1

INTRODUCTION TO EMERGING
TECHNOLOGY

Chapter 3: Artificial Intelligence (AI)

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outline
• What is Artificial Intelligence (AI)
• Need for Artificial Intelligence
• Goals of Artificial Intelligence
• What Comprises to Artificial Intelligence?
• Advantages of Artificial Intelligence
• Disadvantages of Artificial Intelligence
• History of AI
• Levels of AI
• Types of AI
• AI tools and platforms
• AI applications
 3

What is Artificial Intelligence (AI)

• Artificial Intelligence is composed of two words Artificial
and Intelligence.
• Artificial defines "man-made," and intelligence defines
"thinking power", or “the ability to learn and solve
problems” hence Artificial Intelligence means "a man-
made thinking power."
• Artificial Intelligence (AI) can be defined as the branch of
computer science by which we can create intelligent
machines which can behave like a human, think like
humans, and able to make decisions.
 4

Cont…
• Intelligence:-is the ability to acquire and apply
knowledge.
• Knowledge is the information acquired through
experience.
• Experience is the knowledge gained through exposure
(training).
• Summing the terms up, we get artificial intelligence as the
“copy of something natural”.
 5

Cont…
• Artificial Intelligence exists when a machine can have
human-based skills such as learning, reasoning, and
solving problems.
• Intelligence is composed of:
Learning
Reasoning
Problem Solving
Perception
Linguistic Intelligence
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Cont…
• An AI system is composed of an agent and its
environment.
• An agent (e.g. human or robot) is anything that can
perceive its environment through sensors and acts upon
that environment through effectors.
• Intelligent agents must be able to set goals and achieve
them.
• Machine perception is the ability to use input from sensors
(such as cameras, microphones, sensors, etc.) to deduce
aspects of the world. e.g. Computer Vision.
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Cont…
• High-profile examples of AI include
• autonomous vehicles (such as drones and self-driving cars),
• medical diagnosis,
• creating art (such as poetry),
• proving mathematical theorems,
• playing games (such as Chess or Go),
• search engines (such as Google search),
• online assistants (such as Siri),
• image recognition in photographs,
• spam filtering,
• prediction of judicial decisions and
• targeting online advertisements.
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Cont…
• AI deals with the area of developing computing systems
that are capable of performing tasks that humans are very
good at.
• For example recognizing objects, recognizing and making
sense of speech, and decision making in a constrained
environment.
• The advent of Big Data, driven by the arrival of the
Internet, smart mobile and social media has enabled AI
algorithms.
• In particular from Machine Learning and Deep Learning,
to leverage Big Data and perform their tasks more
optimally.
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Cont…
• This combined with cheaper and more powerful hardware
such as Graphical Processing Units (GPUs) has enabled
AI to evolve into more complex architectures.
• Machine Learning is an advanced form of AI where the
machine can learn as it goes rather than having every
action programmed by humans.
• The term machine learning was introduced by Arthur
Samuel in 1959.
• Neural networks are biologically inspired networks that
extract features from the data in a hierarchical fashion.
• The field of neural networks with several hidden layers is
called deep learning.
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Artificial Intelligence (AI), Machine

Learning (ML) and Deep Learning (DL)
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Need for Artificial Intelligence

1. To create expert systems that exhibit intelligent
behavior with the capability to learn, demonstrate,
explain and advice its users.
2. Helping machines find solutions to complex problems
like humans do and applying them as algorithms in a
computer-friendly manner.
 12

Goals of Artificial Intelligence

• Following are the main goals of Artificial Intelligence:
1) Replicate human intelligence
2) Solve Knowledge-intensive tasks
3) An intelligent connection of perception and action
4) Building a machine which can perform tasks that
requires human intelligence such as:
Proving a theorem
Playing chess
Plan some surgical operation
Driving a car in traffic
5) Creating some system which can exhibit intelligent
behavior, learn new things by itself, demonstrate,
explain, and can advise to its user.
 13

What Comprises to Artificial Intelligence?

