iToLoc achieves remarkable performance with an accuracy of 1.92 m and >95% localization success rate. Even 7 months after the original fingerprint database was established, the rate still maintains >90%, which significantly outperforms previous works.

Learning to Locate: Adaptive Fingerprint-Based Localization With Few-Shot Relation Learning in Dynamic Indoor Environments

WiFi fingerprint-based localization has been intensive studies as a promising technology of ubiquitous location-based services. Two main concerns for its wide spread applications are to tackle with the cumbersome efforts of site survey and to combat vulnerable environment changes. To address these issues comprehensively, we propose a novel approach on adaptive fingerprint-based localization with less site survey, named as LESS, by exploring a new paradigm of radio map construction and adaptation with few-shot relation learning. Firstly, we extend sparsely collected fingerprints with the fingerprint augmentation method which produces new related data and derives their location information based on local proximity property in a low-dimensional manifold space. Then, LESS designs deep relation networks to learn not only the appropriate features but also a transferable deep-distance metric for modeling the fundamental relationships of the neighborhood fingerprints. Finally, once trained, LESS can quickly establish the neighborhood relationships among new fingerprints in the changed surroundings to realize adaptive location estimations, even without the network updating. The extensive experimental results demonstrate that LESS can achieve an attractive trade-off between the system overhead and the location performance with the superiorities over others in dynamic indoor environments.

Rank Based Iterative Clustering (RBIC) for indoor localization

In the current era, technological advancements have resulted in a spike in various sensor data-intensive applications including indoor localization services. WiFi fingerprint-based localization for large indoor areas is primarily contingent on laborious site surveys. To ease the flow of research, grossly labeled data collected using a crowd-sourced method could be clustered. However, for a real-time localization approach, the challenge is to generate the clusters containing data samples that are really physically close. In this paper, a two-phase semi-supervised localization approach has been proposed, which is general enough to be applied to indoor localization datasets. In the offline phase, a Rank-Based Iterative Clustering (RBIC) algorithm has been proposed that generates a clustered dataset with a negligible chance of containing physically apart location points within a common cluster. RBIC can be viewed as a clustering ensemble model. Different clustering algorithms are selected as baseline algorithms and assigned unique ranks depending on well-known clustering scores to be fed as input to RBIC. In the online phase, the users’ location is estimated using machine learning (ML) classifiers based on the dynamic received signal strength indicator (RSSI) vector received through its handheld device transceiver. The system is evaluated with three benchmark datasets for WiFi based indoor localization. For the first dataset, JUIndoorLoc, 94% to 99% localization accuracy is achieved for individual supervised classifiers. For the second and third datasets, the ranges of obtained localization accuracies are 96% to 99% and 95% to 98% respectively.

A Boundary Aware WiFi Localization Scheme Based on UMAP and KNN

In this letter, a novel localization scheme is reported to improve WiFi fingerprint-based localization via dimensionality reduction and an adaptive fingerprint matching algorithm. Specifically, an experimental analysis is firstly carried out to investigate the quality of radio maps. Then, the advanced uniform manifold approximation and projection (UMAP) algorithm is applied to refine the mapping relations from a fingerprint space to a location space in a supervised dimensionality reduction approach, which significantly improves the quality of radio maps. In addition, to further enhance the localization accuracy, boundary aware KNN (B-KNN) is proposed to adaptively deal with fingerprint matching according to inner area or boundary area. Extensive experiments are conducted in a real scenario with the area of nearly <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1000~m^{2}$ </tex-math></inline-formula> , and a thorough comparison with several existing popular methods illustrates that the proposed scheme can significantly improve the localization accuracy on average by 33.3%.

