Optimal Granularity Selection for Indoor Localization Detection with Wireless IoT Networks

Indoor localization detection acts as an important issue and has wide applications with wireless Internet of Things (IoT) networks. In recent years, the WiFi-based localization by using the latest artificial intelligence methods for improving the detection accuracy has attracted attention of many re...

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Bibliographic Details
Published in:Wireless communications and mobile computing 2022-08, Vol.2022, p.1-9
Main Authors: Cao, Feng, Zhang, Jing, Li, Deyu, Qian, Yuhua, Tang, Chao, Zhang, Xialei, Hu, Zhiguo
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Ant colony optimization
Artificial intelligence
Computation
Entropy
Internet of Things
Localization
Methods
Neural networks
Optimization
Set theory
Support vector machines
Taxonomy
Wireless networks
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Summary:Indoor localization detection acts as an important issue and has wide applications with wireless Internet of Things (IoT) networks. In recent years, the WiFi-based localization by using the latest artificial intelligence methods for improving the detection accuracy has attracted attention of many researchers. Granular computing is a newly emerged computing paradigm in artificial intelligence, which focuses on the structured thinking based on multiple levels of granularity. Thus, we introduce granular computing approaches to the task of wireless indoor localization detection, and a novel heuristic data discretization method is proposed based on the binary ant colony optimization and rough set (BACORS) for the selection of optimal granularity. For BACORS, the global optimal cut point set is searched based on the binary ant colony optimization to simultaneously discretize multiple attributes. Meanwhile, the accuracy of approximation classifications coined from rough sets is used to determine the consistent of multiple attribute data. To validate the effectiveness of BACORS, it is applied to a wireless indoor localization data set, and the experimental results indicate that it has promising performance.
ISSN:1530-8669
1530-8677
DOI:10.1155/2022/8156917