• To create the AI-first we should know that how intelligence is
composed.
• Intelligence is an intangible part of our brain which is a combination of
Reasoning, learning, problem-solving, perception, language
understanding, etc.
• To achieve the above factors for a machine or software Artificial
Intelligence requires the following disciplines:
 Mathematics
 Philosophy
 Biology
 Psychology
 Sociology
 Computer Science
 Neurons Study
 Statistics
 Linguistic study
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Advantages of Artificial Intelligence

• High Accuracy with fewer errors: AI machines or
systems are prone to fewer errors and high accuracy as it
takes decisions as per pre-experience or information.
• High-Speed: AI systems can be of very high-speed and
fast-decision making, because of that AI systems can beat
a chess champion in the Chess game.
• High reliability: AI machines are highly reliable and can
perform the same action multiple times with high
accuracy.
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Cont…
• Useful for risky areas: e.g defusing a bomb, exploring
the ocean floor, where to employ a human can be risky.
• Digital Assistant: e.g E-commerce websites to show the
products as per customer requirements.
• Useful as a public utility: e.g, self-driving car, facial
recognition for security purposes, Natural language
processing (for search engines, for spelling checker, for
assistant like Siri, for translation like google translate), etc.
 16

Disadvantages of Artificial Intelligence

• High Cost:
• Can't think out of the box: Even we are making smarter
machines with AI, but still they cannot work out of the box.
• No feelings and emotions:
• Increase dependence on machines
• No Original Creativity:
 17

