Location Fingerprinting Research Articles

A Federated Learning Framework for Fingerprinting-Based Indoor Localization in Multibuilding and Multifloor Environments

The participatory nature of federated learning (FL) makes it attractive for fingerprinting-based indoor localization in multibuilding and multifloor environments. A group of sensing clients can collaboratively leverage their private, local fingerprint data to help their edge server update a location prediction model. However, it is challenging to jointly handle the two involved issues, i.e., building-floor classification (BFC) and latitude–longitude regression (LLR), in a wide 3-D space through enabling FL on decentralized yet heterogeneous data and over an imperfect wireless network. In this article, we confront these challenges and propose an FL framework, FedLoc3D, for both BFC and LLR. Specifically, the former issue is addressed by an FedDSC-BFC approach, which generates a multilabel classification model based on a convolutional neural network with depthwise separable convolutions. The latter issue is addressed by an FedADA-LLR approach, which develops a multitarget regression model based on a deep neural network with autoencoder and data augmentation. Extensive experiments on a real-world data set of WiFi fingerprints are carried out, and our approaches with enhanced capabilities of feature extraction, generalization, and convergence are validated to improve both localization accuracy and learning efficiency under data heterogeneity and network instability.

An Enhanced Indoor Positioning Solution Using Dynamic Radio Fingerprinting Spatial Context Recognition

Radio fingerprinting positioning is widely used for smartphone location-based services and Internet of Things applications given its high availability and low cost. Radio fingerprinting-based algorithms, however, are subject to the forced matching problem and often yield estimated positions even when a user is actually located outside of the fingerprint region. A positioning solution in multistory buildings should be able to locate positions accurately on the current floor; but these methods may generate unreasonable positioning trajectories, such as irrationally passing through a wall, when fingerprinting positioning is fused the with inertial measurement unit to further improve the accuracy. A radio map must be surveyed dynamically on-the-move, as a dynamic fingerprint, to reduce the time costs and on-site workload. Unlike static fingerprint-based methods, dynamic fingerprinting samples are sparse. Thus, we propose an enhanced indoor positioning solution using spatial context knowledge, extracted from the sparse dynamic fingerprints. In the offline stage of radio map calculation, we extract the dynamic fingerprint features and store them in a spatial features database to reduce the computational time and storage space complexity. The proposed floor detection, region recognition, and path correction algorithms identify the online spatial contexts from the stored spatial features to improve positioning performance. This solution was applied on smartphones combining WiFi and Bluetooth low-energy radio signals in two typical scenarios. The experimental results show that the floor detection accuracy reached 99% while region recognition accuracy reached 90.75%. The positioning path correction method enhances the accuracy of smartphone indoor positioning from 3.27 to 2.56 m.

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.

Low-Cost Indoor Wireless Fingerprint Location Database Construction Methods: A Review

The fingerprint positioning has achieved remarkable results in indoor localization tasks, but the method usually relies on a large amount of fingerprint data to build a fingerprint database, and the amount and diversity of fingerprint data will directly affect the effectiveness of fingerprint positioning. Since fingerprint acquisition is limited and disturbed by space and time, it consumes a lot of labor and time costs to collect fingerprint data in the localization environment, and wireless fingerprint data is time-sensitive and environment-dependent, and changes in the localization environment will reduce the usability of the existing fingerprint database. The complex and repetitive fingerprint acquisition work seriously affects the feasibility of practical deployment of fingerprint positioning systems in the positioning environment. Therefore, the study of low-cost wireless fingerprint database construction methods has become an inevitable part of promoting the widespread deployment of indoor fingerprint positioning systems. In this paper, we introduce the traditional data augmentation-based approach and the advanced machine learning model-based approach, systematically presenting the underlying models and algorithms of both. The former reviews the application of two traditional data enhancement methods, namely channel propagation models and interpolation or regression, to the construction of low-cost wireless fingerprint databases, while the latter taps into techniques for reducing the cost of fingerprint database construction by combining generative adversarial networks and small-sample learning models with the indoor localization domain. Finally, we discuss the current challenges and future research directions for achieving high-performance indoor localization based on low-cost wireless fingerprint databases, and suggest some useful research guidelines.

