This document discusses various emerging technologies including Internet of Things (IoT), digital transformation, big data, data analytics, machine learning, artificial intelligence, blockchain, Ripple, LiFi, and Mitz technologies. It provides overviews and examples of each technology, noting how IoT is bringing more connected devices and creating challenges around data structures, formats, and analytics. Artificial intelligence can help with IoT data preparation, discovery, visualization, prediction, and geospatial analysis. Blockchain provides benefits for tracking connected devices and enabling secure transactions without centralized control.

1. Leveraging IoT & Latest
Technologies
Mithileysh Sathiyanarayanan
London
Email: mithileysh.s@srishtibiz.in

2. 1. Internet of Things (IoT) & Digital Transformation
(DX)
2. Big Data
3. Data Analytics
4. Data-driven Documents (D3)
5. Machine Learning/Deep Learning (ML/DL)
6. Artificial Intelligence (AI)
7. LiFi Technology
8. Blockchain Technology
9. Ripple Technology
10. Mitz Technology
Agenda

3. Internet of Things (IoT)

4. The Internet of Things (IoT) is an ecosystem of
ever-increasing complexity; it’s the next wave of
innovation that will humanize every object in
our life.

6. IoT is bringing more and more devices (things)
into the digital fold every day, which will likely
make IoT a multi-trillion dollars’ industry in the
near future.

7. However, the rapid evolution of the IoT market
has caused an explosion in the number and
variety of IoT solutions, which created real
challenges as the industry evolves, mainly, the
urgent need for a reliable IoT model to perform
common tasks such as sensing, processing,
storage, and communicating.
That’s the reason for the need of digital transformation……..

8. Digital Transformation (DX)

9. Digital transformation (DX) is the profound
transformation of business and organizational
activities, processes, competencies and models
to fully leverage the changes and opportunities
of a mix of digital technologies and their
accelerating impact across society in a strategic
and prioritized way, with present and future
shifts in mind.

12. • A digital transformation strategy aims to
create the capabilities of fully leveraging the
possibilities and opportunities of new
technologies and their impact faster, better
and in more innovative way in the future.

13. IoT & Big Data

14. Big data are extremely large data sets,
that are voluminous and complex, that
may be analysed computationally to
reveal patterns, trends, and
associations, especially relating to
human behaviour and interactions.

16. IoT & Data Analytics

17. • IoT analytics helps to exploit the information
collected by "things" in many ways — for
example, to understand customer behavior,
to deliver services, to improve products, and
to identify and intercept business moments.

19. There are many challenges facing IoT Analytics
including;
– Data Structures,
– Combing Multi Data Formats
– The Need to Balance Scale and Speed
– Analytics at the Edge
– IoT Analytics and AI

20. Data structures
• Most sensors send out data with a time stamp
and most of the data is "boring" with nothing
happening for much of the time.
• However once in a while something serious
happens and needs to be attended to.

21. • While static alerts based on thresholds are a
good starting point for analyzing this data,
they cannot help us advance to diagnostic or
predictive or prescriptive phases.
• There may be relationships between data
pieces collected at specific intervals of times.
In other words, classic time series challenges.

22. Combining Multiple Data Formats
• While time series data have established
techniques and processes for handling, the
insights that would really matter cannot come
from sensor data alone.
• There are usually strong correlations between
sensor data and other unstructured data.

23. • For example, a series of control unit fault
codes may result in a specific service action
that is recorded by a mechanic.
• Similarly, a set of temperature readings may
be accompanied by a sudden change in the
macroscopic shape of a part that can be
captured by an image or change in the audible
frequency of a spinning shaft.

24. • We would need to develop techniques where
structured data must be effectively combined
with unstructured data or what we call Dark
Data

25. • Dark data is a type of unstructured, untagged
and untapped data that is found in data
repositories and has not been analyzed or
processed.
What is Dark Data ?

26. • Dark data is also known as dusty data.
• Dark data is data that is found in log files and
data archives stored within large enterprise
class data storage locations.
• It includes all data objects and types that
have yet to be analyzed for any business or
competitive intelligence or aid in business
decision making.

27. • Typically, dark data is complex to analyze and
stored in locations where analysis is difficult.
• The overall process can be costly.
• It also can include data objects that have not
been seized by the enterprise or data that are
external to the organization, such as data
stored by partners or customers.

28. • International Data Corporation (IDC), stated
that up to 90 percent of big data is
dark data.

29. The Need to Balance Scale and Speed
• Most of the serious analysis for IoT will
happen in the cloud, a data center, or more
likely a hybrid cloud and server-based
environment.

30. IoT & D3

31. Data-driven documents (D3) is a JavaScript
library for visualizing data in a dynamic &
interactive form in web browsers using
JavaScript, HTML, SVG, and CSS.

33. IoT D3 Dashboard - Demo
https://codepen.io/mithileysh/full/VdmLNr/

34. IoT and ML/DL

39. IoT and AI

40. IoT Analytics and AI
• The greatest—and as yet largely untapped—
power of IoT analysis is to go beyond reacting
to issues and opportunities in real time and
instead prepare for them beforehand.

