Data Analytics for Monitoring IoT

G.Madhusudan

Orange Labs

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 prescriptedequipment 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)

Changing scenario for IoT networks

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

From raw IoT data to IoT dashboard

System architecture

Dashboard

• Provides B2B client-centric view of the IoT

networks

• SLA obligations

• KPIs

• Anomalies

• Predicted events

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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 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 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.

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Anomaly detection - 1

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Anomaly detection - 2

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)

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

Thank you!

Questions