History of AI
• Artificial Intelligence is not a new word and not a new
technology for researchers.
• This technology is much older than you would imagine.
• Even there are the myths of Mechanical men in Ancient
Greek and Egyptian Myths.
• The following are some milestones in the history of AI
which define the journey from the AI generation to till date
development.
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History of AI
A. Maturation of Artificial Intelligence (1943-1952)
• The year 1943: The first AI work was done by Warren
McCulloch and Walter pits in 1943.
• They proposed a model of artificial neurons.
• The year 1949: Donald Hebb demonstrated an updating rule
for modifying the connection strength between neurons.
• His rule is now called Hebbian learning.
• The year 1950: The Alan Turing who was an English
mathematician and pioneered Machine learning in 1950.
• Alan Turing publishes "Computing Machinery and Intelligence"
in which he proposed a test.
• The test can check the machine's ability to exhibit intelligent
behavior equivalent to human intelligence, called a Turing test.
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Cont…
B. The birth of Artificial Intelligence (1952-1956)
• The year 1955: An Allen Newell and Herbert A.
• Simon created the "first artificial intelligence program"
Which was named "Logic Theorist".
• The year 1956: The word "Artificial Intelligence" first
adopted by American Computer scientist John McCarthy
at the Dartmouth Conference.
• For the first time, AI coined as an academic field.
• At that time high-level computer languages such as
FORTRAN, LISP, or COBOL were invented.
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Cont…
C. The golden years-Early enthusiasm (1956-1974)
• The year 1966: The researchers emphasized developing
algorithms that can solve mathematical problems.
• Joseph Weizenbaum created the first chatbot in 1966, which
was named as ELIZA.
• The year 1972: The first intelligent humanoid robot was built in
Japan which was named WABOT-1.
D. The first AI winter (1974-1980)
• The duration between the years 1974 to 1980 was the first AI
winter duration.
• AI winter refers to the time period where computer scientists
dealt with a severe shortage of funding from the government
for AI researches.
• During AI winters, an interest in publicity on artificial intelligence
was decreased.
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Cont…
E. A boom of AI (1980-1987)
• The year 1980: After AI winter duration, AI came back with
"Expert System".
• Expert systems were programmed that emulate the decision-
making ability of a human expert.
• In the Year 1980, the first national conference of the American
Association of Artificial Intelligence was held at Stanford
University.
F. The second AI winter (1987-1993)
• The duration between the years 1987 to 1993 was the second
AI Winter duration.
• Again, Investors and government stopped in funding for AI
research due to high cost but not efficient results.
• The expert system such as XCON was very cost-effective.
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Cont…
G. The emergence of intelligent agents (1993-2011)
• The year 1997: In the year 1997, IBM Deep Blue beats
world chess champion, Gary Kasparov, and became the
first computer to beat a world chess champion.
• The year 2002: for the first time, AI entered the home in
the form of Roomba, a vacuum cleaner.
• The year 2006: AI came into the Business world until the
year 2006.
• Companies like Google, Facebook, Twitter, and Netflix
also started using AI.
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Cont…
H. Deep learning, big data and artificial general intelligence
(2011-present)
• The year 2011: In the year 2011, IBM's Watson won jeopardy,
a quiz show
• The year 2012: Google has launched an Android app feature
"Google now", which was able to provide information to the
user as a prediction.
• The year 2014: In the year 2014, Chatbot "Eugene Goostman"
won a competition in the infamous "Turing test."
• The year 2018: The "Project Debater" from IBM debated on
complex topics with two master debaters and also performed
extremely well.
• Google has demonstrated an AI program "Duplex" which was a
virtual assistant.
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Levels of AI
• Stage 1 – Rule-Based Systems
• The most common uses of AI today fit in this bracket,
covering everything from business software (Robotic
Process Automation) and domestic appliances to aircraft
autopilots.
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Cont…
• Stage 2 – Context Awareness and Retention
• Algorithms that develop information about the specific
domain they are being applied in.
• They are trained on the knowledge and experience of the
best humans, and their knowledge base can be updated
as new situations and queries arise.
• Well known applications of this level are chatbots and
“roboadvisors”.
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Cont…
• Stage 3 – Domain-Specific Expertise
• Going beyond the capability of humans, these systems
build up expertise in a specific context taking in massive
volumes of information which they can use for decision
making.
• Successful use cases have been seen in cancer
diagnosis and the well-known Google Deepmind’s
AlphaGo.
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Cont…
• Stage 4 – Reasoning Machines
• These algorithms have some ability to attribute mental
states to themselves and others – they have a sense of
beliefs, intentions, knowledge, and how their own logic
works.
• This means they could reason or negotiate with humans
and other machines.
• At the moment these algorithms are still in development,
however, commercial applications are expected within the
next few years.
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Cont…
• Stage 5 – Self Aware Systems / Artificial General
Intelligence (AGI)
• These systems have human-like intelligence – the most
commonly portrayed AI in media – however, no such use
is in evidence today.
• It is the goal of many working in AI and some believe it
could be realized already from 2024.
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Cont…
• Stage 6 – Artificial Super intelligence (ASI)
• AI algorithms can outsmart even the most intelligent
humans in every domain.
• Logically it is difficult for humans to articulate what the
capabilities might be, yet we would hope examples would
include solving problems we have failed to so far, such as
world hunger and dangerous environmental change.
• There are a few experts who claim it can be realized by
2029.
• Fiction has tackled this idea for a long time, for example in
the film Ex Machina or Terminator.
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Cont…
• Stage 7 – Singularity and Transcendence
• This is the idea that development provided by ASI (Stage
6) leads to a massive expansion in human capability.
• Human augmentation could connect our brains to each
other and to a future successor of the current internet,
creating a “hive mind” that shares ideas, solves problems.
• Pushing this idea further, we might go beyond the limits of
the human body and connect to other forms of intelligence
on the planet – animals, plants, weather systems, and the
natural environment.
• Google’s Director of Engineering, suggest we could see it
happen by 2045
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3xdth
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Types of AI
• Artificial Intelligence can be divided into various types,
there are mainly two types of the main categorization
which are based on capabilities (Type-1) and based on
functionality (Type-2) of AI.
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A. Based on Capabilities
1. Weak AI (Narrow AI):
• Narrow AI is a type of AI which is able to perform a dedicated
task with intelligence.
• The most common and currently available AI is Narrow AI in
the world of Artificial Intelligence.
• Narrow AI cannot perform beyond its field or limitations, as it is
only trained for one specific task.
• Hence it is also termed as weak AI.
• Narrow AI can fail in unpredictable ways if it goes beyond its
limits.
• Example. Apple Siri, IBM's Watson supercomputer, Google
translate, playing chess, purchasing suggestions on e-
commerce sites, self-driving cars, speech recognition, and
image recognition.
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Cont…
2. General AI
• General AI is a type of intelligence that could perform any
intellectual task with efficiency like a human.
• The idea behind the general AI to make such a system
that could be smarter and think like a human on its own.
• Currently, there is no such system exists which could
come under general AI and can perform any task as
perfect as a human.
• It may arrive within the next 20 or so years
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Cont…
3. Super AI (Strong AI)
• Super AI is a level of Intelligence of Systems at which
machines could surpass human intelligence, and can
perform any task better than a human with cognitive
properties.
• This refers to aspects like general wisdom, problem
solving and creativity.
• It is an outcome of general AI.
• Some key characteristics of strong AI include capability
include the ability to think, to reason solve the puzzle,
make judgments, plan, learn, and communicate on its
own.
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B. Based on the functionality

