Title: Trust and grey model based data aggregation algorithm

Authors: Jun Wang; Ni Wang; Li Li; Yansui Du

Addresses: School of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang, 110142, China ' School of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang, 110142, China ' School of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang, 110142, China ' Party School of Liaoning Provincial Party Committee, Shenyang, Liaoning, 110004, China

Abstract: The reliability of data aggregation is an important problem in wireless sensor networks. Therefore, trust and grey model based data aggregation algorithm (TGDA) is proposed. The algorithm combines technologies such as trust mechanism, data prediction and data aggregation. Before initiating data aggregation, the cluster heads will detect the abnormal nodes whose trust value is lower than a certain value, and add them to the blacklist. According to the high time dependence of the data collected by sensor nodes, the grey model is used to predict the missing data of the abnormal nodes. Finally, the trusted data are aggregated. Simulation results show that compared with other algorithms, the algorithm can effectively improve the security and accuracy of data aggregation under the premise of ensuring the network life cycle.

Keywords: trust mechanism; data prediction; grey model; data aggregation; time dependence; time series; abnormal nodes; weight mechanism; clustering algorithm; wireless sensor networks.

DOI: 10.1504/IJSNET.2021.114752

International Journal of Sensor Networks, 2021 Vol.35 No.4, pp.258 - 266

Received: 09 Oct 2020
Accepted: 06 Dec 2020
Published online: 04 May 2021 *

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