JAMIA
Edge Computing
Group Members:-- Subject Code:CEN-693
Mohammad Arman -22BCS045 Semester:6th
Course:B.Tech(Computer
Mohammad Saif Alam-22BCS047
Engineering)
Sahil-22BCS068
Key Challenges in Edge Computing
Devices Have Less Power and Memory Content
Edge devices (like sensors or small
computers) are not as powerful as cloud
servers.
They may not be able to handle heavy tasks
like big data analysis or AI.
This can slow down performance or limit
what the device can do.
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Key Challenges in Edge Computing
Security Problems and Internet Dependence
These devices are often more open to
hacking because they are closer to
users.
Without strong protection, data can be
stolen or misused.
Also, if the internet connection is weak
or goes down, edge devices may not
work properly.
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Key Challenges in Edge Computing
Hard to Manage and Connect Everything
There are many edge devices in different
places — like cameras, sensors, or small
computers.
It’s hard to keep all of them updated with the
latest software.
Getting all these devices to work together can
be a big challenge.
Also, connecting them to older systems or to
the cloud can take a lot of effort and planning.
Edge Security Concerns
Main Security Challenges
1.Physical Access Risks
Devices can be stolen or tampered with easily.
2.Weak Encryption
Some edge devices don’t use strong encryption, so
data can be stolen during transfer.
3.Inconsistent Updates
Not all devices get regular security updates, making
them vulnerable to attacks.
4.More Attack Points
With many devices spread out, there are more
chances for hackers to find weak spots.
Real World Companies Using Edge Computing
Tesla
Use case: Autonomous driving
Edge Role: Tesla cars process
sensor and camera data locally
for immediate decision-making
(e.g., lane changes, obstacle
detection).
How Autonomous Cars Use Edge Computing
1. Real-Time Decision Making
Sensors (LIDAR, cameras, radar)
generate massive data streams.
Instead of sending that data to the
cloud, the car uses onboard edge
computers (like NVIDIA Drive or
Tesla’s FSD chip) to:
Detect obstacles
Identify lanes and traffic signs
Make decisions (brake,
accelerate, steer)
2. Local AI Processing
AI models are pre-trained in
the cloud, but run locally on
the car.
These models help the car:
Understand road
environments
Predict behavior of other
drivers
Plan safe paths
Connectivity Backup
Cars connect to the cloud for:
Software updates
Sending data for
training/improvement
But they don’t rely on it for
core functions—edge handles
real-time driving.
Future Trends
Smarter Edge Devices
Devices are becoming smart by using
Artificial Intelligence (AI) and Machine
Learning (ML).
They can now analyze data right where it is
created, without sending it to the cloud.
This leads to:
Faster decision-making (like detecting a fire
or intruder immediately).
Less internet usage, which saves cost and
bandwidth.
Better privacy, since data doesn't have to
travel far.
Common in smart homes, smart cameras, and
factory machines.
Future trends
Faster and Stronger Connections
The rollout of 5G networks is a game-
changer for edge computing.
This leads to :
Very low latency – devices respond in
milliseconds.
High-speed data transfer between edge
and cloud.
Real-time processing for critical tasks
like:
Self-driving cars avoiding obstacles.
Doctors performing surgery remotely.
5G + Edge = Perfect match for smart
cities, healthcare, and transportation.
Future trends
Edge + Cloud = Better Together
Hybrid Cloud-Edge Systems
In the future, cloud and edge will work
more closely together.
Critical tasks will be done at the edge,
while the cloud handles big data storage
and analysis.
This creates a smart and balanced system
for speed and efficiency.
Research & Development
Driving Innovation at the Edge
Edge computing is improving through research
in:
AI for real-time tasks
Better security
Low-power devices
Easier system integration
Universities, companies, and governments are
working together to push the tech forward.
Final Thoughts
Edge computing is changing how
we use and process data.
It offers speed and efficiency but
has some challenges.
With ongoing innovation, its future
looks bright in smart tech and
industries.
RESOURCES
https://www.bing.com/search?q=memory+content&qs=HS&pq=memory+&sc=12-
slide 2
7&cvid=E0174F73D1B246A2A8EF743F63B61F95&FORM=QBRE&sp=1&ghc=1&lq=
0
slide 3 https://www.bing.com/key challanges in edge computing/search?
q=security+problem&form=HDRSC3&first=1
slide 4 https://www.bing.com/images/search?
q=how+to+manage+and+connect+every+thing&form=HDRSC3&first=1
Slide 5 :https://developer.nvidia.com/blog/edge-computing-considerations-for-security
Slide 6 :https://www.researchgate.net/figure/Edge-computing-architecture
Slide 7 :https://www.researchgate.net/figure/Edge-computing-architecture
Slide 8 :https://www.digi.com/blog/post/edge-computing-vs-cloud-computing
slide 9 https://www.computerworld.com/article/3808978/how-does-connection-
backup-actually-work.html
slide 10, 11,12 https://chatgpt.com/share/6809f016-fcd8-800b-92a4-35ed3d3dffc2
slide 13 https://ieeexplore.ieee.org/document/9083958
https://www.bing.com/search?
slide 14 q=final+throughput+in+edge+computing&FORM=HDRSC1
THANK YOU
