Wi-Fi Localization Research Articles

Improving Indoor WiFi Localization by Using Machine Learning Techniques

Accurate and robust positioning has become increasingly essential for emerging applications and services. While GPS (global positioning system) is widely used for outdoor environments, indoor positioning remains a challenging task. This paper presents a novel architecture for indoor positioning, leveraging machine learning techniques and a divide-and-conquer strategy to achieve low error estimates. The proposed method achieves an MAE (mean absolute error) of approximately 1 m for latitude and longitude. Our approach provides a precise and practical solution for indoor positioning. Additionally, some insights on the best machine learning techniques for these tasks are also envisaged.

Ada-LT IP: Functional Discriminant Analysis of Feature Extraction for Adaptive Long-Term Wi-Fi Indoor Localization in Evolving Environments.

Wi-Fi fingerprint-based indoor localization methods are effective in static environments but encounter challenges in dynamic, real-world scenarios due to evolving fingerprint patterns and feature spaces. This study investigates the temporal variations in signal strength over a 25-month period to enhance adaptive long-term Wi-Fi localization. Key aspects explored include the significance of signal features, the effects of sampling fluctuations, and overall accuracy measured by mean absolute error. Techniques such as mean-based feature selection, principal component analysis (PCA), and functional discriminant analysis (FDA) were employed to analyze signal features. The proposed algorithm, Ada-LT IP, which incorporates data reduction and transfer learning, shows improved accuracy compared to state-of-the-art methods evaluated in the study. Additionally, the study addresses multicollinearity through PCA and covariance analysis, revealing a reduction in computational complexity and enhanced accuracy for the proposed method, thereby providing valuable insights for improving adaptive long-term Wi-Fi indoor localization systems.

Privacy-Preserving WiFi Localization Based on Inner Product Encryption in a Cloud Environment

Privacy-Preserving WiFi Localization Based on Inner Product Encryption in a Cloud Environment

A Sequential-Multi-Decision Scheme for WiFi Localization Using Vision-Based Refinement

Currently, most mobile devices have WIFI and camera modules to locate their position. However, there are two main challenges in large, highly similar indoor environments (localization accuracy and localization time). Aiming to balance these problems, we propose a sequential-multi-decision integrated system that combines WIFI and vision to acquire users' locations. This system has two phases: sequential fusion localization and adaptive multi-decision fusion localization. The former employs WIFI-based localization first, then image-based localization and fusion localization are used within the constraints of WIFI-based localization. In the WIFI-based localization phase, the gaussian process regression (GPR) model is used to construct a WIFI indoor map. Subsequently, we propose to apply the hybrid whale optimization algorithm (HWOA) to WIFI-based localization to improve its accuracy and stability. The latter uses an adaptive multi-decision fusion mechanism that integrates WIFI-based localization, image-based localization, and fusion localization to obtain the users' location finally. The experiments show the effectiveness of HWOA applied to WIFI-based localization. We also experimentally evaluate the proposed fusion algorithm with other state-of-the-art fusion algorithms (e.g., accuracy and time) in a real environment (an area larger than 10,000 <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> ). The experimental results show that the proposed fusion system is competitive.

Innovative Applications of Intelligent Assistive Systems in Interior Design

Abstract With the development of smart homes, virtual reality, and other technologies, people’s demand for digital modeling and visualization of interior space is increasing. How to quickly and accurately estimate the layout of the interior space has become a matter of great concern. This paper develops a 3D virtual visual interior design platform structure based on this. By studying the indoor multi-sensor fusion localization system, the localization module in the navigation process was designed based on this fusion result. The visual localization algorithm, WiFi localization, PDR localization, and AMCL fusion localization algorithms are introduced, respectively. The extended Kalman filter algorithm is used to fuse the algorithm with the highest state value for localization, which is used for scene recognition according to the algorithm localization effect. To achieve the 3D virtual visual impact, a 3D virtual visual interior design platform is created based on 3D virtual vision. Experiments are conducted on the localization system in the platform, and the error of all the localization points of the localization method combining vision and AMCL is no more than 1.1m, with the maximum error of 1.071m and the average error of 0.481m, which is the lowest error among all the algorithms. The localization sensor constructed by the localization algorithm using vision and AMCL with extended Kalman filtering has an average error of 10.33cm, which allows for good scene recognition. The data analyzes designers’ satisfaction with the use of the platform, and the independent variable in the regression equation that contributes most significantly to total satisfaction is the platform’s time-saving, with a coefficient of 0.324. The platform’s focus should be on enhancing time efficiency.

