An Efficient Data Aggregation Scheme Leveraging Time Series Prediction in Wireless Sensor Networks

Abstract

Abstract-In wireless sensor networks, the data periodically sensed by sensor nodes are usually of high temporal correlation. Therefore, prediction based data aggregation scheme can be designed to reduce the amount of data transmission and save sensor nodes' limited energy and bandwidth. We proposed a temporal correlation based data aggregation scheme in this paper which utilizes time series model to predict the data of next several periods at both ordinary sensors and aggregators based on the same amount of recent sensed values. We show through experiments using real sensor data that our proposed scheme can provide considerable aggregation ratio while maintaining a low prediction error rate.

Data Fusion in Wireless Sensor Network using Simpson's 3/8 rule

“…Guorui Li [2] proposed a temporal correlation based data aggregation scheme using Auto Regressive moving average (ARIMA). This model is used to predict next time series data at both sensor node and aggregator node by using recently sensed data sequence.…”

Current State of Art in Security of Data Aggregator in Smart Grids

“…Concealed data aggregation CDA was addressed by Westhoff in 2006 [4] [28] which uses the multiplicative and additive homomorphism privacy scheme. Another homomorphism privacy scheme EDA was proposed by Castellucia in 2005, [4,36] this scheme aggregates the encrypted data. EDA does not use XOR operation used in stream cipher, since it has modular addition.…”