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Probabilistic Recovery of Incomplete Sensed Data in IoT

Authors
Fekade, BerihunMaksymyuk, TarasKyryk, MaryanJo, Minho
Issue Date
8월-2018
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Internet of Things (IoT); massive sensed data; probabilistic matrix factorization (PMF); recovery of missing sensor data
Citation
IEEE INTERNET OF THINGS JOURNAL, v.5, no.4, pp.2282 - 2292
Indexed
SCIE
SCOPUS
Journal Title
IEEE INTERNET OF THINGS JOURNAL
Volume
5
Number
4
Start Page
2282
End Page
2292
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/73846
DOI
10.1109/JIOT.2017.2730360
ISSN
2327-4662
Abstract
Reliable data delivery in the Internet of Things (IoT) is very important in order to provide IoT-based services with the required quality. However, IoT data delivery may not be successful for different reasons, such as connection errors, external attacks, or sensing errors. This results in data incompleteness, which decreases the performance of IoT applications. In particular, the recovery of missing data among the massive sensed data of the IoT is so important that it should be solved. In this paper, we propose a probabilistic method to recover missing (incomplete) data from IoT sensors by utilizing data from related sensors. The main idea of the proposed method is to perform probabilistic matrix factorization (PMF) within the preliminary assigned group of sensors. Unlike previous PMF approaches, the proposed model measures the similarity in data among neighboring sensors and splits them into different clusters with a K-means algorithm. Simulation results show that the proposed PMF model with clustering outperforms support vector machine (SVM) and deep neural network (DNN) algorithms in terms of accuracy and root mean square error. By using normalized datasets, PMF shows faster execution time than SVM, and almost the same execution time as the DNN method. This proposed incomplete data-recovery approach is a promising alternative to traditional DNN and SVM methods for IoT telemetry applications.
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