Time Series Analysis for Spatial Node Selection in Environment Monitoring Sensor Networks
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 challen...
| Main Authors: | , , , |
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| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
MDPI AG
2017-12-01
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| Colecção: | Sensors |
| Assuntos: | |
| Acesso em linha: | https://www.mdpi.com/1424-8220/18/1/11 |
| _version_ | 1828114730986766336 |
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| author | Siddhartha Bhandari Neil Bergmann Raja Jurdak Branislav Kusy |
| author_facet | Siddhartha Bhandari Neil Bergmann Raja Jurdak Branislav Kusy |
| author_sort | Siddhartha Bhandari |
| collection | DOAJ |
| description | 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 °C 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. |
| first_indexed | 2024-04-11T12:30:21Z |
| format | Article |
| id | doaj.art-94e913a0e48349ffbddc367e324dbb6b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:30:21Z |
| publishDate | 2017-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-94e913a0e48349ffbddc367e324dbb6b2022-12-22T04:23:47ZengMDPI AGSensors1424-82202017-12-011811110.3390/s18010011s18010011Time Series Analysis for Spatial Node Selection in Environment Monitoring Sensor NetworksSiddhartha Bhandari0Neil Bergmann1Raja Jurdak2Branislav Kusy3School of ITEE, University of Queensland, Brisbane 4072, AustraliaSchool of ITEE, University of Queensland, Brisbane 4072, AustraliaCSIRO/Data61, Pullenvale 4069, AustraliaCSIRO/Data61, Pullenvale 4069, AustraliaWireless 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 °C 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.https://www.mdpi.com/1424-8220/18/1/11wireless sensor networkstime series analysisspatio-temporal analysisenvironmental monitoring |
| spellingShingle | Siddhartha Bhandari Neil Bergmann Raja Jurdak Branislav Kusy Time Series Analysis for Spatial Node Selection in Environment Monitoring Sensor Networks Sensors wireless sensor networks time series analysis spatio-temporal analysis environmental monitoring |
| title | Time Series Analysis for Spatial Node Selection in Environment Monitoring Sensor Networks |
| title_full | Time Series Analysis for Spatial Node Selection in Environment Monitoring Sensor Networks |
| title_fullStr | Time Series Analysis for Spatial Node Selection in Environment Monitoring Sensor Networks |
| title_full_unstemmed | Time Series Analysis for Spatial Node Selection in Environment Monitoring Sensor Networks |
| title_short | Time Series Analysis for Spatial Node Selection in Environment Monitoring Sensor Networks |
| title_sort | time series analysis for spatial node selection in environment monitoring sensor networks |
| topic | wireless sensor networks time series analysis spatio-temporal analysis environmental monitoring |
| url | https://www.mdpi.com/1424-8220/18/1/11 |
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