Abstract
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.
Highlights
The importance of the indoor localization has been increased during the last decade due to the need of practical indoor localization system that meets the requirements of the people
Where: initialRSS is the Received Signal Strength (RSS) at 1 m from the access point, RSS is the received RSS value, λ is the path loss exponent, d is the distance between the receiver and the access point
Relying only on the access points that are attached to the ceiling or to the wall of the building makes the received signals clear and in line of sight which leads to accurate distance estimation
Summary
The importance of the indoor localization has been increased during the last decade due to the need of practical indoor localization system that meets the requirements of the people. The pervasiveness of the Wi-Fi in the public buildings such as Airports, shopping malls, etc., and the easiness of getting the Wi-Fi signals have enabled the researchers to employ Wi-Fi technology in indoor localization techniques. Many applications need an accurate indoor localization system to offer localization services to the people, who spend most of their time in indoor environments [1,2]. Providing the user’s position in Airports, shopping malls, and hospitals are considered as location-based services. To estimate the 2D position of the users based on trilateration approach, at least three different signals are required
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