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Fault Diagnosis System for Wireless Sensor Networks

Fault Diagnosis System for Wireless Sensor Networks. Praharshana Perera Supervisors: Luciana Moreira Sá de Souza Christian Decker. Outline. Introduction Sensor Data Analysis Data Correlation Time Dependant Sensor Data Analysis Approaches

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Fault Diagnosis System for Wireless Sensor Networks

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  1. Fault Diagnosis System for Wireless Sensor Networks Praharshana Perera Supervisors: Luciana Moreira Sá de Souza Christian Decker

  2. Outline • Introduction • Sensor Data Analysis • Data Correlation • Time Dependant Sensor Data Analysis • Approaches • Neural Network based Fault Detector • Rule Based fault Detector • Evaluation • Evaluation Neural Fault Detector • Evaluation Rule based Fault Detector • Conclusions and Future Work

  3. Wireless Sensor Networks have the potential to be used in the near future in industrial applications: Inventory Management Items Tracking Environment Health and Safety Monitor Storage Regulations Monitor Patient Conditions Track Personnel (Workers in Hazardous Areas) Introduction

  4. Effects of failures in a Business Process: Economic losses Contamination of the environment Human life risk Quality reduction Maintenance costs WSN Failure in a Business Process WSN

  5. Our Goal • Automatic identification of incorrect sensor readings • Called value failures • Provide a higher maintainability to the business process by • Diagnosing failures before they propagate further to the rest of the system

  6. WSN WSN WSN State of the Art Value Fault Detection for WSNs • Depend heavily on model assumptions and expert knowledge • Lack prior data analysis • Perform fault detection in nodes itself • Hierarchical detection does not provide value failure detection • but shift the task of fault detection to a more powerful device (sink)

  7. Advantages Ability to learn any complex system model No assumptions on mathematical/statistical models Less expert knowledge Disadvantages Require training time Scalability for WSNs Neural and Fuzzy Models in Sensor Fault Detection

  8. Analysis - Sensor Data

  9. Analysis - Incorrect Sensor Readings Light sensor stuck in one value 4 abnormal peaks of temperature sensor data

  10. Sensor Data Correlation y y High Low • Metrics • Correlation coefficient • Multiple correlation coefficient • Gathered Data • Temperature, Light, and Movement data of 3 neighboring nodes • 3 days • To reduce noise (especially movement and light) • Interpolation • Moving Average Results x x

  11. Time Dependant Sensor Data Analysis

  12. Neural Network based Fault Detector • A neural network has the capability of learning these patterns • Requires training data • A neural network is trained to identify • Too high (incorrect) • Too low (incorrect) • Normal (correct) • Temperature Sensor readings

  13. Rule based Fault Detector Input Output • Rule based fault detection algorithm • Rules search phase • Online fault detection phase • Rules are discovered automatically eliminating the need of an expert Sensor Data Statistics σ μ R r Fault Detection Valid/Invalid Rule Base Threshold rules Fuzzy rules

  14. Rules Search Phase Output Input • Threshold Rules • Expected values for a node for the time period T • Mean μ • Standard deviation σ • Multiple correlation coefficient R • Correlation coefficient r (Statistics for Time period T) (Rules for Time period T) If T then μ≈ X μ Threshold Rules Search σ R Fuzzy Rules Search If T and σ = low Then R = high r • Fuzzy Rules • Relationships between statistics for a node for the time period T • μ different sensors • σ and R same sensor • r different sensors

  15. Fault Detection Phase Sensor data Preprocess Sensor Measurements μσ R r Threshold Rules Time Period T If no rule is rejected correct If majority of the rules is rejected incorrect Rule Base Else Threshold rules Fuzzy rules Fuzzy Rules Validate corresponding fuzzy rules If rejected incorrect

  16. Evaluation • Experiment setup • 32 nodes (uParts) deployed on the ground floor • Data collected for a time period of 23 days (3 for training) • Evaluation Metrics • False positive effectiveness (FPE) = actual unreliable / identified unreliable • Fault detection effectiveness (FDE) = identified unreliable / unreliable

  17. Evaluation – Neural Fault Detector Experiment results

  18. Evaluation – Rule based Fault Detector • Identified Rules • Temperature • Light

  19. Evaluation – Threshold Rules

  20. Evaluation - Number of Rejected Threshold rules

  21. Evaluation – Rule based Fault Detector Example

  22. Conclusions and Future Work • Conclusions • Proved to be efficient on identification of failures • A new strategy to evaluate sensor readings in WSNs • Require less expert knowledge of the system • Ability to learn environment and system dynamics • Fault detection performed in back-end • Without putting burden on the nodes • Independent of any hardware platform :- Ideal for enterprise scenarios • Neural fault detector :- potential to be used in specialized scenarios • Rule based fault detector :- Any WSN scenario supporting the users (operators)

  23. Conclusions and Future Work • Future Work • Evaluating the approaches within a second application trial • Long period of time • Introducing errors • Neural network to detect failures in light and movement sensors • Enhancements in the decision scheme in rule based detector • Voting or weighting mechanisms

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