Due to the widespread inaccuracy of wireless sensor networks (WSNs) data, it is essential to ensure that the data is as complete, clean and precise as possible. To address data gaps and replace erroneous data, temporal correlation modelling can be applied, which takes advantage of temporal correlation and is also energy efficient. In this research, the suitability of adapting the ARIMA model into a WSN context is scrutinized, as technological requirements demand special considerations. The necessity of applying a smoothing technique is explored and the selection of an appropriate method is determined. Additionally, the available options with regards to ARIMA set-up are discussed, in terms of achieving accurate and energy friendly predictions. The effect of sufficient historical data and the importance of predictions’ life span on the estimation accuracy are additionally investigated. Finally, an adaptive, online and energy efficient system is proposed for maintaining the accuracy of the model that simultaneously detects outliers and events as well as substitutes any missing or erroneous data with estimated values.
Energy efficient information collection in wireless sensor networks using adaptive compressive sensing