A WiFi Indoor Location Tracking Algorithm Based on Improved Weighted K Nearest Neighbors and Kalman Filter

The Weighted K Nearest Neighbor (WKNN) algorithm is a widely adopted lightweight methodology for indoor WiFi positioning based on location fingerprinting. Nonetheless, it suffers from the disadvantage of a fixed K value and susceptibility to incorrect reference point matching. To address this issue, we present a novel algorithm in this paper, referred to as Static Continuous Statistical Characteristics-Soft Range Limited-Self-Adaptive WKNN (SCSC-SRL-SAWKNN). Our algorithm not only takes into account location tracking in the motion state but also exploits the continuous statistical features of extended periods of inactivity to enhance localization. In the motion state, we initially employ the adaptive WKNN (SAWKNN) algorithm to determine the optimal K value, followed by the employment of the Soft Range Limited KNN (SR-KNN) algorithm to identify the correct reference point and ultimately estimate the position. When a prolonged stationary state is detected, we first utilize the moving window method to obtain a more stable position fingerprint (SCSC), and then proceed with the positioning process in the same motion state. Ultimately, we use Kalman filter to generate the location trajectory. Our experimental findings demonstrate that the proposed SCSC-SRL-SAWKNN algorithm outperforms traditional WKNN, SAWKNN, and SRL-KNN techniques in terms of localization accuracy and location trajectory. Specifically, the localization accuracy of our algorithm is 56.7% and 36.6% higher than that of traditional WKNN in the static state and overall situation, respectively.

Neural Networks-Based Wi-Fi/PDR Indoor Navigation Fusion Methods

Pedestrian dead-reckoning (PDR) technique has high accuracy in short-term positioning and is little affected by environments, but its cumulative error makes long-term positioning infeasible. Received signal strength (RSS) fingerprint positioning is immune to the cumulative error, but its poor radio signal propagation affected by indoor environments leads to a large positioning error. The extended Kalman filter (EKF) is widely applied to improve the two methods, but the approximation of nonlinearity still causes a large localization error. In this study, we propose a new approach based on neural networks for the Wi-Fi/PDR positioning fusion. The nonlinear mapping capability of back-propagation (BP) neural network is used to correct the EKF nonlinear approximation errors. Unlike the commonly used PDR algorithm, a series of pedestrian historical motion states are applied to train the long short-term memory (LSTM) and then make predictions. This method can significantly reduce cumulative errors in PDR. On-site experiments demonstrate that the proposed method achieves an average localization error of 1.18 m with 73% of the errors under 2 m, which outperforms EKF and unscented Kalman filter (UKF) by approximately 45% under the same test environment. Moreover, another fusion method using LSTM localization results as a priori information is described in this article. Different from the fusion at the resulting level of the previous method, this second method achieves the fusion at the algorithm level by limiting the Wi-Fi reference point (RP) search range through the LSTM localization results to reduce training and positioning time.

Position Estimation of Radio Source Based on Fingerprinting With Physical Wireless Parameter Conversion Sensor Networks

Establishing a highly accurate positioning of radio sources for radio wave monitoring and frequency spectrum sharing is attracting considerable attention. Because the positioning method generally requires specific processing of the position target, it is not applicable when the position target cannot perform any processing. The location fingerprinting method uses multiple sensors to observe the received signal strength indication (RSSI) emitted by a radio source and estimates the position from the radio wave propagation characteristics. This does not require a certain process for the target position. However, it takes time to gather RSSI from many sensors by wireless communication. In this study, we propose an RSSI gathering method using physical wireless parameter conversion sensor networks (PhyC-SN) for the high-speed positioning of radio sources. In the proposed method, each sensor selects the radio carrier frequency corresponding to its measured RSSI and transmits the signal. Projecting the RSSI distribution of each sensor onto the frequency distribution of the received signal enables the center to detect the RSSI of multiple sensors simultaneously. Furthermore, to improve the gathering accuracy, we established a sensor group method by considering the regional characteristics and access timing control based on each sensor group. Computer simulations and experimental evaluations show that the proposed method significantly reduces the data-gathering time compared with conventional packet communications and achieves a high positioning accuracy.

De-Correlation Neural Network for Synchronous Implementation of Estimation and Secrecy

Intelligent equipment within the Internet of things (IoT) carries out massive, frequent, and persistent data communication, making privacy protection particularly critical. Different from regular encryption methods or neural networks, this study proposes a de-correlation neural network (DeCNN) which synchronously realizes the estimation and privacy protection by a comprehensive loss function. In addition, a two-stage learning algorithm is utilized for solution optimization and computation enhancement. The DeCNN is deployed in the deep fingerprint positioning, and the experimental results demonstrate that the proposed method decreases the maximal correlation coefficient between the transmission data and target data from 0.95 to 0.34 (and 0.13) when the positioning error reaches 1.31 m (and 2.88 m).

BLS-Location: A Wireless Fingerprint Localization Algorithm Based on Broad Learning

With the rapid growth in the demand for location-based services in indoor environments, wireless fingerprint localization has attracted increasing attention because of its high precision and easy implementation. However, an effective method does not exist owing to the problems of data loss, noise interference in the fingerprint database, and being time-consuming during the offline training phase. Therefore, this paper presents a novel indoor wireless fingerprint localization algorithm, termed BLS-Location, based on a broad learning system (BLS) that utilizes channel state information (CSI) to overcome the aforementioned problems. It includes an offline training phase and an online localization phase. In the offline training phase, the Kalman filter and the expectation-maximization (EM) algorithm are utilized for completing and denoising the data. Moreover, principal component analysis (PCA) is used to reconstruct the CSI data to reduce complexity and train the weights by BLS. In the online localization phase, we employ a novel probabilistic method based on the regression results of BLS to obtain the estimated location. The experimental results show that BLS-Location can significantly reduce the training time with a high accuracy, compared to several machine learning algorithms and four existing methods in two representative indoor environments.

Multifeature-Based Outdoor Fingerprint Localization With Accuracy Enhancement for Cellular Network

Multifeature-Based Outdoor Fingerprint Localization With Accuracy Enhancement for Cellular Network

Comparative Study of Fingerprint-Based Gender Identification

Gender identification is a need in forensics investigation. In addition to their use for identification, fingerprints are used for gender identifications. In this work, we propose a model for fingerprint gender classification focusing on a small dataset with imbalanced classes. First, we applied denoising and equalization filters to the fingerprint images. Then, we cropped the image into a region of interest. After preprocessing the fingerprint image, we extracted fast Fourier transform (FFT) and principal component analysis (PCA) features, and we applied the min-max normalization. We train our models with each of them as well as with their fusion. To balance the initially imbalanced datasets, we used two sampling techniques: random sampling and synthetic minority oversampling Technique (SMOTE). Our comparison of the receiver operating characteristic (ROC) of K-nearest neighbours (KNN), Support Vector Machine (SVM), Decision Tree, and forward neural network showed that the best result for the area under the ROC Curve is given by the SVM classifier. Next, we compared the SVM classifier accuracy for each fingerprint position with FFT features, PCA features, and their fusion. In addition, we applied both random hybrid sampling and SMOTE sampling. We applied our method on the National Institute of Standards and Technology (NIST) special database, 4 gray scale images of fingerprint image. We showed that the fusion of FFT and PCA features gives the best gender identification accuracy when the SMOTE dataset balancing technique is applied. We found that the right ring finger was more appropriate with 75.55% of correct male gender identifications and 91.62% female gender identification. Further investigation into dataset balancing techniques will be applied in the future.

Wireless positioning based on hierarchical symbolic dynamic filtering of RSSI time series

Hierarchical symbolic dynamic filtering (HSDF) is used in literature for anomaly detection by unsupervised classification of time series data. This paper proposes a novel wireless positioning method based on the HSDF concept. This approach aims to tackle the main problems in fingerprinting-based localization methods by eliminating the offline phase, which is the most time-consuming part of a fingerprinting localization. Online learning of Received Signal Strength Indicator (RSSI) time series data causes this method to be efficient in dealing with indoor environments’ dynamical behavior, which is another problem in the localization methods domain. Due to SDF (symbolic dynamic filtering)-based structure, it shows robustness to noise and multipath effect. This paper demonstrates these achievements by results obtained from both simulation of noisy datasets and real-time positioning in an indoor testbed.

