Edge computing is a distributed computing model that processes data closer to the source, enhancing response times and reducing bandwidth costs. Driven by advancements in IoT, real-time analytics, and 5G technology, it supports applications in smart cities, autonomous systems, and industry 4.0. The future of edge computing includes further integration with AI and critical advancements in autonomous technologies.

1. Edge Computing
Edge computing is a distributed computing paradigm that brings
computation and data storage closer to the location where it is
needed, improving response times and saving bandwidth. In this
presentation, we will explore what edge computing is, and how it is
changing the world of technology.
Dr.Irshad Ahmed

2. Definition of Edge Computing
What is Edge
Computing?
Edge computing is a
network architecture
which enables data
processing to be done
at the edge of the
network, this means
that it’s done closer the
users and devices,
rather than in a central
location. Data is
processed locally on
edge devices or servers,
minimizing latency,
reducing data transfer
and bandwidth cost,
while improving
response times and
overall user experience.
Why Edge
Computing?
The rise of edge
computing is driven by
the emergence of new
technologies such as
IoT, real-time analytics,
5G networks, and
autonomous vehicles.
These technologies
generate and rely on a
vast amount of data,
which requires fast and
efficient processing
with low latency. With
Edge computing, this
processing happens
closer to the source,
reducing the time delay
from transferring data
to a central cloud
location.
Examples of Edge
Devices
Edge devices range
from consumer smart
devices like home
assistants, smart
thermostats, door
locks, and surveillance
cameras to industrial
devices like drones,
autonomous machines,
and remote sensors.

3. Difference between Edge Computing
and Cloud Computing
1
Centralized vs. Distributed
Cloud computing is a centralized model
of computing where central servers in
data centers run applications and store
data. Edge computing is a distributed
model of computing where processing is
done locally at the edge of the network.
2
Latency and Response Times
Cloud computing interactions rely solely on the network connection which can be slow and
unreliable. Edge computing provides low latency and high bandwidth, enabling faster data
processing, and better user experience.
3
Bandwidth and Network Control
Cloud computing requires high
bandwidth and centralized network
control, which creates a bottleneck for
data transmission while edge
computing minimizes the amount of
data transmitted over the network,

4. Use cases of Edge Computing
Smart Cities
Edge computing can be used to power
IoT-enabled smart cities, improving
traffic management, public safety,
energy efficiency, and overall quality of
life.
Autonomous Systems
Edge computing can enable autonomous
systems like drones, self-driving cars, and
robots to make real-time decisions in remote
locations without the need for a stable internet
connection.

5. Use cases of Edge Computing
Edge computing can drive industry 4.0, by
enabling real-time analytics on the factory
floor, improving productivity, quality control,
and predictive maintenance.
Industry 4.0
Edge computing can be used in
healthcare, for remote patient
monitoring, managing medical devices,
and improving patient care with real-
time analytics.
Healthcare

6. Benefits of Edge Computing
1 Faster Response Times
Local data processing reduces latency
and improves response times.
2 Lower Network Costs
Reduced bandwidth usage lowers
network costs, especially in remote
locations.
3 Improved Reliability
Edge computing reduces the reliance on
central servers and increases the
reliability of the system as it can operate
autonomously in case of network failure.
4 Better Data Privacy
Edge computing processes data locally,
reducing the need to send sensitive data
over the network, providing better data
privacy and security.

7. Challenges of Implementing Edge
Computing
Challenge Solution
Scalability Adopting cloud-managed edge computing
solutions can ease management and
scalability challenges.
Data Security Ensuring edge devices are secured from
hacking, and vulnerabilities are minimized
through regular maintenance and software
updates.
Interoperability Standardization of edge devices and cloud
communication standards can help to
address interoperability challenges.

8. Future of Edge Computing
1 5G Rollout
The deployment of 5G networks will be a significant catalyst in the edge
computing adoption, enabling higher bandwidth, lower latency, and faster
data processing, ultimately driving innovative technologies like AR and VR.
2 Edge AI
The integration of edge computing and AI will enable real-time decision
making, autonomous control, and intelligent automation in various industries.
Edge AI will power the growth of the IoT and will transform industries.
3 Autonomous Systems
Edge computing is critical to the adoption of autonomous systems like self-
driving cars, drones, and robots, which rely on real-time data processing and
decision-making in remote locations.

9. Conclusion
Transforming
Technology
Edge computing is a
paradigm shift in computing
that is transforming how we
use technology. It has
immense potential to create
new business models,
enhance customer
experiences, and unlock new
revenue streams.
Connected World
As edge computing evolves,
it will provide more powerful
and efficient computing
capabilities, connecting
people, devices, and systems
Even more closely, delivering
new services and
experiences that were
previously thought
impossible.
Speeding up the future
The possibilities of edge
computing are infinite, from
smart cities to autonomous
systems, from AI to VR, and
they will only be limited by
our imagination. Edge
computing is undoubtedly
opening a new chapter of the
tech world, and it’s only just
getting started.