Internet of Things

1. I.O.T. (INTERNET
OF THINGS)
PRESENTED BY
ANKIT CHATTERJEE

2. The Internet of Things (IoT) is a system of
interrelated computing devices, mechanical and
digital machines, objects, animals or people that
are provided with unique identifiers and the ability
to transfer data over a network without requiring
human-to-human or human-to-computer
interaction

3. • Today, Internet application development demand is very high. So IoT
is a major technology by which we can produce various useful internet
applications.
• Basically, IoT is a network in which all physical objects are connected
to the internet through network devices or routers and exchange data.
IoT allows objects to be controlled remotely across existing network
infrastructure. IoT is a very good and intelligent technique which
reduces human effort as well as easy access to physical devices. This
technique also has autonomous control feature by which any device
can control without any human interaction.

4. • “Things” in the IoT sense, is the mixture of hardware,
software, data, and services. “Things” can refer to a
wide variety of devices such as DNA analysis devices
for environmental monitoring, electric clamps in
coastal waters, Arduino chips in home automation and
many other. These devices gather useful data with the
help of various existing technologies and share that
data between other devices. Examples include Home
Automation System which uses Wi-Fi or Bluetooth for
exchange data between various devices of home.

6. • In early 1982 the concept of the network of smart devices was
discussed, with a modified Coke machine. This coke machine is
modified at “Carnegie Mellon University” and becoming the first
Internet-connected appliance. This machine was able to report its
inventory and whether newly loaded drinks were cold.
• In 1994 Reza Raji explained the idea of IoT as “small packets of data
to a large set of nodes, so as to integrate and automate everything from
home appliances to entire factories”. After that many companies
proposed various solutions like Microsoft’s at Work or Novell’s Nest.
Bill Joy proposed Device to Device (D2D) communication as a part of
his “Six Webs” frameworks at the World Economic Forum at Davos in
1999.

7. • The thought of Internet of Things first became
popular in 1999. British entrepreneur Kevin
Ashton first used the term Internet of Things in
1999 while working at Auto-ID labs. Besides that
near field communication, barcode scanners, QR
code scanners and digital watermarking are the
various devices which are working on IoT in the
present scenario.

8. • Data processing is simply the conversion of raw data to meaningful
information through a process. Data is manipulated to produce results
that lead to a resolution of a problem or improvement of an existing
situation. Similar to a production process, it follows a cycle where
inputs (raw data) are fed to a process (computer systems, software,
etc.) to produce output (information and insights). Generally,
organisations employ computer systems to carry out a series of
operations on the data to present, interpret, or obtain information. The
process includes activities like data entry, summary, calculation,
storage, etc. A useful and informative output is presented in various
appropriate forms, such as diagrams, reports, graphics, etc.

9. • The lifecycle of data within an IoT system proceeds from data
production to aggregation, transfer, optional filtering and
preprocessing, and finally to storage and archiving. Querying and
analysis are the endpoints that initiate (request) and consume data
production, but data products can be set to be “pushed” to the IoT
consuming services. Production, collection, aggregation, filtering, and
some basic querying and preliminary processing functionalities are
considered online, communication-intensive operations. Intensive
preprocessing, long-term storage and archival and in-depth
processing/analysis are considered offline storage-intensive
operations.

10. • Storage operations aim at making data available on the
long-term for constant access/updates, while archival is
concerned with read-only data. Since some IoT systems
may generate, process, and store data in-network for
real-time and localised services, with no need to
propagate this data further up to concentration points in
the system, edge devices that combine both processing
and storage elements may exist as autonomous units in
the cycle

11. • QUERYING - data-intensive systems rely on querying as
the core process to access and retrieve data. In the context
of the IoT, a query can be issued either to request real-
time data to be collected for temporal monitoring
purposes or to retrieve a certain view of the data stored
within the system. The first case is typical when a (mostly
localised) real-time request for data is required. The
second case represents more globalised views of data and
in-depth analysis of trends and patterns.

12. • Production: data production involves sensing and transfer of
data by the edge devices within the IoT framework and
reporting this data to interested parties periodically (as in a
subscribe/notify model), pushing it up the network to
aggregation points and subsequently to database servers, or
sending it as a response triggered by queries that request the
data from sensors and smart objects. Data is usually time-
stamped and possibly geo-stamped and can be in the form of
simple key-value pairs, or it may contain rich (unstructured)
audio/image/video content, with varying degrees of
complexity in-between.

13. • Collection: the sensors and smart objects within the IoT may store the data for a
certain time interval or report it to govern components. Data may be collected at
concentration points or gateways within the network, where it is further filtered
and processed, and possibly fused into compact forms for efficient transmission.
Wireless communication technologies such as Zigbee, Wi-Fi and mobile
networks are used by objects to send data to collection points. A collection is the
first stage of the cycle and is very crucial since the quality of data collected will
impact heavily on the output. The collection process needs to ensure that the
data gathered are both defined and accurate so that subsequent decisions based
on the findings are valid. This stage provides both the baseline from which to
measure and a target on what to improve. Some types of data collection include
census (data collection about everything in a group or statistical population),
sample survey (collection method that includes only part of the total
population), and administrative by-product (data collection is a byproduct of an
organisation’s day-to-day operations).

