Received Signal Strength Fingerprints Research Articles

Wi‐Fi fingerprint‐based indoor location: Multiple fingerprints and multiple classifiers with two‐layer fusion weights

SummaryIndoor location based on Wi‐Fi fingerprint has attracted extensive attention recently, since it only requires a mobile device and existing network; it does not require additional infrastructure and hardware. However, localization using only received signal strength fingerprint is susceptible to dynamic environments and device heterogeneity. Localization using signal strength differences fingerprint and hyperbolic location fingerprint which extracted from received signal strength fingerprint is a powerful measure to overcome the device heterogeneity. Existing fusion‐based methods do not fully use of the mutual fusion function of multiple fingerprints, which leads to low positioning accuracy. In order to further improve the positioning accuracy, we propose a multifingerprint multiclassifier and two‐layer fusion weight positioning model. The proposed approach first creates a multiple fingerprints group by gleaning signal strength differences fingerprint and hyperbolic location fingerprint from received signal strength fingerprint. In order to deal with the accuracy decline caused by a single classifier in complex scenes, three typical classifiers, random forest (RF), K‐nearest neighbor (KNN), and support vector machine (SVM), are adopted, and a fusion weight selection algorithm is proposed to improve the localization accuracy by choosing fusion weights intelligently from the two‐layer fusion weights. Experiment results show that the proposed scheme is effective to resolve device heterogeneity, and the position accuracy is improved about 16.9% and 5.8%, respectively, compared with single fingerprint and classifier schemes. The results show that, compared with the single fingerprint scheme, the position accuracy is improved at least 16.9% and 28.4% for the homogeneous and heterogeneous equipments, respectively; as for the proposed multiclassifier fusion method, it achieves at least 5.8% and 8.7% accuracy improvement for the cases with homogeneous and heterogeneous equipments, respectively, compared with other traditional classifiers such as RF, KNN, and SVM.

Simplified Indoor Localization Using Bluetooth Beacons and Received Signal Strength Fingerprinting with Smartwatch.

Variations in Global Positioning Systems (GPSs) have been used for tracking users' locations. However, when location tracking is needed for an indoor space, such as a house or building, then an alternative means of precise position tracking may be required because GPS signals can be severely attenuated or completely blocked. In our approach to indoor positioning, we developed an indoor localization system that minimizes the amount of effort and cost needed by the end user to put the system to use. This indoor localization system detects the user's room-level location within a house or indoor space in which the system has been installed. We combine the use of Bluetooth Low Energy beacons and a smartwatch Bluetooth scanner to determine which room the user is located in. Our system has been developed specifically to create a low-complexity localization system using the Nearest Neighbor algorithm and a moving average filter to improve results. We evaluated our system across a household under two different operating conditions: first, using three rooms in the house, and then using five rooms. The system was able to achieve an overall accuracy of 85.9% when testing in three rooms and 92.106% across five rooms. Accuracy also varied by region, with most of the regions performing above 96% accuracy, and most false-positive incidents occurring within transitory areas between regions. By reducing the amount of processing used by our approach, the end-user is able to use other applications and services on the smartwatch concurrently.

Received signal strength reconstruction using pix2pix generative adversarial network

AbstractPositioning technology based on received signal strength (RSS) fingerprints has emerged as a vital research topic in indoor localization due to its high accuracy and low cost. Increasing the density of offline fingerprints collected can effectively improve localization accuracy in the online stage, but it will result in astronomical collection costs. RSS reconstruction using generative models is an efficient solution to dramatically reduce fingerprint collection costs while maintaining excellent accuracy. In this paper, the authors propose a pix2pix generative adversarial network based RSS reconstruction model, which can generate a dense RSS fingerprint map based on only the location information of the wireless access point. The accuracy and efficiency of the proposed model are verified in an indoor environment.

