Sensing the Future: Anomaly Detection and Event Prediction in Sensor Networks

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. 3

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. 4

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. 5

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. 6

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. 7

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. 10

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 [email protected] https://pratikthedatascientist.github.io/