41. • That is why prediction is central to many IoT
analytics strategies, whether to project
demand, anticipate maintenance, detect
fraud, predict churn, or segment customers.

42. • Artificial Intelligence (AI) use and improves
current statistical models for handling
prediction.

43. • AI will automatically learn underline rules,
providing an attractive alternative to rules-
only systems, which require professionals to
author rules and evaluate their performance.
When AI applied it provides valuable and
actionable insights.

47. There are six types of IoT Data Analysis where AI
can help:
• 1. Data Preparation: Defining pools of data
and clean them which will take us to concepts
like Dark Data, Data Lakes.

48. • 2. Data Discovery: Finding useful data in the
defined pools of data

49. • 3. Visualization of Streaming Data: On the
fly dealing with streaming data by defining,
discovering data, and visualizing it in smart
ways to make it easy for the decision-making
process to take place without delay.

50. • 4. Time Series Accuracy of Data: Keeping the
level of confidence in data collected high with
high accuracy and integrity of data

51. • 5. Predictive and Advance Analytics: Very
important step where decisions can be made
based on data collected, discovered and
analyzed.

52. • 6. Real-Time Geospatial and Location
(logistical Data): Maintaining the flow of data
smooth and under control.

53. Challenges Facing AI in IoT

55. • Many IoT ecosystems will emerge, and
commercial and technical battles between
these ecosystems will dominate areas such as
the smart home, the smart city, financials and
healthcare.

56. • But the real winners will be the ecosystems
with better, reliable, fast and smart IoT
Analytics tools, after all what is matter is how
can we change data to insights and insights to
actions and actions to profit .

57. Blockchain Technology

58. Blockchain technology can be used in tracking
billions of connected devices, enable the
processing of transactions and coordination
between devices; allow for significant savings
to IoT industry manufacturers. This
decentralised approach would eliminate single
points of failure, creating a more resilient
ecosystem for devices to run on. The
cryptographic algorithms used by blockchains,
would make consumer data more private.

60. Two Main Types of Blockchain-
In a public blockchain, everyone can read or
write data. Some public blockchains limit the
access to just reading or writing. Bitcoin, for
example, uses an approach where anyone can
write.
In a private blockchain, all the participants are
known and trusted. This is useful when the
blockchain is used between companies that
belong to the same legal mother entity.

61. The ledger is tamper-proof and cannot be
manipulated by malicious actors because it
doesn’t exist in any single location, and man-in-
the-middle attacks cannot be staged because
there is no single thread of communication that
can be intercepted. Blockchain makes trustless,
peer-to-peer messaging possible and has
already proven its worth in the world of
financial services through cryptocurrencies
such as Bitcoin, providing guaranteed peer-to-
peer payment services without the need for
third-party brokers.

62. Ripple Technology

63. • Ripple connects banks, payment providers,
digital asset exchanges and corporates via
RippleNet to provide one frictionless
experience to send money globally.
• Real-time traceability of funds.
• Provides optional access to the world’s
fastest and most scalable digital asset for
payments.

65. LiFi Technology

66. LiFi is a bidirectional, high-speed wireless
communication technology similar to WiFi but
transmits data by using Light Emitting Diode
(LED) lightbulbs.
The expanding interest for higher bandwidths,
faster transfer speeds and more secure data
transmission for internet of things(IoT).

68. LED bulbs will be turned into wireless access
points, effectively letting you move between
light sources without losing your connection.
Modulated Data passes through LED lights with
Photodetector to produce demodulated data
For more details, refer my article: IEEE Paper

70. Mitz Technology

71. Mitz Technology
=
Eye controlled Technology
+
Telekinesis
Clue: using contact lenses with an Internet surfing
function and controlling objects through mind.
Soon to come………….

72. Conclusion
The building blocks of digital transformation
are; mindset, people, process, and tools. IoT
covers all the blocks since IoT doesn’t just
connect devices, it connects people too.
Blockchain will ensure end-to-end security
and by using AI you will move IoT beyond
connections to intelligence.

73. References
• https://www.linkedin.com/pulse/ai-catalyst-iot-ahmed-banafa/
• https://enterprisersproject.com/article/2017/2/how-iot-enabling-
new-wave-digital-business-transformation
• https://www.huffingtonpost.com/guido-bartels/internet-of-things-
digital_b_8472410.html
• https://www.linkedin.com/pulse/iot-blockchain-convergence-
ahmed-banafa/
• https://www.i-scoop.eu/digital-transformation/
• https://www.linkedin.com/pulse/five-challenges-iot-analytics-
success-ahmed-banafa/?
• https://dzone.com/articles/why-blockchain-and-digital-
transformation-go-hand
• http://www.zdnet.com/article/why-ai-and-machine-learning-need-
to-be-part-of-your-digital-transformation-plans/

74. Mithileysh Sathiyanarayanan
Research Scientist, London
Email: mithileysh.s@srishtibiz.in
LinkedIn: https://www.linkedin.com/in/mithileysh/
Web: https://sites.google.com/site/mithileysh/

75. Feedback Form