WiFi Fingerprint Localization Research Articles

CBWF: A Lightweight Circular-Boundary-Based WiFi Fingerprinting Localization System

CBWF: A Lightweight Circular-Boundary-Based WiFi Fingerprinting Localization System

DeepMetricFi: Improving Wi-Fi Fingerprinting Localization by Deep Metric Learning

DeepMetricFi: Improving Wi-Fi Fingerprinting Localization by Deep Metric Learning

Enhancing WiFi Fingerprinting Localization Through a Co-Teaching Approach Using Crowdsourced Sequential RSS and IMU Data

Enhancing WiFi Fingerprinting Localization Through a Co-Teaching Approach Using Crowdsourced Sequential RSS and IMU Data

Wi-Fi Fingerprint for Indoor Keyless Entry Systems with Ensemble Learning Regression-Classification Model

Keyless entry systems have made human life more comfortable because the possibility of someone leaving a key is non-existent. However, there is a research opportunity to use Wi-Fi fingerprint localization in an indoor keyless entry system. The use of Wi-Fi as a solution is low cost because of the already hdeployed Wi-Fi access points (AP) around buildings. This study uses ensemble learning to utilize Wi-Fi fingerprints for keyless entry systems in indoor environments. The first step of this research is to obtain the UJIIndoorLoc Data Set, an open-source Wi-Fi fingerprint dataset. The next step is to design and implement a keyless entry system based on Wi-Fi Fingerprint. We compared four ensemble learning methods: Random Forest, AdaBoost, gradient boosting, and XGBoost. We use several metrics to compare the four methods, namely r2, the area under the receiver operating curve (AUROC), and the equal error rate (EER). The test results show that for localization based on ensemble regression, XGBoost has the best performance compared to the other three ensemble methods for both longitude and latitude predictions. The r2values are 0.94 and 0.98, respectively. For authentication based on ensemble classification, the gradient-boosting model performance increases as the number of weak learners increases. The optimum number of weak learners is 60. With AUROC = 0.99, gradient boosting outperforms random forest, AdaBoost, and XGBoost on authentication. Finally, our authentication method has EER = 0.01, which outperforms other state-of-the-art keyless entry systems. This research focuses on local building map datasets for future work.

Automatic Update for Wi-Fi Fingerprinting Indoor Localization via Multi-Target Domain Adaptation

Wi-Fi fingerprinting system in the long term suffers from gradually deteriorative localization accuracy, leading to poor user experiences. To keep high accuracy yet at a low cost, we first study long-term variation of access points (APs) and characteristics of their Wi-Fi signals through over-one-year experiments. Motivated by the experimental findings, we then design MTLoc, a Multi-Target domain adaptation network-based Wi-Fi fingerprinting Localization system. As the core, MTDAN (Multi-Target Domain Adaptation Network) model adopts the framework of generative adversarial network to learn time-invariant, time-specific, and location-aware features from the source and target domains. To enhance the alignment among the source and targets, two-level cycle consistency constraints are proposed. Hence, MTDAN is able to transfer location knowledge from the source domain to multiple targets. In addition, domain selection and outlier detection are designed to avoid explosive growth of storage for targets and to limit the impact of random variations of Wi-Fi signals. Extensive experiments are carried out on five datasets collected over two years in various real-world indoor environments with a total area of 8, 350 m2. Experimental results demonstrate that MTLoc retains high localization accuracy with limited storage and training cost in the long term, which significantly outperforms its counterparts. We share our dataset to the community for other researchers to validate our results and conduct further research.

