Survey on the Indoor Localization Technique of Wi-Fi Access Points

2018 ◽  
Vol 10 (3) ◽  
pp. 27-42
Author(s):  
Yimin Liu ◽  
Wenyan Liu ◽  
Xiangyang Luo
Keyword(s):  
Indoor Localization ◽  
Signal Strength ◽  
Access Point ◽  
Distance Measurement ◽  
Basic Principles ◽  
Localization Technique ◽  
Related Research ◽  
Research Results ◽  
Access Points ◽  
Fingerprint Feature

This article describes how indoor localization of Wi-Fi AP (access point) plays an important role in the discovery of illegal indoor Wi-Fi and for the safety inspection of confidential places. There have been many related research results in recent years. In this article, a review is presented on the indoor localization technique of Wi-Fi AP. First, indoor localization methods of Wi-Fi AP can be divided into three categories: localization based on signal strength; fingerprint feature; and distance measurement. Then, the basic principles of the three methods are described respectively, and an evaluation of the typical methods are provided. Finally, the authors point out some research tendency of the indoor localization techniques of Wi-Fi AP.

scholarly journals Received-Signal-Strength-Based Approach for Detection and 2D Indoor Localization of Evil Twin Rogue Access Point in 802.11

2021 ◽  
Vol 11 (1) ◽  
pp. 13-20
Author(s):  
Roman Y. Korolkov ◽  
Serhii V. Kutsak
Keyword(s):  
Indoor Localization ◽  
Signal Strength ◽  
Access Point ◽  
Signal Propagation ◽  
Received Signal Strength ◽  
False Alarms ◽  
Access Points ◽  
Network Analyzers ◽  
Indoor Conditions ◽  
Rogue Access Point

The “Evil twin” rogue access point is one of the most serious security threats to wireless LANs. To solve this problem, a practical approach has been proposed for detecting rogue access points using the received signal strength indicator (RSSI). First, a distributed architecture is presented, which consists of three network analyzers. Then, a cluster analysis of the RSSI vectors is performed to determine the attack. The coordinates of the centroids of clusters obtained were converted into the distance by using an empirical model of signal propagation under indoor conditions. The obtained distances are used to determine the localization of a rogue access point (RAP) using the trilateration method. Finally, we are conducting experiments to evaluate the performance of practical RAP detection. The results show that the proposed approach to detecting rogue access points can significantly reduce the frequency of false alarms, while providing an average localization error of 1.5m, which is quite acceptable for RAP localization in real indoor conditions.

scholarly journals A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2392
Author(s):  
Óscar Belmonte-Fernández ◽  
Emilio Sansano-Sansano ◽  
Antonio Caballer-Miedes ◽  
Raúl Montoliu ◽  
Rubén García-Vidal ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Indoor Localization ◽  
Hidden Markov ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Machine Learning Algorithms ◽  
Forward Algorithm ◽  
Generative Method

Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed.

scholarly journals An Efficient Indoor Wi-Fi Positioning Method Using Virtual Location of AP

2020 ◽  
Vol 9 (4) ◽  
pp. 261
Author(s):  
Fan Xu ◽  
Xuke Hu ◽  
Shuaiwei Luo ◽  
Jianga Shang
Keyword(s):  
Indoor Localization ◽  
Attenuation Factor ◽  
Access Point ◽  
Location Estimation ◽  
Signal Attenuation ◽  
Distance Ratio ◽  
Localization Accuracy ◽  
Access Points ◽  
Attenuation Parameter ◽  
The Impact

Wi-Fi fingerprinting has been widely used for indoor localization because of its good cost-effectiveness. However, it suffers from relatively low localization accuracy and robustness owing to the signal fluctuations. Virtual Access Points (VAP) can effectively reduce the impact of signal fluctuation problem in Wi-Fi fingerprinting. Current techniques normally use the Log-Normal Shadowing Model to estimate the virtual location of the access point. This would lead to inaccurate location estimation due to the signal attenuation factor in the model, which is difficult to be determined. To overcome this challenge, in this study, we propose a novel approach to calculating the virtual location of the access points by using the Apollonius Circle theory, specifically the distance ratio, which can eliminate the attenuation parameter term in the original model. This is based on the assumption that neighboring locations share the same attenuation parameter corresponding to the signal attenuation caused by obstacles. We evaluated the proposed method in a laboratory building with three different kinds of scenes and 1194 test points in total. The experimental results show that the proposed approach can improve the accuracy and robustness of the Wi-Fi fingerprinting techniques and achieve state-of-art performance.

