Nodes deployment optimization algorithm based on improved evidence theory of underwater wireless sensor networks

Underwater wireless sensor networks (UWSNs) applications for ocean monitoring, deep sea surveillance, and locating natural resources are gaining more and more popularity. To monitor the underwater environment or any object within a certain area of interest, these applications are required to deploy...

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Bibliographic Details
Published in:Photonic network communications 2019-04, Vol.37 (2), p.224-232
Main Authors: Song, Xiaoli, Gong, Yunzhan, Jin, Dahai, Li, Qiangyi
Format: Article
Language:English
Subjects:
Algorithms
Characterization and Evaluation of Materials
Computer Communication Networks
Computer Science
Computer simulation
Conditional probability
Data integration
Deep sea
Deep sea environments
Electrical Engineering
Multisensor fusion
Natural resources
Nodes
Optimization
Original Paper
Remote sensors
Sensors
Underwater
Viability
Wireless sensor networks
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Summary:Underwater wireless sensor networks (UWSNs) applications for ocean monitoring, deep sea surveillance, and locating natural resources are gaining more and more popularity. To monitor the underwater environment or any object within a certain area of interest, these applications are required to deploy underwater node sensors connected for obtaining useful data. For thriving UWSNs, it is essential that an efficient and secure node deployment mechanism is in place. This paper presents a novel node deployment scheme, which is based on evidence theory approach and caters for 3D USWNs. This scheme implements sonar probability perception and an enhanced data fusion model to improve prior probability deployment algorithm of D–S evidence theory. The viability of our algorithm is verified by performing multiple simulation experiments. The simulation results reveal that our algorithm deploys fewer nodes with enhanced network judgment criteria and expanded detection capabilities for a relatively large coverage area compared to other schemes. In addition, the generated nodes are also less resource intensive, i.e., low-power sensor nodes.
ISSN:1387-974X
1572-8188
DOI:10.1007/s11107-018-0807-3