14. • Aggregation/fusion: transmitting all the raw data out of the network
in real-time is often prohibitively expensive, given the increasing data
streaming rates and the limited bandwidth. Aggregation and fusion
techniques deploy summarisation and merging operations in real-time
to compress the volume of data to be stored and transmitted.
• Delivery: as data is filtered, aggregated, and possibly processed either
at the concentration points or at the autonomous virtual units within
the IoT, the results of these processes may need to be sent further up
the system, either as final responses or for storage and in-depth
analysis. Wired or wireless broadband communications may be used
there to transfer data to permanent data stores.

15. • Preprocessing: the IoT data will come from different sources with varying formats and
structures. Data may need to be preprocessed to handle missing data, remove
redundancies and integrate data from different sources into a unified schema before being
committed to storage. Preparation is the manipulation of data into a form suitable for
further analysis and processing. Raw data cannot be processed and must be checked for
accuracy. Preparation is about constructing a dataset from one or more data sources to be
used for further exploration and processing. Analysing data that has not been carefully
screened for problems can produce highly misleading results that are heavily dependent
on the quality of data prepared. This preprocessing is a known procedure in data mining
called data cleaning. Schema integration does not imply brute-force fitting of all the data
into a fixed relational (tables) schema, but rather a more abstract definition of a consistent
way to access the data without having to customise access for each source's data
format(s). Probabilities at different levels in the schema may be added at this phase to the
IoT data items in order to handle the uncertainty that may be present in data or to deal
with the lack of trust that may exist in data sources.

16. • Storage/update and archiving: This phase handles the efficient storage and organisation of
data, as well as the continuous update of data with new information as it becomes available.
Archiving refers to the offline long-term storage of data that is not immediately needed for
the system's ongoing operations. The importance of this step is that it allows quick access
and retrieval of the processed information, allowing it to be passed on to the next stage
directly when needed. The core of centralised storage is the deployment of storage
structures that adapt to the various data types and the frequency of data capture. Relational
database management systems are a popular choice that involves the organisation of data
into a table schema with predefined interrelationships and metadata for efficient retrieval at
later stages. NoSQL key-value stores are gaining popularity as storage technologies for
their support of big data storage with no reliance on a relational schema or strong
consistency requirements typical of relational database systems. Storage can also be
decentralised for autonomous IoT systems, where data is kept at the objects that generate it
and is not sent up the system. However, due to the limited capabilities of such objects,
storage capacity remains limited in comparison to the centralised storage model.

17. • Processing/analysis: This phase involves the ongoing retrieval and analysis
operations performed and stored and archived data in order to gain insights
into historical data and predict future trends, or to detect abnormalities in the
data that may trigger further investigation or action. Task-specific
preprocessing may be required to filter and clean data before meaningful
operations can take place. When an IoT subsystem is autonomous and does not
require permanent storage of its data, but rather keeps the processing and
storage in the network, then in-network processing may be performed in
response to real-time or localised queries.
• Output and interpretation: This is the stage where processed information is
now transmitted to the user. An output is presented to users in various visual
formats like diagrams, infographics, printed report, audio, video, etc. The
output needs to be interpreted so that it can provide meaningful information
that will guide future decisions of the company.

18. • IOT is Growing fast and in
people's daily needs are going to
depend on the internet.
• By 2025, IOTs are expected to
generate 79.4 Zettabytes of data
by IDC. It will grow at a
compound annual growth rate of
28.7% over 2020 to 2025.
• According to projection of
Statista Research Departrment,
75.44 billion devices will be
connected with IOT worldwide
by 2025.

19. 1. REQUEST -
RESPONSE MODEL
Here the server processes
and categorises the request
sent by the client and
provides a response
accordingly.

20. 2. PUBLISHER-SUBSCRIBE
MODEL

21. PUBLISHER-SUBSCRIBE
MODEL CONTD.
•Publisher sends the data to the topics managed
by the broker.
•Broker sends the data to the subscriber who is
subscribed to the topic.

22. 3. PUSH-PULL MODEL
• Publisher and consumer are
involved.
• Publisher sends the topic to the
consumer through a Queue.
• Publisher pushes the topic to the
queue and the consumer pulls the
topic from the queue.
• Queues act as buffers whenever
there is any mismatch between
the push-pull data rates.

23. 4. EXCLUSIVE PAIR MODEL
•Full duplex
•Bidirectional
•Connection is
setup until the
client sends a
request to close the
correction.

24. • Unique Identity
• Dynamic Nature
• Self Adapting
• Self Configuring
• Heteroginity
• Integrated to Information Network

25. •EFFICIENT RESOURCE UTILISATION
•SAVES TIME
•REDUCTION OF EFFORTS AND
ERRORS
•SECURITY
•USER FRIENDLY AND EASY TO USE

26. Resources are very precious. They should not be wasted. They must
be utilised in a proper manner.
Eg- Smart Home. It reduces wastage of electricity.

27. “Saving time is equivalent to saving
the life”
IOT technology reduces human efforts
and saves time.

28. Devices mainly aim for reducing huiman efforts and errors.
Technology is advancing rapidly.
Eg - Considering a smart home, when moving from one
room to the other, the lights of the room you are moving to
get switched on and the lights of the room from where you
have shifted are automatically put off through sensing.

29. IOT provides more security.

30. No technical knowledge required. Everything is
provided in human understandable form in the
inteace provided.

31. •DOS ( Denial of Service)
•DDOS (Distributed Denial of
Service)
•Unauthorized Access
•Information Manipulation
•Information disclosure

32. •SMART CITY
•HEALTH CARE
•EDUCATION
•AGRICULTURE
•SMART HOME
•VEHICLE INDUSTRY