scholarly journals Fusion of Channel State Information and Received Signal Strength for Indoor Localization Using a Single Access Point

2020 ◽  
Vol 12 (12) ◽  
pp. 1995
Author(s):  
David Sánchez-Rodríguez ◽  
Miguel A. Quintana-Suárez ◽  
Itziar Alonso-González ◽  
Carlos Ley-Bosch ◽  
Javier J. Sánchez-Medina
Keyword(s):  
Channel State Information ◽  
Indoor Localization ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Location Estimation ◽  
Channel State ◽  
State Information ◽  
Single Access ◽  
5 Ghz

In recent years, indoor localization systems based on fingerprinting have had significant advances yielding high accuracies. Those approaches often use information about channel communication, such as channel state information (CSI) and received signal strength (RSS). Nevertheless, these features have always been employed separately. Although CSI provides more fine-grained physical layer information than RSS, in this manuscript, a methodology for indoor localization fusing both features from a single access point is proposed to provide a better accuracy. In addition, CSI amplitude information is processed to remove high variability information that can negatively influence location estimation. The methodology was implemented and validated in two scenarios using a single access point located in two different positions and configured in 2.4 and 5 GHz frequency bands. The experiments show that the methodology yields an average error distance of about 0.1 m using the 5 GHz band and a single access point.

scholarly journals Hybrid Machine Learning Classifiers for Indoor User Localization Problem

2021 ◽  
Vol 10 (3) ◽  
pp. 49-53
Author(s):  
Hamza Turabieh ◽  
Ahmad S. Alghamdi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Indoor Localization ◽  
Signal Strength ◽  
Access Point ◽  
Support Vector ◽  
Linear Discriminant ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
User Location

Wi-Fi technology is now everywhere either inside or outside buildings. Using Wi-fi technology introduces an indoor localization service(s) (ILS). Determining indoor user location is a hard and complex problem. Several applications highlight the importance of indoor user localization such as disaster management, health care zones, Internet of Things applications (IoT), and public settlement planning. The measurements of Wi-Fi signal strength (i.e., Received Signal Strength Indicator (RSSI)) can be used to determine indoor user location. In this paper, we proposed a hybrid model between a wrapper feature selection algorithm and machine learning classifiers to determine indoor user location. We employed the Minimum Redundancy Maximum Relevance (mRMR) algorithm as a feature selection to select the most active access point (AP) based on RSSI values. Six different machine learning classifiers were used in this work (i.e., Decision Tree (DT), Support Vector Machine (SVM), k-nearest neighbors (kNN), Linear Discriminant Analysis (LDA), Ensemble-Bagged Tree (EBaT), and Ensemble Boosted Tree (EBoT)). We examined all classifiers on a public dataset obtained from UCI repository. The obtained results show that EBoT outperforms all other classifiers based on accuracy value/

scholarly journals Penggunaan Metode Simulated Annealing untuk Optimasi Penempatan Posisi Access Point pada Jaringan WI-FI

2019 ◽  
Vol 5 (1) ◽  
pp. 10
Author(s):  
Ganang Eko Noviardianto ◽  
Muhammad Novel ◽  
Mercurius Broto Legowo
Keyword(s):  
Simulated Annealing ◽  
Statistical Mechanics ◽  
Signal Strength ◽  
Access Point ◽  
Simulated Annealing Method ◽  
Access Points ◽  
Optimum Solution ◽  
Received Signal Strength Indication ◽  
Annealing Method

