A Fault Detection Method for Wireless Sensor Networks Based on Credible Sensor Nodes Set

2012 ◽  
Author(s):  
Zhaoxing Wang ◽  
Qiaoyan Wen ◽  
Teng Wang ◽  
Hua Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Detection ◽  
Detection Method ◽  
Sensor Nodes ◽  
Wireless Sensor

scholarly journals A Data-Based Fault-Detection Model for Wireless Sensor Networks

2019 ◽  
Vol 11 (21) ◽  
pp. 6171 ◽  
Author(s):  
Jangsik Bae ◽  
Meonghun Lee ◽  
Changsun Shin
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Detection ◽  
Data Transmission ◽  
Sensor Node ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Sensors And Actuators ◽  
Detection Model ◽  
Management Platform

With the expansion of smart agriculture, wireless sensor networks are being increasingly applied. These networks collect environmental information, such as temperature, humidity, and CO2 rates. However, if a faulty sensor node operates continuously in the network, unnecessary data transmission adversely impacts the network. Accordingly, a data-based fault-detection algorithm was implemented in this study to analyze data of sensor nodes and determine faults, to prevent the corresponding nodes from transmitting data; thus, minimizing damage to the network. A cloud-based “farm as a service” optimized for smart farms was implemented as an example, and resource management of sensors and actuators was provided using the oneM2M common platform. The effectiveness of the proposed fault-detection model was verified on an integrated management platform based on the Internet of Things by collecting and analyzing data. The results confirm that when a faulty sensor node is not separated from the network, unnecessary data transmission of other sensor nodes occurs due to continuous abnormal data transmission; thus, increasing energy consumption and reducing the network lifetime.

scholarly journals An Adaptive Approach to Discriminate the Persistence of Faults in Wireless Sensor Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Arunanshu Mahapatro ◽  
Pabitra Mohan Khilar
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Detection ◽  
Detection Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Transient Fault ◽  
Intermittent Faults ◽  
Parametric Fault ◽  
Simulation Results

This paper presents a parametric fault detection algorithm which can discriminate the persistence (permanent, intermittent, and transient) of faults in wireless sensor networks. The main characteristics of these faults are the amount the fault appears. We adopt this state-holding time to discriminate transient from intermittent faults. Neighbor-coordination-based approach is adopted, where faulty sensor nodes are detected based on comparisons between neighboring nodes and dissemination of the decision made at each node. Simulation results demonstrate the robustness of the work at varying transient fault rate.

scholarly journals A Compromised Cluster Detection Method in Dynamic En-route Filtering Utilizing False Reports of Wireless Sensor Networks

10.29007/sx5j ◽  
2018 ◽  
Author(s):  
Jung Sub Ahn ◽  
Dongjin Park ◽  
Tae Ho Cho
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Detection Method ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Losses ◽  
False Alarms ◽  
False Report ◽  
Entire Network

Sensor nodes of wireless sensor networks are deployed in open environments. Hence, an attacker can easily compromise the node. An attacker can compromise a node to generate false reports and inject them into the network. This causes unnecessary energy consumption associated with the process of transmitting false alarm messages and false data reports to the system. If the attacker keeps repeatedly attacking, the attacks will cause problems such as a reduction in the entire network life or disabling of the networks. Yu and Guan proposed a dynamic en-route filtering scheme to detect and drop these false reports before they reach the base station. In dynamic en-route filtering, the energy waste of the intermediate nodes occurs until it is detected early. In this paper, we propose a method to save the energy of the intermediate nodes by searching for the compromised node and blocking the reports generated at that node. When verifying a false report at the verification node, it can know its report information. The base station is able to find the cluster of compromised nodes using that information. In particular, by knowing the location of the node that has been compromised, we can block false alarms and energy losses by blocking reports generated in that cluster.

Sensor nodes fault detection for agricultural wireless sensor networks based on NMF

2019 ◽  
Vol 161 ◽  
pp. 214-224 ◽  
Author(s):  
Jimmy Ludeña-Choez ◽  
Juan J. Choquehuanca-Zevallos ◽  
Efraín Mayhua-López
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Detection ◽  
Sensor Nodes ◽  
Wireless Sensor

scholarly journals FCS-MBFLEACH: Designing an Energy-Aware Fault Detection System for Mobile Wireless Sensor Networks

Mathematics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 28 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Javad Hassannataj Joloudari ◽  
Mohammad GhasemiGol ◽  
Hamid Saadatfar ◽  
Amir Mosavi ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Detection ◽  
Large Scale ◽  
Detection System ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Support Vector ◽  
Detection Accuracy ◽  
Classification Methods

Wireless sensor networks (WSNs) include large-scale sensor nodes that are densely distributed over a geographical region that is completely randomized for monitoring, identifying, and analyzing physical events. The crucial challenge in wireless sensor networks is the very high dependence of the sensor nodes on limited battery power to exchange information wirelessly as well as the non-rechargeable battery of the wireless sensor nodes, which makes the management and monitoring of these nodes in terms of abnormal changes very difficult. These anomalies appear under faults, including hardware, software, anomalies, and attacks by raiders, all of which affect the comprehensiveness of the data collected by wireless sensor networks. Hence, a crucial contraption should be taken to detect the early faults in the network, despite the limitations of the sensor nodes. Machine learning methods include solutions that can be used to detect the sensor node faults in the network. The purpose of this study is to use several classification methods to compute the fault detection accuracy with different densities under two scenarios in regions of interest such as MB-FLEACH, one-class support vector machine (SVM), fuzzy one-class, or a combination of SVM and FCS-MBFLEACH methods. It should be noted that in the study so far, no super cluster head (SCH) selection has been performed to detect node faults in the network. The simulation outcomes demonstrate that the FCS-MBFLEACH method has the best performance in terms of the accuracy of fault detection, false-positive rate (FPR), average remaining energy, and network lifetime compared to other classification methods.

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