IOT TECHNOLOGIES

1. IoT Enabling
Technologies
Lecture -4
Vishal Choudhary
vishalhim@yahoo.com

2.  Wireless Sensor Network
 Cloud Computing
 Big Data Analytics
 Embedded Systems

3. Wireless Sensor Network
Wireless Sensor Networks (WSNs) can be defined as a
self-configured and infrastructure-less wireless networks to
monitor physical or environmental conditions, such as
temperature, sound, vibration, pressure, motion or
pollutants and to cooperatively pass their data through the
network to a main location or sink where the data can be
observed and analysed.

4. The Characteristics of Wireless
Sensor Network
The basic characteristics if WSN are
 Computing, memory, and power consumption
constraints.
 Heterogeneous network topology
 Dynamic network topology.
 Dynamic reconfiguration
 Ability to operate in a harsh environment.
 Small node size
 Scalability
 Mobility
 Short communication range
 Limited life cycle.

6. Example of WSNs in IoT
 Weather monitoring system
 Indoor Air quality monitoring system
 Soil moisture monitoring system
 Surveillance systems
 Health monitoring systems

7. Cloud computing
Cloud computing is the delivery of on-demand computing services -- from
applications to storage and processing power -- typically over the internet and on a
pay-as-you-go basis.

8. How does cloud computing
work?
 Rather than owning their own computing
infrastructure or data centers, companies can rent
access to anything from applications to storage
from a cloud service provider.
 One benefit of using cloud computing services is
that firms can avoid the upfront cost and
complexity of owning and maintaining their own IT
infrastructure, and instead simply pay for what
they use, when they use it.
 In turn, providers of cloud computing services can
benefit from significant economies of scale by
delivering the same services to a wide range of
customers.

9. What cloud computing services
are available?
Cloud computing services cover a vast range of options
now, from the basics of storage, networking, and
processing power through to natural language processing
and artificial intelligence as well as standard office
applications. Pretty much any service that doesn't require
you to be physically close to the computer hardware that
you are using can now be delivered via the cloud.

10. What are examples of cloud
computing?
 Cloud computing underpins a vast number of services. That includes
consumer services like Gmail or the cloud back-up of the photos on
your smartphone, though to the services which allow large enterprises
to host all their data and run all of their applications in the cloud.
Netflix relies on cloud computing services to run its its video streaming
service and its other business systems too, and have a number of other
organisations.
 Cloud computing is becoming the default option for many apps:
software vendors are increasingly offering their applications as services
over the internet rather than standalone products as they try to switch to
a subscription model. However, there is a potential downside to cloud
computing, in that it can also introduce new costs and new risks for
companies using it.

11. Why is it called cloud
computing?
A fundamental concept behind cloud computing is that
the location of the service, and many of the details
such as the hardware or operating system on which it
is running, are largely irrelevant to the user. It's with
this in mind that the metaphor of the cloud was
borrowed from old telecoms network schematics, in
which the public telephone network (and later the
internet) was often represented as a cloud to denote
that the just didn't matter -- it was just a cloud of stuff.
This is an over-simplification of course; for many
customers location of their services and data remains
a key issue.

12. Big Data Analytics
Big Data analytics is the process of collecting, organizing and
analyzing large sets of data (called Big Data) to discover
patterns and other useful information. Big Data analytics can
help organizations to better understand the information
contained within the data and will also help identify the data that
is most important to the business and future business decisions.
Analysts working with Big Data typically want the knowledge that
comes from analyzing the data.

13. High-Performance Analytics
Required
to analyze such a large volume of data, Big Data analytics is
typically performed using specialized software tools and
applications for predictive analytics, data mining, text mining,
forecasting and data optimization. Collectively these processes
are separate but highly integrated functions of high-performance
analytics. Using Big Data tools and software enables an
organization to process extremely large volumes of data that a
business has collected to determine which data is relevant and
can be analyzed to drive better business decisions in the future.

14. The Challenges
 For most organizations, Big Data analysis is a
challenge. Consider the sheer volume of data and the
different formats of the data
(both structured and unstructured data) that is
collected across the entire organization and the many
different ways different types of data can be combined,
contrasted and analyzed to find patterns and other
useful business information.
 The first challenge is in breaking down data silos to
access all data an organization stores in different
places and often in different systems. A second
challenge is in creating platforms that can pull in
unstructured data as easily as structured data. This
massive volume of data is typically so large that it's

15. Embedded System
as its name suggests, Embedded means something that is
attached to another thing. An embedded system can be
thought of as a computer hardware system having
software embedded in it. An embedded system can be an
independent system or it can be a part of a large system.
An embedded system is a microcontroller or
microprocessor based system which is designed to
perform a specific task. For example, a fire alarm is an

16. An embedded system has three
components
 It has hardware.
 It has application software.
 It has Real Time Operating system (RTOS) that
supervises the application software and provide
mechanism to let the processor run a process as
per scheduling by following a plan to control the
latencies. RTOS defines the way the system
works. It sets the rules during the execution of
application program. A small scale embedded
system may not have RTOS.So we can define an
embedded system as a Microcontroller based,
software driven, reliable, real-time control system.

17. Characteristics of an Embedded System
 Single-functioned − An embedded system
usually performs a specialized operation and
does the same repeatedly. For example: A pager
always functions as a pager.
 Tightly constrained − All computing systems
have constraints on design metrics, but those on
an embedded system can be especially tight.
Design metrics is a measure of an
implementation's features such as its cost, size,
power, and performance. It must be of a size to fit
on a single chip, must perform fast enough to
process data in real time and consume minimum
power to extend battery life.

18.  Reactive and Real time − Many embedded systems
must continually react to changes in the system's
environment and must compute certain results in real
time without any delay. Consider an example of a car
cruise controller; it continually monitors and reacts to
speed and brake sensors. It must compute
acceleration or de-accelerations repeatedly within a
limited time; a delayed computation can result in
failure to control of the car.
 Microprocessors based − It must be
microprocessor or microcontroller based.
 Memory − It must have a memory, as its software
usually embeds in ROM. It does not need any
secondary memories in the computer.

19.  Connected − It must have connected peripherals
to connect input and output devices.
 HW-SW systems − Software is used for more
features and flexibility. Hardware is used for
performance and security.

20. Advantages
 Easily Customizable
 Low power consumption
 Low cost
 Enhanced performance

21. Disadvantages
 High development effort
 Larger time to market

22. Basic Structure of an Embedded
System

23.  Sensor − It measures the physical quantity and
converts it to an electrical signal which can be read
by an observer or by any electronic instrument like
an A2D converter. A sensor stores the measured
quantity to the memory.
 A-D Converter − An analog-to-digital converter
converts the analog signal sent by the sensor into a
digital signal.
 Processor & ASICs − Processors process the data
to measure the output and store it to the memory.
 D-A Converter − A digital-to-analog converter
converts the digital data fed by the processor to
analog data
 Actuator − An actuator compares the output given
by the D-A Converter to the actual (expected) output