1.
Cloud Options for
Wearable Data Analysis
Gene Leybzon, November 2015

2.
Data
Collection
Mobile
Device or
Hub
Delivery to
Cloud
Analysis
Notifications
“Traditional“ Wearable Data Stream

3.
Data
Collection
Mobile
Device or
Hub
Delivery to
Cloud
Analysis
Notifications
Wearable Data Stream
with partner API
Partner Cloud

4.
Wearable Data Stream with Machine
Learning
Data
Collection
R/T Data
Analysis based
on ML model
Local
Notification
Analysis ML model
tweaking Notifications
Updated ML params

5.
Why Cloud?
• Scale (50 billion connected devices
by 2020)
• Connectivity options
• Make sense of data takes
computing power
• Reliable customer experience

6.
 Wide range options to connect (both message-
based and streaming data)
 Fast and global network
 Big Data storage and real-time analysis
 99.99% (or better) uptime and availability
 Security and authentication
IoT developers expectations from
the Cloud

7.
Viable Cloud Options

8.
AWS

9.
AWA IoT message-basedc
ommunicaton

10.
AWS IoT connection-oriented
communication

11.
AWS Cloud-based data processing

12.
AWS IoT Landscape

13.
AWS IoT Features
Connect
devices to
AWS Cloud
Connect
between
devices
Secure data
and
interactions
Process data
in the cloud
Message-based (offline)
communication

14.
Google Cloud Platform

15.
Google for IoT
 Google backbone network
 BigQuery (Big data database)
 Cloud dataflow (data streaming)
 Cloud pub/sub (messaging infrastructure )
 Brillo OS and Weave coming soon

16.
Google Brillo OS

17.
Google Weave
Easy setup
Direct
connectivity
Interpretability

18.
Google Weave Hub

19.
Google Nest Weave

20.
VMware

21.
VMware
 Managing devices: AirWatch
 Stream data: vRealize Log
 Cloud: vCloud Air or Hybrid Cloud

22.
VMware vCloudAir

23.
Microsoft Azure IoT Suite
 Remote
monitoring
 Predictive
mantanace

24.
Microsoft Azure Cloud Services
Virtual
Machines
Mobile
Push
SQL
Database
Media
Streaming
Websites
Active
Directory
Hadoop

25.
Questions?