A Novel Cluster-Based Wireless Sensor Network Reliability Model Using the Expectation Maximization Algorithm

Wireless sensor networks (WSNs) have been used widely across various industries and business fields that require the coverage of large geographical regions that are difficult for humans to reach. It is therefore important to be able to model, assess, and predict the reliability of WSNs. Masked data...

Full description

Saved in:
Bibliographic Details
Published in:Journal of sensors Vol. 2021; no. 1
Main Authors: Yang, Jianfeng, Chen, Jing, Huo, Yujia, Liu, Yanyu
Format: Journal Article
Language:English
Published: New York Hindawi 20.03.2021
John Wiley & Sons, Inc
Subjects:
Algorithms
Clusters
Energy efficiency
Failure
Growth models
Maximization
Maximum likelihood estimation
Missing data
Network reliability
Optimization
Parameter estimation
Reliability analysis
Sensors
Software
System reliability
Topology
Wireless networks
Wireless sensor networks
ISSN:1687-725X, 1687-7268
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Wireless sensor networks (WSNs) have been used widely across various industries and business fields that require the coverage of large geographical regions that are difficult for humans to reach. It is therefore important to be able to model, assess, and predict the reliability of WSNs. Masked data is a type of missing data used to represent system failure when the exact cause of the failure is unknown. This paper proposed a novel additive reliability model for a cluster-based WSN system using general masked data and uses the expectation maximization (EM) algorithm to solve the problem of the maximum likelihood estimation (MLE). Moreover, the proposed model assumes that a WSN comprises several clusters, and the failure processes of these clusters are independent. The probability characteristics of the system are determined according to the topology of the WSN system to evaluate the system reliability. Finally, the proposed model is demonstrated to be powerful for estimating WSN system reliability using a simulated dataset.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1687-725X
1687-7268
DOI:10.1155/2021/8869544