1921. Leveraging Bluetooth Low-Energy Technology to Improve Contact Tracing in Healthcare Settings during the COVID-19 Pandemic

Abstract Background COVID-19 rapidly evolved into a global pandemic. Contact tracing with isolation and quarantine contribute to epidemic control but they are time consuming, costly and may be incomplete. We set out to assess the usability and performance characteristics of Bluetooth Low-Energy (BLE) wireless technology for indoor localization applied to contact tracing in healthcare settings. Methods Consented healthcare workers (HCW) from 2 designated COVID-19 wards (one intensive care unit (ICU) and one medical ward) were equipped with coin-sized BLE- emitting beacons. The signal was captured by small embedded computers (anchors) placed at designated locations, time-stamped and transmitted to an edge server via secure Wi-Fi where data were stored and real time contact algorithms were run (Fig.1). We developed experiments mimicking clinical scenarios and tested indoor localization during observed clinical activity for 6 months. We constructed our algorithms based on room structure (e.g. open spaces vs computer rooms) and activity characteristics (e.g. rounding in a large group vs 2 healthcare workers sitting together). We used 1) radio fingerprint localization where an initial virtual radio map was developed, 2) semantic localization which carries additional information such as proximity to a computer to define indirect transmission via fomites, and 3) clustering contact tracing to identify individuals rounding together. Close contact was defined as per the CDC guidelines. Fig. 1System configuration Results Consent rate was 43.3% with 187 HCW enrolled in the study. Consent rate was higher in the ICU and among attendings. All participants were compliant with wearing the beacons for the duration of the study. The performance characteristics for contact tracing using fingerprinting methods were AUROC 0.93, AUPRC 0.96, sensitivity 0.9, specificity 0.77 with F1 score of 0.89 and overall accuracy of 0.85. The clustering contact tracing registered a sensitivity of 0.86, specificity 0.89, F1 score 0.91 and accuracy 0.87. Computation time necessary to generate a list of close contacts as per specified criteria was less than 30 minutes. Conclusion We have developed and tested a reliable and accurate, low-cost and easily deployable system based on BLE technology to improve contact tracing among healthcare workers. Disclosures M Cristina Vazquez Guillamet, MD, AUPH: Stocks/Bonds|BNGO: Stocks/Bonds|OCGN: Stocks/Bonds|SESN: Stocks/Bonds.

LocID: A Secure and Usable Location-Based Smartphone Unlocking Scheme Using Wi-Fi Signals and Light Intensity

A user’s location information can be used to identify the user. For example, in Android, we can keep our smartphone unlocked when it is located near a place that was previously registered as a trusted place. However, existing location-based user authentication solutions failed to support fine-grained indoor location registration. In this article, we present a highly accurate identification scheme named location-based identification (LocID) using the smartphone’s physical fingerprints that are sensed at the user’s secure area. Our key idea is to use Wi-Fi signals and the light intensity of smartphones’ fingerprints to accurately identify their locations within adjacent indoor areas. To generate stable and reliable location fingerprints, we develop a novel mathematical location-wise signal signature (LSS) algorithm by minimizing the bias highly affected by various environmental factors. To show the feasibility of LocID, we evaluated the performance using machine learning classifiers with a real-world data set containing Wi-Fi and light fingerprints from 68 locations in a building for five weeks. The experimental results show that LocID achieved an F1-score of 98.3%, false-negative rate (FNR) of 2.9%, and false-positive rate (FPR) of 1.7%, respectively, when LocID was retrained daily. Moreover, LocID achieved an F1-score of 94.4%, FNR of 2.7%, and FPR of 3.7%, respectively, when LocID was trained using the data set during the first week and tested with the remaining four weeks data set. Also, we tested LocID using only one-scan of location fingerprints and achieved 90.6% and 90.8% AUC scores for support vector machine and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -nearest neighbor classifiers, respectively.

