WiFi-based Localization Systems Research Articles

The Adaptive Fingerprint Localization in Dynamic Environment

Indoor localization service is an indispensable part of modern intelligent life, among which Wi-Fi based fingerprint localization system is popular in indoor positioning researches due to its advantages of low cost and widely deployment. However, Wi-Fi based localization system is susceptible to dynamic environment, and fingerprint collection and updating are time-consuming and labor-intensive. To address this problem, we propose a novel positioning framework based on multiple transfer learning fusion using Generalized Policy Iteration (GPI). Firstly, a 1-Dimension Convolutional Autoencoder (1-D CAE) is designed to extract features from one-dimensional fingerprint data; similar to Convolutional Neural Network (CNN), it can not only pay more attention to the information of different dimensions of fingerprints, but also compress redundant information and reduce noise. After that, Domain Adversarial Neural Network (DANN) and Passive Aggressive (PA) algorithm are fused to train localization model based on unlabeled fingerprint of target domain using the theory of GPI in offline stage. Finally, the model is fine-tuned with unlabeled fingerprints and few labeled fingerprints in daily online predictions to improve the performance of the localization system. Various evaluations in five typical scenarios validate the effectiveness of proposed algorithm in dynamic environment, with low tendency, easy recalibration, long-term stabilization high accuracy and so on.

WheelLoc: Practical and Accurate Localization for Wheeled Mobile Targets via Integrated Sensing and Communication

Practical and accurate localization systems are important to mobile targets that enable promising services such as navigation and augmented reality. With the proliferation of WiFi, existing WiFi-based localization systems have leveraged RSSI, fingerprints, landmarks, time of arrival, or angle of arrival to locate targets, while no related work pays attention to mobile targets themselves. For wheel-driven mobile targets, such as vehicles, bikes, and wheeled robots, we design and implement WheelLoc, a novel WiFi-based localization system leveraging the rotation of wheels. The specially designed WheelLoc hardware is cost-effective and self-powered with the composition of three commercial antennas and a solar cell, which is also easy to be installed on wheels. A hybrid WheelLoc algorithm is further proposed to realize accurate localization in diverse environments, whether the wheel of targets is static or mobile, indoor or outdoor, on flat or bumpy ground. The movements of individual antennas are exploited to emulate linear, cycloid, and circular antenna arrays using a new formulation of Synthetic Aperture Radar (SAR). Extensive experiments are conducted on bikes in the real world. Performance results demonstrate that WheelLoc does not require any user interaction, yet achieves comparable accuracy with the state-of-the-art localization systems using WiFi.

Fidora: Robust WiFi-Based Indoor Localization via Unsupervised Domain Adaptation

Emerging Internet of Things (IoT) applications, such as cashier-less shopping, mobile ads targeting, and geo-based augmented reality (AR), are expected to bring us much more convenience and infotainment. To realize this amazing future, we need to feed these applications with user locations of (sub)meter-level resolution anytime and anywhere. Unfortunately, many widely used location sources are either unavailable indoor (e.g., global positioning system) or coarse grained (e.g., user check-ins). In order to provide ubiquitous localization services, the widespread WiFi signals are being leveraged to establish (sub)meter-level localization systems. Fine-grained WiFi propagation characteristics, which are sensitive to human body locations, have been employed to create location fingerprints. However, these WiFi characteristics are also sensitive to: 1) the body shapes of different users and 2) the objects in the background environment. Consequently, systems based on WiFi fingerprints are vulnerable in the presence of: 1) new users with different body shapes and 2) daily changes of the environment, e.g., opening/closing doors. To tackle this issue, this article proposes a WiFi-based localization system based on domain-adaptation with cluster assumption, named Fidora. Fidora is able to: 1) localize different users with labeled data from only one or two example users and 2) localize the same user in a changed environment without labeling any new data. To achieve these, Fidora integrates two major modules. It first adopts a data augmenter that introduces data diversity using a variational autoencoder (VAE). It then trains a domain-adaptive classifier that adjusts itself to newly collected unlabeled data using a joint classification-reconstruction structure. We conducted real-world experiments to evaluate Fidora against the state of the art. It is demonstrated that when tested on an unlabeled user, Fidora increases the average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> score by 17.8% and improves the worst case accuracy by 20.2%. Moreover, when applied in a varied environment, Fidora outperforms the state of the art by 23.1%.