A Cryptographic Protocol for Efficient Mutual Location Privacy Through Outsourcing in Indoor Wi-Fi Localization

A Cryptographic Protocol for Efficient Mutual Location Privacy Through Outsourcing in Indoor Wi-Fi Localization

Application and Optimization of WIFI Positioning algorithm in College Classroom Roll Call: A Positioning Strategy based on Mathematical Optimization

Abstract In this paper, the WIFI localization algorithm based on the signal intensity feature matching is studied and combined with the mathematical optimization strategy. By constructing accurate mathematical models, the algorithm can process signal intensity data more accurately and reduce the influence of environmental factors on the localization results. This positioning strategy based on mathematical optimization not only improves the positioning accuracy, but also improves the stability and practicability of the system, and provides an innovative technical means for the classroom roll call system in universities, which has important practical value and promotion prospects.

Smartphone-Key: Hands-Free Two-Factor Authentication for Voice-Controlled Devices Using Wi-Fi Location

In the last few years, Internet of Things (IoT) devices have evolved at an accelerated rate. Smart technology is leaning towards new human-device interaction interfaces that lack screens or buttons, thus reducing the cost of the devices while offering a more natural user experience. Voice is one of the most extended IoT user interfaces nowadays and has been popularized with the advent of smart assistants. However, with the adoption of voice interfaces, new challenges arise in terms of security. According to our research, the complexity of speaker recognition has led to non-reliable voice authentication on IoT devices. Recent approaches to solve this problem include multi-factor authentication (MFA) schemes, but existing solutions, such as one-time passwords (OTP), virtual/physical buttons, and others, have a significant impact on the user experience that is not suitable for all applications and can negate the hands-free benefits of voice interfaces. In addition, the privacy concerns of voice biometrics hinder the development of personalized voice applications that require user accounting and access control policies. Motivated by this security concern, in this paper, we present a two-factor authentication (2-FA) scheme for voice-activated devices that uses a smartphone as a key to authorize voice commands when the user’s presence is detected. The proposed security model, which makes use of Wi-Fi location for presence detection, was implemented on an Amazon Echo device and an Android smartphone, the results proved that the denominated Smartphone-key model can protect against the most common attack vectors on voice-controlled devices without affecting the user experience.

Perancangan Website Sistem Informasi Penunjuk Arah Wifi id Wilayah Semarang

Wifi@id internet is a public service of technology-based Wifi / Hotspot provided by TELKOM Group. WiFi@id wifi is a promising business for the company because of its CDMA business declined previously lived. Problems that occur at this time is the lack of information between users with id being wifi locations. It causes the user to access the wifi en manual are based on the location, while the location of wifi id contained in the shared location. Design website information systems and route search nearby directions wifi @ id is located. This website system enables users to find the location nearest wifi id with the position where the user is located. This mapping information design using a model of the development of the SDLC (system development life cycle), namely the development of a model system in stages starting from the analysis, the initial design, through implementation phase object-oriented programming is UML (unified modeling language), the data collection techniques namely observation and interviews. Conclusion of this website information system that can manage data wifi, managing user data, and know the location of the user's location.

