Autonomous Fault Detection and Diagnosis in Wireless Sensor Networks Using Decision Trees

Laiou, Angeliki E.; Malliou, Christina M.; Lenas, Sotirios-Angelos; Tsaoussidis, Vassilis

doi:10.12720/jcm.14.7.544-552

Cited by 20 publications

(12 citation statements)

References 12 publications

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“…The first type of method is based on model analysis. It is defined as simulating the cognitive process of things, which includes expert system-based models, fuzzy logic-based models, decision tree-based models and hypothesis testing-based models [ 2 , 3 , 4 , 5 , 6 , 7 ]. Febriansyah, II.…”

Section: Related Workmentioning

confidence: 99%

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A wireless sensor network node fault diagnosis model based on belief rule base with power set

Sun

Zhu

et al. 2022

Heliyon

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Section: Related Workmentioning

confidence: 99%

“…Laiou, A. et al. constructed an autonomous fault diagnosis model in a WSN based on a decision tree [ 6 ]. Sun, QY.…”

Section: Related Workmentioning

confidence: 99%

A wireless sensor network node fault diagnosis model based on belief rule base with power set

Sun

Zhu

et al. 2022

Heliyon

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“…The next type of fault detection in WSN is based on machine learning. Numerous fault detection approaches have been proposed by various researchers by using machine learning; among them, authors proposed a machine learning‐based decision tree approach for detecting and diagnosing the faults in WSN 19 . In this work, the node faults are identified by using the data collected from the sensor nodes.…”

Section: Classification Of Node Faults In Wsnmentioning

confidence: 99%

Comprehensive analysis on sensor node fault management schemes in wireless sensor networks

Babu

Kumar

2022

Int J Communication

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Wireless sensor networks (WSNs) are ad hoc networks that consist of tiny sensor nodes deployed in the sensed environment to monitor the natural phenomenon and send the monitored data to the base station for further processing. Due to the unfriendly deployment of the sensor nodes and their resource-constrained nature, the sensor nodes are subjected to frequent node failure. Detecting sensor node failures and recovering them is a major challenge in WSN. Due to the failure of the nodes in WSN, there exist various problems such as a change in the network topology, communication link failure, a disjoint partition of the network, and failure to transmit the data to the sink. Various authors have proposed various techniques to detect and recover from node failure in the WSN. In this paper, a comprehensive analysis of the various node failure and node recovery techniques has been carried out to highlight the advantages and limitations along with the open research challenges for addressing the failure of the nodes in an effective manner.

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“…For thousands of years, the atmospheric environment has been and will always be one of the important components of the natural environment on which all living things in the world depend [1]. Air pollution is still a key link in ecological environment pollution, and air quality directly affects people's physical and mental health [2].…”

Section: Introductionmentioning

confidence: 99%

Practice of Air Environment Quality Monitoring Data Visualization Technology Based on Adaptive Wireless Sensor Networks

Yin

2022

Wireless Communications and Mobile Computing

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This paper begins by outlining the evolution and current state of traditional wireless sensor networks, followed by an examination of the principle and architecture of wireless sensor networks, as well as the challenges that traditional wireless sensor networks face in environmental monitoring. This paper designs and develops a data visualization system for wireless sensor networks based on the visualization system and the advantages of other visualization systems, after analyzing and comparing the current visualization systems for monitoring wireless sensor networks. A real-time monitoring system for urban air quality based on wireless sensor network is designed to solve the problem that the traditional method of combining manual sampling with laboratory analysis for air quality monitoring is expensive and cannot carry out real-time online monitoring for multiple points at the same time. The system’s implementation is based primarily on air quality monitoring data collected in the Shenyang area for visual display and analysis. The air quality monitoring system proposed in this paper has a complete structure and independent modules and is simple to use. The dynamic adaptability of the monitoring system is greatly improved by utilizing the self-adaptability and self-organization of wireless sensor networks, which brings great convenience to air quality monitoring.

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Autonomous Fault Detection and Diagnosis in Wireless Sensor Networks Using Decision Trees

Cited by 20 publications

References 12 publications

A wireless sensor network node fault diagnosis model based on belief rule base with power set

A wireless sensor network node fault diagnosis model based on belief rule base with power set

Comprehensive analysis on sensor node fault management schemes in wireless sensor networks

Practice of Air Environment Quality Monitoring Data Visualization Technology Based on Adaptive Wireless Sensor Networks

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