Abstract
The trust computing mechanism has an increasing role in the cooperative work of wireless sensor networks. However, the computing speed, resource overhead, and anti-collaborative attack ability of a trust mechanism itself are three key challenging issues for any open and resource-constrained wireless sensor networks. In this study, we propose a fast, resource-saving, and anti-collaborative attack trust computing scheme (FRAT) based on across-validation mechanism for clustered wireless sensor networks. First, according to the inherent relationship among three network entities (which are made up of three types of network nodes, namely base stations, cluster heads, and cluster members), we propose the cross-validation mechanism, which is effective and reliable against collaborative attacks caused by malicious nodes. Then, we adopt a fast and resource-saving trust computing scheme for cooperation between between cluster heads or cluster members. This scheme is suitable for wireless sensor networks because it facilitates resource-saving. Through theoretical analysis and experiments, the feasibility and effectiveness of the trust computing scheme proposed in this study are verified.
Highlights
Wireless sensor networks (WSNs [1,2,3,4,5]) are widely used in several fields such as intelligent perception, military, disaster warning, medical care, etc
By identifying the inherent relationship among cluster member (CM), cluster head (CH), and base station (BS), we propose a fast, resource-saving, and anti-collaborative attack trust computing scheme based on the cross-validation mechanism
We extended the traditional trust schemes in clustered WSNs and proposed a cross-validation trust mechanism based on multiple trust factors, which has a stronger anti-collaborative attack ability against collaborative attacks compared with existing trust mechanisms
Summary
Wireless sensor networks (WSNs [1,2,3,4,5]) are widely used in several fields such as intelligent perception, military, disaster warning, medical care, etc. The main application of WSN is to sense the surrounding environment and send the obtained information to the base station (BS) for subsequent processing. For clustered WSNs such as EEHC [6], EC [7], HEED [8], TRAST [9], and LDTS [10], clustering algorithms can significantly improve the performance and efficiency of wireless sensor networks [11]. The clustering algorithm is used to divide nodes into multiple clusters. A node with powerful computing capability is selected as the cluster head (CH).
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