Algorithm for Quasi-Stationary Measurement Data Aggregation of Sensor Nodes

Purpose of research. Wireless sensor networks are actively developing and perspective direction in the information and communication field. Computational complexity optimization of data processing algorithms for wireless sensor network devices is still an important scientific and technical problem. This article is devoted to an algorithm that realized the method of intelligent quasi-indifferent data aggregation for decentralized devices - sensor nodes. The aim of the study is to further aggregation technologies improvement in wireless sensor networks by creating the new algorithm for quasi-stationary measurement data aggregation of sensor nodes. The developed algorithm ensures efficient quasistationary measurement data aggregation of sensor nodes by presenting this data in the form of the parabolic regression model coefficients vectors and combining them into groups based on the dynamic variations correlation of the recorded parameters, also takes into account undefined values and outliers in data segments and implements their elimination.

Methods. Methods of algorithms theories, probability theory, mathematical statistics, calculations in terms of complexity theory and technical calculations application software Matlab were used in the study during the theoretical research and algorithm development.

Results. The algorithm for quasi-stationary measurement data aggregation of sensor nodes that allows to reduce their volume when transmitting via wireless communication channels is developed. The practical significance of the developed algorithm lies in implementing the proposed theoretical and algorithmic structures to a level of programs.

Conclusion. The developed algorithm ensures efficient aggregation by presenting quasi-stationary measurement data of sensor nodes in the form of the parabolic regression model coefficients vectors, minimizes temporal and spatial correlations of data on the receiving side, eliminates undefined values and outliers in data segments exceeding three average absolute deviations.

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