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.
Highlights
Wireless Sensor Networks (WSNs) allow dense spatiotemporal measurement of environmental phenomena such as temperature, humidity, solar radiation and rainfall [1] which in turn can be used to better understand local environmental conditions and processes
Data still needs to be sampled at high temporal resolution, and there is no investigation of what the best sampling interval should be. They propose round-robin scheduling on sensors in spatial clusters. In general these previous works have used time series analysis to model the statistical behavior of the data
Univariate time series analysis is performed on an environmental sensors array deployed for monitoring outdoor environmental temperatures
Summary
Wireless Sensor Networks (WSNs) allow dense spatiotemporal measurement of environmental phenomena such as temperature, humidity, solar radiation and rainfall [1] which in turn can be used to better understand local environmental conditions and processes. Around 175 microclimate sensor nodes have been deployed for more than 5 years, and they have recorded temperature readings (as well as other environmental phenomena) every 5 min during this time This provides a rich source of data for further analysis. It analyzes the reduction in measurement accuracy if the sampling interval is extended with temperature interpolated between these values.
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