Wi-Fi based indoor localization using trilateration and fingerprinting methods

Nowadays, mobile devices such as personal computers and smartphones are emerging as a major key in today’s computing platforms for indoor object localization systems due to the object localization in indoor areas strongly suffers from limitation of using GNSS (Global navigation satellite system) systems due to low satellite availability and high signal attenuation. During the last decade, many researchers have developed indoor localization systems which are the process of obtaining user or device location through mobile devices using Wireless Fidelity (Wi-Fi) network signals with promising results and acceptable performance. In these Wi-Fi based localization systems, indoor positioning relies on different types of measurements including Time-Of-Arrival (TOA), Time-Difference-Of-Arrival (TDOA), Angle-Of-Arrival (AOA), and Received Signal Strength (RSS) of Wi-Fi signal.In this paper, the techniques and algorithms that used for the RSS-based localization such as Trilateration and Fingerprinting which depend on the RSS from the access point (WI-FI). Using Received Signal Strength Ranging approach in the Trilateration method which depends on database that contains path-loss-exponent and shadowing parameter that differ according to the environment, solving the equation using different Access Points (APs) at least 3 APs, the no of the APs and there locations were varied to get the best accuracy which depends on the horizontal dilution of position (HDOP). At the Fingerprinting method depends on matching the recorded offline RSS from nearby access points (AP) to the online RSS received by the user on the move is reviewed. A comparison of location fingerprinting methods involving deterministic method (k-nearest neighbor method and weighted k-nearest neighbor method), probabilistic methods by estimation of likelihood functions with several approaches (non-parametric and parametric)are also explained. The performance parameters of this study include the two-dimensional root mean square error (2D-rms) which measures the localization accuracy. Moreover, the effect of increasing/decreasing the number of APs on the system accuracy is also discussed. The aim of this paper is to announce which method can provide better performance than the others and under what conditions.

Data Information Fusion From Multiple Access Points for WiFi-Based Self-localization

In this letter, we propose a novel approach for fusing information from multiple access points in order to enhance WiFi-based self-localization. A common approach for designing WiFi-based localization systems is to learn location-to-signal strength mappings for each access point in an environment. Each mapping is then used to compute the likelihood of the robot's location conditioned on sensed signal strength data, yielding as many likelihood functions as mappings are available. Office buildings typically have from several tens to a few hundreds of access points, making it essential to properly combine all available likelihoods into a single, coherent, joint likelihood that yields precise likelihoods, yet is not overconfident. While most research has focused on techniques for learning these mappings and improving data acquisition; research on techniques to adequately fuse them has been neglected. Our approach for data information fusion is based on information theory and yields considerably better joint distributions than previous approaches. Furthermore, through extensive testing, we show that these joint likelihoods considerably increase the system's localization performance.

Fast indoor localization for exhibition venues with calibrating heterogeneous mobile devices

Indoor localization has attracted much attention these years and been applied into different fields and sites. This paper focuses on Wi-Fi based localization system in exhibition venues which have some unique features, for example, many heterogeneous mobile devices with different transmission characteristics. We propose a customized indoor localization system and an efficient positioning approach which can give fast and accurate location estimations. For tackling heterogeneous mobile device problem, we use Expectation-maximization (EM) algorithm to infer the parameters of the signal-strength-to-distance model so that each mobile device has its own signal strength degradation model. This is suitable for the exhibition scenario since it not only improves the localization accuracy for diverse mobile devices, but also reduces the offline data acquisition time as in the case of employing the fingerprinting approach. We have conducted experiments in our testbed as well as pilot tests at the Hong Kong Convention and Exhibition Centre (HKCEC). The experimental results show that our system has good localization performance and is robust to heterogeneous mobile devices.

