Fault Matters: Sensor Data Fusion for Detection of Faults using Dempster-Shafer Theory of Evidence in IoT-Based Applications
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Summary
This work has adopted Dempster-Shafer Theory of Evidence which is a popular learning method to collate the information from sensors to come up with a decision regarding the faulty status of a sensor node to verify the validity of the proposed method.
- Type
- preprint
- Published
- 2019-06-24
- Cited by
- 44
- References
- 44
- Access
- Open access
- OpenAlex
- https://openalex.org/W2950877942
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:195345258
Keywords
Correctness, Benchmark (surveying), Computer science, Dempster–Shafer theory, Context (archaeology)
References
- Application of Dempster Shafer Theory to Assess the Status of Sealed Fire in a Cole Mine
- Statistical Techniques for Network Security: Modern Statistically-Based Intrusion Detection and Protection
- Fast and Efficient Outlier Detection Method in Wireless Sensor Networks
- Identifying best practices for supporting broadband growth: Methodology and analysis
- Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence
- Outliers detection and classification in wireless sensor networks
- Multisensor data fusion for fire detection
- Fault Detection in Wireless Sensor Networks: A Machine Learning Approach
- A Distributed Architecture for HVAC Sensor Fault Detection and Isolation
- Detection of Urban-Induced Rainfall Anomalies in a Major Coastal City
- A Review of Data Fusion Techniques
- Labelled data collection for anomaly detection in wireless sensor networks
- Sensor data fusion for context-aware computing using dempster-shafer theory
- Outlier Analysis
- Context-dependent combination of sensor information in Dempster–Shafer theory for BDI
- Dempster-Shafer evidence theory for multi-bearing faults diagnosis
- Intelligent Vehicle Embedded Sensors Fault Detection and Isolation Using Analytical Redundancy and Nonlinear Transformations
- Outlier detection for wireless sensor networks using density-based clustering approach
- Unsupervised Sequential Outlier Detection With Deep Architectures
- Recursive Principal Component Analysis-Based Data Outlier Detection and Sensor Data Aggregation in IoT Systems
Cited by
- IoT based Smart Access Controlled Secure Smart City Architecture Using Blockchain
- A Fault Diagnosis and Visualization Method for High-Speed Train Based on Edge and Cloud Collaboration
- Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach
- Architectural framework for Multi sensor Data fusion and validation in IoT Based system
- A data fusion based data aggregation and sensing technique for fault detection in wireless sensor networks
- Parabolic Detection Algorithm of Tennis Serve Based on Video Image Analysis Technology
- A new approach of obstacle fusion detection for unmanned surface vehicle using Dempster-Shafer evidence theory
- Risk Analysis with the Dempster–Shafer Theory for Smart City Planning: The Case of Qatar
- Animation Design Based on 3D Visual Communication Technology
- Prediction of the Remaining Useful Life of Lithium-ion Batteries Based on Dempster-Shafer Theory and the Support Vector Regression-particle Filter
- Consensus reaching model for counter-intuitive in D-S evidence theory and application under 2-tuple linguistic representation
- A roadmap towards energy‐efficient data fusion methods in the Internet of Things
- Recovery schemes of Hop Count Matrix via topology inference and applications in range-free localization
- Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions
- D-S evidence based FMECA approach to assess potential risks in ballast water system (BWS) on-board tanker ship.
- Multisensor Data-Fusion-Based Gas Hazard Prediction Using DSET and 1DCNN for Underground Longwall Coal Mine
- UniPreCIS : A data pre-processing solution for collocated services on shared IoT
- Time-series clustering for sensor fault detection in large-scale Cyber-Physical Systems
- P2DF: Privacy-Preserving Data Fusion Protocol
- Fault-tolerant multi-sensor data fusion system for underground mine gas hazard prediction using Dempster Shafer Evidence Theory
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