A Fuzzy Logic-Based Energy-Adaptive Localization Scheme by Fusing WiFi and PDR

Fusing WiFi fingerprint localization and pedestrian dead reckoning (PDR) on smartphones is attractive because of their obvious complementarity in localization accuracy and energy consumption. Although fusion localization algorithms tend to improve localization accuracy, extra hardware and software involved will result in extra computations, such that energy consumption is inevitably increased. Thus, in this study, we propose a novel fusion localization scheme based on fuzzy logic, which aims to achieve ideal localization accuracy by consuming as little energy as possible. Specifically, energy-efficient inertial measurement unit (IMU) sensors are routinely called to provide the displacement of a smartphone user in the manner of PDR, whereas a fuzzy inference system is employed to adaptively schedule energy-hungry WiFi scans to fulfill WiFi fingerprint localization according to a coarse metric for fusion localization errors and the remaining energy of the smartphone, so as to achieve a trade-off between localization accuracy and energy consumption. Moreover, in order to mitigate the effect of drift errors induced by PDR, straight trajectories of the user are further identified using a series of WiFi localization results to calibrate heading estimates of PDR. Extensive experimental results demonstrate that the proposed scheme achieves the same accuracy as the complementary filter, but consumes 38.02% energy than the complementary filter, confirming that the proposed scheme can effectively balance the localization accuracy and energy consumption.

CRCLoc: A Crowdsourcing-Based Radio Map Construction Method for WiFi Fingerprinting Localization

The WiFi-based fingerprint indoor-positioning system has attracted increasing interest from industry and academia, benefiting from the widespread deployment of the wireless local area network (WLAN) infrastructure. However, with the expansion of application scenarios, this system suffers from labor-intensive work for received signal strength (RSS) fingerprint construction. In this article, we propose a crowdsourcing-based radio map construction method and trajectory matching algorithm to solve the time-consuming preliminary fingerprint data collection in the offline phase, where the tedious collection work is replaced by the massive crowdsourcing sensor data. First, the latent information of the map is extracted by using some image processing methods, and all possible routes are obtained simultaneously with the depth-first traversal method. Then, considering the strict restrictions on walking in indoor environments, the crowdsourcing trajectories are produced by matching the result of pedestrian dead reckoning (PDR) with the candidate routes based on the Shape Context algorithm. Further, a crowdsourcing trajectory can be represented by uniformly distributed reference points based on step detection, and each point corresponds to a unique RSS of access points (APs). Since such a fingerprint construction method can be performed while smartphone holders are moving and not aware of their actual positions, it can be referred to the dynamic construction method of the radio map. The real scenario experiments reveal that the proposed solution can significantly reduce the time and manpower consumption to build the radio map. Moreover, compared with traditional schemes, our crowdsourcing-based radio map construction method for the WiFi fingerprinting localization (CRCLoc) system can improve the accuracy and robustness of the indoor-positioning results.

CROWDSOURCED WIFI FINGERPRINT LOCALIZATION IN URBAN CANYON

Abstract. Although Global Navigation Satellite System (GNSS) has achieved success in outdoor localization, it does not often work well in urban canyon, which is due to the weak signals and the loss of satellites. WiFi technology is widely used at present, and the crowdsourced WiFi data has the advantages of rich sources and low cost. Therefore, utilizing the crowdsourced WiFi data for localization may effectively improve the deficiency of GNSS in the urban canyon. In this paper, we propose a novel method of crowdsourced WiFi fingerprint localization in urban canyon. Considering that the crowdsourced data is noisy, discontinuous and unstable, we carry out pre-processes for data refining, and grid-based statistical method for noise smoothing. Then in order to quickly locate the terminals in large-scale area, the AP coverage intersection method is proposed, in which the coverage range, centers and density of all APs are inferred, and the personal hotspots as well as the mobile APs are removed. To further enhance the positioning accuracy, the fine localization is carried out, which is based on the iterative KWNN algorithm. Extensive field tests are carried out in a typical urban canyon, results show that the average positioning error of our method is 16.82 m, which shows the effectiveness of the proposed method for crowdsourced positioning in urban canyon.