A Comparison Study of Indoor Localization Methods Using Available WI-FI Signals

Object localization in indoor areas strongly suffers from limitation of using GNSS (Global navigation satellite system ) systems due to low satellite availability and high signal attenuation. Nowadays, mobile devices such as personal computers and smart phones are emerging as a major key in today’s computing platforms for indoor object localization systems due to the rapid developments in wireless communications and mobile computing. During last decade, many researchers have developed indoor localization systems through mobile devices using Wireless Fidelity (Wi-Fi) network signals with promising results and acceptable performance. In these Wi-Fi based localization systems, indoor positioning relies on different types of measurements including Time-Of-Arrival (TOA), Time-Difference-Of-Arrival (TDOA), Angle-Of-Arrival (AOA), and Received Signal Strength (RSS) of Wi-Fi signal. In this paper, the algorithms and techniques used for the RSS-based localization systems using fingerprinting method which depends on matching the recorded offline RSS from nearby access points (AP) to the online RSS received by the user on the move is reviewed. A comparison of location fingerprinting methods involving deterministic method (k-nearest neighbor method and weighted k-nearest neighbor method), probabilistic methods by estimation of likelihood functions with several approaches (non-parametric and parametric)are also explained. The performance parameters of this comparative study include the two-dimensional root mean square error (2D-rms) which measures the localization accuracy. Moreover, the effect of increasing/decreasing the number of APs on the system accuracy is also discussed. The aim of this comparison is to announce which method can provide better performance than the others and under what conditions.

Real-Time Indoor Localization in Smart Homes Using Semi-Supervised Learning

&#x0D; &#x0D; &#x0D; Long-term automated monitoring of residential or small in- dustrial properties is an important task within the broader scope of human activity recognition. We present a device- free wifi-based localization system for smart indoor spaces, developed in a collaboration between McGill University and Aerˆıal Technologies. The system relies on existing wifi net- work signals and semi-supervised learning, in order to au- tomatically detect entrance into a residential unit, and track the location of a moving subject within the sensing area. The implemented real-time monitoring platform works by detect- ing changes in the characteristics of the wifi signals collected via existing off-the-shelf wifi-enabled devices in the environ- ment. This platform has been deployed in several apartments in the Montreal area, and the results obtained show the poten- tial of this technology to turn any regular home with an ex- isting wifi network into a smart home equipped with intruder alarm and room-level location detector. The machine learn- ing component has been devised so as to minimize the need for user annotation and overcome temporal instabilities in the input signals. We use a semi-supervised learning framework which works in two phases. First, we build a base learner for mapping wifi signals to different physical locations in the en- vironment from a small amount of labeled data; during its lifetime, the learner automatically re-trains when the uncer- tainty level rises significantly, without the need for further supervision. This paper describes the technical and practical issues arising in the design and implementation of such a sys- tem for real residential units, and illustrates its performance during on-going deployment.&#x0D; &#x0D; &#x0D;

Use of negative information in positioning and tracking algorithms

To avoid additional hardware deployment, indoor localization systems have to be designed in such a way that they rely on existing infrastructure only. Besides the processing of measurements between nodes, localization procedure can include the information of all available environment information. In order to enhance the performance of Wi-Fi based localization systems, the innovative solution presented in this paper considers also the negative information. An indoor tracking method inspired by Kalman filtering is also proposed.

High-Speed Optical Wireless Communication System for Indoor Applications

A novel high-speed optical wireless communication system for indoor personal area networking applications is proposed and studied. A proof-of-concept experiment at 12.5-Gb/s wireless transmission has been successfully demonstrated with limited mobility. When integrated with a WiFi-based localization system, high-speed optical wireless communication with a mobility feature can be achieved over the entire room. The performance trade-offs between the maximum beam footprint and overall bit rate have also been studied and quantified experimentally and the results show that error-free (BER<;10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-9</sup> ) reception can always be achieved for a wide range of bit rates from 1 to 12.5 Gb/s.