SE-Loc: Security-Enhanced Indoor Localization With Semi-Supervised Deep Learning

Wireless indoor localization has become unavoidable for industrial indoor location-based services. Given the ubiquitous deployment of wireless access points (APs), WiFi fingerprinting of Received Signal Strength (RSS) has been widely adopted for indoor localization. Meanwhile, existing RSS fingerprint-based methods lack security-oriented considerations and are vulnerable to malicious attacks. When security vulnerabilities are exploited, mobile users may confront indoor localization mismatches, faults and even localization system failures. In this paper, we propose SE-Loc, a semi-supervised learning-based technique to enhance security and resiliency of fingerprint-based localization. The architecture of SE-Loc consists of two parts: (1) a correlation-based AP selection for processing RSS fingerprints and fingerprint-image generation, and (2) a deep learning model based on a denoising autoencoder and convolutional neural networks for robust feature learning and location matching. Extensive experiments show that under potential AP attacks, SE-Loc demonstrates superior performance on indoor localization over the state-of-the-art methods. With up to 100 malicious attacking APs in the UJIIndoorLoc edge server, SE-Loc can still achieve the lowest error fluctuation of 1.7 m and the highest average localization accuracy of 8.9 m.

WiFi round-trip time (RTT) fingerprinting: an analysis of the properties and the performance in non-line-of-sight environments

ABSTRACT Indoor positioning systems based on WiFi round-trip time (RTT) measurement were reported to deliver sub-metre-level accuracy using trilateration, under ideal indoor conditions. However, the performance of WiFi RTT positioning in complex, non-line-of-sight environment remains an open research question. To this end, this article investigates the properties of WiFi RTT in several real-world indoor environments on heterogeneous smartphones. We present three datasets collected on a large-scale building floor, an office room and an apartment. The datasets contain both RTT and received signal strength (RSS) signal measures with correct ground-truth labels for further research. Our results indicated that in a complex indoor environment, RTT fingerprinting system delivered an accuracy of 0.6 m which was 107% better than traditional RSS fingerprinting and 6 m better than RTT trilateration which failed to deliver sub-metre accuracy as claimed.

Pedestrian Positioning Using an Enhanced Ensemble Transform Kalman Filter.

Due to the unavailability of GPS indoors, various indoor pedestrian positioning approaches have been designed to estimate the position of the user leveraging sensory data measured from inertial measurement units (IMUs) and wireless signal receivers, such as pedestrian dead reckoning (PDR) and received signal strength (RSS) fingerprinting. This study is similar to the previous study in that it estimates the user position by fusing noisy positional information obtained from the PDR and RSS fingerprinting using the Bayes filter in the indoor pedestrian positioning system. However, this study differs from the previous study in that it uses an enhanced state estimation approach based on the ensemble transform Kalman filter (ETKF), called QETKF, as the Bayes filer for the indoor pedestrian positioning instead of the SKPF proposed in the previous study. The QETKF estimates the updated user position by fusing the predicted position by the PDR and the positional measurement estimated by the RSS fingerprinting scheme using the ensemble transformation, whereas the SKPF calculates the updated user position by fusing them using both the unscented transformation (UT) of UKF and the weighting method of PF. In the field of Earth science, the ETKF has been widely used to estimate the state of the atmospheric and ocean models. However, the ETKF algorithm does not consider the model error in the state prediction model; that is, it assumes a perfect model without any model errors. Hence, the error covariance estimated by the ETKF can be systematically underestimated, thereby yielding inaccurate state estimation results due to underweighted observations. The QETKF proposed in this paper is an efficient approach to implementing the ETKF applied to the indoor pedestrian localization system that should consider the model error. Unlike the ETKF, the QETKF can avoid the systematic underestimation of the error covariance by considering the model error in the state prediction model. The main goal of this study is to investigate the feasibility of the pedestrian position estimation for the QETKF in the indoor localization system that uses the PDR and RSS fingerprinting. Pedestrian positioning experiments performed using the indoor localization system implemented on the smartphone in a campus building show that the QETKF can offer more accurate positioning results than the ETKF and other ensemble-based Kalman filters (EBKFs). This indicates that the QETKF has great potential in performing better position estimation with more accurately estimated error covariances for the indoor pedestrian localization system.

