Wireless LAN Positioning based on Received Signal Strength from Mobile device and Access Points

2007 ◽  
Author(s):  
Wilson M. Yeung ◽  
Joseph K. Ng
Keyword(s):  
Mobile Device ◽  
Wireless Lan ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Access Points

Wireless LAN Security Management with Location Detection Capability in Hospitals

2012 ◽  
Vol 51 (03) ◽  
pp. 221-228 ◽  
Author(s):  
H. Atarashi ◽  
I. Yamaguchi ◽  
H. Watanabe ◽  
R. Yamamoto ◽  
K. Ohe ◽  
...  
Keyword(s):  
Wireless Lan ◽  
Radio Signal ◽  
Security Management ◽  
Signal Strength ◽  
Detection Performance ◽  
Stable Operation ◽  
Wireless Devices ◽  
Monitoring Method ◽  
Access Points ◽  
Location Detection

SummaryObjectives: In medical institutions, unauthorized access points and terminals obstruct the stable operation of a large-scale wireless local area network (LAN) system. By establishing a real-time monitoring method to detect such unauthorized wireless devices, we can improve the efficiency of security management.Methods: We detected unauthorized wireless devices by using a centralized wireless LAN system and a location detection system at 370 access points at the University of Tokyo Hospital. By storing the detected radio signal strength and location information in a database, we evaluated the risk level from the detection history. We also evaluated the location detection performance in our hospital ward using Wi-Fi tags.Results: The presence of electric waves outside the hospital and those emitted from portable game machines with wireless communication capability was confirmed from the detection result. The location detection performance showed an error margin of approximately 4 m in detection accuracy and approximately 5% in false detection. Therefore, it was effective to consider the radio signal strength as both an index of likelihood at the detection location and an index for the level of risk.Conclusions: We determined the location of wireless devices with high accuracy by filtering the detection results on the basis of radio signal strength and detection history. Results of this study showed that it would be effective to use the developed location database containing radio signal strength and detection history for security management of wireless LAN systems and more general-purpose location detection applications.

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.

Indoor Tracking and Navigation Using Received Signal Strength and Compressive Sensing on a Mobile Device

2013 ◽  
Vol 12 (10) ◽  
pp. 2050-2062 ◽  
Author(s):  
Anthea Wain Sy Au ◽  
Chen Feng ◽  
Shahrokh Valaee ◽  
Sophia Reyes ◽  
Sameh Sorour ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Mobile Device ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Indoor Tracking

scholarly journals Fingerprint Database Variations for WiFi Positioning

2013 ◽  
Vol 11 (10) ◽  
pp. 3101-3107
Author(s):  
Nelson Acosta ◽  
Juan Toloza ◽  
Carlos Kornuta
Keyword(s):  
Mobile Device ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Relative Precision ◽  
Received Signal Strength Indicator ◽  
Positioning Systems ◽  
Fingerprinting Technique ◽  
Euclidian Distance ◽  
Fingerprint Database

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.

scholarly journals Wi-Fi RSS-Based Approach for Locating the Position of Indoor Wi-Fi Access Point

2019 ◽  
Vol 21 (4) ◽  
pp. 69-74
Author(s):  
Ahmad Abadleh
Keyword(s):  
Small Area ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
High Accuracy ◽  
Movement Direction ◽  
Dynamic Threshold ◽  
Access Points ◽  
Two Phases

This paper presents an approach to automatically detect the position of the Wi-Fi access points. It uses Wi-Fi received signal strength as well as some characteristics of the buildings such as the height of the building and the movement direction of the user to detect the position of the access points. This approach comprised of two phases: in phase one, a dynamic threshold is computed for each detected access point using the highest received signal strength. Then the threshold is used to detect a small area surrounding the access point. In phase two, it detects the position of the access point by monitoring the angle between the user and the access point, if the angle is in a certain range, then the position of the access point is detected. The experiments results show a high accuracy achieved by the proposed approach. Moreover, the results show that the proposed approach is promising.

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