Presents architecture and capabilities for an end-to-end Gateway/Cloud Internet-Of-Things solution

1. © 2015 IBM Corporation
IBM Analytics
IBM Internet of Things - Architecture
and Capabilities
Pradeep Natarajan - Manager, IoT Solutions and Services
April 30th, 2015

2. Agenda
• IoT Architectural Components Overview
• Informix for the Intelligent Gateway
• Informix for the Cloud
• IoT Partners and Platforms
2

3. 3
Informix for the Intelligent
Gateway

4. 4
Internet of Things Topography
Streams
Deep Analytics Zone
Device/Sensors
Smart Gateways
Sensor Analytics Zone
4

5. • Gateways can reduce the cost of the backend cloud
• Reduces cloud storage by filtering/aggregating/analyzing data locally
• Reduces cloud CPU requirements by precomputing values
• Reduces latency since actions can be taken immediately
• Intelligent gateways can detect and respond to local events as they
happen rather than waiting for transfer to the cloud
• Some users are not comfortable putting all their data in the cloud
• Gateways allow customers to capture and get value from their sensors
without sending data to the cloud
• Protocol Consolidation
• Cloud does not need to implement the 100’s of IoT protocols
Over time more and more of the processing will move from the cloud
to gateway devices
How Do Gateways Help IoT Solutions?
5

6. What are the Requirements for a Gateway Database?
• The database management system must:
▪ Have a small install footprint, less than 100 MB
▪ Run with low memory requirements – less than 256 MB
▪ Use lossless compression or other techniques to minimize
storage space
▪ Have built-in support for common types of IoT data like time series
and spatial/GIS data
▪ Simple application development supporting both NoSQL and SQL
▪ Driverless, easy access to the data
▪ Require absolutely no administration
▪ Ideally should be able to network multiple gateways together to
create a single distributed database
6
The database must be powerful enough to ingest, process and
analyze data in real-time

7. IBM Informix: The Ideal Database for Gateways
Simple to use
▪ Hands-Free operation – No administration
▪ Supports popular interfaces such as REST and Mongo as well
as ODBC/JDBC
▪ Handles SQL and JSON data in the same database
Performance
▪ One of a kind support for Time Series and Spatial data
▪ Stream data continuously into the database
▪ Run analytics as data arrives
▪ Dynamically add and update analytics when needed
▪ Storage is typically 1/3 the size compared to other vendors
Invisible
Agile
7
Informix is the only database management system
perfectly suited to run in Gateways

8. Sensor Data is Time Series Data
• What is a Time Series?
▪ A logically connected set of records ordered by time
• What are the Key Strengths of Informix TimeSeries?
▪ Much less space required
• Typically about 1/3 the space required by other vendors
▪ Queries run orders of magnitude faster
• Unique optimized storage means codes paths are shorter and more
data fits in memory
▪ Purpose built streaming data loader for sensor data
• Automatically run analytic and/or aggregate functions on new data
▪ Can store structured (SQL) or unstructured (JSON) data for
quick application development
• REST/ODBC/JDBC/JSON interfaces available
▪ 100’s of functions predefined
• Programming APIs available to create your own analytics
8

9. Traditional Table Approach
Informix TimeSeries Approach
Meter_ID Time KWH Voltage ColN
1 1-1-11 12:00 Value 1 Value 2 ……… Value N
2 1-1-11 12:00 Value 1 Value 2 ……… Value N
3 1-1-11 12:00 Value 1 Value 2 ……… Value N
… … … … ……… …
1 1-1-11 12:15 Value 1 Value 2 ……… Value N
2 1-1-11 12:15 Value 1 Value 2 ……… Value N
3 1-1-11 12:15 Value 1 Value 2 ……… Value N
… … … … ……… …
Meter_ID Series
1 [(1-1-11 12:00, value 1, value 2,…, value N), (1-1-11 12:15, value 1, value 2, …, value N), …]
2 [(1-1-11 12:00, value 1, value 2,…, value N), (1-1-11 12:15, value 1, value 2, …, value N), …]
3 [(1-1-11 12:00, value 1, value 2,…, value N), (1-1-11 12:15, value 1, value 2, …, value N), …]
4 [(1-1-11 12:00, value 1, value 2,…, value N), (1-1-11 12:15, value 1, value 2, …, value N), …]
…
Traditional Sensor data storage vs Informix TimeSeries
Storage
9