Two-stage clustering for improve indoor positioning accuracy

High accurate positioning, as a key factor for the most resource management algorithms, has attracted a lot of attention due to its crucial role in some new applications such as smart factories, IoT, and autonomous cars. Indoor positioning, as another major category of positioning in addition to outdoor positioning, is a very complicated process and achieving high accuracy is a tough process. This paper proposes a two-stage clustering-based approach (TSCA) for indoor positioning to deal with highly complex data sets which was collected from a large-scale indoor radio system. In the first stage, a new clustering algorithm, called group matching method is developed, which divides the dataset into several groups (sub-datasets) according to the different reference signal received power (RSRP) values of the real-world dataset. In the second stage, KNN and its variants, are used to match and evaluate the location of each device in one of sub-datasets instead of the entire dataset, which can increase the accuracy of the positioning. This method can perfectly solve the problem of uneven distribution of reference point data in the process of data acquisition, which is a popular challenge for most real scene data acquisition. The proposed method is compared with several state-of-the-art ML methods such as Support Vector Regression (SVR), and clustering methods such as K-means. The results indicate a high positioning accuracy improvement of more than 55% compared to a modified KNN method use our own RSRP fingerprint dataset, and a 2D accuracy improvement of 13.36% and a 3D accuracy improvement of 10.3% compared to a traditional KNN method use a Wi-Fi Received Signal Strength fingerprint.

Cramér–Rao Lower Bound Analysis of Differential Signal Strength Fingerprinting for Crowdsourced IoT Localization

Crowdsourcing is considered an efficient and promising paradigm for constructing large-scale signal fingerprint radio maps due to the proliferation of Wi-Fi-enabled devices. However, a crowdsourced Indoor Positioning System (IPS) has to handle diverse devices and the inherent heterogeneity in Received Signal Strength (RSS) measurements. To address the device heterogeneity problem, differential fingerprinting methods have been explored, which mitigate the device characteristics that cause RSS from different commercial devices to report differently. In this paper, we focus on Mean Differential Fingerprinting (MDF) that produces the differential fingerprints by subtracting the mean RSS value of all APs from the original RSS fingerprints. We study the localization performance of the MDF method by means of the Cramér-Rao Lower Bound (CRLB) and show analytically that it outperforms another method that addresses device diversity. Furthermore, we evaluate the localization accuracy of existing solutions using real-life Wi-Fi RSS datasets collected by multiple consumer devices. The experimental results confirm our analytical findings and demonstrate the effectiveness of the MDF method to mitigate device diversity, as well as other factors that affect the RSS readings including the device carrying mode and power control schemes of the Wi-Fi infrastructure, thus contributing to the wider adoption of crowdsourced IPS.

Indoor Localization With Wireless Heterogeneous Devices by Composite Fingerprint Sets and Hybrid Classification

Wi-Fi fingerprint-based indoor localization has recently attracted significant research interest since Wi-Fi devices are widely deployed and practical, and no additional infrastructure is required. However, the Received Signal Strength (RSS) is significantly different on heterogeneous devices, and this difference has a negative impact on localization results. In this paper, we propose a multi-fingerprint and multi-classifier fusion (MFMCF) localization method to improve the localization accuracy and solve the problem of heterogeneous hardware. First, the individual feature set of original RSS fingerprint, signal strength difference (SSD) fingerprint and hyperbolic location fingerprint (HLF) are fused as a composite fingerprint set (CFS), and then the data dimension is reduced by linear discriminant analysis (LDA). Second, three representative machine learning algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF) are selected and trained to construct an integrated fusion model. Finally, in order to get more accurate predictions, in the online phase, a selective strategy based on entropy is proposed by calculating the entropy of each classifier’s prediction result. Experiments show that MFMCF is an effective scheme to solve the localization problem of heterogeneous devices and improve the localization accuracy.

MULTIPLE RSS FINGERPRINT BASED INDOOR LOCALIZATION IN RIS-ASSISTED 5G WIRELESS COMMUNICATION SYSTEM

Abstract. The received signal strength (RSS) fingerprint based localization is a widely used technique for location estimation in the indoor environment with the fifth generation (5G) wireless communication. However, the RSS feature is easily affected by the noise and other variations of the propagation channel, thus limiting the localization accuracy. In this paper, we propose a multiple RSS fingerprint based localization scheme in the reconfigurable intelligent surface (RIS) assisted system, where the RSS values under different RIS configurations are leveraged as the fingerprints. However, it is challenging to set the favorable RIS configurations. To tackle this challenge, we design an optimization method based on Cramér-Rao Lower Bound (CRLB) to derive the optimal RIS configurations to achieve a robust and accurate location estimation, where the CRLB is minimized, and projected gradient descent (PGD) method is applied to solve this optimization problem. After the fingerprints are collected, deep neural network (DNN) is employed for location estimation. Simulation results reveal that the proposed scheme performs well in terms of localization accuracy and stability.

