Automatic ARIMA modeling-based data aggregation scheme in wireless sensor networks

Guorui Li,Ying Wang

doi:10.1186/1687-1499-2013-85

Abstract

Data aggregation is a very important method to conserve energy by eliminating the inherent redundancy of raw data in wireless sensor networks (WSNs). In this article, we developed an automatic auto regressive-integrated moving averagemodeling-based data aggregation scheme in WSNs. The main idea behind this scheme is to decrease the number of transmitted data values between sensor nodes and aggregators by utilizing time series prediction model. The proposed scheme can effectively save the precious battery energy of wireless sensor nodes while keeping the predicted data values of aggregators within application-defined error threshold. We show through experiments with real data that the predicted data values of our proposed scheme fit the real sensed data values very well and fewer messages are transmitted between sensor nodes and aggregators than the native data aggregation scheme. Furthermore, the characteristics of the proposed data aggregation scheme are also discussed in this article.

Highlights

Wireless sensor networks(WSNs) are made up of a mass of spatially distributed autonomous sensor nodes, to jointly monitor physical or environmental conditions, such as temperature, humidity, vibration, pressure, sound, motion, or pollutants [1]
We proposed an automatic auto regressiveintegrated moving average (ARIMA)modeling-based data aggregation scheme which utilizes time series model to predict the data of several periods at both ordinary sensor nodes and aggregators based on the same amount of recent data values
Performance comparison In automatic ARIMA modeling-based data aggregation scheme, ordinary sensor node will transmit the sensed data value to the aggregator only when the prediction error between sensed value and predicted value is beyond the application-specified error threshold

Summary

Introduction

Wireless sensor networks(WSNs) are made up of a mass of spatially distributed autonomous sensor nodes, to jointly monitor physical or environmental conditions, such as temperature, humidity, vibration, pressure, sound, motion, or pollutants [1]. We proposed an automatic auto regressiveintegrated moving average (ARIMA)modeling-based data aggregation scheme which utilizes time series model to predict the data of several periods at both ordinary sensor nodes and aggregators based on the same amount of recent data values.

Results

Conclusion