Missing Data Estimation in Mobile Sensing Environments

Zhou, Yuchao, De, Suparna, Wang, Wei, Wang, Ruili and Moessner, Klaus (2018) Missing Data Estimation in Mobile Sensing Environments IEEE Access, 6 (1). pp. 69869-69882.

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Abstract

Mobile sensing techniques have been increasingly deployed in many Internet of Things based applications because of their cost efficiency, wide coverage and flexibility. However, these techniques are unreliable in many situations due to noise of different kinds, loss of communication, or insufficient energy. As such, datasets created from mobile sensing scenarios are likely to contain large amount of missing data, which makes further data analysis difficult, inaccurate, or even impossible. We find that the existing estimation models and techniques developed for static sensing do not work well in the mobile sensing scenarios. To address the problem, we propose a spatio-temporal method, which is specifically designed for answering queries in such applications. Experiments on a real-world, incomplete mobile sensing dataset show that the proposed method outperforms the state-of-the-art noticeably in terms of estimation errors. More importantly, the proposed model is tolerant to datasets with extremely high missing data rates. Training with the proposed model is also efficient, which makes it suitable for deployment on computationally constrained devices and platforms that need to process massive amounts of data in real time.

Item Type:	Article
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :	Name Email ORCID Zhou, Yuchao yz0003@surrey.ac.uk De, Suparna S.De@surrey.ac.uk Wang, Wei Wang, Ruili Moessner, Klaus K.Moessner@surrey.ac.uk
Date :	24 October 2018
Funders :	European Horizon 2020
DOI :	10.1109/ACCESS.2018.2877847
Copyright Disclaimer :	Copyright 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information
Uncontrolled Keywords :	Missing sensor data, data estimation, mobile sensing, support vector regression, spatiotemporal model.
Depositing User :	Melanie Hughes
Date Deposited :	23 Oct 2018 11:43
Last Modified :	11 Dec 2018 11:24
URI:	http://epubs.surrey.ac.uk/id/eprint/849759

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