1. Reactive Machines
• Purely reactive machines are the most basic types of
Artificial Intelligence.
• Such AI systems do not store memories or past
experiences for future actions.
• These machines only focus on current scenarios and
react on it as per possible best action.
• Examples IBM's Deep Blue system, Google's AlphaGo
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Cont…
2. Limited Memory
• Limited memory machines can store past experiences or
some data for a short period of time.
• These machines can use stored data for a limited time
period only.
• E.g Self-driving cars are one of the best examples of
Limited Memory systems.
• These cars can store the recent speed of nearby cars, the
distance of other cars, speed limits, and other information
to navigate the road.
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Cont…
3. Theory of Mind
• Theory of Mind AI should understand human emotions,
people, beliefs, and be able to interact socially like
humans.
• This type of AI machines is still not developed, but
researchers are making lots of efforts and improvement
for developing such AI machines.
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Cont…
4. Self-Awareness
• Self-awareness AI is the future of Artificial Intelligence.
• These machines will be super intelligent and will have
their own consciousness, sentiments, and self-
awareness.
• These machines will be smarter than the human mind.
• Self-Awareness AI does not exist in reality still and it is a
hypothetical concept.
 41

How does a human being think?

• The goal of many researchers is to create strong and
general AI that learns like a human and can solve general
problems as the human brain does.
• Intelligence or the cognitive process is composed of three
main stages:
• Observe and input the information or data in the brain.
• Interpret and evaluate the input that is received from the
surrounding environment.
• Make decisions as a reaction towards what you received
as input and interpreted and evaluated.
• AI researchers are simulating the same stages in building
AI systems or models.
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Mapping human thinking to artificial intelligence

components
• In AI models, the first stage is sensing.
• The sensing layer- perceives information from the
surrounding environment.
• This information is specific to the AI application.
• Example: voice recognition for sensing voice and visual
imaging recognition for sensing images.
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Cont…
• The second stage is related to interpreting and evaluating
the input data.
• In AI, this stage is represented by the interpretation layer,
that is, reasoning and thinking about the gathered input
acquired by the sensing layer.
• The third stage is related to taking action or making
decisions.
• After evaluating the input data, the interacting layer
performs the necessary tasks.
• Example: Robotic movement control and speech
generation are implemented in the interacting layer.
 44

Influencers of artificial intelligence

• Big data: Structured data versus unstructured data
• Advancements in computer processing speed and new
chip architectures
• Cloud computing and APIs
• The emergence of data science
 45

Big data: Structured data versus

unstructured data
• Big data refers to huge amounts of data.
• Big data requires innovative forms of information
processing to draw insights, automate processes, and
help decision making.
• Big data can be structured data that corresponds to a
formal pattern, such as traditional data sets and
databases.
• Also, big data includes semi-structured and unstructured
formats, like word-processing documents, videos, images,
audio, presentations, social media interactions, streams,
web pages,
 46

Cont…
• Structured data, that is, information with an organized
structure, such as a relational database is searchable by
simple and straightforward search engine algorithms or
SQL statements.
• The real-world data such as the type that humans deal
with constantly does not have a high degree of
organization.
• Unstructured data is not contained in a regular database
and is growing exponentially, making up most of the data
in the world.
 47

Advancements in computer processing speed,

new chip architectures
• Significant advancements in computer processing and
memory speeds enable us to make sense of the
information that is generated by big data more quickly.
• Processing speeds and new computer chip architectures
contribute to the rapid evolution of AI applications.
 48

Cloud Computing
• Cloud computing is a general term that describes the
delivery of on-demand services, usually through the
Internet, on a pay-per-use basis.
• Companies worldwide offer their services to customers
over cloud platforms.
• These services might be data analysis, social media,
video storage, e-commerce, and AI capabilities that are
available through the internet and supported by cloud
computing.
 49

Cont…
• All the significant companies in the AI services market
deliver their services and tools on the internet through
APIs over cloud platforms, for example:
• IBM delivers Watson AI services over IBM Cloud.
• Amazon AI services are delivered over Amazon Web
Services (AWS).
• Microsoft AI tools are available over the MS Azure cloud.
• Google AI services are available in the Google Cloud
Platform.
 50

Emergence of data science

• Data science has emerged in the last few years as a new
profession that combines several disciplines, such as
statistics, data analysis, machine learning, and others.
• The goal of data science is to extract knowledge or
insights from data in various forms, either structured or
unstructured, which is like data mining.
• Data science uses machine learning and AI to process big
data.
 51