Exploiting temporal dependency of RSS data with deep learning for IoT‐oriented wireless indoor localization

With the ubiquitous demand for indoor location‐based services and the pervasive deployment of Wi‐Fi hotspots, wireless indoor localization has been widely studied by utilizing various Wi‐Fi signal measurements. Most existing schemes leverage the Received Signal Strength (RSS) of Wi‐Fi to conduct cost‐efficient indoor localization. However, the RSS data are not only prone to multi‐path effects, but also sensitive to time‐varying environmental dynamics, making it quite daunting to achieve robust indoor localization. In contrast to existing solutions that focus on spatial features of RSS, in this article, we exploit the temporal dependency of RSS time‐series data by integrating the Kalman filter with deep neural networks. In particular, to tame time‐varying noises and preserve valuable temporal features in RSS measurements, we propose a time‐varying RSS filtering algorithm based on the Kalman filter and a refined post‐processing module. Moreover, a deep learning model based on deep neural network (DNN) is further utilized for effective feature extraction on one‐dimension RSS fingerprints. The experiment results show that the proposed Kalman‐DNN model improves at least 25% localization accuracy in comparison with conventional DNN model. Furthermore, with the localization time as 0.02 ms, the Kalman‐DNN model outperforms the Kalman‐CNN model in localization accuracy by at least 10%.

EdgeLoc: A Robust and Real-Time Localization System Toward Heterogeneous IoT Devices

Indoor localization has become an essential demand driven by indoor location-based services (ILBSs) for mobile users. With the rising of Internet of Things (IoT), heterogeneous smartphones and wearables have become ubiquitous. However, the ILBSs for heterogeneous IoT devices confront significant challenges, such as received signal strength (RSS) variances caused by hardware heterogeneity, multipath reflections from complex environments, and localization time restricted by computation resources. This article proposes EdgeLoc, a robust and real-time indoor localization system toward heterogeneous IoT devices to solve the above challenges. In particular, the RSS fingerprinting data of Wi-Fi is employed for localization and tackling the heterogeneity of IoT devices in twofold. First, feature-level and signal-level solutions are presented to address the random RSS variances. At the feature level, this work proposes a novel capsule neural network model to efficiently extract incremental features from RSS fingerprinting data. At the signal level, a multistep dataflow is further devised to process RSS fingerprints into image-like data, which utilizes the feature matrix to reduce absolute sensing errors introduced by hardware heterogeneity. Second, an edge-IoT framework is designed to utilize the edge server to train the deep learning model and further supports real-time localization for heterogeneous IoT devices. Extensive field experiments with over 33 600 data points are conducted to validate the effectiveness of EdgeLoc with a large-scale Wi-Fi fingerprint data set. The results show that EdgeLoc outperforms the state-of-the-art SAE-CNN method in localization accuracy by up to 14.4%, with an average error of 0.68 m and an average positioning time of 2.05 ms.

How to Get the Best Out of Your Fingerprint Database: Hierarchical Fingerprint Indoor Positioning for Databases With Variable Density

In this paper, we consider wireless positioning using Received Signal Strength (RSS) fingerprinting. To obtain good accuracy, this technique requires a database containing a high density of up-to-date fingerprints. However, as acquiring fingerprints through training is labor intensive and the indoor topology is subject to changes, a high density fingerprint database cannot always be obtained. On the other hand, the time to retrieve data from a database with high density can be too high for real-time positioning. To tackle these issues, we introduce the Hierarchical Positioning Algorithm (HPA). In this algorithm, we divide the database into a number of sub-databases with different densities, each containing a sufficiently small number of fingerprints to reduce the data retrieval time. The algorithm starts with a coarse estimate at the highest level, and gradually improves the accuracy in going to the lowest level. This HPA technique requires the construction of sub-databases containing fingerprints that are properly selected to obtain the wanted level of accuracy. This paper considers two algorithms to construct the database: the Minimum Distance Algorithm (MDA) to select the reference points, and the Local Gaussian Process (LGP) algorithm to determine the RSS values at the selected reference points. Simulation results show that the hierarchical algorithm, combined with MDA and LGP to construct the sub-databases, is a fast algorithm that can achieve high accuracy, even with a database having a variable density of fingerprints.

