Delve into the realm of sensor networks and uncover the sophisticated techniques employed for anomaly detection and event prediction. From statistical analysis to machine learning algorithms, explore how these technologies empower proactive decision-making in various domains, including industrial monitoring, environmental sensing, and healthcare systems. To learn more about detection and other techniques visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

1. ANOMALY DETECTION AND EVENT
PREDICTION IN SENSOR NETWORKS
Pratik Kamble

2. INTRODUCTION
Presentation title 2
Anomaly detection is a crucial task in sensor
networks to identify unusual patterns that may
indicate faults or malicious activities. This
project focuses on developing a system for
anomaly detection and event prediction in
sensor networks, aiming to enhance system
reliability and security.

3. PROBLEM STATEMENT
Presentation title
The problem involves detecting anomalies in sensor data to ensure the integrity and
reliability of sensor networks. Anomalies can indicate faults, errors, or potential security
threats, making their detection vital for maintaining system performance and security.
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4. PROBLEM
DEFINITION
Presentation title
Objectives:
• Develop an anomaly detection system for sensor networks.
• Predict events based on sensor data to anticipate potential
issues. Scope:
• Focus on detecting anomalies in sensor readings.
• Utilize machine learning and deep learning techniques for
analysis.
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5. DATASET DESCRIPTION
Presentation title
The dataset consists of sensor data collected from various nodes in a sensor network. It comprises five features:
Area, Sensing Range, Transmission Range, Number of Sensor Nodes, and Number of Barriers. Total samples: 182.
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6. DATA PREPROCESSING
Presentation title
Data preprocessing steps:
• Handling missing values, duplicates, and outliers.
• Feature scaling for standardization. Standardization ensures that all features have a
similar scale, preventing certain features from dominating the analysis.
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7. ANOMALY DETECTION
METHODOLOGY
Presentation title
Methods Used:
• K-means clustering for unsupervised anomaly
detection.
• Deep learning with Keras Sequential and TensorFlow
for more complex pattern recognition. Approach:
• Train models on preprocessed data to identify
anomalies in sensor readings.
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8. EVALUATION METRICS
Presentation title 8
Evaluation metrics:
• Silhouette score for K-means clustering to assess cluster separation.
• Number of anomalies detected by deep learning models.

9. RESULTS
Presentation title 9
Summary of Results:
• Silhouette score obtained from K-means clustering: 0.57
• Two anomalies detected by deep learning models. User Input Prediction:
• The system predicts anomalies based on user input, providing real-time anomaly detection
capabilities.

10. FUTURE
SCOPE
Presentation title
Future Research Areas:
• Incorporating additional features for enhanced
anomaly detection.
• Exploring other machine learning and deep
learning techniques for improved performance.
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11. Presentation title 11
CONCLUSION
Summary of Project:
• Developed an anomaly detection system for sensor networks using
machine learning and deep learning techniques. Key Findings:
• Achieved a silhouette score of 0.57 with K-means clustering.
• Successfully detected anomalies using deep learning models. Significance:
• Enhances system reliability and security in sensor networks.

12. ANY QUESTIONS?
Feel free to ask any questions.

13. THANK
YOU
Pratik Kamble
9137674770
pratikakamble7467@gmail.com
https://pratikthedatascientist.github.io/