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Research paper | Open Access

Missing observation approximation for spatio-temporal profile reconstruction in participatory sensor networks

Assad Mehmood1, Kashif Zia2, Arshad Muhammad2( ), Dinesh Kumar Saini2
Saudi Ministry of Defense, Riyadh, Saudi Arabia
Department of Computing and IT, Sohar University, Sohar, Oman
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Abstract

Purpose

Participatory wireless sensor networks (PWSN) is an emerging paradigm that leverages existing sensing and communication infrastructures for the sensing task. Various environmental phenomenon – P monitoring applications dealing with noise pollution, road traffic, requiring spatio-temporal data samples of P (to capture its variations and its profile construction) in the region of interest – can be enabled using PWSN. Because of irregular distribution and uncontrollable mobility of people (with mobile phones), and their willingness to participate, complete spatio-temporal (CST) coverage ofP may not be ensured. Therefore, unobserved data values must be estimated for CST profile construction of P and presented in this paper.

Design/methodology/approach

In this paper, the estimation of these missing data samples both in spatial and temporal dimension is being discussed, and the paper shows that non-parametric technique – Kernel Regression – provides better estimation compared to parametric regression techniques in PWSN context for spatial estimation. Furthermore, the preliminary results for estimation in temporal dimension have been provided. The deterministic and stochastic approaches toward estimation in the context of PWSN have also been discussed.

Findings

For the task of spatial profile reconstruction, it is shown that non-parametric estimation technique (kernel regression) gives a better estimation of the unobserved data points. In case of temporal estimation, few preliminary techniques have been studied and have shown that further investigations are required to find out best estimation technique(s) which may approximate the missing observations (temporally) with considerably less error.

Originality/value

This study addresses the environmental informatics issues related to deterministic and stochastic approaches using PWSN.

Keywords

RegressionApproximationEnvironmental informaticsSpatial-Temporal profile

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International Journal of Crowd Science
Volume 2 Issue 2,
November 2018
Pages 108-122
DOI: 10.1108/IJCS-05-2018-0009
Cite this article:
Mehmood A, Zia K, Muhammad A, et al. Missing observation approximation for spatio-temporal profile reconstruction in participatory sensor networks. International Journal of Crowd Science, 2018, 2(2): 108-122. https://doi.org/10.1108/IJCS-05-2018-0009

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Received: 30 May 2018
Revised: 28 August 2018
Accepted: 03 September 2018
Published: 31 October 2018
© The author(s)

Assad Mehmood, Kashif Zia, Arshad Muhammad and Dinesh Kumar Saini. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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