AI tools and platforms

• AI has developed a large number of tools to solve the
most difficult problems in computer science, like:
Search and optimization
Logic
Probabilistic methods for uncertain reasoning
Classifiers and statistical learning methods
Neural networks
Control theory
Languages
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Applications of AI
1. AI in agriculture
• Agriculture is applying AI as agriculture robotics, solid and
crop monitoring, predictive analysis.
2. AI in Healthcare
• AI can help doctors with diagnoses and can inform when
patients are worsening so that medical help can reach the
patient before hospitalization.
3. AI in education
• AI can automate grading so that the tutor can have more
time to teach.
• AI chatbot can communicate with students as a teaching
assistant.
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Cont…
4. AI in Finance and E-commerce
5. AI in Gaming
6. AI in Data Security
7. AI in Social Media
• Facebook, Twitter, and Snapchat
10. AI in Robotics:
11. AI in Entertainment

•
 54

Cont…
13. Commuting
➢ Google’s AI-Powered Predictions
➢ Ridesharing Apps Like Uber and Lyft
➢ Commercial Flights Use an AI Autopilot
14. Email
➢ Spam Filters
➢ Smart Email Categorization
15. Social Networking
• Facebook, Pinterest, Instagram, Snapchat
 55

Cont…
16. Mobile Use
• Voice-to-Text
• Siri and Google Assistant (which could perform internet
searches, set reminders, and integrate with your calendar).
• Alexa, an AI-powered personal assistant that accepts voice
commands to create to-do lists, order items online, set
reminders, and answer questions (via internet searches)
• Echo (later, Dot) smart speakers that allow you to integrate
Alexa into your living room and use voice commands to ask
natural language questions, play music, order pizza, hail an
Uber, and integrate with smart home devices.
• Microsoft’s Cortana, pre-loaded on Windows computers and
Microsoft smartphones.
 56

Cont…
• The most common artificial intelligence platforms include
Microsoft AZURE Machine Learning,
Google Cloud Prediction API,
IBM Watson,
TensorFlow,
Infosys Nia,
Wipro HOLMES,
API.AI,
Premonition,
Rainbird,
Ayasdi,
MindMeld, and
Meya.
Introduction to Emerging
Technology
1

CHAPTER 4:INTERNET OF THINGS

(IOT)
 Outline
2

 Features of IoT
 What is IoT?
 History of IoT
 IoT − Advantages
 Disadvantages of IoTs
 Challenges of IoTs
 Architectures of IoTs
 IoT Tools and Platforms
 Applications of IoTs
 Overview of IoT
3

 The most important features of IoT include artificial

intelligence, connectivity, sensors, active engagement, and
small device use.
 AI − IoT makes virtually anything “smart”, meaning it
enhances every aspect of life with the power of data
collection, artificial intelligence algorithms, and networks.
 Connectivity − New enabling technologies for networking
and specifically IoT networking, mean networks are no
longer exclusively tied to major providers.
 Networks can exist on a much smaller and cheaper scale
while still being practical.
 IoT creates small networks between its system devices.
 Cont…
4

 Sensors − IoT loses its distinction without sensors.

 They act as important instruments that transform IoT from a
standard passive network of devices into an active system
capable of real-world integration.
 Active Engagement − Much of today's interaction with
connected technology happens through passive engagement.
 IoT introduces a new paradigm for active content, product, or
service engagement.
 Small Devices − Devices, as predicted, have become smaller,
cheaper, and more powerful over time.
 IoT exploits purpose-built small devices to deliver its precision,
scalability, and versatility.
 What is IoT?
5

 According to the Internet Architecture Board’s (IAB)

definition, “ IoT is the networking of smart objects,
meaning a huge number of devices intelligently
communicating in the presence of internet protocol that
cannot be directly operated by human beings but exist as
components in buildings, vehicles or the environment”.
 According to the Internet Engineering Task Force (IETF)
organization’s definition, “IoT is the networking of smart
objects in which smart objects have some constraints such
as limited bandwidth, power, and processing accessibility
for achieving interoperability among smart objects”.
 Cont…
6

 According to the IEEE Communications category

magazine’s definition, “IoT is a framework of all things that
have a representation in the presence of the Internet in
such a way that new applications and services enable the
interaction in the physical and virtual world in the form of
Machine-to-Machine (M2M) communication in the cloud”.
 According to the Oxford dictionary’s definition, “IoT is the
interaction of everyday object’s computing devices through
the Internet that enables the sending and receiving of
useful data”.
 Cont…
7

 The term Internet of Things (IoT) according to the 2020

conceptual framework is expressed through a simple
formula such as:
“IoT= Services+ Data+ Networks + Sensors”
 Cont…
8

 Generally, The Internet of Things (IoT) is the network of

physical objects or "things" embedded with electronics,
software, sensors, and network connectivity, which enables
these objects to collect and exchange data.
 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.
 9