Intelligent Perception and Positioning Technology of Internet of Things by K -Nearest Neighbor Matching Algorithm

To study the intelligent sensing and positioning technology of the Internet of Things (IoT) combined with the K -nearest neighbor algorithm, the K -nearest neighbor matching algorithm and optimization algorithm are introduced using the indoor Wi-Fi positioning technology. The study proposes weighting K -nearest neighbor (WKNN) by weighted Euclidean distance, adaptive weighted Euclidean distance K-nearest neighbor Wi-Fi localization algorithm, and optimal K -value Wi-Fi fingerprint localization algorithm. The experimental error is verified. The experimental results show that the lowest error of continuous acquisition of 3 s signal values in experimental environment A is 1.8815 m, which is 10.13% lower than the error of only acquiring 1 s for the same K -value. The lowest error of environment B scheme two can reach 1.8862, which is 7.06% lower than the error of the same K -value. The optimal K -value Wi-Fi fingerprint positioning algorithm by distance constraint has better positioning accuracy than other KNN positioning algorithms, and the positioning fluctuation is smaller. The average positioning error of the optimal K in environment A is 1.2987 m, which is 0.2797 m less than the average of the traditional positioning algorithm. In environment B, the average positioning error of the optimal K is 1.5353 m, which is 0.3253 m less than the average of the traditional positioning algorithm. Therefore, the optimal K -value Wi-Fi positioning algorithm proposed has better performance.

An Indoor Navigation Algorithm Using Multi-Dimensional Euclidean Distance and an Adaptive Particle Filter.

The inertial navigation system has high short-term positioning accuracy but features cumulative error. Although no cumulative error occurs in WiFi fingerprint localization, mismatching is common. A popular technique thus involves integrating an inertial navigation system with WiFi fingerprint matching. The particle filter uses dead reckoning as the state transfer equation and the difference between inertial navigation and WiFi fingerprint matching as the observation equation. Floor map information is introduced to detect whether particles cross the wall; if so, the weight is set to zero. For particles that do not cross the wall, considering the distance between current and historical particles, an adaptive particle filter is proposed. The adaptive factor increases the weight of highly trusted particles and reduces the weight of less trusted particles. This paper also proposes a multidimensional Euclidean distance algorithm to reduce WiFi fingerprint mismatching. Experimental results indicate that the proposed algorithm achieves high positioning accuracy.

A universal Wi-Fi fingerprint localization method based on machine learning and sample differences

Wi-Fi technology has become an important candidate for localization due to its low cost and no need of additional installation. The Wi-Fi fingerprint-based positioning is widely used because of its ready hardware and acceptable accuracy, especially with the current fingerprint localization algorithms based on Machine Learning (ML) and Deep Learning (DL). However, there exists two challenges. Firstly, the traditional ML methods train a specific classification model for each scene; therefore, it is hard to deploy and manage it on the cloud. Secondly, it is difficult to train an effective multi-classification model by using a small number of fingerprint samples. To solve these two problems, a novel binary classification model based on the samples’ differences is proposed in this paper. We divide the raw fingerprint pairs into positive and negative samples based on each pair’s distance. New relative features (e.g., sort features) are introduced to replace the traditional pair features which use the Media Access Control (MAC) address and Received Signal Strength (RSS). Finally, the boosting algorithm is used to train the classification model. The UJIndoorLoc dataset including the data from three different buildings is used to evaluate our proposed method. The preliminary results show that the floor success detection rate of the proposed method can reach 99.54% (eXtreme Gradient Boosting, XGBoost) and 99.22% (Gradient Boosting Decision Tree, GBDT), and the positioning error can reach 3.460 m (XGBoost) and 4.022 m (GBDT). Another important advantage of the proposed algorithm is that the model trained by one building’s data can be well applied to another building, which shows strong generalizable ability.

WiFi Fingerprinting Indoor Localization Based on Dynamic Mode Decomposition Feature Selection with Hidden Markov Model.