A fingerprint-based localization algorithm based on LSTM and data expansion method for sparse samples

The accuracy of WiFi fingerprint-based localization is related to the number of reference points, generally, to obtain better positioning accuracy, enough samples must be collected, which will inevitably lead to a huge sampling workload. Thus, it will be of great significance to design an algorithm using sparse samples to achieve positioning accuracy like that of dense samples. This paper proposes a WiFi fingerprint-based localization algorithm using Long Short-Term Memory Network (LSTM) with explainable feature and a sparse sample expansion algorithm (PGSE) based on Principal component analysis and Gaussian process regression for sparse samples. Specifically, in the case of limited number of collected reference points, principal component analysis is used to select the access point, and Gaussian process regression is used to model the reference point coordinates and the corresponding received signal strength values in the training sample set, to expand the signal data and construct a new fingerprint database. The effectiveness of the PGSE algorithm is verified by using the public dataset ’UJIIndoorLoc’. At the same time, the applicability of PGSE expansion algorithm to data with temporal information is verified in the fingerprint-based localization method. In addition, this paper also proposes a WiFi-RSSI indoor localization method based on Long Short-Term Memory Network. Lots of experiments are conducted in the actual scenes and the results are compared with several existing methods. The results indicate that the proposed method improves the precision of indoor localization on an average level compared to state-of-the-art methods.

An Efficient Radio Map Learning Scheme Based on Kernel Density Function

Training an accurate and up-to-date radio map has always been a primary concern for implementing a WiFi fingerprint-based localization system. This paper presents a novel radio map learning scheme for online learning a set of kernel density functions, which function like a traditional radio map in WiFi fingerprint-based localization systems. To be specific, each kernel density function corresponds to one specific access point (AP) and predicts the probability of having a received signal strength (RSS) measurement from this AP at any position. In the offline phase, a set of coarse-grained kernel density functions are initially established by the Multivariate Kernel Density Estimation (MKDE) through a fast site survey. In the online phase, these kernel density functions are recursively refined through Incremental-MKDE (IMKDE) using freshly crowdsourced fingerprints, which essentially fuses the historical features in the previous kernel density functions and up-to-date features in the current fingerprints. A compression technique is applied to improve the computation and storage efficiency. In addition, pedestrian dead reckoning (PDR) is leveraged to calibrate the location labels of crowdsourced fingerprints. Extensive experiments are carried out in a real scenario of nearly 1000 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m^2$</tex-math></inline-formula> for five months, and a comparison made between the IMKDE based method and several existing popular solutions confirms the superiority of the proposed IMKDE in terms of both computational complexity and localization accuracy.

Wireless Localization with Spatial-Temporal Robust Fingerprints

Indoor localization has gained increasing attention in the era of the Internet of Things. Among various technologies, WiFi fingerprint-based localization has become a mainstream solution. However, RSS fingerprints suffer from critical drawbacks of spatial ambiguity and temporal instability that root in multipath effects and environmental dynamics, which degrade the performance of these systems and therefore impede their wide deployment in the real world. Pioneering works overcome these limitations at the costs of ubiquity as they mostly resort to additional information or extra user constraints. In this article, we present the design and implementation of ViViPlus, an indoor localization system purely based on WiFi fingerprints, which jointly mitigates spatial ambiguity and temporal instability and derives reliable performance without impairing the ubiquity. The key idea is to embrace the spatial awareness of RSS values in a novel form of RSS Spatial Gradient (RSG) matrix for enhanced WiFi fingerprints. We devise techniques for the representation, construction, and localization of the proposed fingerprint form and integrate them all in a practical system. Extensive experiments across 7 months in different environments demonstrate that ViViPlus significantly improves the accuracy in localization scenarios by about 30% to 50% compared with the state-of-the-art approaches.

A Theoretical Analysis on Sampling Size in WiFi Fingerprint-Based Localization

This paper deals with a key problem in WiFi fingerprint-based localization, namely how to sample a sufficient number of received signal strength (RSS) measurements during an offline site survey. To this end, a probabilistic framework is firstly presented to characterize the ability of distinguishing two fingerprints, and is then applied in both the ideally infinite sampling case and the realistically finite sampling case with correlated samples. On these grounds, it is shown that the Euclidean distance between the vectors of mean RSS measurements at any two positions is the key factor of determining localization performance, and how several other factors affect localization performance. More importantly, based on the central limit theory, a quantitative analysis is conducted to describe the degradation in localization performance introduced by finite and correlated samples. In addition, provided that correlated samples satisfy the first order autoregressive model, an explicit formula is derived to describe the relationship between the correlation coefficient and the sampling sizes, which can be employed to guide the offline site survey. Extensive simulations are conducted to confirm the effectiveness of the probabilistic framework as well as the correctness of different analytical results, and an experiment is also carried out for validation. This paper not only helps to understand the basic mechanism of WiFi fingerprint-based localization, but also provides insightful guidelines for efficiently building a fingerprint database.

