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Bringing iot data to life, IoT Israel 2014
- 1. Bringing IoT Data to Life! Date Dr. Joachim Schaper, VP Research
- 2. 2 The Potential…and Challenges…of IoT Data
- 3. DATA REAL TIME VAST AMOUNTS REAL WORLD HETEROGENEOUS NOISY
- 4. sense How do we make of IoT data?
- 5. I T A An IoT Analytics Platform
- 6. 6 IoTA: Analytics in Action! Mobility Pattern Analytics Behavior Learning & Prediction Crowd Analytics Anomaly Detection Detection
- 7. 7 Anomaly Detection Answers Difficult Questions What just happened that shouldn‘t have? •What does something that shouldn‘t have happened look like? How can I find it in time? •Before there is serious damage •Before supply chains, customers, competitors and VIPs are impacted Why are you disturbing my sleep?! •False alarms are costly
- 8. 8 Anomaly Detection Addresses Multiple Problems Anomaly Detection Framework Traffic Incidents Electrical Grid Smart Home Social Media Crowd Dike Stability
- 9. 9 Traffic Anomaly Detection 86% 14% Detection performance True positives False positives Customer problems addressed •Ensure road network efficiency, safety •Minimize impact of traffic incidents •Real-time, automatic detection of abnormal traffic congestion based on sensor data 2.00 0.11 0.00 5.00 Alert rates per day and road segment Rule-based detection Detection Anomaly detection
- 10. 10 Traffic Anomalies Captured Live Different anomalies are identified depending on index threshold and event filtering. Abnormal traffic congestion identified Construction worker caused traffic changes Taxi parked for >15 min caused traffic changes 28-Nov 13:15 22-Nov 04:15 20-Nov 21:26
- 11. 11 Engine Configuration for Traffic Incident Detection Use-Case Specific Plugin Generic Plugin Detection and Classification API Context Dependency Modeling Preprocessing Normality Model Learning Robust Density Estimation Support Vector Machines Apache Thrift Timestamp Discretization Auto Partitioning Discrete Context Switching Hypothesis & Persistence Test Noise Reduction Data Imputation Nearest Neighbor Python Storm Spark MapReduce Principal Component Analysis Clustering Manifold Learning Feature Extraction Traffic Parameter Extraction Enhanced HMM System Identification Random Forest Event Filter Normalization
- 12. 12 Traffic Anomaly Detection – Data and Extracted Patterns Raw Data: LPR, VA Characteristic Features Normal Pattern Model •Speed and volume per lane •High frequency noise (10% - 30% std. dev.) •Full/partial sensor outages •4 feature vectors: •Average speed •Total volume •Lane average speed •Lane speed difference •Aggregation to 1 min interval •Data cleaning •Noise filtering Multi-dimensional model •Traffic features (4 dim.) •Context dependency •Time of day •Day of week; public holidays •Covariance optimization for robustness against anomalies in training set
- 13. 13 Traffic Anomaly Detection – Anomaly Index Anomaly Example – Features and Model Mahalanobis Distance •Mahalanobis Distance indicates magnitude of deviation between model and measurements •Index threshold (red line) determines detection sensitivity •Anomalies affect multiple traffic characteristics •Deviation vectors used to further classify the type of anomaly
- 14. 14 Results from Large-scale Deployment 1,000 LPR cameras 16 million vehicle detections/day 230 road segments analyzed Same configuration applied across highways, on-ramps, urban arterials and side streets Events validated on CCTV Low false alert rate Recurring congestion suppressed
- 15. 15 Moonscape Ventures • Corporate development and investment company • Launched August 2014; operates in TLV, NYC, Silicon Valley • Grows startups: IoT, smart cities, big data, news and media, other • Invests in late-seed stage, Series A round • Led by Tammy Mahn
- 16. 16 Bringing IoT Data to Life!