Connectivity-Based Localization Scheme for Social Internet of Things

Different from the social network which only focuses on the interaction between people, the integration of social networks and the internet of things (IoT) leads to multi-directional interactions of human to human, human to thing, and thing to thing. The social IoT is composed of a large number of heterogeneous devices, which can improve the scalability of resource and service. Meanwhile, the heterogeneous devices have uneven computing power and different location information measurement types (e.g., the distance, angle, and hop count). Therefore, a localization approach with easy-to-obtain measurement data and low requirements on the computing power is needed. In this article, we propose a localization approach for the social IoT by combining the fuzzy rough set theory and the ridge regression extreme learning machine (RRELM). First of all, a location fingerprint database is constructed. Different from the traditional location fingerprint database, the location fingerprint database here stores the minimum hop counts between the reference node (RN) and the anchor node (AN) instead of the received signal strength (RSS). Second, the fuzzy rough set theory is used to compute the significant degree of each AN, and the ANs that contribute little to the positioning result are removed. This approach not only relieves the storage pressure of the location fingerprint database but also reduces the computational complexity of user position estimation. Third, the RRELM is trained by using the samples in the location fingerprint database. Finally, by inputting the newly collected minimum hop counts from the user to each AN into the trained RRELM, the user’s position is estimated. From the extensive experimental results, the proposed approach has high positioning accuracy and low computational complexity, which is suitable for the social IoT.

Hybrid Fingerprinting and Ray Extension Localization in NLOS Regions

A general non-asymptotic theoretical analysis is developed for fingerprinting localization system designs. Based on this analysis, hybrid fingerprinting and propagation-based methods are proposed using 5G-like received signal strength (RSS), time of arrival (TOA), and direction of arrival (DOA) measurements which have been a focus in recent 3GPP Rel 16 and Rel 17 positioning activities. The proposed hybrid methods have the flexibility and robustness of fingerprinting methods in dealing with none-line-of-sight (NLOS) problem while inheriting the efficiency and accuracy of propagation-based methods in 3-D localization. Specifically, a ray extension technique is developed as the propagation-based method. Then the ray extension is combined with two fingerprinting methods, the conventional weighted k-nearest neighbors (WKNN) and the proposed optimal WKNN (OWKNN), in order to remedy the geometrical deficiency in fingerprinting methods. Based on the non-asymptotic study, the proposed hybrid methods are guaranteed to outperform the fingerprinting methods without ray extension. Verification of the proposed methods is performed in a large none-line-of-sight (NLOS) urban San Jose region using simulation data provided by a previously developed super-efficient ray launcher.

RSSI Fingerprint Height Based Empirical Model Prediction for Smart Indoor Localization.

Smart indoor living advances in the recent decade, such as home indoor localization and positioning, has seen a significant need for low-cost localization systems based on freely available resources such as Received Signal Strength Indicator by the dense deployment of Wireless Local Area Networks (WLAN). The off-the-shelf user equipment (UE's) available at an affordable price across the globe are well equipped with the functionality to scan the radio access network for hearable single strength; in complex indoor environments, multiple signals can be received at a particular reference point with no consideration of the height of the transmitter and possible broadcasting coverage. Most effective fingerprinting algorithm solutions require specialized labor, are time-consuming to carry out site surveys, training of the data, big data analysis, and in most cases, additional hardware requirements relatively increase energy consumption and cost, not forgetting that in case of changes in the indoor environment will highly affect the fingerprint due to interferences. This paper experimentally evaluates and proposes a novel technique for Received Signal Indicator (RSSI) distance prediction, leveraging transceiver height, and Fresnel ranging in a complex indoor environment to better suit the path loss of RSSI at a particular Reference Point (RP) and time, which further contributes greatly to indoor localization. The experimentation in different complex indoor environments of the corridor and office lab during work hours to ascertain real-life and time feasibility shows that the technique's accuracy is greatly improved in the office room and the corridor, achieving lower average prediction errors at low-cost than the comparison prediction algorithms. Compared with the conventional prediction techniques, for example, with Access Point 1 (AP1), the proposed Height Dependence Path-Loss (HEM) model at 0 dBm error attains a confidence probability of 10.98%, higher than the 2.65% for the distance dependence of Path-Loss New Empirical Model (NEM), 4.2% for the Multi-Wall dependence on Path-Loss (MWM) model, and 0% for the Conventional one-slope Path-Loss (OSM) model, respectively. Online localization, amongst the hearable APs, it is seen the proposed HEM fingerprint localization based on the proposed HEM prediction model attains a confidence probability of 31% at 3 m, 55% at 6 m, 78% at 9 m, outperforming the NEM with 26%, 43%, 62%, 62%, the MWM with 23%, 43%, 66%, respectively. The robustness of the HEM fingerprint using diverse predicted test samples by the NEM and MWM models indicates better localization of 13% than comparison fingerprints.