Wi-Fi Fingerprint-Based Indoor Mobile User Localization Using Deep Learning

In recent years, deep learning has been used for Wi-Fi fingerprint-based localization to achieve a remarkable performance, which is expected to satisfy the increasing requirements of indoor location-based service (LBS). In this paper, we propose a Wi-Fi fingerprint-based indoor mobile user localization method that integrates a stacked improved sparse autoencoder (SISAE) and a recurrent neural network (RNN). We improve the sparse autoencoder by adding an activity penalty term in its loss function to control the neuron outputs in the hidden layer. The encoders of three improved sparse autoencoders are stacked to obtain high-level feature representations of received signal strength (RSS) vectors, and an SISAE is constructed for localization by adding a logistic regression layer as the output layer to the stacked encoders. Meanwhile, using the previous location coordinates computed by the trained SISAE as extra inputs, an RNN is employed to compute more accurate current location coordinates for mobile users. The experimental results demonstrate that the mean error of the proposed SISAE-RNN for mobile user localization can be reduced to 1.60 m.

Lightweight Privacy-Preserving Scheme in Wi-Fi Fingerprint-Based Indoor Localization

Wi-Fi fingerprint-based localization, a common indoor localization technique, plays an important role in the Internet of Things, supporting a number of mobile applications, e.g., navigation indoor, trackingetc. However, Wi-Fi fingerprint-based localization suffers from the privacy issue, disclosing users’ location privacy and the data privacy of localization server (LS). Existing work cannot completely protect LS's data privacy, and moreover, incurs larger amount of computation and communication overhead. In this article, we propose a lightweight privacy-preserving scheme (LWP $^{2}$ ) which protects both location privacy and data privacy with lower cost. The main idea of LWP $^{2}$ is to first formalize the privacy-preserving localization problem as minimizing the least-squared-error for an overdetermined linear formulation, and then design a lightweight solution in ciphertext space using the special structure of the overdetermined linear formulation. Extensive experiments have validated the privacy preservation and the efficiency improvement of LWP $^{2}$ .

A Hybrid Wi-Fi Fingerprint-Based Localization Scheme Achieved by Combining Fisher Score and Stacked Sparse Autoencoder Algorithms

Along with the advancement of wireless technology, indoor localization technology based on Wi-Fi has received considerable attention from academia and industry. The fingerprint-based method is the mainstream approach for Wi-Fi indoor localization and can be easily implemented without additional hardware. However, signal fluctuations constitute a critical issue pertaining to the extraction of robust features to achieve the required localization performance. This study presents a fingerprint feature extraction method commonly referred to as the Fisher score–stacked sparse autoencoder (Fisher–SSAE) method. Some features with low Fisher scores were eliminated, and the representative features were then extracted by the SSAE. Furthermore, this study establishes a hybrid localization model constructed with the use of the global model and the submodel to avoid significant coordinate localization errors attributed to subregional localization errors. Combined with three accessible fingerprint-based positioning methods, namely, the support vector regression, random forest regression, and the multiplayer perceptron classification, the experimental results demonstrate that the proposed methods improve the localization accuracy and response time compared to other feature extraction methods and the single localization model. Compared with some state-of-the-art methods, the proposed methods have better localization performances when large number of features are used.

DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble Classifier.

