CFDDR: A Centralized Faulty Data Detection and Recovery Approach for WSN With Faults Identification

Yemeni, Zaid; Wang, Haibin; Ismael, Waleed M.; Hawbani, Ammar; Chen, Zhengming

doi:10.1109/jsyst.2021.3099830

Cited by 10 publications

(4 citation statements)

References 42 publications

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“…The efficiency of the proposed energy aware HRSFD framework is examined and compared with the existing software fault detection models: DBN, 19 LRT, 18 and CFDDR. 21 In order to confirm whether the comparisons were adequate, all the above models utilized a similar disseminated methodology for recognizing the fault detection of nodes. These models are analyzed under the same generated fault dataset and similar training parameters.…”

Section: Resultsmentioning

confidence: 99%

“…Centralized Faulty Data Detection and Recovery (CFDDR) has been proposed for classifying, recovering, and distinguishing various software faults in IWSN. 21 It functioned in two stages: the first one utilized the temporal fluctuation of the acquired data to detect and recover incorrect data. The identification of fault types was the second stage in which several software faults were detected and informed to the supervisor for an appropriate decision.…”

Section: Fault Classification Modelsmentioning

confidence: 99%

“…Centralized Faulty Data Detection and Recovery (CFDDR) has been proposed for classifying, recovering, and distinguishing various software faults in IWSN 21 . It functioned in two stages: the first one utilized the temporal fluctuation of the acquired data to detect and recover incorrect data.…”

Section: Related Workmentioning

confidence: 99%

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An energy‐aware software fault detection system based on hierarchical rule approach for enhancing quality of service in internet of things‐enabled wireless sensor network

Balraj,

Prasanth

2024

Trans Emerging Tel Tech

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Of late, the Internet of Things (IoT) has progressed in its pervasiveness across the globe for diverse applications. Wireless Sensor Network (WSN) is one of the prominent technologies employed in IoT environments where multiple tiny sensor nodes are distributed to sense real‐time observations about unforeseeable areas for control and managerial purposes. Owing to the presence of sensors in inaccessible regions and their battery restrictions, different types of software faults occur in IoT‐enabled WSNs (IWSNs). These faults create uncertainty in data reading which causes serious damage to the sensor network. Hence, the IWSN necessitates an effective fault‐detection methodology to continue optimal activity despite the existence of software faults. This work proposes a novel Energy‐Aware Hierarchical Rule‐based Software Fault Detection (HRSFD) model to identify various software faults with minimum energy depletion in the IWSN environment. Primarily, the proposed model extracts antecedent attributes from the characteristics of the sensed data. Its abnormal values can be identified based on the obtained antecedent attributes. Subsequently, the category of the software fault is determined by applying a hierarchical rule strategy. Finally, from the simulation results, it is apparent that the fault detection accuracy rate of the proposed HRSFD model attains 99.12% for dense networks. The lifetime of the network is also prolonged by 18% as compared to the existing state‐of‐the‐art models.

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Section: Resultsmentioning

confidence: 99%

Section: Fault Classification Modelsmentioning

confidence: 99%

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An energy‐aware software fault detection system based on hierarchical rule approach for enhancing quality of service in internet of things‐enabled wireless sensor network

Balraj,

Prasanth

2024

Trans Emerging Tel Tech

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“…Various authors have proposed various centralized‐based fault detection mechanisms among them. A centralized faulty data detection and recovery approach (CFDDR) 13 works based on the collected data in temporal manner variation. In the proposed system, fault‐type recognition is implemented to determine the type of fault and report it to the end user for better decision making.…”

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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A novel approach for missing data recovery and fault nodes detection in wireless sensor networks

Thiyagarajan,

Nagabhooshanam,

Prasad

et al. 2024

Int J Communication

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SummaryEnsuring data integrity in wireless sensor networks (WSNs) is crucial for accurate monitoring, yet missing data due to sensor faults present a significant challenge. This research introduces an innovative approach that integrates advanced data recovery techniques with leading‐edge methods to address this issue. The system begins by identifying and isolating fault nodes using a specialized algorithm that analyzes network behavior. By applying fuzzy density‐based spatial clustering of applications with noise (FDBSCAN), potential fault nodes are precisely located based on deviations from expected patterns. Subsequently, an intelligent missing data recovery mechanism powered by bidirectional long short‐term memory (Bi‐LSTM) networks takes action. The Bi‐LSTM model is trained on existing sensor data to capture intricate patterns and dependencies, enabling accurate prediction and reconstruction of missing values caused by identified faults. The synergy between Bi‐LSTM for missing data recovery and FDBSCAN for fault node detection comprehensively addresses the missing data problem in WSNs. In missing data recovery, it demonstrates low mean absolute deviation (MAD) ranging from 0.021 to 0.13 and mean squared deviation (MSD) ranging from 0.0025 to 0.05 across various missing data ratios. Data reliability remains consistently high at 96% to 98%, even with up to 80% missing data. For fault node detection, the approach achieves precision of 95.7%, recall of 96.3%, F1‐score of 96.1%, and accuracy of 97.4%, outperforming existing techniques. The computational cost during training is noted at 5.79 h, presenting a limitation compared to other methods. This research highlights the importance of integrating fault node detection into missing data recovery mechanisms, presenting an innovative solution for the advancement of WSNs.

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CFDDR: A Centralized Faulty Data Detection and Recovery Approach for WSN With Faults Identification

Cited by 10 publications

References 42 publications

An energy‐aware software fault detection system based on hierarchical rule approach for enhancing quality of service in internet of things‐enabled wireless sensor network

An energy‐aware software fault detection system based on hierarchical rule approach for enhancing quality of service in internet of things‐enabled wireless sensor network

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

A novel approach for missing data recovery and fault nodes detection in wireless sensor networks

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