A data fusion method in wireless sensor network based on belief structure

EURASIP Journal on Wireless Communications and Networking

Gelan Yang¹  Xiaoyong Tang²  Kui Fang³  Tao Zhang³  Chengfeng Long³  Siqiao Tan³  Yakun Yang³  Xingxin Liu³ 

[1] Department of Computer Science, Hunan City University;School of Computer and Communications Engineering, Changsha University of Science and Technology;School of Information and Intelligence Science and Technology, Hunan Agricultural University;

关键词: Wireless sensor network;    Granular computing;    Rough set;    Dempster–Shafer theory;    Reduction;   

DOI  :  10.1186/s13638-021-01916-4

来源: DOAJ

【 摘 要 】

Abstract Considering the issue with respect to the high data redundancy and high cost of information collection in wireless sensor nodes, this paper proposes a data fusion method based on belief structure to reduce attribution in multi-granulation rough set. By introducing belief structure, attribute reduction is carried out for multi-granulation rough sets. From the view of granular computing, this paper studies the evidential characteristics of incomplete multi-granulation ordered information systems. On this basis, the positive region reduction, belief reduction and plausibility reduction are put forward in incomplete multi-granulation ordered information system and analyze the consistency in the same level and transitivity in different levels. The positive region reduction and belief reduction are equivalent, and the positive region reduction and belief reduction are unnecessary and sufficient conditional plausibility reduction in the same level, if the cover structure order of different levels are the same the corresponding equivalent positive region reduction. The algorithm proposed in this paper not only performs three reductions, but also reduces the time complexity largely. The above study fuses the node data which reduces the amount of data that needs to be transmitted and effectively improves the information processing efficiency.

【 授权许可】

Unknown