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Fault Diagnosis System for Wireless Sensor Networks
Praharshana Perera
Supervisors: Luciana Moreira Sá de Souza Christian Decker
Page 2 of 23
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
Page 3 of 23
Introduction
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)
Page 4 of 23
WSN Failure in a Business Process
WSN
Effects of failures in a Business Process: Economic losses Contamination of the environment Human life risk Quality reduction Maintenance costs
Page 5 of 23
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
Page 6 of 23
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)
WSN
WSN
WSN
Page 7 of 23
Neural and Fuzzy Models in Sensor Fault Detection
Advantages
Ability to learn any complex system model
No assumptions on mathematical/statistical models
Less expert knowledge
Disadvantages
Require training time
Scalability for WSNs
Page 8 of 23
Analysis - Sensor Data
Page 9 of 23
Analysis - Incorrect Sensor Readings
4 abnormal peaks of temperature sensor data
Light sensor stuck in one value
Page 10 of 23
Sensor Data Correlation
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
Sensor Multiple correlation coefficient
Temperature 0.91
Light 0.93
Sensor Sensor Correlation coefficient
Temperature Light 0.73
Temperature Movement 0.69
Light Movement 0.69
x x
y yHigh Low
Page 11 of 23
Time Dependant Sensor Data Analysis
Page 12 of 23
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
Page 13 of 23
Rule based Fault Detector
Rule based fault detection algorithm
Rules search phase
Online fault detection phase
Rules are discovered automatically eliminating the need of an expert
SensorData Statistics
σ μ R r
Input
Rule Base
Threshold rules Fuzzy rules
Fault Detection
Output
Valid/Invalid
Page 14 of 23
Rules Search Phase
Threshold Rules
Expected values for a node for the time period T Mean μ Standard deviation σ
Multiple correlation coefficient R Correlation coefficient r
Threshold RulesSearch
Fuzzy Rules Search
Input
(Statistics for Time period T)
σ
μ
R
r
(Rules for Time period T)
Output
If T then μ ≈ X
If T and σ = lowThen R = high
Fuzzy Rules
Relationships between statistics for a node for the time period T μ different sensors σ and R same sensor r different sensors
Page 15 of 23
Fault Detection Phase
Time Period T
Sensor Measurementsμ σ R r
Sensor data
Preprocess
Rule Base
Threshold rules
Fuzzy rules
Threshold Rules
If no rule is rejected
If majority of the rules is rejected
Else
correct
incorrect
Fuzzy Rules
Validate corresponding fuzzy rules
If rejected incorrect
Page 16 of 23
EvaluationExperiment 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
Page 17 of 23
Evaluation – Neural Fault Detector
Experiment results
Fault detection effectiveness (FDE) False positive effectiveness (FPE)
0.75 0.80
Page 18 of 23
Evaluation – Rule based Fault Detector
Identified Rules
Temperature
Light
Page 19 of 23
Evaluation – Threshold Rules
Page 20 of 23
Evaluation - Number of Rejected Threshold rules
Page 21 of 23
Evaluation – Rule based Fault Detector
Example
Page 22 of 23
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)
Page 23 of 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