 IoT is a network of devices that can sense, accumulate and

transfer data over the internet without any human
intervention.
 The Internet of Things consists of any device with an on/off
switch connected to the Internet.
 This includes almost anything ranging from cellphones to
building maintenance to the jet engine of an airplane.
 Medical devices, such as a heart monitor implant or a
biochip transponder in a farm animal, can transfer data
over a network and are members of the IoT.
 Cont…
10

 The Internet of things (IoT) has found its application in several

areas such as
 Smart class Room
 connected industry,
 smart-city,
 smart-home,
 smart-energy,
 connected car,
 smart agriculture,
 connected building and campus,
 health care,
 logistics, among other domains
 History of IoT
11

 The Internet of Things has not been around for very long.
 Machines have been providing direct communications since
the telegraph (the first landline) was developed in the
1830s and 1840s, described as “wireless telegraphy”.
 The first radio voice transmission took place on June 3,
1900.
 The development of computers began in the 1950s.
 The Internet, itself a significant component of the IoT,
started out as part of DARPA (Defense Advanced Research
Projects Agency) in 1962 and evolved into ARPANET in
1969.
 Cont…
12

 Global Positioning Satellites (GPS) became a reality in early

1993, with the Department of Defense providing a stable,
highly functional system of 24 satellites.
 One additional and important component in developing a
functional IoT was IPV6’s intelligent decision to increase
address space.
 The Internet of Things, as a concept, wasn’t officially
named until 1999.
 First examples of an Internet of Things is from the early
1980s and was a Coca Cola machine, located at the
Carnegie Melon University.
 Cont…
13

 By the year 2013, the Internet of Things had evolved into a

system using multiple technologies, ranging from the
Internet- wireless communication and from micro-
electromechanical systems (MEMS) to embedded systems.
 The traditional fields of automation (including the
automation of buildings and homes), wireless sensor
networks, GPS, control systems, and others, all support the
IoT.
 Cont…
14

 Kevin Ashton, the Executive Director of Auto-ID Labs at

MIT, was the first to describe the Internet of Things, during
his 1999 speech.
 Kevin Ashton stated that Radio Frequency Identification
(RFID) was a prerequisite for the Internet of Things.
 He concluded if all devices were “tagged,” computers could
manage, track, and inventory them.
 IoT − Advantages
15

 Improved Customer Engagement − Current analytics

suffer from blind-spots and significant flaws inaccuracy;
and as noted, engagement remains passive.
 IoT completely transforms this to achieve richer and more
effective engagement with audiences.
 Technology Optimization − The same technologies and
data which improve the customer experience also improve
device use, and aid in more potent improvements to
technology.
 Cont…
16

 Reduced Waste − IoT makes areas of improvement clear.

 Current analytics give us superficial insight, but IoT
provides real-world information leading to the more
effective management of resources.
 Enhanced Data Collection − Modern data collection
suffers from its limitations and its design for passive use.
 IoT breaks it out of those spaces and places it exactly where
humans really want to go to analyse our world.
 Disadvantages IoT
17

 As the number of connected devices increases and more

information is shared between devices, the potential that a
hacker could steal confidential information also increases.
 If there’s a bug in the system, it’s likely that every
connected device will become corrupted.
 Since there’s no international standard of compatibility for
IoT, it’s difficult for devices from different manufacturers to
communicate with each other.
 Enterprises may eventually have to deal with massive
numbers maybe even millions of IoT devices and collecting
and managing the data from all those devices will be
challenging.
 Challenges of IoT
18
 Though IoT delivers an impressive set of advantages, it also presents a significant
set of challenges.
 Security :
 The system offers little control despite any security measures.
 This leaves users exposed to various kinds of attackers.
 Privacy
 The sophistication of IoT provides substantial personal data in extreme detail
without the user's active participation.
 Complexity:
 complicated in terms of design, deployment, and maintenance given their use of
multiple technologies
 Flexibility :
 Many are concerned about the flexibility of an IoT system to integrate easily with
another. They worry about finding themselves with several conflicting or locking
systems.
 Compliance :
 Its complexity makes the issue of compliance seem incredibly challenging when
many consider standard software compliance a battle.
 How does it(IoT) work?
19

 An IoT ecosystem consists of

 web-enabled smart devices that use embedded processors,
sensors and communication hardware to collect, send and
act on data they acquired from their environments.
 IoT devices share the sensor data they collect by connecting
to an IoT gateway.
 The devices do most of the work without human
intervention, although people can interact with the devices.
 For instance, to set them up, give them instructions or
access the data.
 Devices and Networks
20