A quickly growing location-based services area has led to increased demand for indoor positioning and localization. Undoubtedly, Wi-Fi fingerprint-based localization is one of the promising indoor localization techniques, yet the variation of received signal strength is a major problem for accurate localization. Magnetic field-based localization has emerged as a new player and proved a potential indoor localization technology. However, one of its major limitations is degradation in localization accuracy when various smartphones are used. The localization performance is different from various smartphones even with the same localization technique. This research leverages the use of a deep neural network-based ensemble classifier to perform indoor localization with heterogeneous devices. The chief aim is to devise an approach that can achieve a similar localization accuracy using various smartphones. Features extracted from magnetic data of Galaxy S8 are fed into neural networks (NNs) for training. The experiments are performed with Galaxy S8, LG G6, LG G7, and Galaxy A8 smartphones to investigate the impact of device dependence on localization accuracy. Results demonstrate that NNs can play a significant role in mitigating the impact of device heterogeneity and increasing indoor localization accuracy. The proposed approach is able to achieve a localization accuracy of 2.64 m at 50% on four different devices. The mean error is 2.23 m, 2.52 m, 2.59 m, and 2.78 m for Galaxy S8, LG G6, LG G7, and Galaxy A8, respectively. Experiments on a publicly available magnetic dataset of Sony Xperia M2 using the proposed approach show a mean error of 2.84 m with a standard deviation of 2.24 m, while the error at 50% is 2.33 m. Furthermore, the impact of devices on various attitudes on the localization accuracy is investigated.

Selecting Critical WiFi APs for Indoor Localization Based on a Theoretical Error Analysis

The radio map built during the offline phase of any WiFi fingerprint-based localization system usually scales with the number of WiFi access points (APs) that can be detected at a single location by any mobile device, but this number in practice can be as large as 100, only a few of which essentially contribute to localization. Simply involving all the APs in location fingerprints and thus in the radio map not only wastes excessive storage but also leads to a large computational overhead. In this paper, a theoretical analysis of WiFi-based location determination is conducted to investigate the resulting localization errors and reveals that some critical APs contribute significantly more to localization than the others, implying that it is unnecessary to include every AP in localization. Consequently, a heuristic AP selection algorithm based on the error analysis is proposed to efficiently select a subset of APs for use in localization. Finally, extensive experiments are carried out by using both the UJI dataset available online and the dataset collected in our laboratory, and it is shown that the proposed algorithm not only significantly reduces the redundancy of APs in WiFi fingerprint-based localization but also substantially improves the localization accuracy of the $k$ nearest neighbor (KNN) method.

Automatic Radio Map Adaptation for Indoor Localization Using Smartphones

The proliferation of mobile computing has prompted WiFi-based indoor localization to be one of the most attractive and promising techniques for ubiquitous applications. A primary concern for these technologies to be fully practical is to combat harsh indoor environmental dynamics, especially for long-term deployment. Despite numerous research on WiFi fingerprint-based localization, the problem of radio map adaptation has not been sufficiently studied and remains open. In this work, we propose AcMu, an automatic and continuous radio map self-updating service for wireless indoor localization that exploits the static behaviors of mobile devices. By accurately pinpointing mobile devices with a novel trajectory matching algorithm, we employ them as mobile reference points to collect real-time RSS samples when they are static. With these fresh reference data, we adapt the complete radio map by learning an underlying relationship of RSS dependency between different locations, which is expected to be relatively constant over time. Extensive experiments for 20 days across six months demonstrate that AcMu effectively accommodates RSS variations over time and derives accurate prediction of fresh radio map with average errors of less than 5dB, outperforming existing approaches. Moreover, AcMu provides 2 $\times$ improvement on localization accuracy by maintaining an up-to-date radio map.

A novel indoor localization scheme based on refined fingerprint-based autoencoder network

Indoor positioning systems have attracted increasing interests for the emergency of location based service in indoor environments. Wi-Fi fingerprint-based localization scheme has become a promising indoor localization technique due to the availability of access point (AP) and its low cost. However, the received signal strength (RSS) values are easily fluctuated by the interference of multi-path effects, which introduce propagation errors into localization results. In order to address the issue, a fingerprint-based autoencoder network scheme is proposed to learn the essential features from the measured coarse RSS values and extract the trained weight parameters of autoencoder network as refined fingerprints. The extracted fingerprints are able to represent the environmental properties and display strong robustness with fluctuated signals. The proposed scheme is further implemented in complex indoor scenes, which substantiate the effectiveness and accuracy improvement compared with other RSS-based schemes.

