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Data analytics for monitoring IoT infrastructures by G.Madhusudan, Orange Labs

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Madhusudan Giyyarpuram, Orange Labs, Data analytics for monitoring IoT infrastructures

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Data analytics for monitoring IoT infrastructures by G.Madhusudan, Orange Labs

  1. 1. Data Analytics for Monitoring IoT G.Madhusudan Orange Labs
  2. 2. LPWA Use cases Smart City • Street Lighting • Waste management • Air monitoring • Parking • Traffic Lightning • Advertising monitoring • Tracking (bikes, mobile ads, …) • Fire Hydrant monitoring • Flood detection / monitoring Smart Building • Heating / T°, humidity, CO2 monitoring • Water, Gas, Elec metering • Presence detection • Zoning / indoor location • Smoke detection / security equipment monitoring • Access control & monitoring • Alerting, monitoring • Monitoring of prescripted equipment use • Home services and badging • Feedback buttons • Bridge, railway , tank, vibration, road T°… sensors • Objects and people tracking • Weighing machines • Lightning receptor for wind turbine Smart Agriculture • Connected beehives • Ground sensors • Animal monitoring • High end home objects • Tracking • Secondary residence automation • Smoke detection monitoring • Oil/Gas tank monitoring Technical • Coverage verification smart territories industry healthcare retail smart home transport logistics Best TTM in bold: existing use cases, with improved ROI due to deployment facility (on battery, no repeaters, use of global network)
  3. 3. Changing scenario for IoT networks
  4. 4. Déploiement du réseau LoRa ® dans 18 agglomérations françaises et progressivement au niveau national A fin S1 2016, dans 18 agglomérations soit 1200 communes A fin janvier 2017, dans 120 agglomérations soit 2600 communes Capacité d’étendre cette couverture par une offre site
  5. 5. From raw IoT data to IoT dashboard
  6. 6. System architecture
  7. 7. Dashboard • Provides B2B client-centric view of the IoT networks • SLA obligations • KPIs • Anomalies • Predicted events
  8. 8. 8 Interne Orange Orange Labs Research Exhibition 2016 B2B client-centric view of IoT • multi tenant • multiple services • provide a B2B client centric view of the functioning of the IoT network • how are my set of devices functioning? • KPIs adapted to the services • All this on unlicensed bands for LPWA i.e. radio frequencies that are free for everyone to use if a few conditions are respected (transmit power, duty cycle,)
  9. 9. 9 Interne Orange Orange Labs Research Exhibition 2016 IoT NMS – Data analysis • data collection • data cleaning • exploratory data analysis visualization scatter plot correlation • Modeling • Machine learning
  10. 10. 10 Interne Orange Orange Labs Research Exhibition 2016 IoT NMS – machine learning aspects Activity Machine Learning techniques Examples Anomaly detection k-means clustering. DCs that need more analysis. Can be extended to use external open data sets such as road works and meteorological inputs. Model construction Random forest , Open ML in the future? Establishing the variables that most influence a KPI (such as packet delivery rate), which model to use? Event prediction in a streaming context Incremental learning on non- stationary streams – concept drift, Adaptive Hoeffding Trees The goal is for the model to adapt itself dynamically to potentially changing environments. The prediction is verified against the real label and the model adapted accordingly.
  11. 11. 11 Interne Orange Orange Labs Research Exhibition 2016 Anomaly detection - 1
  12. 12. 12 Interne Orange Orange Labs Research Exhibition 2016 Anomaly detection - 2
  13. 13. Challenges - stream processing • Integration of ML libraries such as Samoa with Stream processing engines • Delayed/Missing labels • Missing features – imputation? • Concept Drift (change in seasons, new building sites)
  14. 14. Challenges – system view • Prediction model is at the level of devices or links. • How do we go from these atomic predictions to network level and system level views? • Use traffic pattern profiles and map low level prediction to KPIs associated with the profiles
  15. 15. Thank you! Questions

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