10. IoT Requirements for SpatioTemporal Data
• Many IoT applications have a
spatial component to them
▪ Vehicles, cell phones, even pets…
tracking is common
• In these cases both location and
time is important
▪ Show me the vehicles that have
passed by location X in the last
hour
▪ Where has my car been over the
last few hours?
• Informix allows you to combine
Time series and Spatial data in the
same query
10

11. Informix Handles Structured and Unstructured Data
• There is no common format for sensor data
▪ Vendor 1 sends just temperature, Vendor 2 sends temperature plus pressure
• Informix is the only product to handle structured and unstructured data
simultaneously in the same database
▪ Store meta data about your devices (e.g. manufacturer) as JSON
▪ Store device history data (e.g. temperature) as SQL time series
• Informix translates between structured and unstructured on the fly
▪ Allows SQL apps to access the unstructured data and Mongo apps to access
the structured data
▪ Structured and unstructured data can be combined in a single query
• “Give me all the temperature data for sensors made by manufacturer X
between Jan 1 and Feb 1”
11

12. Data Access Options
12
MongoDB
Client
REST Client
SQLI Client
DRDA Client
Informix
DBMS
Informix NoSQL
Listener
Informix
• NoSQL ↔ SQL Translation
• REST, MongoDB Protocol
Support
• SQLI, DRDA Protocol Support
• Relational, Collection, Time
Series, and Spatial Data
Support
Spatial
Time Series
JSON Collection
Relational Table
A REST client is any
program capable of
making a HTTP
request

13. Data Access Options
13
MongoDB
Client
REST Client
SQLI Client
DRDA Client
Informix
DBMS
Informix NoSQL
Listener
Informix
• NoSQL ↔ SQL Translation
• REST, MongoDB Protocol
Support
• SQLI, DRDA Protocol
Support
• Relational, Collection, Time
Series, and Spatial Data
Support
Spatial
Time Series
JSON Collection
Relational Table
You can use the
MongoDB PHP drivers
are available for
MongoDB with the
NoSQL Listener

14. What is Node-Red
• nodered.org
• A visual tool for wiring the Internet of
Things
• Node.js application. Uses building
blocks of reusable code to control
flow of data
14
• “Flows” are created to process
data.
• Example: Take data in, transpose,
process and store in a database
and/or publish to a message
broker
• Collaboration among node
developers

15. Node-Red Benefits
• Rapid Development
• Simple use with JSON
• Simple REST
• Simple MQTT
• Contributor Nodes – simple to use other services
15

16. Node-RED
16

17. IoT Developers - Get Started!
• Gateway starter - https://ibm.biz/BdXr2W
• Code samples - https://ibm.biz/BdX4QV
• Github - https://github.com/IBM-IoT/
17

18. 18
Informix for the Cloud and
Operational Zone

19. 19
What are the IoT Requirements for the Cloud?
• Requirements - similar to gateways (but for different reasons):
• Potentially 1000’s of servers means zero administration is a must
• Data volume adds up very quickly so low storage overhead is required
• Data flows into the cloud continuously and must be processed in real-
time
• Must be able to handle time series, spatial, and NoSQL data natively
• Additional requirements
• Must be able to scale-out
• Must be available as a service
The database must be able to ingest, process and analyze
data in real-time

20. 20
Why use Informix in the “Operational Zone”?
Simple to use
• Hands-Free operation
• Supports REST and Mongo APIs as well as ODBC/JDBC
• Stores SQL and JSON database in the same database
Highly Available
• Close to zero down time
• Partition or Hash your data across servers in the cloud
• Dynamically add/remove additional servers
Performance
• Continuous High Performance Analytics
• Specialized support for Time Series and Spatial data
Invisible
Agile
Resilient

21. 21
Shards: Scale-out your Database across Servers
or Gateways
• Distribute data among servers by
range or hash partitioning
• Each shard can have an associated
secondary server for high
availability
• Run queries across all shards or a
subset of the shards
• Only shards that could qualify are
searched
• Shards are searched in parallel
• Ignores shards that are offline
Shards in a

