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A Probabilistic Tensor Factorization approach for Missing Data Inference in Mobile Crowd-Sensing

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Abstract
Mobile crowd-sensing (MCS) is a promising sensing paradigm that leverages mobile users with smart devices to perform large-scale sensing tasks in order to provide services to specific applications in various domains. However, MCS sensing tasks may not always be successfully completed or timely completed for various reasons, such as accidentally leaving the tasks incomplete by the users, asynchronous transmission, or connection errors. This results in missing sensing data at specific locations and times, which can degrade the performance of the applications and lead to serious casualties. Therefore, in this paper, we propose a missing data inference approach, called missing data approximation with probabilistic tensor factorization (MDI-PTF), to approximate the missing values as closely as possible to the actual values while taking asynchronous data transmission time and different sensing locations of the mobile users into account. The proposed method first normalizes the data to limit the range of the possible values. Next, a probabilistic model of tensor factorization is formulated, and finally, the data are approximated using the gradient descent method.
The performance of the proposed algorithm is verified by conducting simulations under various situations using different datasets.
Author(s)
악터 샤티윤석훈
Issued Date
2021
Type
Article
Keyword
mobile crowd-sensingmissing data inferenceprobabilistic tensor factorizationgradient descent
DOI
10.7236/IJIBC.2021.13.3.63
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9166
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_nrf_kci_oai_kci_go_kr_ARTI_9857756&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,A%20Probabilistic%20Tensor%20Factorization%20approach%20for%20Missing%20Data%20Inference%20in%20Mobile%20Crowd-Sensing&offset=0&pcAvailability=true
Publisher
The International Journal of Internet, Broadcasting and Communication
Location
대한민국
Language
영어
ISSN
2288-4920
Citation Volume
13
Citation Number
3
Citation Start Page
63
Citation End Page
72
Appears in Collections:
Engineering > IT Convergence
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