A Novel Indoor Fingerprint Localization System Based on Distance Metric Learning and AP Selection

A desirable fingerprint-based indoor localization (FIL) system aims to achieve an accurate positioning result within an acceptable time consumption, which is still challenging for application. Building a practical FIL system is a composite task of feature extraction and location estimation, resulting in related methods that is often hard to consider both the positioning accuracy and time consumption. This article proposes a novel FIL system that uses a combination of distance metric learning (DML) and access point (AP) selection method to tradeoff the positioning accuracy and time consumption. Specially, we first abstract the localization process to develop a mathematical model from the perspective of probability theory and reveal the significant impact of the received signal strength (RSS) similarity comparison on FIL. Then, we propose a perturbation theory-based AP selection method to select the best-position-discrimination AP subset from all to reduce the positioning time consumption. Meanwhile, we propose a DML-based method to extract the RSS distribution which involves the indoor environmental information, and further use it in RSS fingerprint similarity comparison to improve the positioning accuracy. We introduce the signal path-loss model into the proposed method for training to get the best similarity metric function. Finally, experimental results show that both the positioning accuracy and the time consumption are comparatively improved in the online phase by the proposed FIL system.

Triple-Coil-Structure-Based Coil Positioning System for Wireless EV Charger

In this article, a triple-coil-based coil positioning approach is proposed for the wireless electric vehicle charger to realize the accurate coil positioning with low electromagnetic field. Based on the working principle of the proposed triple-coil architecture and the discussion of the sensing-coil configuration, the received signal strength (RSS) equations are derived, revealing the way of enhancing the positioning signals by taking advantage of the quality factor (Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sub> ). Thereafter, an RSS fingerprint positioning algorithm is proposed for obtaining coordinates in x-, y-, and z-direction. Specific considerations are also given for designing the exciting frequency and the coil parameters. Finally, the proposed positioning system is tested on a 3.3-kW wireless charger. The RSS of the proposed positioning system has been proved to be 3.2 times as that of the state-of-the-art one, whereas the excitation current is reduced by more than half. A number of positioning cases, with the targeted coil placed in an area of 32 cm × 32 cm, are tested in the lab. The experimental results show that more than 95% of these test cases have been well positioned with test errors less than 1.5 cm.

Error model and simulation for multisource fusion indoor positioning

Seamless positioning services are of a critical concern in building smart cities. In a multisource fusion indoor positioning system, providing the guidance information for the deployment of positioning sources is a key technology, which can optimize the infrastructure resources to provide higher positioning accuracy. The error models of single-source positioning such as the received signal strength (RSS) fingerprint and the pedestrian dead reckoning (PDR) should be extended to meet the requirement of multisource indoor positioning for positioning error estimation. This paper proposes a model that combines the RSS fingerprint and PDR positioning error models for fusion positioning error simulation, which weights the PDR and RSS fingerprint positioning results and calculates the mean square error for the fusion positioning according to their positioning variances. This model is also used to establish an indoor positioning simulation system. To validate the proposed model, an experiment is performed which compared the actual positioning errors using the fusion positioning with the errors of the simulate model. The results show that the actual positioning error curves and the error curve predicted by the model are consistent. As a result, the proposed error model provides a solution for optimizing the deployment of positioning sources.

Multi-Floor Indoor Pedestrian Dead Reckoning with a Backtracking Particle Filter and Viterbi-Based Floor Number Detection.

