Abstract
One of the most significant researches in location-based services is the development of effective indoor localization. In this work, we propose a novel model of fingerprint localization, which divides location area into different subareas by fuzzy C-means and calculates location via relative distance fuzzy localization. In offline training stage, fuzzy C-means algorithm is used in localization model to divide localization area into different subareas and then to select the useful access points in subareas to reduce the dimensions of fingerprint. In online location stage, we use the nearest neighbor algorithm to select the subareas and to calculate the coordinate of the target point according to relative distance fuzzy localization algorithm, which converts traditional fingerprint of reference points into distance fingerprint and calculates the coordinate of the target point by fuzzy C-means algorithm. The noise and non-linear attenuation between the wireless signal and distance are taken into full consideration in relative distance fuzzy localization algorithm, which eliminates the random environmental noise. Experiments show that our proposed model is able to save the calculation time and improve the localization accuracy.
Highlights
Today, the Internet of Things (IoT) has received growing attention and has been investigated in widespread fields such as mobile communication, wireless network, and intelligent devices
Target subarea is selected utilizing nearest neighbor (NN) algorithm based on useful access points (APs), after which the target location is calculated by relative distance fuzzy localization (RDFL) algorithms proposed in this article
An indoor positioning model based on fingerprint has been designed
Summary
The Internet of Things (IoT) has received growing attention and has been investigated in widespread fields such as mobile communication, wireless network, and intelligent devices. Traditional fingerprint localization technology requires that all the fingerprint information be compared and calculated, which would result in the high complexity and low real time of the system.[11,12] Virtual Reference Elimination (VIRE)[13] requires only a small amount of fingerprint information to create fingerprint database through D-value and sparse reconstruction It could be influenced by the difference of heterogeneous devices, leading to the larger errors. Target subarea is selected utilizing nearest neighbor (NN) algorithm based on useful APs, after which the target location is calculated by relative distance fuzzy localization (RDFL) algorithms proposed in this article. A novel model of indoor location, used to reduce the computation complexity of fingerprint during the online stage, is shown in section ‘‘Methodology.’’ In order to evaluate the performance of the proposed model, several experiments have been conducted, which is shown in section ‘‘Experimental result.’’ Section ‘‘Conclusion’’ concludes the article
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Distributed Sensor Networks
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
