{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T21:40:36Z","timestamp":1652391636856},"reference-count":38,"publisher":"Tsinghua University Press","issue":"2","license":[{"start":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T00:00:00Z","timestamp":1540944000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCS"],"published-print":{"date-parts":[[2018,11,29]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Participatory wireless sensor networks (PWSN) is an emerging paradigm that leverages existing sensing and communication infrastructures for the sensing task. Various environmental phenomenon \u2013 <jats:italic>P<\/jats:italic> 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 \u2013 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 of <jats:italic>P<\/jats:italic> may not be ensured. Therefore, unobserved data values must be estimated for CST profile construction of <jats:italic>P<\/jats:italic> and presented in this paper.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>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 \u2013 Kernel Regression \u2013 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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This study addresses the environmental informatics issues related to deterministic and stochastic approaches using PWSN.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijcs-05-2018-0009","type":"journal-article","created":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T14:32:18Z","timestamp":1540996338000},"page":"108-122","source":"Crossref","is-referenced-by-count":1,"title":["Missing observation approximation for spatio-temporal profile reconstruction in participatory sensor networks"],"prefix":"10.26599","volume":"2","author":[{"given":"Assad","family":"Mehmood","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kashif","family":"Zia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arshad","family":"Muhammad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinesh","family":"Kumar Saini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"11138","published-online":{"date-parts":[[2018,10,31]]},"reference":[{"key":"key2021041414000691600_ref001","first-page":"230","article-title":"Metrotrack: predictive tracking of mobile events using mobile phones","volume-title":"International Conference on Distributed Computing in Sensor Systems","year":"2010"},{"issue":"4","key":"key2021041414000691600_ref002","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/S1389-1286(01)00302-4","article-title":"Wireless sensor networks: a survey","volume":"38","year":"2002","journal-title":"Computer Networks"},{"issue":"2","key":"key2021041414000691600_ref003","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MPRV.2007.27","article-title":"Data management in the worldwide sensor web","volume":"6","year":"2007","journal-title":"IEEE Pervasive Computing"},{"issue":"1","key":"key2021041414000691600_ref004","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1504\/IJDATS.2016.075973","article-title":"Route prioritisation in a multi-agent transportation environment via multi-attribute decision making","volume":"8","year":"2016","journal-title":"International Journal of Data Analysis Techniques and Strategies"},{"key":"key2021041414000691600_ref005","volume-title":"Engineering Noise Control: theory and Practice","year":"2009"},{"key":"key2021041414000691600_ref006","year":"2006"},{"key":"key2021041414000691600_ref007","first-page":"13","article-title":"Sensing data market","volume-title":"Proceedings of Poster Papers","year":"2007"},{"issue":"11","key":"key2021041414000691600_ref008","doi-asserted-by":"crossref","first-page":"1928","DOI":"10.1016\/j.jss.2011.06.073","article-title":"A survey on privacy in mobile participatory sensing applications","volume":"84","year":"2011","journal-title":"Journal of Systems and Software"},{"key":"key2021041414000691600_ref009","year":"1978"},{"key":"key2021041414000691600_ref010","first-page":"73","article-title":"Mauvedb: supporting model-based user views in database systems","volume-title":"Proceedings of the 2006 ACM SIGMOD international conference on Management of data\u2019","year":"2006"},{"key":"key2021041414000691600_ref011","volume-title":"Springer Handbook of Acoustics","year":"2015"},{"key":"key2021041414000691600_ref012","first-page":"77","article-title":"Context-aware sensors","volume-title":"\u2018European Workshop on Wireless Sensor Networks","year":"2004"},{"key":"key2021041414000691600_ref013","year":"2003"},{"key":"key2021041414000691600_ref014","first-page":"1","article-title":"Distributed regression: an efficient framework for modeling sensor network data","volume-title":"Information Processing in Sensor Networks, 2004. 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