 The IoT devices can be categorized into: consumer,

enterprise and industrial.
 Consumer connected devices include smart TVs, smart
speakers, toys, wearables (smart watch), and smart
appliances.
 Examples of industrial and enterprise IoT devices smart
meters, commercial security systems and smart city
technologies such as those used to monitor traffic and
weather conditions.
 Other technologies, including smart air conditioning, smart
thermostats, smart lighting, and smart security, span home,
enterprise, and industrial uses.
 Cont…
21

 IoT landscape is depicted by an increasing number of connected

devices characterized by their heterogeneity and the presence of
resources constrained networks.
 To ensure the correct functioning of those (IOT) connected
devices, they must be remotely accessed to configure,
monitoring their status, and so forth.
 It is necessary to take into account several elements such as
scalability, interoperability, energy efficiency, topology control,
Quality of Service (QoS), fault tolerance, and security.
 Traditional management solutions cannot be used for low power
devices networks given their resources limitation and scalability
issues.
 Cont…
22

 Efficient and autonomic management of IoT networks is

needed.
 Thus, platform for IoT networks and devices management,
called M4DN.IoT (Management for Device and Network in
the Internet of Things) is proposed.
 M4DN.IoT defines a management structure in two scopes:
 local management: the platform runs in the same
environment as the devices
 remote management: the platform controls the devices in
different networks
 Architecture of IoT
23

 In general, an IoT device can be explained as a network of

things that consists of hardware, software, network
connectivity, and sensors.
 The architecture of IoT devices comprises four major
components:
 Sensing layer

 Network layer

 Data processing layer, and

 Application layer
 Sensing Layer
24

 The sensing layer is to identify any phenomena in the

devices’ peripheral and obtain data from the real world.
 This layer consists of several sensors.
 A sensor hub is a common connection point for multiple
sensors that accumulate and forward sensor data to the
processing unit of a device.
 Cont…
25

 Sensors in IoT devices can be classified into three broad categories as described
below:
A. Motion Sensors
 Motion sensors measure the change in motion as well as the orientation of the
devices. There are two types of motions : linear and angular motions.
B. Environmental Sensors: (e.g Light sensors, Pressure sensors)
 They are embedded in IoT devices to sense the change in environmental parameters
in the device’s peripheral.
C. Position sensors: (e.g Magnetic sensors and Global Positioning system (GPS)
sensors)
 Position sensors of IoT devices deal with the physical position and location of the
device.
 Magnetic sensors-used as digital compass and help to fix the orientation of the
device display.
 GPS is used for navigation purposes in IoT devices.
 Network Layer
26

 The network layer acts as a communication channel to

transfer data, collected in the sensing layer, to other
connected devices.
 In IoT devices, the network layer is implemented by using
diverse communication technologies:
 (e.g., Wi-Fi, Bluetooth, Zigbee, Z-Wave, LoRa, cellular
network(e.g 4G LTE, 5G), etc.) to allow data flow between
other devices within the same network.
 Data Processing Layer
27

 The data processing layer takes data collected in the

sensing layer and analyses the data to make decisions
based on the result.
 This layer may share the result of data processing with
other connected devices via the network layer.
 Application Layer: The application layer implements
and presents the results of the data processing layer to
accomplish disparate applications of IoT devices.
 The application layer is a user-centric layer that executes
various tasks for the users.
28
 IoT Tools and Platforms
29

 KAA
 SiteWhere
 ThingSpeak
 DeviceHive
 Zetta
 ThingsBoard
 Applications of IoT
30

 Agriculture
 Consumer Use
 Healthcare
 Insurance
 Manufacturing
 Retail
 Transportation
 Utilities
Introduction to Emerging
Technologies
1

CHAPTER 5:AUGMENTED REALITY (AR)

 Overview of Augmented Reality
2

 The fundamental idea of AR is to combine, or mix,

the view of the real environment with additional,
virtual content that is presented through computer
graphics.
 Its convincing effect is achieved by ensuring that the
virtual content is aligned and registered with
the real objects.
 Cont…
3

 Augmented reality (AR) is a form of emerging

technology that allows users to overlay computer-
generated content in the real world.
 AR refers to a live view of a physical real-world
environment whose elements are merged with
augmented computer-generated images creating a
mixed reality.
 Cont…
4

 The augmentation is typically done in real-time and

in semantic context with environmental elements.
 By using the latest AR techniques and technologies,
the information about the surrounding real world
becomes interactive and digitally usable.
 5

Virtual reality (VR), Augmented Reality (AR) vs

Mixed reality (MR)
 Virtual Reality (VR)
6

 It is also called a computer-simulated reality.

