2. Google Cloud Platform 2
How can we…
By harnessing IoT data, we can answer pressing
business and operational questions
● Track device status, state or location?
● Prevent downtime by predicting device failure?
● Optimize the performance of every device, in real-time?
● Automatically predict when a device needs maintenance?
● Detect anomalies?
3. Google Cloud Platform 3
Scaling
on-demand
Secure
device connection
Actionable
Insights
Three key challenges in building an IoT solution
4. Google Cloud Platform 4
We’ve built our IoT stack on the same
technology and tools we use at Google
Confidential & ProprietaryGoogle Cloud Platform 4
6. Google Cloud Platform 6
HK-G (HK, GU) 2019
Havfrue (US, IE, DK) 2019
Curie (CL, US) 2019
JGA (AU, GU, JP) 2019
Network
SJC (JP, HK, SG) 2013
Unity (US, JP) 2010
Faster (US, JP, TW) 2016
PLCN (HK, LA) 2019
Monet (US, BR) 2017
Junior (Rio, Santos) 2018
Tannat (BR, UY, AR) 2018
Indigo (SG, ID, AU) 2019
Asia Pacific Americas Europe, Middle East, & Africa
Google Cloud Platform
Regions, PoPs, Network
Edge points of
presence 110+
3London
3
Netherlands
3
3 Finland
3
Belgium
3Los Angeles
4Iowa
3 N. Virginia
3
S. Carolina
3 Montreal
3
São Paulo
3
Tokyo
3Osaka
3
Taiwan
3
Hong Kong
3
Mumbai
3
Sydney
3
Singapore
3Oregon
Current region and
number of zones
Future region and
number of zones
Frankfurt
Zürich
3
3
9. Google Cloud Platform 9
Fully-managed service to securely connect and
manage your global device network
Cloud IoT Core
Device Manager :
● Configure individual devices
● Update and control devices
● Role level access control
● Logical device representation
● Console and APIs for device
deployment and monitoring
Protocol Bridge :
● MQTT , HTTP protocol
endpoints
● Automatic load balancing
● Global data access with
Pub/Sub
11. Google Cloud Platform 11
Ingestion at scale with Cloud Pub/Sub
Topic
Subscription Subscription Subscription
IoT DeviceIoT DeviceIoT Device
3rd party
services
IoT Core
Pub/Sub
Functions Dataflow
● Durable message persistence
● Global service
● Powerful real-time features
With Dataflow & Cloud Functions
● Separation of application
and devices
13. Google Cloud Platform 13
Unlockbusiness insights in real time
Build and execute ad hoc
analyses on petabyte-scale
IoT data sets
Run advanced analytics
and apply machine learning
with to your IoT data
Visualize IoT data
results with built-in reports
and dashboards
Globally dispersed device data
14. Google Cloud Platform 14
Make real-time changes
to the device state
Seamlessly work
with Android Things,
and supports other
embedded OS
Out-of-the-box support
from leading
manufacturers like
Intel and QualComm
Manage device firmware
updates or carry out repairs
from one central network.
Get a new level of insight into the operational
efficiency of your devices
15. or Linux OS
Cloud IoT Edge
Real-time analytics & ML
Edge ML
Edge IoT Core
Edge Device
Data
Control
Update Config
& Deploy ML
model
Data
Cloud IoT Core
for device connection
and management
Cloud ML Engine
for training, deploying,
and running ML model
Cloud Pub/Sub
for ingest event streams
at any scale
Other
Cloud services
BigQuery for data
warehouse and fast
querying
Build and Train ML model
in the Cloud
Predict & act at the Edge
Cloud Dataflow
for streaming and
batch analytics
CPU GPU Edge TPU
Cloud IoT Edge
Locally store, process, and derive intelligence from data at the Edge
16. Features of Cloud IoT Edge
Local compute
ML inference at the Edge
Run on Android Things and Linux-based OS
Supports enhanced security through hardware-root-of-trust
Edge TPU™ support
Securely connect devices to the Google Cloud
Works seamlessly with Cloud for hassle-free device provisioning
17. Exciting new features of Cloud IoT Core
Cloud IoT
provisioning
High speed Cloud to
device messaging
Device management:
Groups
Cloud IoT Edge
Gateways
Onboard millions of devices
seamlessly and securely
Hit 100 msg/sec to each
device at high speed
Better control access to groups
of devices with ACL
Bring the power of AI
to the Edge
Securely connect any IoT
device to Cloud
Extensive logging
Extremely fine-grained logging
data for devices
18. Google Cloud Platform 18
Ingest, manage
and optimize your
IoT device data:
• Cloud IoT Edge
• Cloud IoT Core
• Cloud Pub/Sub
Process, clean
and store:
• Cloud Dataflow
• Cloud Functions
• Cloud Bigtable
• Cloud Spanner
• GCS
Analyze, visualize and
predict outcomes:
• BigQuery
• Cloud ML Engine
• Data Studio
• Cloud Functions
• Cloud Dataflow
A platform for end-to-end IoT data processing
Google Cloud is built on the same infrastructure and computing principles as that of the popular Google internet services that handle billions of users across the globe.
We understand that building an enterprise IoT solution is incredible difficult and we’re working to make it easier.
Google Cloud IOT is a set of fully managed and integrated services that allow you to easily and securely connect, manage, and ingest IoT data from globally dispersed industrial devices at a large scale, process and analyze/visualize that data in real time, and implement operational changes and take actions (such as predictive maintenance) as needed based on that data.
Cloud IoT Core is a fully managed service that allows you to easily and securely connect, manage, and ingest data from millions of globally dispersed devices. Cloud IoT Core, in combination with other services on Google Cloud IoT platform, provides a complete solution for collecting, processing, analyzing, and visualizing IoT data in real time to support improved operational efficiency.
100% managed service
No need for autoscaling, redundancy, sharding or resource pre-provisioning. Connect one of millions of devices.
Bridges MQTT protocol with highest level of security (TLS 1.2 with certificates)
GLOBAL end-point (mqtt.googleapis.com)
Data is published to PubSub automatically and is accessible globally
Update and control devices via the Device Manager
Seamlessly move IoT data across Google Cloud services. Ingest data with Cloud IoT Core, distribute data with Cloud Pub/Sub, apply data transformations with Cloud Dataflow, and store data in Cloud Storage, BigQuery or in Bigtable .Perform ad hoc analysis using Google BigQuery, visualize data using Cloud Data Studio, and derive intelligence using Cloud Machine Learning
To touch on just a couple, Cloud IoT provisioning: Millions of devices can be connected to the Cloud IoT Core with zero provisioning operations. We are working with select secure element vendors to make this possible. High speed and scalable messaging will make control scenarios much easier where you want to be able to directly influence the state of your devices from the Cloud. Support for gateway devices to represent a multitude of less powerful or less secure on-premise devices. All these features being added to IoT Core represent more robust scaling.
We plan to release a set of software and hardware as part of our edge offering as an extension of the IoT platform.
Edge TPU: a hardware accelerator chip for local ML inference
Edge ML: a client library which uses Tensorflow-lite runtime to perform machine learning inference on the Edge TPU hardware chip. Since it uses Tensorflow-lite runtime, Edge ML will have support for ML inference on GPUs and other hardware accelerators.
Edge IoT Core: a client library to securely connect to Google Cloud IoT Core and transfer data and ML models with Cloud.