Wi-Fi signal strengths database construction for indoor positioning systems using Wi-Fi RFID (original) (raw)

Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

Data

WiFi fingerprinting, one of the most popular methods employed in indoor positioning, currently faces two major problems: lack of robustness to short and long time signal changes and difficult reproducibility of new methods presented in the relevant literature. This paper presents a WiFi RSS (Received Signal Strength) database created to foster and ease research works that address the above-mentioned two problems. A trained professional took several consecutive fingerprints while standing at specific positions and facing specific directions. The consecutive fingerprints may enable the study of short-term signals variations. The data collection spanned over 15 months, and, for each month, one type of training datasets and five types of test datasets were collected. The measurements of a dataset type (training or test) were taken at the same positions and directions every month, in order to enable the analysis of long-term signal variations. The database is provided with supporting materials and software, which give more information about the collection environment and eases the database utilization, respectively. The WiFi measurements and the supporting materials are available at the Zenodo repository under the open-source MIT license.

Fingerprint Database Variations for WiFi Positioning

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY

Indoor positioning systems calculate the position of a mobile device (MD) in an enclosed environment with relative precision. Most systems use WiFi infrastructure and several positioning techniques, where the most commonly used parameter is RSSI (Received Signal Strength Indicator). In this paper, we analyze the fingerprinting technique to calculate the error window obtained with the Euclidian distance as main metric. Build variations are presented for the Fingerprint database analyzing various statistical values to compare the precision achieved with different indicators.

A Study of Fingerprint-based Methods for Training Phase in Wi-Fi Indoor Positioning Systems

The use of Indoor Positioning Systems (IPSs) is growing in the last years on different scopes, from faculties to airports, hospitals and big malls among others. There is a group contained in these kind of systems to be highlighted, those based on the most common technologies like Wi-Fi because the deployment effort is already done. As there is a new trending study topicon using Fingerprinting collection techniques for positioning,this document exposes a survey of Wi-Fi Indoor Positioning Systems in an offline phase, specifically those that use the Fingerprint collection technique, focusing on the cost of that process in terms of the sampling mode and the interaction required with the user.

A new method for improving Wi-Fi-based indoor positioning accuracy

Wi-Fi and smartphone based positioning technologies are play-ing a more and more important role in Location Based Service (LBS) industries due to the rapid development of the smartphone market. However, the low positioning accuracy of these technologies is still an issue for indoor positioning. To ad-dress this problem, a new method for improving the indoor posi-tioning accuracy was developed. The new method initially used the Nearest Neighbor (NN) algorithm of the fingerprinting meth-od to identify the initial position estimate of the smartphone us-er. Then two distance correction values in two roughly perpen-dicular directions were calculated by the path loss model based on the two signal strength indicator (RSSI) values observed. The errors from the path loss model were eliminated by differencing two model-derived distances from the same access point. The new method was tested and the results compared and assessed against that of the commercial Ekahau RTLS system and the NN algorithm. The preliminary results showed that the positioning accuracy has been improved consistently after the new method was applied and the root mean square accuracy improved to 3.3 m from 3.8 m compared with the NN algorithm.

FaMWiFi: Fingerprinting/Sampling & Monitoring of WiFi signals for Indoor Positioning

Wi-Fi based location estimation systems are popular indoor positioning technologies, which are based on Received Signal Strength (RSS) of the Wireless LAN (WLAN). These systems uses RSS signals obtained from several Access Points (AP) installed at serving area to provide user location. The sampling process must be performed before running phase of the location determination services. Sampling is a process, which required collection of samples at installation area for location estimation services which is a time-consuming and manual process of indoor positioning system. Several softwares exist to perform this process automatically, that will then require less manual works. However there is still deficiency of fully automated process and requires a lot of manual work for the user to do the sampling tasks. Our developed sampling software FaMWIFI (Fingerprinting And Monitoring of Wi-Fi), which provides fully automated, flexible features in addition to manage environments for the user to perform sampling concept. Our software provides different functioning controls to the user to collect and manage samples in an easy and efficient manner.

Wireless Indoor Localization Using Fingerprinting Technique

Journal of Advanced Research in Dynamical and Control Systems, 2020

Indoor positioning has gained more interest as one of the upcoming applications due to its use in a variety of services. Multiple technologies such as Bluetooth, Wi-Fi, RFID. However, Wi-Fi based localization in indoor environment offers significant advantages utilizing installed wireless infrastructures and good performances with low cost. With this study, we aim to provide a compromise between accurate positioning and feasibility of the system for practical applications. For this purpose, we minimize the fluctuations of Wi-Fi received signal strength (RSS) by filtering and we combine two approaches to locate a mobile user. At first, we implemeted the traditional fingerprinting technique that uses a real time matching of pre-recorded received signal strength (RSS) from the location data of the user transmitted to nearby access points (AP). Secondly, we used distance-based trilateration technique which determines positions using three known access points. The combination of the two methods provides enhancement of accuracy and wide indoor locating coverage. Regardless the locating data number, experiment confirmed a significant and a consistent performance in term of execution time and accuracy.