We present a smartphone-based indoor localisation system, able to track pedestrians over multiple floors. The system uses Pedestrian Dead Reckoning (PDR), which exploits data from the smartphone’s inertial measurement unit to estimate the trajectory. The PDR output is matched to a scaled floor plan and fused with model-based WiFi received signal strength fingerprinting by a Backtracking Particle Filter (BPF). We proposed a new Viterbi-based floor detection algorithm, which fuses data from the smartphone’s accelerometer, barometer and WiFi RSS measurements to detect stairs and elevator usage and to estimate the correct floor number. We also proposed a clustering algorithm on top of the BPF to solve multimodality, a known problem with particle filters. The proposed system relies on only a few pre-existing access points, whereas most systems assume or require the presence of a dedicated localisation infrastructure. In most public buildings and offices, access points are often available at smaller densities than used for localisation. Our system was extensively tested in a real office environment with seven 41 m × 27 m floors, each of which had two WiFi access points. Our system was evaluated in real-time and batch mode, since the system was able to correct past states. The clustering algorithm reduced the median position error by in real-time and in batch mode, while the floor detection algorithm achieved a and floor number accuracy in real-time and batch mode, respectively.

An Efficient Indoor Positioning Method Based on Wi-Fi RSS Fingerprint and Classification Algorithm.

Wi-Fi received signal strength (RSS) fingerprint-based indoor positioning has been widely used because of its low cost and universality advantages. However, the Wi-Fi RSS is greatly affected by multipath interference in indoor environments, which can cause significant errors in RSS observations. Many methods have been proposed to overcome this issue, including the average method and the error handling method, but these existing methods do not consider the ever-changing dynamics of RSS in indoor environments. In addition, traditional RSS-based clustering algorithms have been proposed in the literature, but they make clusters without considering the nonlinear similarity between reference points (RPs) and the signal distribution in ever-changing indoor environments. Therefore, to improve the positioning accuracy, this paper presents an improved RSS measurement technique (IRSSMT) to minimize the error of RSS observation by using the number of selected RSS and its median values, and the strongest access point (SAP) information-based clustering technique, which groups the RPs using their SAP similarity. The performance of this proposed method is tested by experiments conducted in two different experimental environments. The results reveal that our proposed method can greatly outperform the existing algorithms and improve the positioning accuracy by 89.06% and 67.48%, respectively.

Pseudo Label-Driven Federated Learning-Based Decentralized Indoor Localization via Mobile Crowdsourcing

Received signal strength (RSS) fingerprint-based indoor localization has received increasing popularity over the past decades. However, it suffers from the high calibration effort for fingerprint collection. In this paper, a C entralized indoo R localizatio N method using P seudo-label (CRNP) is proposed, which employs a small set of labeled data (RSS fingerprint) along with large volumes of unlabeled data (RSS values without coordinates) to reduce the workload of labeled data collection and improve the indoor localization performance. However, the rich location data is large in quantity and privacy sensitive, which may lead to high network cost (i.e., data transmission cost, data storage cost) and potential privacy leakage for data transmission to the central server. Therefore, a decentralized indoor localization method incorporating CRNP and federated learning is devised, which keeps the location data on local users’ devices and improves the shared CRNP model by aggregating users’ updates of the model. The experiment results demonstrate that (i) the proposed CRNP enables to improve the indoor localization accuracy by using unlabeled crowdsourced data; (ii) the designed decentralized scheme is robust to different data distribution and is capable to reduce the network cost and prevent users’ privacy leakage.

A Semi-Simulated RSS Fingerprint Construction for Indoor Wi-Fi Positioning

Fingerprinting-based Wi-Fi positioning has increased in popularity due to its existing infrastructure and wide coverage. However, in the offline phase of fingerprinting positioning, the construction and maintenance of a Received Signal Strength (RSS) fingerprint database yield high labor. Moreover, the sparsity and stability of RSS fingerprinting datasets can be the most dominant error sources. This work proposes a minimally Semi-simulated RSS Fingerprinting (SS-RSS) method to generate and maintain dense fingerprints from real spatially coarse RSS acquisitions. This work simulates dense fingerprints exploring the cosine similarity of the directions to Wi-Fi access points (APs), rather than only using either a log-distance path-loss model, a quadratic polynomial fitting, or a spatial interpolation method. Real-world experiment results indicate that the semi-simulated method performs better than the coarse fingerprints and close to the real dense fingerprints. Particularly, the model of RSS measurements, the ratio of the simulated fingerprints to all fingerprints, and a two dimensions (2D) spatial distribution have been analyzed. The average positioning accuracy achieves up to 1.01 m with 66.6% of the semi-simulation ratio. The SS-RSS method offers a solution for coarse fingerprint-based positioning to perform a fine resolution without a time-consuming site-survey.