Multimodel Framework for Indoor Localization Under Mobile Edge Computing Environment

Location estimation technology under the wireless environment has become a vital technology in the field of mobile edge computing. Especially, under the mobile edge of entire networks environment, indoor location estimation is gradually getting the interest research and application topic, due to tec...

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Veröffentlicht in:IEEE internet of things journal Jg. 6; H. 3; S. 4844 - 4853
Hauptverfasser: Li, Wenjun, Chen, Zhenyu, Gao, Xingyu, Liu, Wei, Wang, Jin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Buildings
Combinatorial analysis
Combinatorial optimization problem
Data collection
Edge computing
Estimation
Global positioning systems
GPS
Indoor environments
indoor localization
Internet of Things
Localization
Mathematical model
Mobile computing
mobile edge computing
multimodel framework
NP-hardness
Optimization
Wireless fidelity
Wireless networks
ISSN:2327-4662, 2327-4662
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Zusammenfassung:Location estimation technology under the wireless environment has become a vital technology in the field of mobile edge computing. Especially, under the mobile edge of entire networks environment, indoor location estimation is gradually getting the interest research and application topic, due to technical constraints of global positioning system technology for indoor environment and the popularity of the mobile edge computing servers. In this paper, the widely used single-model framework for indoor localization is presented as an introduction, which consists of three stages: 1) sample data collection; 2) model building; and 3) localization estimation. And then, through analyzing of the actual scene of indoor localization, a new framework for indoor localization under mobile edge computing environment, named Multimodel, is proposed from the theoretical perspective. It is mainly based on the observation that the environment of the sample data collection and that of localization data collection may change seriously. In order to make up for the shortcomings of this framework, two combinatorial optimization problems are proposed. Later, we discuss the NP-hardness of them in several different cases. In addition, two heuristic algorithms are given, and the performance of which are illustrated by the corresponding experimental results.
Bibliographie:ObjectType-Article-1
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2018.2872133