22. 22
IoT Analytics - Operational and
Big Data Analytics
• Operational Analytics
• Needed when results are required in (near) real-time
• Real-time monitoring, situational detection, correlation of events, e.g.
• Dynamic advertising based on customer movement
• Real-time equipment failure prediction
• Operational analytics are required in gateways as well as the cloud
• Gateways need to aggregate, filter, monitor for conditions
• Analytics must run efficiently while new data is being loaded
• Must be able to dynamically add and update analytics in the cloud
and gateways
• Big Data analytics are required when you have the time to do
“Deeper/Wider” analysis
• Latency between data arrival and results not an issue
• Efficient ETL process from the operational repository is a must

23. 23
TCP/IP
Bulk Loader
SQL Queries (from apps)
Informix Warehouse Accelerator
Compressed
DB partition
Query
Processor
Data Warehouse
Informix SQL
Query Router Results
Informix Warehouse Accelerator:
• Connects to Informix via TCP/IP & DRDA
• Analyzes, compresses, and loads to memory
• Copy of (portion of) warehouse
• Processes routed SQL query and
• returns answer to Informix
Informix Warehouse Accelerator (IWA)
In-Memory Analytics
Informix:
• Routes SQL queries to accelerator
• User need not change SQL or apps.
• Can always run query in Informix
• Too short an est. execution time

24. 24
TimeSeries Service on BlueMix
Every IoT deployment will need to store time series data and possibly spatial data
• Provides REST, JSON, ODBC/JDBC based services to:
• Store, retrieve, and join time series, spatial, relational, and NoSQL data
• Built-in time series analytics
• Multi-tenancy support
• Scales out automatically as needed
• No Administration needed
• Quickly and simply add new sensors
• Interface for continuously loading sensor data
• Adapters for MQTT and MessageSight
IoT Foundation Service on BlueMix
BlueMix Services

25. Docker Engine
• Open source project
• Supported on every major Linux distro (MS Windows in 2015)
• Client-server architecture with daemon deployed on physical or virtual host
• Uses Linux kernel cgroups and namespaces for process resource management and
isolation
• Uses copy-on-write filesystem for git-like image change management
25
A portable, lightweight application runtime and packaging
tool built on top of kernel container primitives
Build Ship Run
Docker Engine

26. Containers
26
App
A’
Host OS
Server
Bins/Libs
App
A
App
B
App
B’
App
B’
App
B’
Container
What is a Container
• An isolated user space within a running Linux OS
• Shared kernel across containers
• Direct device access
• All packages and data in an isolated run-time, saved as a
filesystem.
• Resource management implemented with cgroups
• Resource isolation through namespaces
Run
Bins/Libs

27. Docker Hub
• Enable sharing and collaboration of Docker Images
• Private and public repositories of images
• Certified base images by ISVs
27
Ship
Docker Hub
$ docker push $ docker pull

28. Informix on Docker Hub
https://registry.hub.docker.com/u/ibmcom/informix-innovator-c/
• IBM Informix Innovator-C
• 12.10.FC4
28
Docker Hub
$docker pull ibmcom/informix-innovator-c

29. IoT Partners &
Platforms
29

30. IBM Informix and Intel Deliver Leading Edge Solutions for IoT
▪ Informix support for
Intel’s new Quark
processor, now supporting
Intel family, from Quark to
Xeon.
▪ Quark port in the free
Informix Developer
Edition
▪ Informix Developer
Edition available as part of
standard Intel Gateway
developer SDK
Combining IBM and Intel’s strength at the Edge, Gateway and in the Cloud
provides an intelligent, e2e IoT solution for rapid time to market.
Intel® Quark™ SoC / Intel®
Atom™ / Intel® Xeon™
Windriver
McAfee Security
Supports OSGI/TR69
Informix Database
30

31. Summary
• IBM Informix - best fit for IoT architecture
• IoT gateway
• IoT cloud analytics
• Supported on a wide array of platforms
• Best in class embeddability
• Native support for sensor data - TimeSeries & Spatial data
• Native support for unstructured (JSON) data
• Ease of application development - REST access
• High availability and dynamic scaling
31

32. 32
Pradeep Natarajan – pnatara@us.ibm.com

33. IBM Analytics © 2015 IBM Corporation33
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