2015
DOI: 10.1007/s11277-015-2690-x
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Prediction Models for Energy Efficient Data Aggregation in Wireless Sensor Network

Abstract: In sensor networks, the periodically aggregated data often exhibit high temporal coherency. Huge energy consumption incurred in transmitting these redundant information results in network disconnection thereby leading to service disruption. In order to effectively manage the energy consumption in concurrent data gathering rounds, temporal data prediction model is proposed. The proposed model provides near accurate predictions that successfully restricts redundant transmissions. The communication energy conserv… Show more

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Cited by 19 publications
(19 citation statements)
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“…The model is oriented to improve the prediction speed while ensuring accurate prediction. Sinha et al [10] proposed a data aggregation model TDPA based on time data prediction. The model generates an estimate of future data to analyze the prediction error and uses the predicted value to save transmission energy consumption when the prediction meets a predefined threshold.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…The model is oriented to improve the prediction speed while ensuring accurate prediction. Sinha et al [10] proposed a data aggregation model TDPA based on time data prediction. The model generates an estimate of future data to analyze the prediction error and uses the predicted value to save transmission energy consumption when the prediction meets a predefined threshold.…”
Section: Related Workmentioning
confidence: 99%
“…Using data prediction methods to reduce unnecessary data transmission is an effective way to improve the quality of data collection and increase the network lifetime. The current methods usually use the periodicity and redundancy to predict the specific sensory data based on historical data, which often results in low prediction stability and biased predictions [6][7][8][9][10][11].…”
Section: Introductionmentioning
confidence: 99%
“…First, the node computes the rank for each measure (lines 7-8). Then, it uses the Behavior function [14] to adapt its sensing rate only if the calculated difference between measures is less than the Kruskal-Wallis threshold (lines [10][11][12][13][14]. As explained in [14], we define the application risk level and we express this level by a quantitative variable R which can take values between 0 and 1 representing the low and the high risk level respectively.…”
Section: A Illustrative Examplementioning
confidence: 99%
“…Then, the filters are ready to predict the future data. Other data prediction approaches are also studied to conserve energy in WSN [6], [7], [8], [9], [10]. It means to predict future information with the use of various algorithms and prevent transmitting the raw data.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation