Real-Time Sensor Monitoring
Total Readings
0
Anomalies Detected
0
Detection Accuracy
0.0%
Avg Processing Time
0.0ms
Temperature (°C)
Vibration (g)
Pressure (PSI)
Recent Anomalies
No anomalies detected yet. Start the stream to begin monitoring.
Technical Implementation
ML Algorithms
- •Isolation Forest: Primary anomaly detection algorithm with 94.5% accuracy
- •Random Forest: Secondary classifier for anomaly type identification
- •Statistical Methods: Z-score and IQR-based outlier detection
Infrastructure
- •Apache Spark: Distributed processing for real-time sensor streams
- •AWS S3 & Glue: Data lake architecture for historical analysis
- •Docker & Kubernetes: Containerized microservices deployment
Performance Metrics
- •Latency: Sub-second processing time for real-time detection
- •Throughput: 10K+ sensor readings per second
- •Accuracy: 94.5% anomaly detection with 2.1% false positive rate
Key Features
- •Real-time Monitoring: Live sensor data visualization and alerting
- •Automated Alerts: Severity-based notification system
- •Scalable Architecture: Handles millions of IoT devices
Technology Stack
Apache SparkPySparkIsolation ForestRandom ForestAWS S3AWS GlueDockerKubernetesPythonscikit-learnReal-time ML