IoT and IIoT
Data collection, storage, processing and visualization
Cloud infrastructure and platform services for Analytics
Architecture - Example
3. IOT & IIOT
Billions of connected devices to server via network and deliver connected
industry solutions. The connectivity is just an enabler but the real value of
IoT is on data (business insight/data-driven economy).
Use of smart sensors and actuators to enhance manufacturing and
Industry 4.0 focusses on the interconnectedness of machines and systems
to improve operational efficiency and productivity
4. IoT- Key Technology Enablers
(1) Cloud Computing
(2) Big Data and Analytics
(3) Web 2.0 and 3.0
(4) Evolution of high speed communication technologies
5. Technology Landscape
Industry verticals -
Sensors ,Devices and
• API Security
• Data At
• Data At Rest
6. IOT- Sensors to Application
IoT & Cloud platform
Edge Devices/Sensors source for the real time data .
Device gateway collects data from multiple edge devices, filter aggregate and ingest the data to the cloud
platform for further processing and analyzing
IoT platform enable device onboarding, data ingestion, device to cloud and cloud to device communication
Cloud platform receive data, store, process and generate insights
Application helps to visualize the dashboard ,monitor and control the devices.
Management Compute Hosting
7. IOT Sensors &Gateway
Acquire and Transmit
Monitor Transmit Aggregate Analyze Send to Cloud
Analyze and ActAggregate
Control and management
Communication between sensors and
Gateway , Gateway to cloud platform
Different field sensors/Devices
Sensors: Temperature, pressure, accelerometer ,vibration ,RPM,
Devices: Camera, activity tracker, smart glass etc..
Data from Sensors/Devices
• Structured, semi-structured, or unstructured, or any combination of these varieties.
• Velocity, Variety and Volume
• What can we do with Vast amount of data?
• Real time streaming analysis and insights
• Derive meaningful KPI’s to help business
• Detect anomaly in operation, device behavior, Alert
• Predictive analysis
• Visualization, Control/Operate and report
• Quality inspection
• Process automation
• Improve quality of life
• Research and improvements
• Analyze the data
• How to analyze Terabytes of streaming data ?
• Large repositories
• Complex data analysis techniques
• Distributed/parallel processing .
• Data lake/warehousing /Business Intelligence .
9. Data Collection, Storage and processing
With growing volumes of available data
and affordable data storage.
Computational processing is also cheaper
Analyzing bigger and complex data helps
in delivering faster and more accurate
Process real-time data such as video,
audio, application logs, website
clickstreams, and IoT telemetry data.
What happened and why?.
Real time Streaming Analytics
human-interpretable patterns that
describe the data
correlations, trends, clusters,
trajectories, and anomalies
summarize the underlying relationships
Use some variables to predict unknown
or future values of other variables
Data Processing Descriptive Tasks
Real time Streaming
Step 1: Data Collection
How to collect Continuous data from multiple sources/industry machines?
Choose the important parameters and monitor the parameters
Select the frequency at which the data needs to be collected
Choose appropriate Cloud Gateway/broker and streaming platform
Choose proper data lake to store different type of data
Step 2: Data Storage
Store large amount of data
Real Time Streaming Data
Scalable ,Reliable and high availability
Relational (SQL): Structure with defined attributes
MS SQL, POSTGRESQL ,oracle SQL
Can be queried using SQL
No-relational(NO-SQL): Free flow operations
Utilize a variety of data models, including document, graph, key-value and columnar
Unique way to query the data
Mongo DB(Document) , Redis (Key-value) ,Amazon Redshift (Columnar) , Cassandra
(Columnar), HBase (Columnar) ,Dynamo DB(Document DB-stores JSON/XML) , GraphDB
Processing – Tools/Framework
Processing Big Data
Spark - distributed stream processing
Storm - distributed stream processing
Kafka – Event Processing
Splunk – Log analysis Platform
Hive – Data warehouse
Hbase – No SQL
Zookeeper – Centralized config & coordination
Streams and Complex Event Processing
Kafka, AWS Kinesis, JMS , Azure Event
hub, Google pub/sub
Where to install Big Data Tools ? Who is providing process/memory/Storage/
R Programming (Statistical
Amazon Machine Learning
Azure ML studio
Analyzes and visualizes data in real time
Ex: production floor manager wants to have real-time insights from the
sensor data, patterns and take actions on them
Use historical data
Association rule analysis
Finding groups of objects/points such that the objects/points in a group
will be similar (or related) to one another and different from (or unrelated
to) the objects in other groups
16. Predictive Analytics
• What else most likely to happen?
• Data/Text mining ,forecast and statistical analysis
• Intelligent/scientific estimates about the future values (Ex:customer demand, interest rates, stock
market movements etc..).
• Deploy to take business decision
• Predictive Shipping
Input Param 3
Input Param 1
Input Param 2
Classification and Regression
Output of an algorithm after it has been trained on a historical dataset and applied to
new data to know the likelihood of a particular outcome.
Set of algorithms & methods to predict categorical values.
Classification, which is the task of assigning objects to one of several predefined
Tid Attrib1 Attrib2 Attrib3 Class
1 Yes Large 125K No
2 No Medium 100K No
3 No Small 70K No
4 Yes Medium 120K No
5 No Large 95K Yes
6 No Medium 60K No
7 Yes Large 220K No
8 No Small 85K Yes
9 No Medium 75K No
10 No Small 90K Yes
Tid Attrib1 Attrib2 Attrib3 Class
11 No Small 55K ?
12 Yes Medium 80K ?
13 Yes Large 110K ?
14 No Small 95K ?
15 No Large 67K ?
Classification and Regression
• Algorithm Selection depends on
• Training time
• Number of parameters
• Feature count
• Memory footprint
• Linear/Non-linear data
Support Vector Machine
To capture and communicate insights from Big Data analytics, move from standard
reporting to more sophisticated visualization.
Visualization -> presenting information in such a way that people can consume it
The most impactful visualizations are often the most interactive
Explore and have a conversation with the data.
it capitalizes on visual advantage to recognize and understand patterns, represents
a large amount of data in one place, and gives users access to actionable insights.
Heat Map Tag Cloud History Flow
23. How to Visualize Raw/Processed/Analytics output?
• Application that is accessed via a web browser over a
• Web apps became really popular when HTML5 came
around and people realized that they can obtain
native-like functionality in the browser.
• Native apps are written in languages that the platform
• Swift or Objective-C for iOS
• Java for Android
• C# for Windows Mobile
• Combination of Native with Web Component
• Xamarin -Slack, Pinterest.
• React Native -Facebook, Walmart, Tesla, and Airbnb
• Titanium -eBay, ZipCar, PayPal
• Angular JS -PubNub Chat, YouTube on PS3
• Advanced BI tools – Power BI, Qlikview, Tableau
24. IoT Platforms
IBM Watson IoT
Google Cloud IoT
Platform ability to centrally manage of
multiple devices at scale, provide
remote configuration, monitoring and
Facilitate seamless connection
between device to platform, platform
to device and direct connectivity
between sensors to platform.
Ability to provide infrastructure, tools
to manage, store, process and real
time analysis of streaming data.
Platform to host in public, private,
Friendly environment, programming,
framework options to develop,
integrate, connect, host and run the
Platform capability to provide fine
grained security and data privacy.
25. Cloud Platforms
PaaS and IaaS
Ramp up or ramp down resource on need
Route the load to difference instances
Virtual Network Environment
Environment to host applications and run
Auto scaling and Load Balancing
Automatic Deployment with Zero downtime
Scaling and Elasticity
Managed Machine learning, Deep Learning ,
Identity and Access Management
Compute, Storage, Network