Novel Data Fusion Algorithm Based on Event-Driven and Dempster–Shafer Evidence Theory

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 consumpti...

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Published inWireless personal communications Vol. 100; no. 4; pp. 1377 - 1391
Main Authors Yu, Xiuwu, Zhang, Feng, Zhou, Lixing, Liu, Qin
Format Journal Article
LanguageEnglish
Published New York Springer US 01.06.2018
Springer Nature B.V
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ISSN0929-6212
1572-834X
DOI10.1007/s11277-018-5644-2

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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.
AbstractList 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.
Author Liu, Qin
Yu, Xiuwu
Zhou, Lixing
Zhang, Feng
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Keywords Wireless sensor network
Data fusion
Event driven
Dempster–Shafer evidence theory
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Snippet 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...
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SubjectTerms Algorithms
Clusters
Communications Engineering
Computer Communication Networks
Computer simulation
Data integration
Datum (elevation)
Energy conservation
Energy consumption
Engineering
Multisensor fusion
Networks
Packets (communication)
Remote sensors
Sampling
Signal,Image and Speech Processing
Wireless sensor networks
Title Novel Data Fusion Algorithm Based on Event-Driven and Dempster–Shafer Evidence Theory
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