Paper Info
Title
Novel Data Fusion Algorithm Based on Event-Driven and Dempster–Shafer Evidence Theory
Abstract
When a new event occurs, the nodes in the neighborhood of the event sense and then send many packets to the sink node. Such circumstances need their networks to be simultaneously reliable and event-driven. Moreover, it should be remove redundant packets in order to lower the average energy consumption. A data fusion algorithm based on event-driven and Dempster–Shafer evidence theory is proposed in this paper to reduce data packet quantities and reserve energy for wireless sensor networks upon detecting abnormal data. Sampling data is compared against the set threshold, and the nodes enter the relevant state only when there are abnormal datum; at this point, cluster formation begins. All cluster members incorporate a local forwarding history to decide whether to forward or to drop recent sampling data. Dempster–Shafer evidence theory is exploited to process the data. The basic belief assignment function, with which the output of each cluster member is characterized as a weighted-evidence, is constructed. Then, the synthetic rule is subsequently applied to each cluster head to fuse the evidences gathered from cluster member nodes to obtain the final fusion result. Simulation results demonstrate that the proposed algorithm can effectively ensure fusion result accuracy while saving energy.
Year
DOI
Venue
2018
https://doi.org/10.1007/s11277-018-5644-2
Wireless Personal Communications
Keywords
Field
DocType
Data fusion,Dempster–Shafer evidence theory,Event driven,Wireless sensor network
Data mining,Geodetic datum,Computer science,Network packet,Real-time computing,Sensor fusion,Sampling (statistics),Fuse (electrical),Dempster–Shafer theory,Energy consumption,Wireless sensor network
Journal
Volume
Issue
ISSN
100
4
0929-6212
Citations 
PageRank 
References 
2
0.37
14
Authors
4
Name
Order
Citations
PageRank
Xiuwu Yu120.37
Feng Zhang220.37
Lixing Zhou320.37
Qin Liu466.93