New Reconstructed Database for Cost Reduction in Indoor Fingerprinting Localization

IEEE Access, 2019

Location fingerprinting is a technique widely suggested for challenging indoor positioning. Despite the significant benefits of this technique, it needs a considerable amount of time and energy to measure the Received Signal Strength (RSS) at Reference Points (RPs) and build a fingerprinting database to achieve an appropriate localization accuracy. Reducing the number of RPs can reduce this cost, but it noticeably degrades the accuracy of positioning. In order to alleviate this problem, this paper takes the interior architecture of the indoor area and signal propagation effects into account and proposes two novel recovery methods for creating the reconstructed database instead of the measured one. They only need a few numbers of RPs to reconstruct the database and even are able to produce a denser database. The first method is a new zone-based path-loss propagation model which employs fingerprints of different zones separately and the second one is a new interpolation method, zone-based Weighted Ring-based (WRB). The proposed methods are compared with the conventional path-loss model and six interpolation functions. Two different test environments along with a benchmarking testbed, and various RPs configurations are also utilized to verify the proposed recovery methods, based on the reconstruction errors and the localization accuracies they provide. The results indicate that by taking only 11% of the initial RPs, the new zone-based pathloss model decreases the localization error up to 26% compared to the conventional path-loss model and the proposed zone-based WRB method outperforms all the other interpolation methods and improves the accuracy by 40%.

Indoor Location Estimation Utilizing Wi-Fi Signals

International Journal of Emerging Trends in Engineering Research, 2020

Global Positioning System is commonly been used for locating a position of a specific structure in finding geographical coordinates of a target area. Though, this application is still having a restricted in term of the signals, might not well operated and ineffective for indoor usage. The study aim is to develop positioning and localization systems by using Wi-Fi signal. Estimation was made based on the measurement of wireless distance for estimation the user's coordinates. Analysis of views called the fingerprint algorithm is used in this study. The algorithm involved two phases over an offline and the online phases of the survey. Unidentified user's coordinates will be in the online phase by comparative databases collected in the survey phase. MATLAB Graphical User Interface and Android has been used to develop a user interface for simulation purposes. Several analyses were performed to define the precision and efficiency of occurred error as the number of access points and the traffic environment. Finally, the user required to provide several inputs e.g. the exact location and the RSS from AP's number at the present location. The simulation-based software will evaluate the estimation location and positioning of the user and will match to user's precise location.

Wi-Fi Indoor Positioning Fingerprint Health Analysis for a Large Scale Deployment

Indoor positioning systems (IPS) have witnessed continuous improvements over the years. However, large-scale commercial deployments remain elusive due to various factors such as high deployment cost and lack market drivers. Among the state of the art indoor positioning approaches, the Wi-Fi fingerprinting technique, in particular, is gaining much attention due to its ease of deployment. This is largely due to widespread deployment of WiFi infrastructure and its availability in all existing mobile devices. Although WiFi fingerprinting approach is relatively low cost and fast to deploy, the accuracy of the system tends to deteriorate over time due to WiFi access points (APs) being removed and shifted. In this paper, we carried out a study on such deterioration, which we refer to as fingerprint health analysis on a 2 million square feet shopping mall in South of Kuala Lumpur, Malaysia. We focus our study on APs removal using the actual data collected from the premise. The study reveals the following findings: 1) based on per location pin analysis, ~50% of APs belong to the mall operator which is a preferred group of APs for fingerprinting. For some location, however, the number of operator-managed APs are too few for fingerprinting positioning approach. 2) To maintain mean error distance of ~5 meters, up to 80% of the APs can be removed using the selected positioning algorithms at some locations. At some other locations, however, the accuracy will exceed 5m upon >20% of APs being removed. 3) On average, around 40%-60% of the APs can be removed randomly in order to maintain the accuracy of ~5m.

Multiple simultaneous Wi-Fi measurements in fingerprinting indoor positioning

2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2017

The accuracy of fingerprinting-based positioning methods accuracy is limited by the fluctuations in the radio signal intensity mainly due to reflections, refractions, and multipath interference, among other factors. We consider that the fluctuations (often modelled as a Gaussian process for simplification purposes) can be minimized by exploiting the richness of multiple signals collected simultaneously through independent network interfaces. This paper introduces an analysis of Wi-Fi signals' statistics using simultaneous measurements which shows that RSSI values obtained from independent devices are not highly correlated. The low correlation between Wi-Fi interfaces might be exploited to improve the positioning accuracy. The validation of the proposed fingerprinting approach in a real scenario shows that the mean and maximum error in positioning can be reduced by more than 40% when five Wi-Fi interfaces are simultaneously used for fingerprinting.