Gain Without Pain

Among numerous indoor localization systems proposed during the past decades, WiFi fingerprint-based localization has been one of the most attractive solutions, which is known to be free of extra infrastructure and specialized hardware. However, current WiFi fingerprinting suffers from a pivotal problem of RSS fluctuations caused by unpredictable environmental dynamics. The RSS variations lead to severe spatial ambiguity and temporal instability in RSS fingerprinting, both impairing the location accuracy. To overcome such drawbacks, we propose fingerprint spatial gradient (FSG), a more stable and distinctive form than RSS fingerprints, which exploits the spatial relationships among the RSS fingerprints of multiple neighbouring locations. As a spatially relative form, FSG is more resistant to RSS uncertainties. Based on the concept of FSG, we design novel algorithms to construct FSG on top of a general RSS fingerprint database and then propose effective FSG matching methods for location estimation. Unlike previous works, the resulting system, named ViVi, yields performance gain without the pains of introducing extra information or additional service restrictions or assuming impractical RSS models. Extensive experiments in different buildings demonstrate that ViVi achieves great performance, outperforming the best among four comparative start-of-the-art approaches by 29% in mean accuracy and 19% in 95th percentile accuracy and outweighing the worst one by 39% and 24% respectively. We envision FSG as a promising supplement and alternative to existing RSS fingerprinting for future WiFi localization.

Robust Cooperative Wi-Fi Fingerprint-Based Indoor Localization

Wi-Fi fingerprint-based localization has attracted significant research interest recently. Previous works in this area mainly focus on locating an individual user, whereas the additional assistance from peer-to-peer interactions has not been fully exploited. In this paper, we propose a cooperative localization method which not only utilizes the initial results by the fingerprint-based algorithm but also takes into account the physical constraint of pairwise distances to refine the localization estimates for multiple users simultaneously. The experimental results demonstrate that our algorithm is robust against the ranging error and the outdated fingerprint database. With the proposed peer selection scheme, it considerably improves localization accuracy. We further extend our framework to single-user motion tracking and localization based only on access-point-connectivity data.

Privacy-preserving crowdsourced site survey in WiFi fingerprint-based localization

Typically, site survey is an inevitable phase for WiFi fingerprint-based localization which is regarded as one of the most promising techniques for indoor localization. However, the site survey can cause potential location privacy leakage for the participants who contribute their WiFi fingerprint measurements. In this paper, we propose a privacy-preserving site survey scheme for WiFi fingerprint-based localization. In the proposed scheme, we use homomorphic encryption to protect the location privacy of the participants which get involved in the site survey. Further, we employ differential privacy model to ensure that the released data will not breach an individual’s location privacy regardless of whether she is present or absent in the site survey group. We theoretically analyze the security of the proposed scheme and use simulation experiments on a real-world data to validate the efficiency of the proposed scheme.

PDR-Aided Algorithm with WiFi Fingerprint Matching for Indoor Localization

Recently, indoor localization is essential to enable location-based services for many mobile and social network applications. Due to fluctuation of the wireless signal, the accuracy of a simple WiFi fingerprint-based localization is not high. In this paper, we first exploit Pedestrian Dead Reckoning (PDR) technology to overcome the problem of the wireless signal fluctuation, then propose a PDR-aided algorithm with WiFi fingerprint matching for indoor localization, which using the PDR technology aided indoor localization. Experiments show that our algorithm has better accuracy than other indoor localization methods.

Accuracy Performance Analysis between War Driving and War Walking in Metropolitan Wi-Fi Localization

Wi-Fi ingerprint-based localization is currently the most promising approach to building metropolitan-scale localization systems for both indoor and outdoor environments. A Wi-Fi fingerprint localization system makes use of a radio map that contains Wi-Fi signals received at various locations, and uses the map for location estimation. Thus, the accuracy of a Wi-Fi fingerprint localization system greatly depends on the quality of its radio map. This paper explores various properties of a metropolitan radio map that may affect the accuracy of Wi-Fi fingerprint localization systems. In this study, metropolitan-scale radio maps are obtained using war walking and war driving. These maps contain hundreds of thousands of access points and signal samples. A detailed comparison analysis of selected radio map properties reveals how different map properties affect the difference between the positional accuracies of the driving and walking radio maps in Wi-Fi fingerprint-based localization.