Multi-level data fusion algorithm towards privacy protection in wireless sensor networks. (5th May 2020)

• Record Type: Journal Article
• Authors: Wang, Zheng; Li, Liang; Ao, Chen; Wu, Dapeng; Zhou, Wenjun; Yu, Xiaojun
• Is Part Of: International journal of communication networks and distributed systems. Volume 25:Number 3(2021)
• Page Start: 265
• Page End: 283
• Publication Date: 2020-05-05
• Subjects: wireless sensor network -- data fusion -- privacy protection -- data compression -- BP neural network; Computer networks -- Periodicals; Telecommunication systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; 004.6
• Languages: English
• ISSNs: 1754-3916

Abstract

Data fusion is one of the key technologies in wireless sensor networks. To promote secure and efficient data fusion for wireless sensor networks, a privacy protection-based multi-level data fusion algorithm is proposed. In order to minimise the network energy consumption, the optimal number of cluster heads is selected and the adaptive clustering is performed according to the node energy and the positional relationship between nodes. Then, the cluster members collect and encrypt data, whereas the cluster head cleans and integrates the encrypted data. Furthermore, by analysing the correlation between data and constructing the BP neural network, cluster heads and the sink node can fuse the data in a cluster and the data between clusters to achieve the optimal data fusion. Results show that the mechanism proposed in this paper can significantly reduce resource overheads, effectively guarantee data security and fusion efficiency.