{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T17:48:51Z","timestamp":1787161731197,"version":"build-2736575974"},"reference-count":29,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,12,22]],"date-time":"2017-12-22T00:00:00Z","timestamp":1513900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wireless sensor networks are widely used in environmental monitoring. The number of sensor nodes to be deployed will vary depending on the desired spatio-temporal resolution. Selecting an optimal number, position and sampling rate for an array of sensor nodes in environmental monitoring is a challenging question. Most of the current solutions are either theoretical or simulation-based where the problems are tackled using random field theory, computational geometry or computer simulations, limiting their specificity to a given sensor deployment. Using an empirical dataset from a mine rehabilitation monitoring sensor network, this work proposes a data-driven approach where co-integrated time series analysis is used to select the number of sensors from a short-term deployment of a larger set of potential node positions. Analyses conducted on temperature time series show 75% of sensors are co-integrated. Using only 25% of the original nodes can generate a complete dataset within a 0.5 \u00b0C average error bound. Our data-driven approach to sensor position selection is applicable for spatiotemporal monitoring of spatially correlated environmental parameters to minimize deployment cost without compromising data resolution.<\/jats:p>","DOI":"10.3390\/s18010011","type":"journal-article","created":{"date-parts":[[2017,12,22]],"date-time":"2017-12-22T05:50:19Z","timestamp":1513921819000},"page":"11","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Time Series Analysis for Spatial Node Selection in Environment Monitoring Sensor Networks"],"prefix":"10.3390","volume":"18","author":[{"given":"Siddhartha","family":"Bhandari","sequence":"first","affiliation":[{"name":"School of ITEE, University of Queensland, Brisbane 4072, Australia"},{"name":"CSIRO\/Data61, Pullenvale 4069, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6279-1053","authenticated-orcid":false,"given":"Neil","family":"Bergmann","sequence":"additional","affiliation":[{"name":"School of ITEE, University of Queensland, Brisbane 4072, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raja","family":"Jurdak","sequence":"additional","affiliation":[{"name":"CSIRO\/Data61, Pullenvale 4069, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Branislav","family":"Kusy","sequence":"additional","affiliation":[{"name":"CSIRO\/Data61, Pullenvale 4069, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1175\/JAMC-D-11-025.1","article-title":"Observing local-scale variability of near-surface temperature and humidity using a wireless sensor network","volume":"51","author":"Lengfeld","year":"2012","journal-title":"J. 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