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.
Li and Wang REF develop an automatic regressive-integrated moving average modeling-based data aggregation scheme in WSNs.
Automatic ARIMA modeling-based data aggregation scheme in wireless sensor networks
EURASIP Journal on Wireless Communications and Networking
Computer Science