Presentation used during SAP Tech days 2018 in Tokyo during a joint presentation between Hortonworks & Vupico represented by myself, what to think when implementing an IoT strategy. Why use Fog / Edge computing, showcased in a fun use case that I built: a cocktail machine built using raspberry pies with AndroidThings, cameras, TensorFlow lite, Mobilenet 1.0, peristaltic pumps and orchestrated by NiFi.

1. |
Road to success – What to think when implementing a
Next generation Iot & big data platform with SAP
solutions
SAP TechDays
Damien Contreras
Director Solution Architecture

2. |
Difficulty with IoT

3. |
What to think when starting an IoT Journey

4. |
IoT architecture
Observed item Sensor Device Gateway Data Center / Cloud
Local Central
Intelligence
Intelligence
Intelligence
Decision / Action
Decision / Action
Decision / Action
Lower latency Higher latency

5. |
Sensors
Definition:
Sensors capture and measure something. They can be active like a Lidar that emits a
laser or passive that detects variation occurring in the subject’s environment.
What to think when selecting a sensor:
§ Define what we want to observe / measure
§ Understand the sensor accuracy required (not all sensors can operates on all
range, most would loose accuracy at the extremes)
§ What is the operating environment will define the durability & tolerances required
(temperature, exposure to rain, …)
§ What type of communication with the device i2C, SPI, GPIO, UART,…
§ Analog or digital output (e.g: PWM)
§ Other specs coming from the device side or platform

6. |
Devices What to think when selecting a sensor:
§ Which device: processing power /Memory: Raspberry PI, Nvidia jetson TX1 or TK1, Arduino, NXP I MX8M,
Qualcomm SDA212, …
§ OS: Amazon FreeRTOS, Raspbian, AndroidThings, Contiki, JanOS, NodeOS, Lua-RTOS,, …
§ POSIX support
§ Which standard / framework to follow: OpenADR, Microsoft Azure IoT Suite, AWS IoT
§ Which protocol libraries to support: Lora, EnOcean, BLE, PROFIBUS, openThread, MQTT, CoAP, WeMo, AMQP,
OPC UA, RESTFull (Ability to support a full TCP/IP stack)
§ Which hardware communication: BLE, NFC, Serial, Zigbee, Z-Wave, WiFi, …
§ Multiple datatypes: JSON, XML, CSV, raw data, raw text, binaries,…
§ Power supply constrained
§ Tolerances imposed by the environment
§ Constrained to be Real time or not
§ Ability to create User Interface & GUI
§ Security
§ Update over-the-air (capability to update without physically being on site) FOTA or Application Over the Air
§ Ability to store data locally
§ Ability to manage back pressure / Data buffering
§ Ability to integrate with existing iOT framework / architecture
§ Processing at the edge: What decision can be made at the edge ?
§ How many sensors do you plan to manage with one device ?
Definition:
Devices are the first processing unit that connect
wired/ wirelessly to transmit data. They can be
intelligent and embed many functionalities or just
forwarding the data points captured by the sensors.

7. |
Gateway & Communication
Definition:
• Can be hardware or software and represent the connection point between the cloud
/ data center and the sensors and smart devices. It can also offer a place to
preprocess data points at the edge before sending it. Provide also additional security
when the IoT network is left unprotected or uses protocols that do not enable
encryption and high security standards.
Used for:
• Which protocol will be used or bridged
• Do we have bi-directional communication
• Be able to convert process data (aggregate, filter, analyze,…)
• Can be used to go from one topology to another (e.g: mesh network with a unique
access to internet)
• Data buffering /Queueing
• Security (Intrusion detection, anomalies, blocking compromised IoT devices,
templer detection, encryption)

8. |
Central Instance
Definition:
• Where you will accumulate all your historical & real time data to give
you a 360 view of your company
Used for:
§ Leverage advanced hardware: clusters or GPUs
§ Accumulate data massively
§ Cloud based or data center
§ Containerized or native applications
§ KPI & Dashboarding
§ Advanced Analytics
§ Model training & processing
§ Combine internal & External Dataset
AWS IoT

9. |
Fog & edge computing
Definition:
• Fog computing: According to Cisco is devices that extends the cloud to be closer to the devices
Benefits:
• Reduce latency for critical application à provide a better user experience, faster decisions & actions
• Reduce data transfer foot print and therefore cost by sending only relevant information
• Remove noise
• Transform visual data (video stream, photos) into numerical insight
• Reduce probability of failure by having the intelligent part earlier on the transmission chain

10. |
How to leverage edge computing in a fun example

11. |
Let’s imagine a plant…
Raw Material
Finished Goods
Mixing raw material
1
2
3

12. |
…that mixes
Cocktails
INPUT
MIX
OUTPUT
Cocktail
§ How to manage a set of raw
material (bottles) and
produce cocktails ?
1
2
3

13. |
Our POC
Device:
§ Run on AndroidThings
§ Control the Hbridge / pump
§ Get picture from sensor of
the bottle and identify the
bottle type with tensorflow
Ligth
Sensor:
Identify bottles:
Capture a picture of
the bottle every
second
Hbridge:
Control the pump
voltage
Cocktail
Peristaltic Pump:
Pump cocktail raw material
Website/Kafka:
§ Manage Cocktail list /
orders
§ Cocktail
recommendations
§ Analytics on cocktail
sales
§ Control stock level
§ Control device
1
2
3
Cocktail

14. |
Why Android Things ?
For developers:
§ Easy to jump from a pure Mobile android environment to AndroidThings
§ You can use the same IDE as for Android dev
§ Reusability of Java libraries
§ Hardware abstraction layer (HAL) to separate Application / OS and the hardware: board
on which it runs
§ Easy to design & build interfaces following android paradigm
§ Easily deploy on popular dev boards like the Raspberry Pi 3
For project manager:
§ Easy to deploy application & updates over-the-air
§ Write once deploy on many platforms with minimal modifications
§ Complete integration with Google ecosystem (assistant, nearby, Tensorflow light,
Cloud,…)

15. |
Why TensorFlow Lite MobileNet 1.0:
§ Class of Convolutional neural network designed by Google
§ Low footprint: Classification can happen
directly on the device (no need to send to a
cloud resource)
§ Gain in response time
§ Works great on AndroidThings
§ Accuracy is not too bad
(70%)
§ Easy to retrain and use in
Tensorflow
Why TensorFlow Light ?
Orange juice
Whiskey
Apple juice
…

16. |
Enjoy a cocktail on us,
Thank you
all for listening
Damien Contreras
damien.contreras@vupico.com

17. | Let s mix cocktails with IoT
Raw Material
Transformation
Finished Good
1
2
3
Cocktail