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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Published in:	IEEE internet of things journal Vol. 6; no. 3; pp. 4844 - 4853
Main Authors:	Li, Wenjun, Chen, Zhenyu, Gao, Xingyu, Liu, Wei, Wang, Jin
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.06.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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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Abstract	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.
AbstractList	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.
Author	Wang, Jin Gao, Xingyu Chen, Zhenyu Liu, Wei Li, Wenjun
Author_xml	– sequence: 1 givenname: Wenjun orcidid: 0000-0001-6121-588X surname: Li fullname: Li, Wenjun organization: School of Computer and Communication Engineering and the Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha, China – sequence: 2 givenname: Zhenyu surname: Chen fullname: Chen, Zhenyu organization: State Key Laboratory of Power Grid Security and Energy Saving and the Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology, China Electric Power Research Institute, Beijing, China – sequence: 3 givenname: Xingyu surname: Gao fullname: Gao, Xingyu organization: Chinese Academy of Sciences, Institute of Microelectronics, Beijing, China – sequence: 4 givenname: Wei surname: Liu fullname: Liu, Wei organization: School of Information Engineering, Yangzhou University, Yangzhou, China – sequence: 5 givenname: Jin orcidid: 0000-0001-5473-8738 surname: Wang fullname: Wang, Jin email: jinwang@csust.edu.cn organization: School of Computer and Communication Engineering and the Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha, China
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SubjectTerms	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
Title	Multimodel Framework for Indoor Localization Under Mobile Edge Computing Environment
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