Wi-Fi Localization Obfuscation: An implementation in openwifi

Wi-Fi sensing as a side-effect of communications is opening new opportunities for smart services integrating communications with environmental properties, first and foremost the position of devices and people. At the same time, this technology represents an unprecedented threat to people’s privacy, as personal information can be collected directly at the physical layer without any possibility to hide or protect it. Several works already discussed the possibility of safeguarding users’ privacy without hampering communication performance, using signal pre-processing at the transmitter side to introduce pseudo-random (artificial) patterns in the channel response estimated at the receiver, preventing the extraction of meaningful information from the channel state, a process called obfuscation. One step beyond the proof-of-concept for obfuscation feasibility, is its implementation in working systems. In this work, we present the implementation of a location obfuscation technique within the openwifi project that enables fine manipulation of the radio signal at transmitter side and yields acceptable, if not good, performance, the system has been implemented for both 802.11a/g/h and 802.11n systems, including MPDU aggregation, while implementation for 802.11ac or ax is still not feasible because openwifi does not support 40MHz channelization and beyond. This contribution discusses the implementation of the obfuscation subsystem, its performance, possible improvements, and further steps to allow authorized devices to “de-obfuscate” the signal and retrieve the sensed information.

Indoor Localization Based on Factor Graphs: A Unified Framework

Indoor localization is of pivotal significance for a wide variety of services in the context of the Internet of Things (IoT). Both ranging-based and fingerprint-based localization techniques are promising for employment in harsh indoor environments. Hence, we propose a unified framework based on factor graphs for ubiquitous high-accuracy indoor localization. Our unified framework efficiently integrates ranging and fingerprinting for striking an appealing accuracy versus deployment cost tradeoff, where the crowdsourcing required for the construction of fingerprinting databases can also be addressed with little human intervention. By intrinsically amalgamating the global grid sampling and the regularized importance-resampling techniques, a nonparametric belief propagation algorithm is proposed for achieving the accurate position estimation at the cost of a moderate computational complexity. For improving the robustness to environmental variations, a likelihood-ratio-based approach is employed to detect ranging outliers. Moreover, a low-complexity serial scheduling scheme defined over factor graphs is designed for real-time localization. We design a hybrid ultrawide bandwidth and Wi-Fi localization system relying on off-the-shelf commercial devices and evaluate the proposed unified framework in a typical office building. Our experimental results show that the proposed algorithm outperforms the existing state-of-the-art methods and it is capable of achieving submeter localization accuracy.

An extreme value based algorithm for improving the accuracy of WiFi localization

More mobile devices can obtain WiFi Received Signal Strength (RSS) for indoor positioning, so selecting reasonable Access Points (APs) for rapid positioning is important. Furthermore, RSS is vulnerable to diverse interference, making the positioning accuracy decrease. To solve the above mentioned issues, two novel algorithms, AP selection and positioning algorithms, are proposed, in this paper, which both use the location distance between RPs to construct a circle and treat the RSS from each AP individually. RPs in the same circle are seen as having similar RSS. 1) For each received signal, the proposed selection algorithm leverages the RSS extreme values (maximum and minimum values) of circles to find out RPs which may surround Test Point (TP), then it uses the degree of RSS difference and location concentration to evaluate the importance of each AP. 2) For the positioning algorithm, it uses the extreme values to find the useful APs and determines the weight of RPs. Extensive experiments are conducted on simulation and two real-world sites, results show the effectiveness of the proposed algorithms.

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.

PaCNN-LSTM: A Localization Scheme Based on Improved Contrastive Learning and Parallel Fusion Neural Network

