Time Series Data Analysis of Wireless Sensor Network Measurements of Temperature

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.

Big Data Analytics for Large-scale Wireless Networks

“…There are several approaches proposed to address this challenge. In particular, an autocorrelation-based scheme was propsed in [124] to preprocess time-series temperature data and remove duplicated data. In [125], a novel data cleaning mechanism was proposed to clean erroneous data in environmental sensing applications in WSNs.…”

Coalition Formation Based Compressive Sensing in Wireless Sensor Networks

“…The sensor type is SHT75 sensor Sensirion. This dataset is sparse which made it as a very interesting dataset to evaluate recent compressive sensing solutions [41] [42][43] [44].…”

Less is more

“…Most of the environmental phenomena like the temperature data studied in first part of the thesis are usually non-stationary [82,215,11]. Therefore, we select this type of signal for evaluation and analysis of the effect of the transform basis on compressive sensing performance.…”

“…These prediction models are changed and updated dynamically so as to track and predict the environmental data change [77,78,79]. Therefote, these techniques is further divided into stochastic [80,81], time series prediction [82,83,84] and adaptive sampling methods [85,86,87].…”

“…The sensor type is SHT75 sensor Sensirion. This dataset is sparse which made it as a very interesting dataset to evaluate recent compressive sensing solutions [82] [239][240] [241].…”