Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter

Guiyi Wei, Yun Ling, Binfeng Guo, Bin Xiao, Athanasios V. Vasilakos

Research output: Journal article publicationJournal articleAcademic researchpeer-review

197 Citations (Scopus)


In many environmental monitoring applications, since the data periodically sensed by wireless sensor networks usually are of high temporal redundancy, prediction-based data aggregation is an important approach for reducing redundant data communications and saving sensor nodes' energy. In this paper, a novel prediction-based data collection protocol is proposed, in which a double-queue mechanism is designed to synchronize the prediction data series of the sensor node and the sink node, and therefore, the cumulative error of continuous predictions is reduced. Based on this protocol, three prediction-based data aggregation approaches are proposed: Grey-Model-based Data Aggregation (GMDA), Kalman-Filter-based Data Aggregation (KFDA) and Combined Grey model and Kalman Filter Data Aggregation (CoGKDA). By integrating the merit of grey model in quick modeling with the advantage of Kalman Filter in processing data series noise, CoGKDA presents high prediction accuracy, low communication overhead, and relative low computational complexity. Experiments are carried out based on a real data set of a temperature and humidity monitoring application in a granary. The results show that the proposed approaches significantly reduce communication redundancy and evidently improve the lifetime of wireless sensor networks.
Original languageEnglish
Pages (from-to)793-802
Number of pages10
JournalComputer Communications
Issue number6
Publication statusPublished - 3 May 2011


  • Data aggregation
  • Data collection protocol
  • Grey model
  • Kalman Filter
  • Wireless sensor networks

ASJC Scopus subject areas

  • Computer Networks and Communications


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