Over the years, WiFi received signal strength indicator (RSSI) measurements have been widely implemented for determining the location of a user’s position in an indoor environment, where the GPS signal might not be received. This method utilizes a huge RSSI dataset collected from numerous access points (APs). The WiFi RSSI measurements are nonlinear with distance and are largely influenced by interference in the indoor environment. Therefore, machine learning (ML) techniques such as a hidden Markov model (HMM) are generally utilized to efficiently identify a trend of RSSI values, which corresponds to locations around a region of interest. Similar to other ML tools, the performance and computing cost of the HMM are dependent on the feature dimension since a large quantity of RSSI measurements are required for the learning process. Hence, this article introduces a feature extraction method based on dynamic mode decomposition (DMD) for the HMM to effectively model WiFi fingerprint indoor localization. The DMD is adopted since it decomposes RSSIs to meaningful spatial and temporal forms over a given time. Here, the mode forms are analytically reconstructed to produce low-dimensional feature vectors, which are used with the HMM. The localization performance of the proposed HMM-DMD is compared with other well-known ML algorithms for WiFi fingerprinting localization using simulations. The results show that the HMM-DMD algorithm yields a significant localization performance improvement, accuracy, and reasonable processing time in comparison with the state-of-the-art algorithms.

A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting.

Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed.

CCpos: WiFi Fingerprint Indoor Positioning System Based on CDAE-CNN.

WiFi is widely used for indoor positioning because of its advantages such as long transmission distance and ease of use indoors. To improve the accuracy and robustness of indoor WiFi fingerprint localization technology, this paper proposes a positioning system CCPos (CADE-CNN Positioning), which is based on a convolutional denoising autoencoder (CDAE) and a convolutional neural network (CNN). In the offline stage, this system applies the K-means algorithm to extract the validation set from the all-training set. In the online stage, the RSSI is first denoised and key features are extracted by the CDAE. Then the location estimation is output by the CNN. In this paper, the Alcala Tutorial 2017 dataset and UJIIndoorLoc are adopted to verify the performance of the CCpos system. The experimental results show that our system has excellent noise immunity and generalization performance. The mean positioning errors on the Alcala Tutorial 2017 dataset and the UJIIndoorLoc are 1.05 m and 12.4 m, respectively.

Analysis of Received Signal Strength Quantization in Fingerprinting Localization.

In recent times, Received Signal Strength (RSS)-based Wi-Fi fingerprinting localization has become one of the most promising techniques for indoor localization. The primary aim of RSS is to check the quality of the signal to determine the coverage and the quality of service. Therefore, fine-resolution RSS is needed, which is generally expressed by 1-dBm granularity. However, we found that, for fingerprinting localization, fine-granular RSS is unnecessary. A coarse-granular RSS can yield the same positioning accuracy. In this paper, we propose quantization for only the effective portion of the signal strength for fingerprinting localization. We found that, if a quantized RSS fingerprint can carry the major characteristics of a radio environment, it is sufficient for localization. Five publicly open fingerprinting databases with four different quantization strategies were used to evaluate the study. The proposed method can help to simplify the hardware configuration, enhance security, and save approximately 40–60% storage space and data traffic.

Learning RSSI Feature via Ranking Model for Wi-Fi Fingerprinting Localization

Wi-Fi fingerprinting is widely used in indoor localization due to the ubiquitous availability of Wi-Fi infrastructure in indoor environments. The basic assumption of fingerprinting localization is that the received signal strength indicator (RSSI) distance is consistent with the location distance. However, due to the fluctuation of Wi-Fi signals in indoor environments, the nearest neighbors selected using the RSSI distance may not be those whose corresponding locations are nearest to the target, which could lead to a large localization error. In this paper, we propose a novel fingerprinting method for indoor localization by transforming raw RSSI into features with a learned non-linear mapping function. To learn such mapping function, we design a triple loss function that measures the difference between the rank of RSSI distance and that of location distance. By minimizing the loss function iteratively, we can learn the non-linear mapping function with the gradient boosting regression forest (GBRF) method. Experiments have been conducted in a complex environment and experimental results show that our method outperforms the state-of-the-art methods.

Toward Scalable and Robust Indoor Tracking: Design, Implementation, and Evaluation

Although indoor localization has been studied over a decade, it is still challenging to enable many IoT applications, such as activity tracking and monitoring in smart home and customer navigation and trajectory mining in smart shopping mall, which typically require meter-level localization accuracy in a highly dynamic and large-scale indoor environment. Therefore, this article aims at designing and implementing an adaptive and scalable indoor tracking system in a cost-effective way. First, we propose a zero site-survey overhead (ZSSO) algorithm to enhance the system scalability. It integrates the step information and map constraints to infer user’s positions based on the particle filter and supports the auto labeling of scanned Wi-Fi signal for constructing the fingerprint database without the extra site-survey overhead. Further, we propose an iterative-weight-update (IWU) strategy for ZSSO to enhance system robustness and make it more adaptive to the dynamic changing of environments. Specifically, a two-step clustering mechanism is proposed to delete outliers in the fingerprint database and alleviate the mismatch between the auto-tagged coordinates and the corresponding signal features. Then, an iterative fingerprint update mechanism is designed to continuously evaluate the Wi-Fi fingerprint localization results during online tracking, which will further refine the fingerprint database. Finally, we implement the indoor tracking system in real-world environments and conduct a comprehensive performance evaluation. The field testing results conclusively demonstrate the scalability and effectiveness of the proposed algorithms.

A Probabilistic Approach for WiFi Fingerprint Localization in Severely Dynamic Indoor Environments

Taking advantage of widely deployed access points (AP), WiFi fingerprint based localization is of importance in indoor internet-of-things (IOT) environments. Nevertheless, spatio-temporal variation is one of its intractable problems, indicating severely environmental dynamics and uncertainty of decision. In this case, the localization accuracy drops significantly. In this paper, we attempt to overcome effects of spatio-temporal variations from two aspects: filtering of the training data and selection of partially valuable APs for matching in test phase. The key idea is to match partial unaffected measurements with `clean' unaffected fingerprints. Bayesian framework and category model are presented for WiFi fingerprints. Two binary hidden variables with different dimensions are introduced to identify singular fingerprints and affected measurements respectively by employing expectation-maximization (EM) algorithms. EM based filter and simultaneous AP selection and localization are then proposed to obtain an optimal matching. Experimental results show that our proposed scheme greatly improves the localization accuracy in severely dynamic indoor environments.

Building information aided Wi-Fi fingerprinting positioning system

In Wi-Fi fingerprinting localization systems, good positioning accuracy requires accurate fingerprint databases; however, the collection of accurate is a labor intensive work. Therefore, the collection, calibration and maintenance of fingerprints in Wi-Fi positioning have always been hot research topics. In this paper, a parameterized fingerprint optimization system is proposed to obtain an accurate fingerprint database. The pairs of RSS (received signal strength) and corresponding position are collected through path survey several times, which makes the collection process more efficient but brings more noise. The wall-induced wireless signal propagation theory taking floor plans into account is used to model the APs through the least squares. The predicted values from theory are combined with survey values for de-noising in fingerprint database. An experiment performed at indoor environment shows that compared with the original fingerprints, the positioning accuracy of the optimized fingerprints is improved by 0.6 m on average from many tests.

Low-Overhead WiFi Fingerprinting

WiFi-fingerprint localization is recognized as a promising indoor localization technique. However, it suffers from high implementation overhead such as heavy initial training and fingerprint map maintenance overtime. In this paper, we present the design, implementation, and evaluation of AP-Sequence. It is a fingerprint-based localization system that achieves extremely low overhead in fingerprint map construction and maintenance. AP-Sequence achieves this by treating a scan from any reference locations as an input to adjust a large portion of the fingerprint map. The power of AP-Sequence comes from dynamic region partitioning mechanism generating a fingerprint based on relative RSS values. AP-Sequence offers several advantages over existing methods with respect to robustness against environment noises, ability to handle dynamic power control, and mobile device heterogeneity. We have implemented AP-Sequence on an Android platform. Experiment results with over one month of evaluation demonstrate that our design achieves an average localization accuracy of 4-7.6 m over an extended time period with low-overhead in fingerprint map construction and maintenance.