The document discusses the event 'Scale Up for a Real Smart Future' focusing on advanced machine learning and AI techniques integrated with IoT devices. It addresses the challenges of transforming data into intelligence through a hybrid cloud-edge architecture that enables efficient data management and smart services. Key use cases include parking prediction and traffic estimation, highlighting the need for reliable data processing and model synchronization in IoT scenarios.

1. Scale Up for a Real Smart Future
Berlin, Germany
23-24 October, 2019
Advanced ML/AI Techniques with FIWARE
and Connected IoT Devices
Adrián Arroyo
IoE Lab. Research & Innovation, Atos Spain, Spain
adrian.arroyo@atos.net
@arroyadr

2. How to address the challenge of transforming data
into intelligence?
§ IoT ↔ Big Data
§ AI technologies and Machine Learning techniques are
suitable for IoT scenarios
• Detecting patterns and behaviours from gathered data
§ Analysing these patterns, it is possible to transform data
into intelligence
• Generating lots of smart services

3. How to address the challenge of transforming data
into intelligence?
§ Appearance of new IoT hardware with better performance
capabilities on AI in the Edge
§ Power demanding is an issue for IoT
• Hardware performance
• Connectivity issues
• Energy efficiency
§ Move data and intelligence to the cloud is not a solution for
IoT
• Accessibility to the cloud is not guaranteed every time
(24h/7d)
• Delays in responses may appear

4. Our solution: EASIER
A Data Intelligence platform with a Hybrid
Architecture Cloud-Edge

5. EASIER: Architecture
§ Data manager: brings IoT data to the
platform (NGSIv2 supported)
§ Automatic Cloud/Edge data
synchronization
§ Cloud platform to implement your data-
science tasks: create, train and test
models with the Micro Trainers
§ Use the models in the edge (or cloud) with
automatic model synchronization with
the Micro inferencers
§ Models optimized for specific hardware
platforms (TPU, Raspberry Pi, etc.).

6. EASIER: Artificial Intelligence as a Service
Raspberry Pi
Data
Manager
DataData
Google Coral
dev board

7. EASIER: Artificial Intelligence as a Service
Raspberry Pi
Google Coral
dev board
Data

8. EASIER: Artificial Intelligence as a Service
Trainer
Raspberry Pi
Google Coral
dev board
Data

9. EASIER: Artificial Intelligence as a Service
Inferencer
Raspberry Pi
Google Coral
dev board

10. EASIER: Artificial Intelligence as a Service
Trainer
Trainer
Inferencer
Inferencer
Trainer Inferencer
Raspberry Pi
Google Coral
dev board

11. Data Storage
& Processing
Data
Transform
Data
Manager
Data
Transport
Data
Intelligence
Data
Management
Smart
Services
Data Sources
Deployment
EASIER: Architecture technologies
Parking prediction Traffic estimation Vehicle insurance Worker safety Failure prediction

12. Use Case:
§ https://synchronicity-iot.eu/
§ Data ingestion (NGSIv2): Orion, STH
Comet, …
§ Data sources: Parking, Traffic, Noise
§ Smart services
• Prediction of future data
Data
Manager
• Free/occupied ratio vs time of day

13. Smart Service Demo: Parking area availability
prediction

14. Contact for related proposals
Adrián Arroyo Pérez <adrian.arroyo@atos.net>
Internet of Everything Lab
Atos Research and Innovation

15. Thank you!
Keystone Sponsor:
Community Partners:
Join our newsletter
Follow us!!!
Media Partners: