edge computing slides - these slides include

Definition
Edge computing is a distributed computing
paradigm that brings computation and data
storage closer to the sources of data.
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Introduction
• The origins of edge computing lie in content
distributed networks that were created in the late
1990s to serve web and video content from edge
servers that were deployed close to users.
• In the early 2000s, these networks evolved to host
applications and application components on edge
servers, resulting in the first commercial edge
computing services that hosted applications such as
dealer locators, shopping carts, real-time data
aggregators, and ad insertion engines. 3

Facts About Edge Computing
• The first computers were large, bulky
machines that could only be accessed
directly or via terminals that were
basically an extension of the computer.
• With the invention of personal computers,
computing could take place in a much
more distributed fashion.
●●●
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• For a time, personal computing was the
dominant computing model.
• Applications ran and data was stored
locally on a user's device, or sometimes
within an on-premise data center.
• Cloud computing, a more recent
development, offered a number of
advantages over this locally based, on-
premise computing.
●●●
6

• Cloud services are centralized in a vendor-
managed "cloud" (or collection of data
centers) and can be accessed from any
device over the Internet.
• However, cloud computing can introduce
latency because of the distance between
users and the data centers where cloud
services are hosted.
●●●
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• Early computing: Centralized applications
only running on one isolated computer
• Personal computing: Decentralized
applications running locally
• Cloud computing: Centralized applications
running in data centers
• Edge computing: Centralized applications
running close to users, either on the
device itself or on the network edge.
8

Example of Edge Computing
• Consider a building secured with dozens of
high-definition IoT video cameras. These are
"dumb" cameras that simply output a raw
video signal and continuously stream that
signal to a cloud server.
• On the cloud server, the video output from all
the cameras is put through a motion-detection
application to ensure that only clips featuring
activity are saved to the server’s database.
●●●
9

• This means there is a constant and significant
strain on the building’s Internet infrastructure,
as significant bandwidth gets consumed by the
high volume of video footage being
transferred.
• Additionally, there is very heavy load on the
cloud server that has to process the video
footage from all the cameras simultaneously.
●●●
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• Now imagine that the motion sensor
computation is moved to the network edge.
What if each camera used its own internal
computer to run the motion-detecting
application and then sent footage to the cloud
server as needed?
• This would result in a significant reduction in
bandwidth use, because much of the camera
footage will never have to travel to the cloud
server.
●●●
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• Additionally, the cloud server would now
only be responsible for storing the
important footage, meaning that the server
could communicate with a higher number of
cameras without getting overloaded.
• This is what edge computing looks like.
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Cases of Edge Computing
• Security system monitoring: As described
above.
• IoT devices: Smart devices that connect to the
Internet can benefit from running code on the
device itself, rather than in the cloud, for more
efficient user interactions.
• Self-driving cars: Autonomous vehicles need to
react in real time, without waiting for
instructions from a server.
●●●
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Cases of Edge Computing
• More efficient caching: By running code on a
CDN edge network (Content Delivery Network), an
application can customize how content is
cached to more efficiently serve content to
users.
• Medical monitoring devices: It is crucial for
medical devices to respond in real time
without waiting to hear from a cloud server.
14

Benefits of Edge Computing
Cost savings
• As seen in the example above, edge computing
helps minimize bandwidth use and server
resources. Bandwidth and cloud resources are
finite and cost money.
Performance
• Another significant benefit of moving processes
to the edge is to reduce latency. Every time a
device needs to communicate with a distant
server somewhere, that creates a delay.
●●●
15

Benefits of Edge Computing
In addition, edge computing can provide new
functionality that wasn’t previously available. For
example, a company can use edge computing to
process and analyze their data at the edge, which
makes it possible to do so in real time.
• Decreased latency
• Decrease in bandwidth use and associated cost
• Decrease in server resources and associated cost
• Added functionality
16

Drawback of Edge Computing
• One drawback of edge computing is that it
can increase attack vectors.
• With the addition of more "smart" devices
into the mix, such as edge servers and IoT
devices that have robust built-in computers,
there are new opportunities for malicious
attackers to compromise these devices.
• Another drawback with edge computing is
that it requires more local hardware.
●●●
17

Drawback of Edge Computing
• For example, while an IoT camera needs a
built-in computer to send its raw video data
to a web server, it would require a much
more sophisticated computer with more
processing power in order for it to run its
own motion-detection algorithms. But the
dropping costs of hardware are making it
cheaper to build smarter devices.
18

Common Types of Edge Computing
Devices
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Smart Sensors
• Devices that collect and preprocess data, such as IoT sensors for monitoring
temperature, motion, or environmental conditions.
• Examples: Environmental monitoring sensors, industrial IoT devices.
Edge Gateways
• Serve as intermediaries between edge devices and cloud servers, capable of
aggregating, filtering, and processing data locally.
• Examples: Cisco IoT Gateways, Dell Edge Gateways.

20
Single-Board Computers (SBCs)
• Compact and affordable computing platforms often used for prototyping and
lightweight edge applications.
• Examples: Raspberry Pi, NVIDIA Jetson Nano, Google Coral Dev Board.
Embedded AI Devices
• Specialized devices optimized for AI inference at the edge, often including dedicated
accelerators like TPUs or GPUs.
• Examples: Google Coral USB Accelerator, NVIDIA Jetson series, Intel Neural Compute
Stick.
Industrial Edge Devices
• Ruggedized devices designed for use in industrial environments with capabilities for
real-time processing and connectivity.
• Examples: Siemens Industrial Edge devices, Advantech gateways.

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Smart Cameras
• Cameras with built-in computational capabilities to analyze video feeds
locally.
• Examples: Hikvision AI cameras, Axis smart cameras.
Wearable Devices
• Devices worn by individuals, capable of processing and analyzing data in real-
time for applications like health monitoring or augmented reality.
• Examples: Smartwatches, AR/VR headsets.
Autonomous Systems
• Devices like drones, robots, or autonomous vehicles with onboard processing
capabilities.
• Examples: DJI drones, autonomous delivery robots.

Conclusion
 Edge computing is an emerging computing
paradigm which refers to a range of networks and
devices at or near the user.
 Edge is about processing data closer to where it's
being generated, enabling processing at greater
speeds and volumes, leading to greater action-led
results in real time.
22

References
• Wikipedia.org
• Google.com
• Seminarppt.com
• Studymafia.org

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