Paper Info
Title
A Self-Adaptive AP Selection Algorithm Based on Multiobjective Optimization for Indoor WiFi Positioning
Abstract
With the widely deployed wireless access points (APs) and the worldwide popularization of smartphones, WiFi-based indoor positioning has attracted great attention to both industry and academia. Locating and tracking objects within an indoor environment plays an important role in Internet of Things application and service. However, it is a challenging problem to achieve high accuracy using WiFi positioning technique due to the high instability in received signal strength from AP. Thus, it is desirable to select APs by considering both signal strength and connection quality. In this article, an AP selection algorithm based on multiobjective optimization is proposed to improve indoor WiFi positioning accuracy. The self-adaptive AP selection algorithm can be easily applied to various real scenarios and the performance of the new method is considerably better than classical algorithms. Learning algorithm is exploited to obtain the optimal solution for the self-adaptive AP selection algorithm. Experiments are conducted and the proposed algorithm is compared with classical algorithms. The experimental results demonstrate that the performance of the self-adaptive AP selection algorithm is at least a few decimeters better than classical algorithms in terms of RMSE of position estimation. Meanwhile, the new method is robust to the random generation of initial particles and normalizing factor as their effect on the positional accuracy is less than 1 decimeter.
Year
DOI
Venue
2021
10.1109/JIOT.2020.3011402
IEEE Internet of Things Journal
Keywords
DocType
Volume
Access point (AP) selection,indoor WiFi positioning,Internet of Things (IoT),multiobjective optimization,self-adaptive algorithm
Journal
8
Issue
ISSN
Citations 
3
2327-4662
1
PageRank 
References 
Authors
0.37
0
4
Name
Order
Citations
PageRank
Wei Zhang144072.00
Kegen Yu255657.05
Weixi Wang315.77
Xiaoming Li451760.22