A Self-Adaptive AP Selection Algorithm Based on Multiobjective Optimization for Indoor WiFi Positioning

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 a...

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Veröffentlicht in:IEEE internet of things journal Jg. 8; H. 3; S. 1406 - 1416
Hauptverfasser: Zhang, Wei, Yu, Kegen, Wang, Weixi, Li, Xiaoming
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Access point (AP) selection
Accuracy
Adaptive algorithms
Algorithms
Calibration
Fingerprint recognition
Fixtures
Indoor environments
indoor WiFi positioning
Internet of Things
Internet of Things (IoT)
IP networks
Machine learning
multiobjective optimization
Multiple objective analysis
Normalizing
Optimization
self-adaptive algorithm
Signal quality
Signal strength
Wireless access points
Wireless communication systems
Wireless fidelity
ISSN:2327-4662, 2327-4662
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Zusammenfassung: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.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2020.3011402