Hybrid ARIMA and Neural Network Model for Measurement Estimation in Energy-Efficient Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are composed of many sensor nodes using limited power resources. Therefore efficient power consumption is the most important issue in such networks. One way to reduce power consumption of sensor nodes is reducing the number of wireless communication between nodes by dual prediction. In this approach, the sink node instead of direct communication, exploits a time series model to predict local readings of sensor nodes with certain accuracy. There are different linear and non-linear models for time series forecasting. In this paper we will introduce a hybrid prediction model that is created from combination of ARIMA model as linear prediction model and neural network that is a non-linear model. Then, we will present a comparison between effectiveness of our approach and previous hybrid models. Experimental results show that the proposed method can be an effective way to reduce data transmission compared with existing hybrid models and also either of the components models used individually.

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