 It refers to computer technologies using reality headsets
to generate realistic sounds, images and other sensations
that replicate a real environment or create an imaginary
world.
 Advanced VR environment will engage all five senses
(taste, sight, smell, touch, sound), but it is not always
possible.
 Most VR headsets are
 Connected to a computer (Oculus Rift) or
 A gaming console (PlayStation VR) but there are
 Standalone devices (Google Cardboard).
 Cont…
7

 Most standalone VR headsets work in combination

with smartphones-you insert a smartphone, wear a
headset, and immerse in the virtual reality .
 The most advanced VR experiences even provide
freedom of movement – users can move in a digital
environment and hear sounds.
 Using VR devices such as HTC Vive, Oculus Rift or
Google Cardboard, users can be transported into a
number of real-world and imagined environments.
 Augmented Reality (AR)
8

 In augmented reality, users see and interact with the real

world while digital content is added to it.
 If you own a modern smartphone, you can easily
download an AR app and try this technology.
 There’s a different way to experience augmented reality,
though – with special AR headsets, such as Google Glass,
where digital content is displayed on a tiny screen in
front of a user’s eye.
 AR adds digital elements to a live view often by using the
camera on a smartphone.
 Examples of augmented reality experiences include
Snapchat lenses and the game Pokemon Go.
 Cont…
9

 AR is a live, direct or indirect view of a physical, real-

world environment whose elements are augmented
(or supplemented) by computer-generated sensory
input such as sound, video, graphics or GPS data.
 Mixed Reality (MR)
10

 Mixed Reality (MR) or hybrid reality, is the merging

of real and virtual worlds to produce new
environments and visualizations where physical and
digital objects co-exist and interact in real-time.
 The key characteristic of MR is that the synthetic
content and the real-world content are able to react
to each other in real-time.
 Cont…
11

 One of the most common differences among

augmented reality, virtual reality, and mixed reality
is the hardware requirements and also VR is content
which is 100% digital and can be enjoyed in a fully
immersive environment, AR overlays digital content
on top of the real-world.
 MR is a digital overlay that allows interactive virtual
elements to integrate and interact with the real-
world environment.
 Cont…
12

 Numerous augmented reality apps and games can run on

almost every smartphone on the market.
 Virtual reality programs require specialized VR headsets,
noise-canceling headphones, cameras to track room
space and boundaries, and sometimes even motion
capture technology.
 Mixed reality applications sometimes require
exponentially more processing power and thus require
more powerful hardware.
 Mixed reality hardware is still emerging and hasn’t quite
broken into the mainstream consumer market, most
likely due to the price.
 The Architecture of AR Systems
13

 The first Augmented Reality Systems (ARS) were

usually designed with a basis on three main blocks.
 (1) Infrastructure Tracker Unit, (2) Processing Unit,
and (3) Visual Unit
 The Infrastructure Tracker Unit was responsible for
collecting data from the real world, sending them to
the Processing Unit, which mixed the virtual content
with the real content and sent the result to the Video
Out module of the Visual Unit.
 Cont…
14

 The Visual Unit can be classified into two types of

system, depending on the followed visualization
technology:
 1. Video see-through: It uses a Head-Mounted
Display (HMD) that employs a video-mixing and
displays the merged images on a closed-view HMD.
 2. Optical see-through: It uses an HMD that
employs optical combiners to merge the images
within an open-view HMD.
 Applications of AR Systems
15

 One of the newest developing technologies is

augmented reality (AR), which can be applied to
many different disciplines such as education,
medicine, entertainment, military, etc.
 AR In education
16

 Affordable learning materials

 Interactive lessons
 Higher engagement
 Higher retention
 Boost intellectual curiosity
 AR In Medicine
17

 Augmented reality is one of the current technologies

changing all industries, including healthcare and
medical education.
 surgery (minimally invasive surgery);
 education of future doctors;
 diagnostics;
 AR tools may also aid to detect the signs of
depression and other mental illnesses by reading
from facial expressions, voice tones, and physical
gestures.
 AR In Medicine
18

 Describing symptoms
 Nursing care
 Surgery
 Ultrasounds
 Diabetes management
 Navigation
 AR In Entertainment
19

 AR in games - the AR games were praised for

increasing physical activity in people.
 AR in music
 AR on TV
 AR in eSports
 AR in the theater