Improved trust management based on the strength of ties for secure data aggregation in wireless sensor networks

Yun Liu (), Chen-xu Liu () and Qing-An Zeng ()
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Yun Liu: Beijing Municipal Commission of Education, Beijing Jiaotong University
Chen-xu Liu: Beijing Municipal Commission of Education, Beijing Jiaotong University
Qing-An Zeng: North Carolina Agricultural and Technical State University

Telecommunication Systems: Modelling, Analysis, Design and Management, 2016, vol. 62, issue 2, No 6, 319-325

Abstract: Abstract Secure data aggregation exerts more and more important effects on the research of wireless sensor network. There occurs many groping research subject, via managing nodes’ trust and reputation to keep data aggregation secure. The nodes in wireless sensor networks are often compromised and those compromised nodes will do a lot of harmful behaviors to decrease the security of wireless sensor networks. These behaviors include impersonate legal nodes to join routing paths, selectively forward data to an adversary, inject erroneous data, and disrupt data transmission. Thus, the conception of nodes’ trust is introduced into data aggregation to judge and evaluate whether the nodes are trustable or not. In this paper, we propose an improved trust management method for data aggregation based on the relationship between nodes that is called the strength of the ties between the nodes. The improved method is developed from the trust model in the iRTEDA protocol and increasing the utilization efficiency of second-hand information coming from neighboring nodes. The aim of the proposed trust model is to obtain a higher level of security for data aggregation. The simulation results show us that data aggregation, based on proposed trust management method, can get higher accuracy of evaluating nodes’ trust and reputation and achieve higher data accuracy of aggregating.

Keywords: Wireless sensor network; Secure data aggregation; Trust management; Strength of ties; Energy-efficient (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11235-015-0078-6

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