<p><em>Abstrak</em><strong> </strong>– <strong>Tujuan dari penelitian ini adalah untuk menempatkan titik akses pada jaringan Wi-Fi</strong><strong>. </strong><strong>Dengan demikian, kekuatan sinyal yang diterima dari pemancar ke penerima adalah optimal. Masalah muncul ketika menempatkan titik akses untuk mempengaruhi nilai kekuatan sinyal. Selanjutnya, nilai ini akan digunakan untuk menentukan area jangkauan (jangkauan sinyal) dari pemancar</strong><strong> (<em>access point</em></strong><strong>). Penelitian ini adalah studi kasus di Kampus Institut Perbanas di mana pengukuran kekuatan sinyal dari titik akses terhadap penerima di unit 2 dan unit 6 dari Perbanas Institute diukur menggunakan aplikasi inSSIDer. Studi kasus ini menghasilkan nilai Indikasi Kekuatan Sinyal yang Diterima (RSSI) dari pemancar ke penerima. Metode</strong><strong> </strong><strong><em>Simulated</em></strong><strong><em> </em></strong><strong><em>Annealing</em></strong><strong> diterapkan dalam penelitian ini, dengan alasan untuk optimasi generik. Berdasarkan probabilitas dan mekanika statistik, algoritma ini dapat digunakan untuk menemukan pendekatan pada solusi optimal untuk suatu masalah. Hasil penelitian menunjukkan bahwa </strong><strong>setelah dilakukan optimisasi menggunakan </strong><strong>m</strong><strong>etode</strong><strong> </strong><strong><em>Simulated</em></strong><strong><em> </em></strong><strong><em>Annealing</em></strong><strong><em> </em></strong><strong>prosentase <em>koverage area</em> sebesar 98.66 % dan, diperoleh</strong> <strong>kenaikan persentase <em>koverage area</em> sebesar 87.15%.</strong><strong> Dengan demikian, penelitian ini akan memberikan kontribusi untuk memodelkan penempatan </strong><strong>posis </strong><strong>titik akses</strong><strong> </strong><strong>dan kekuatan sinyal </strong><strong>pada jaringan Wi-Fi </strong><strong>yang diperoleh di Unit 2 dan Unit 6 Kampus Institut Perbanas</strong></p><p><em>Abstract</em> – <strong>The purpose of this study is to place access points on Wi-Fi networks. Thus, the signal strength received from the transmitter to the receiver is optimal. Problems arise when placing access points to influence the signal strength value. Furthermore, this value will be used to determine the koverage area (signal koverage) of a transmitter (access point). This research is a case study at the Perbanas Institute Campus where measurements of the signal strength of the access point towards recipients in unit 2 and unit 6 of Perbanas Institute were measured using the inSSIDer application. This case study produced a Received Signal Strength Indication (RSSI) value from a transmitter to the receiver. </strong><strong>The Simulated Annealing method applied in this study, with reasons for generic optimization. </strong><strong>Based on probability and statistical mechanics, this algorithm can be used to find an approach to the optimum solution to a problem. </strong><strong>The results showed that after optimization using the Simulated Annealing method the percentage of koverage area was 98.66%. And, the percentage of the koverage area was increased by 87.15%.</strong> <strong>Thus, this study will contribute to modeling the placement of access points and signal strength in Wi-Fi networks obtained in Units 2 and 6 of the Perbanas Institute Campus.</strong></p><p><strong><em>Keywords</em></strong> -  <em>Koverage Area</em><em>, </em><em>RSSI</em><em>, </em><em>Simulated Annealing</em><em>, </em><em>Propaga</em><em>tion, </em><em>Wi-F</em><em>i</em><strong><em></em></strong></p>

scholarly journals Indoor localization system based on virtual access points with filtering schemes

2019 ◽  
Vol 15 (7) ◽  
pp. 155014771986613 ◽  
Author(s):  
Dong Myung Lee ◽  
Boney Labinghisa
Keyword(s):  
Kalman Filter ◽  
Indoor Environment ◽  
Performance Testing ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Indoor Environments ◽  
Received Signal Strength Indicator ◽  
Access Points ◽  
Error Distance

In indoor positioning techniques, Wi-Fi is one of the most used technology because of its availability and cost-effectiveness. Access points are usually the main source of Wi-Fi signals in an indoor environment. If access points are optimized to cover the indoor area, this could improve Wi-Fi signal distribution. This article proposed an alternative to optimizing access point placement and distribution by introducing virtual access points that can be virtually placed in any part of the indoor environment without installation of actual access points. Virtual access points will be created heuristically by correlating received signal strength indicator of already existing access points and through linear regression. After introducing virtual access points in the indoor environment, next will be the addition of filters to improve signal fluctuation and reduce noise interference. Kalman filter has been previously used together with virtual access point and showed improvement by decreasing error distance of Wi-Fi fingerprinting results. This article also aims to include particle filter in the system to further improve localization and test its effectiveness when paired with Kalman filter. The performance testing of the algorithm in different indoor environments resulted in 3.18 and 3.59 m error distances. An improvement was added on the system by using relative distances instead of received signal strength indicator values in distance estimation and gave an error distance average of 1.85 m.

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

2020 ◽  
Vol 10 (1) ◽  
pp. 117-123
Author(s):  
Bhulakshmi Bonthu ◽  
M Subaji
Keyword(s):  
Signal Strength ◽  
Access Point ◽  
Indoor Positioning ◽  
Positioning System ◽  
Radio Waves ◽  
Direct Path ◽  
Received Signal Strength Indicator ◽  
Access Points ◽  
Measuring Techniques ◽  
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.

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