An indoor location-based service using access points as signal strength data collectors

2010 ◽  
Author(s):  
Kuo-Fong Kao ◽  
I-En Liao ◽  
Jia-Siang Lyu
Keyword(s):  
Signal Strength ◽  
Location Based Service ◽  
Strength Data ◽  
Access Points ◽  
Indoor Location

scholarly journals Improving Accuracy and Simplifying Training in Fingerprinting-Based Indoor Location Algorithms at Room Level

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Mario Muñoz-Organero ◽  
Claudia Brito-Pacheco
Keyword(s):  
Artificial Intelligence ◽  
Radio Wave ◽  
Mobile Devices ◽  
Signal Strength ◽  
Received Signal Strength Indicator ◽  
Access Points ◽  
Indoor Location ◽  
Improving Accuracy ◽  
Indoor Location Systems ◽  
Decision Algorithms

Fingerprinting-based algorithms are popular in indoor location systems based on mobile devices. Comparing the RSSI (Received Signal Strength Indicator) from different radio wave transmitters, such as Wi-Fi access points, with prerecorded fingerprints from located points (using different artificial intelligence algorithms), fingerprinting-based systems can locate unknown points with a few meters resolution. However, training the system with already located fingerprints tends to be an expensive task both in time and in resources, especially if large areas are to be considered. Moreover, the decision algorithms tend to be of high memory and CPU consuming in such cases and so does the required time for obtaining the estimated location for a new fingerprint. In this paper, we study, propose, and validate a way to select the locations for the training fingerprints which reduces the amount of required points while improving the accuracy of the algorithms when locating points at room level resolution. We present a comparison of different artificial intelligence decision algorithms and select those with better results. We do a comparison with other systems in the literature and draw conclusions about the improvements obtained in our proposal. Moreover, some techniques such as filtering nonstable access points for improving accuracy are introduced, studied, and validated.

Indoor Location Position Based on Bluetooth Signal Strength

2015 ◽  
Author(s):  
Mohamed Er Rida ◽  
Fuqiang Liu ◽  
Yassine Jadi ◽  
Amgad Ali Abdullah Algawhari ◽  
Ahmed Askourih
Keyword(s):  
Signal Strength ◽  
Indoor Location
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