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Sensors 2017, 17(6), 1221; doi:10.3390/s17061221

Time Series Data Analysis of Wireless Sensor Network Measurements of Temperature

1
School of ITEE, University of Queensland, Brisbane 4072, Australia
2
CSIRO/Data61, Pullenvale 4069, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Mario Collotta
Received: 21 February 2017 / Revised: 8 May 2017 / Accepted: 23 May 2017 / Published: 26 May 2017
(This article belongs to the Section Sensor Networks)
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Abstract

Wireless sensor networks have gained significant traction in environmental signal monitoring and analysis. The cost or lifetime of the system typically depends on the frequency at which environmental phenomena are monitored. If sampling rates are reduced, energy is saved. Using empirical datasets collected from environmental monitoring sensor networks, this work performs time series analyses of measured temperature time series. Unlike previous works which have concentrated on suppressing the transmission of some data samples by time-series analysis but still maintaining high sampling rates, this work investigates reducing the sampling rate (and sensor wake up rate) and looks at the effects on accuracy. Results show that the sampling period of the sensor can be increased up to one hour while still allowing intermediate and future states to be estimated with interpolation RMSE less than 0.2 °C and forecasting RMSE less than 1 °C. View Full-Text
Keywords: wireless sensor networks; time series analysis; interpolation; forecasting; temperature; environmental monitoring wireless sensor networks; time series analysis; interpolation; forecasting; temperature; environmental monitoring
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Bhandari, S.; Bergmann, N.; Jurdak, R.; Kusy, B. Time Series Data Analysis of Wireless Sensor Network Measurements of Temperature. Sensors 2017, 17, 1221.

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