The deep learning technique plays an important role in Wi-Fi localization systems as it could mine deep features of measurement data. The main challenges are to combat the signal fluctuation resulting in decrease of sample discrimination, and to leverage the broadest information of sample measurements during the training phase, since they are directly related to the location accuracy and robustness. Hence, to address the above issues, this paper proposes an indoor Wi-Fi localization scheme which mainly contains two modules. Firstly, an improved contrastive learning is introduced to handle the sample signal measurements to increase the discrimination. It is from the perspective of learning and encoding, and avoids the drawbacks brought by traditional processing methods. Then, we build a parallel fusion network named as PaCNN-LSTM based on convolutional neural network (CNN) and long short-term memory network (LSTM). Compared with existing networks, PaCNN-LSTM connects neural networks in parallel rather than serial, which improves the generalization performance of model when extracting the spatial and temporal features of signal measurements. In addition, it also considers the large amount of middle layer information that is always ignored. By adding a Flatten layer after the pooling layer, the available information of samples has been broadened. Extensive experimental results show that the localization performance of the proposed scheme is outperformed than others, where the location accuracy is improved by about 22%.

The Improvement and Comparison Study of Distance Metrics for Machine Learning Algorithms for Indoor Wi-Fi Localization

The Improvement and Comparison Study of Distance Metrics for Machine Learning Algorithms for Indoor Wi-Fi Localization

Experimental validation of the ResNet layer number design method for Wi-Fi location estimation in different environments

Experimental validation of the ResNet layer number design method for Wi-Fi location estimation in different environments

Simultaneous WiFi Ranging Compensation and Localization for Indoor NLoS Environments

Smartphone-based WiFi ranging using fine time measurement (FTM) is severely impacted by Non-line-of-sight (NLoS) environments, which causes significant positioning errors. To address this problem, we propose a novel WiFi FTM positioning (WFP) approach based on the geomagnetism and enhanced genetic algorithm (EGA), which can simultaneously execute WiFi localization and ranging compensation. Based on the distribution of the ranging error in NLoS environments, a semiparametric error model-based ranging compensation method is proposed. To construct the EGA searching model, geomagnetism-based positioning is adopted and fed to the EGA together with the measured WiFi ranging data and the ranging compensation method. During online localization, the EGA model dynamically compensates for the erroneous ranging data until it finds the optimal position. Experimental results show that the ranging and localization accuracy of this EGA-based WFP are 1.33 m and 1.64 m, being an improvement of 30.7% and 56.5% compared to the uncompensated ranging data and the trilateration algorithm using the weighted least square (WLS) method, 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%.

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

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

AEDNav: indoor navigation for locating automated external defibrillator

BackgroundIn a sudden cardiac arrest, starting CPR and applying an AED immediately are the two highest resuscitation priorities. Many existing mobile applications have been developed to assist users in locating a nearby AED. However, these applications do not provide indoor navigation to the AED location. The time required to locate an AED inside a building due to a lack of indoor navigation systems will reduce the patient’s chance of survival. The existing indoor navigation solutions either require special hardware, a large dataset or a significant amount of initial work. These requirements make these systems not viable for implementation on a large-scale.MethodsThe proposed system collects Wi-Fi information from the existing devices and the path’s magnetic information using a smartphone to guide the user from a starting point to an AED. The information collected is processed using four techniques: turn detection method, Magnetic data pattern matching method, Wi-Fi fingerprinting method and Closest Wi-Fi location method to estimate user location. The user location estimations from all four techniques are further processed to determine the user’s location on the path, which is then used to guide the user to the AED location.ResultsThe four techniques used in the proposed system Turn detection, Magnetic data pattern matching, Closest Wi-Fi location and Wi-Fi fingerprinting can individually achieve the accuracy of 80% with the error distance ± 9.4 m, ± 2.4 m, ± 4.6 m, and ± 4.6 m respectively. These four techniques, applied individually, may not always provide stable results. Combining these techniques results in a robust system with an overall accuracy of 80% with an error distance of ± 2.74 m. In comparison, the proposed system’s accuracy is higher than the existing systems that use Wi-Fi and magnetic data.ConclusionThis research proposes a novel approach that requires no special hardware, large scale data or significant initial work to provide indoor navigation. The proposed system AEDNav can achieve an accuracy similar to the existing indoor navigation systems. Implementing this indoor navigation system could reduce the time to locate an AED and ultimately increase patient survival during sudden cardiac arrest.