LRF-WiVi: A WiFi and Visual Indoor Localization Method Based on Low-Rank Fusion.

In this paper, a WiFi and visual fingerprint localization model based on low-rank fusion (LRF-WiVi) is proposed, which makes full use of the complementarity of heterogeneous signals by modeling both the signal-specific actions and interaction of location information in the two signals end-to-end. Firstly, two feature extraction subnetworks are designed to extract the feature vectors containing location information of WiFi channel state information (CSI) and multi-directional visual images respectively. Then, the low-rank fusion module efficiently aggregates the specific actions and interactions of the two feature vectors while maintaining low computational complexity. The fusion features obtained are used for position estimation; In addition, for the CSI feature extraction subnetwork, we designed a novel construction method of CSI time-frequency characteristic map and a double-branch CNN structure to extract features. LRF-WiVi jointly learns the parameters of each module under the guidance of the same loss function, making the whole model more consistent with the goal of fusion localization. Extensive experiments are conducted in a complex laboratory and an open hall to verify the superior performance of LRF-WiVi in utilizing WiFi and visual signal complementarity. The results show that our method achieves more advanced positioning performance than other methods in both scenarios.

Hybrid Wi-Fi and BLE Fingerprinting Dataset for Multi-Floor Indoor Environments with Different Layouts

Indoor positioning has garnered significant interest over the last decade due to the rapidly growing demand for location-based services. As a result, a multitude of techniques has been proposed to localize objects and devices in indoor environments. Wireless fingerprinting, which leverages machine learning, has emerged as one of the most popular positioning approaches due to its low implementation cost. The prevailing fingerprinting-based positioning mainly utilizes wireless fidelity (Wi-Fi) and Bluetooth low energy (BLE) signals. However, the RSS of Wi-Fi and BLE signals are very sensitive to the layout of the indoor environment. Thus, any change in the indoor layout could potentially lead to severe degradation in terms of localization performance. To foster the development of new positioning methods, several open-source location fingerprinting datasets have been made available to the research community. Unfortunately, none of these public datasets provides the received signal strength (RSS) measurements for indoor environments with different layouts. To fill this gap, this paper presents a new hybrid Wi-Fi and BLE fingerprinting dataset for multi-floor indoor environments with different layouts to facilitate the future development of new fingerprinting-based positioning systems that can provide adaptive positioning performance in dynamic indoor environments. Additionally, the effects of indoor layout change on the location fingerprint and localization performance are also investigated.

Chirp-Loc: Multi-factor authentication via acoustically-generated location signatures

Context-based authentication has been proposed as a way to enable secure authentication with minimal or even no user interaction requirements by using sensor data to ensure the device being authenticated is in possession of the person initiating the authentication request. A key limitation of practically all context-based authentication systems is that they are vulnerable to context manipulation attacks where an attacker manipulates the environment to create a desired response in the sensor data. We contribute Chirp-Loc as a system that has been designed to improve the robustness of context-based authentication solutions against context-manipulation attacks. Chirp-Loc integrates an innovative approach based on room impulse response (RIR) to establish a location fingerprint that characterizes the physical environment instead of the ambient environment. We describe the design and development of an Android prototype of Chirp-Loc. We also conduct extensive accuracy and security analysis of Chirp-Loc by considering a multi-factor authentication solution that uses Chirp-Loc to verify the proximity of an authentication token, such as a smartphone. Through extensive experiments, we demonstrate that Chirp-Loc offers high degree of security and usability. Our work paves the way for improving the resilience of context-based authentication against attackers that manipulate the context information and offers a way to implement authentication systems that minimize user interaction demands while offering a high degree of security.