Internet Of Things

1. THE INTERNET OF THINGS
(IoT)

2. BACKGROUND
INTERNET OF THINGS
• Embedded with electronics and networks which enables the
same to collect and exchange data
• Each thing is uniquely identifiable
KEVIN ASHTON
• Coined the term “internet of things”

3. EXAMPLES
Things in the “Internet of Things” can refer to but
not limited to the ff:
• Heart monitoring implants
• Biochip transponders on farm animals
• Electric clams in coastal waters
• Automobiles with built-in sensor
• Field operation devices for rescue and
operation teams
• Smart thermostats
• Wi-fi enables washers and dryers

4. SIGNIFICANT APPLICATIONS
• Media
• Environmental Monitoring
• Infrastructure Management
• Energy Management
• Medical and Healthcare Systems
• Building and Home Automation
• Transportation
• Large Scale Deployments

5. EFFECT OF IoT to SOCIETY
• More efficient travel time
(by monitoring traffic conditions)
• Predict product “health”
(fast proactive product
monitoring)
• New job roles (like data jobs
and internet analytic jobs)
• Productivity at work (using
applications like facebook etc)
• Structure to large data
(data is well analyzed and
structured)
• Greener Business (sensors
can automatically turn off
electronic devices)

6. EFFECT OF IoT to SOCIETY
• Location (IoT can help track locations easier)
• Smarter Machines (Equipment can easily remember personal
preference ie coffee machine)

7. EFFECT OF IoT to SOCIETY
• Medical (help doctors diagnose better and monitor patients’
conditions outside of hospitals)
• Plan days better (predict weather more accurately)

8. THE FUTURE OF IoT
FAST DATA
• Analyze data and detect problems before they
even occur
Example :
Take a large wind turbine farm, for example. A sensor that collects real-
time data from turbines that is quickly analyzed and turned into actionable
insights is a key competitive advantage. Companies are already using
advanced sensors to continually assess acceleration, temperature and
vibration. Extracting data from wind turbines uncovers trends for
performance optimization to increase productivity and predictive
maintenance to minimize downtime.

9. THE FUTURE OF IoT
HORIZONTAL INTERGRATION, VERTICAL
APPLICATION
• Today’s business world is seeing a tremendous shift in the speed at
which we have to respond to market changes, leading to the need for
a very flexible solution that can be adapted on the fly, ideally by
business people, and not requiring complex tasks such as IT-support,
coding or deployment.
• we will see more horizontal components with tremendous market
success. Organizations will use building blocks optimized for certain
infrastructural tasks, including device management, data collection,
storage, analytics and application management. However, we will also
see vertical applications, such as in the renewable energy sector,
because it is the best way to put data in context and convey insight to
a specific, unique user-group.
Example :
analyzing turbine sensor data with greater granularity shows the relevant information
in a way that the target audience easily understands. Analytic front ends, as beautiful
as they may be, are only good for analytically minded people. In my experience, not
everyone enjoys “surfing data;” most prefer a context-specific presentation of just the
most relevant data.

10. THE FUTURE OF IoT
DECENTRALIZATION
• We will see much growth in data volume coming from
disparate devices that require both real-time and post-mortem
analysis.
EXAMPLE:
• We will see a decentralization of data storage, processing and
analytics. Technology in renewable energy running on a
system’s edge (the sensors on the wind farm, for example)
benefits from the ability to run queries at any given time,
whether in its own data center, in the cloud, or at customer
locations by using edge analytics. These requirements are not
possible with legacy or general-purpose technologies, which
are not optimized for IoT.

11. THE FUTURE OF IoT
INTEGRATION OF ADVANCED ANALYTI CS AND MACHINE
LEARNING
• There is so much talk about advanced analytics (AA), artificial
intelligence (AI) and machine learning (ML) that most people
have a hard time understanding these technologies. The good
news is, the majority of people do not have to understand; they
should spend their time on their operational processes and
products – not on the math. My view on AA (including AI, ML,
etc.) is that it delivers hints that normal people might have
missed due to volume, speed and complexity of the IoT data
available.