WiFi-based Indoor Positioning System Research Articles

Maximum convergence algorithm for WiFi based indoor positioning system

WiFi-based indoor positioning is widely exploited thanks to the existing WiFi infrastructure in buildings and built-in sensors in smartphones. The techniques for indoor positioning require the high-density training data to archive high accuracy with high computation complexity. In this paper, the approach for indoor positioning systems which is called the maximum convergence algorithm is proposed to find the accurate location by the strongest receiver signal in the small cluster and K nearest neighbours (KNN) of other clusters. Also, the K-mean clustering is deployed for each access point to reduce the computation complexity of the offline databases. Moreover, the pedestrian dead reckoning (PDR) method and Kalman filter with the information from the received signal strength (RSS) and inertial sensors are applied to the WiFi fingerprinting to increase the efficiency of the mobile object's position. The different experiments are performed to compare the proposed algorithm with the others using KNN and PDR. The recommended framework demonstrates significant proceed based on the results. The average precision of this system can be lower than 1.02 meters when testing in the laboratory environment with an area of 7x7 m using three access points.

WiFi Based Fingerprinting Positioning Based on Seq2seq Model.

Indoor positioning technologies are of great use in GPS-denied areas. They can be partitioned into two types of systems—infrastructure-free based and infrastructure-dependent based. WiFi based indoor positioning system is somewhere between the infrastructure-free and infrastructure-dependent systems. The reason is that in WiFi based systems, Access Points (APs) as pre-installed infrastructures are necessary. However, the APs do not need to be specially installed, because WiFi APs are already widely deployed in many indoor areas, for example, offices, malls and airports. This feature makes WiFi based indoor positioning suitable for many practical applications. In this paper, a seq2seq model based, deep learning method is proposed for WiFi based fingerprinting. The model can learn from different length of training sequences, and thus can exploit the context information for positioning. The context information denotes the information contained in the sequence, which can help finding the correspondences between RSS fingerprints and the coordinate positions. A simple example piece of context information is human walking routine (such as no sharp turns). The proposed method shows an improvement with an open source dataset, when compared against deep learning based counterpart methods.

An effective algorithm to overcome the practical hindrance for Wi-Fi based indoor positioning system

AbstractIndoor tracking has evolved with various methods. The most popular method is using signal strength measuring techniques like triangulation, trilateration and fingerprinting, etc. Generally, these methods use the internal sensors of the smartphone. All these techniques require an adequate number of access point signals. The estimated positioning accuracy depends on the number of signals received at any point and precision of its signal (Wi-Fi radio waves) strength. In a practical environment, the received signal strength indicator (RSSI) of the access point is hindered by obstacles or blocks in the direct path or Line of sight. Such access points become an anomaly in the calculation of position. By detecting the anomaly access points and neglecting it during the computation of an indoor position will improve the accuracy of the positioning system. The proposed method, Practical Hindrance Avoidance in an Indoor Positioning System (PHA-IPS), eliminate the anomaly nodes while estimating the position, so then enhances the accuracy.

A Wi-Fi Based Indoor Positioning System for Hospitals using Fingerprinting

How to ensure an efficient medical personnel and equipment management within a hospital and help patients specially in emergency medical conditions. This paper presents a Wi-Fi based indoor positioning system for hospitals. It firstly presents the proposed indoor positioning procedure, analyses the advantages of Wi-Fi compared to the common positioning technologies; then designs the server-based indoor positioning system for hospitals. Finally, fingerprinting algorithm is used to position in real time. Shown as result of the conceived system tests, indoor positioning within hospitals can be realized.

MFA-OSELM Algorithm for WiFi-Based Indoor Positioning System

Indoor localization is a dynamic and exciting research area. WiFi has exhibited a tremendous capability for internal localization since it is extensively used and easily accessible. Facilitating the use of WiFi for this purpose requires fingerprint formation and the implementation of a learning algorithm with the aim of using the fingerprint to determine locations. The most difficult aspect of techniques based on fingerprints is the effect of dynamic environmental changes on fingerprint authentication. With the aim of dealing with this problem, many experts have adopted transfer-learning methods, even though in WiFi indoor localization the dynamic quality of the change in the fingerprint has some cyclic factors that necessitate the use of previous knowledge in various situations. Thus, this paper presents the maximum feature adaptive online sequential extreme learning machine (MFA-OSELM) technique, which uses previous knowledge to handle the cyclic dynamic factors that are brought about by the issue of mobility, which is present in internal environments. This research extends the earlier study of the feature adaptive online sequential extreme learning machine (FA-OSELM). The results of this research demonstrate that MFA-OSELM is superior to FA-OSELM given its capacity to preserve previous data when a person goes back to locations that he/she had visited earlier. Also, there is always a positive accuracy change when using MFA-OSELM, with the best change achieved being 27% (ranging from eight to 27% and six to 18% for the TampereU and UJIIndoorLoc datasets, respectively), which proves the efficiency of MFA-OSELM in restoring previous knowledge.

Unsupervised WiFi-Enabled IoT Device-User Association for Personalized Location-Based Service

A fundamental building block toward personalized location-based service and context-aware service in smart buildings is the knowledge about the identity and mobility of users in indoor environments. Conventional user identification systems require the deployment of dedicated infrastructure or the active user involvement. Motivated by the widespread usage of the WiFi-enabled mobile device (MD), e.g., people usually carry at least one MD in their daily lives, in this paper, we propose WinDUA, a WiFi-enabled nonintrusive device and user association scheme to infer user identity and mobility via a novel unsupervised association learning algorithm. First, we utilize our WiFi-based indoor positioning system to obtain the historical location data of each MD using only existing WiFi infrastructure in a nonintrusive manner. Then, we classify all the MDs into two categories: 1) static device (SD) and 2) mobile phone (MP), according to their location variations and overnight presences. Subsequently, we estimate the correct mapping between each SD and its user through hierarchical clustering and location similarity matching between its location and user's personal space. Finally, we make possible pairs of MP and SD according to their duration of coexistence as well as the historical location similarity to associate the owner of each MP. Real-world experiments are conducted in an office, verifying that WinDUA is able to associate the MD to the correct users in a nonintrusive and unsupervised manner.

A Hybrid Model Based on Constraint OSELM, Adaptive Weighted SRC and KNN for Large-Scale Indoor Localization

In this paper, a novel hybrid model based on the constraint online sequential extreme learning machine (COSELM) classifier with adaptive weighted sparse representation classification (WSRC) and k nearest neighbor (KNN) is proposed for the WiFi-based indoor positioning system. It is referred to as A Fast-Accurate-Reliable Localization System (AFARLS). AFARLS exploits the speed advantage of COSELM to reduce the computational cost, and the accuracy advantage of WSRC to enhance the classification performance, by utilizing KNN as the adaptive sub-dictionary selection strategy. The understanding is that the original extreme learning machine (ELM) is less robust against noise, while sparse representation classification (SRC) and KNN suffer a high computational burden when using the over-complete dictionary. AFARLS unifies their complementary strengths to resolve each other’s limitation. In large-scale multi-building and multi-floor environments, AFARLS estimates a location that considers the building, floor, and position (longitude and latitude) in a hierarchical and sequential approach according to a discriminative criterion to the COSELM output. If the classifier result is unreliable, AFARLS uses KNN to achieve the best relevant sub-dictionary. The sub-dictionary is fed to WSRC to re-estimate the building and the floor, while the position is predicted by the ELM regressor. AFARLS has been verified on two publicly available datasets, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EU Zenodo</i> and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UJIIndoorLoc</i> . The experimental results demonstrate that AFARLS outperforms the state-of-the-art algorithms on the former dataset, and it provides near state-of-the-art performance on the latter dataset. When the size of the dataset increases remarkably, AFARLS shows that it can maintain its real-time high-accuracy performance.

Understanding occupancy pattern and improving building energy efficiency through Wi-Fi based indoor positioning

Detailed visualisation and data analysis of occupancy patterns including spatial distribution and temporal variations are of great importance to delivering energy efficient and productive buildings. An experimental study comprising 24-h monitoring over 30 full days was conducted in a university library building. Occupancy profiles have been monitored and analysis has been carried out. Central to this monitoring study is the Wi-Fi based indoor positioning system based on the measured Wi-Fi devices' number and locations and data mining methods. Distinct from traditional occupancy and energy studies, more detailed information related to the indoor positions and number of occupants has offered a better understanding of building user behaviour. The implication of the occupancy patterns for energy (e.g. lighting and other building services) efficiency is assessed, assisted with data from lighting sensors where needed. It is found occupancy patterns change dramatically with time. Also, the energy waste patterns have been identified through the method of data association rule mining. If the identified energy waste is removed, the total energy consumption can be reduced by 26.1%. The indoor positioning information also has implications for optimizing space use, opening hours as well as staff deployment. The work could be extended to more rooms with diverse functions, other seasons and other types of non-domestic buildings for a more comprehensive understanding of building user behaviour and energy efficiency.

The Quick Radio Fingerprint Collection Method for a WiFi-Based Indoor Positioning System

In the WiFi-based indoor positioning system (IPS), by using WiFi access points (APs) on hand and without requiring the APs' positions, the scene analysis method has better positioning accuracy and attracts many researchers to devote their efforts to it. However, this method needs to perform a sampling process in order to collect the received signal strength indicators (RSSIs) of the APs from the places of interest to build the building's WiFi radio fingerprint database in advance. It also needs to resample frequently in order to keep the fingerprint database updated and accurate. Both tasks are time-consuming, therefore we propose a quick radio fingerprint collection (QRFC) algorithm for collecting the sampling information. The Android smartphone and its built-in motion sensors were used to help the collection process. QRFC makes the sampling process simpler, and the time needed for the sampling is close to the time of walking slowly through the path. We also propose a heuristic AP RSSI shaping algorithm to compensate for the signal attenuation caused by multi-path, shadowing, and mask effects. Several field tests were performed to compare the QRFC method to the traditional method. Experiments show that there is no significant difference in their accuracy, but QRFC takes much less time to complete.

Application of WiFi-based indoor positioning system for labor tracking at construction sites: A case study in Guangzhou MTR

For construction management, tracking the locations of construction resources such as labor, materials machinery, and vehicles is important. Some alternative form of location monitoring system is necessary for indoor environments such as tunnels and buildings under construction, because of systematic limitations of the Global Positioning System (GPS). This study investigated the feasibility of a WiFi-based indoor positioning system for construction sites. The system was developed using the fingerprint method of Received Signal Strength Indication (RSSI) from each Access Point (AP). A series of experiments were conducted at a shield tunnel construction site in Guangzhou, China. The results showed that the WiFi-based indoor positioning system was accurate within 5 m of error for that site, thus proving the utility of the system for tracking the approximate locations of labor at construction sites. Additionally, this system could be used for monitoring the locations of other construction resources such as vehicles and materials.