BECE351L
IOT Fundamentals
IOT_ETH_M4_01
Dr. Om Prakash Sahu
Centre for Innovation and Product Development
Assistant Professor (SG-2)
SENSE
6/22/2023
VIT
VIT University
CC, IOT BECE351L
1
Module: 4
Edge Computing
1. Introduction to Edge/Fog computing
2. Front end Edge Devices
3. Gateway
4. Edge ML for Industry automation
Introduction to Edge/Fog computing
6/22/2023
VIT CC, IOT BECE351L
3
Introduction to Edge/Fog computing
Introduction to Edge/Fog computing
How the technology reached today?
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•
•
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Mainframes (centralized)
Client server (distributed)
Cloud computing (centralized)
Edge computing (distributed)
Introduction to Edge/Fog computing
Cloud Computing
•
In Cloud Computing, either in public, private or hybrid types of accessibility models, data
processing or computation occurs at the data center, where it has a substantial computational
resource and data needs to be transferred back and forth.
Gmail, Office 365, etc.
Using software but using the cloud resource as hardware
providing infrastructure, such as computing, storage, and cloud technology to the end-users
Introduction to Edge/Fog computing
What is Edge Computing
• Edge computing is a
distributed
information
technology
(IT)
architecture in which
client data is processed
at the periphery of the
network, as close to the
originating source as
possible.
Edge computing moves some portion of storage and compute
resources out of the central data center and closer to the
source of the data itself.
Introduction to Edge/Fog computing
What is Edge Computing
• Edge Computing brings the decentralization of networks that allows to bring
computation of data and storage too close to the source (where data is required).
• Minimize the bandwidth, improve response time, and use of latency.
Introduction to Edge/Fog computing
What is Edge Computing
• Edge computing is the computational processing of sensor data away from the
centralized nodes and close to the logical edge of the network, toward individual
sources of data.
• Distributed IT network architecture that enables mobile computing for data
produced locally.
Introduction to Edge/Fog computing
History of Edge Computing
Instead of sending the data to cloud data centers, edge computing decentralizes processing power to
ensure real-time processing without latency while reducing bandwidth and storage requirements
on the network.
Introduction to Edge/Fog computing
Edge Vs. Cloud Computing
Introduction to Edge/Fog computing
Edge Vs. Cloud Computing
Introduction to Edge/Fog computing
Edge Vs. Cloud Computing
Introduction to Edge/Fog computing
Need for Edge Computing?
• A novel distributed and large-scale computing paradigm is required to
effectively treat and analyze such large-scale dataset in a timely manner.
• Edge Computing model was introduced idea to bring data storage and
compute power closer to the device or data source where it is mostly needed.
• Edge Computing paradigm allows computing resources and application
services to be distributed along the communication path, via decentralized
computing infrastructures organised to treat in a hierarchical fashion the
data analytic work flow.
• The hierarchy coupled with the distribution of computing capabilities aims at
solving the bandwidth bottleneck identified for general Cloud architectures.
Introduction to Edge/Fog computing
Edge Computing
Edge Computing
• Push from the Cloud Services
• Push from the Internet of Things
• Change from a Data Consumer to
• a Producer
Cloud Computing
Introduction to Edge/Fog computing
Edge Computing
The objective of Edge Computing is to improve the network technology by moving the computation
of data close to the edge of the network and away from the data centers.
Introduction to Edge/Fog computing
Edge Computing Architecture
Introduction to Edge/Fog computing
Edge Computing Architecture
Cloudlet Computing:
This refers to computing resources (small cluster) connected via WLAN the endusers.
Acts as a data centre in a box which provides support (computing and storage) to the
end-users over the WLAN network.
Cloudlet Computing is based on three layers:
• the component layer,
• the node layer, and
• the cloudlet layer.
This is designed to have
• higher bandwidth, and
• lowering the latency for the applications.
Introduction to Edge/Fog computing
Edge Computing Architecture
Fog Computing,
• a decentralised computing resource that can be placed anywhere between the
cloud and the end-users.
• Fog computing utilizes Fog Computing Nodes (FCNs).
FCNs:
• heterogeneous, including switches, routers, and access points.
• heterogeneous environment facilitates the devices at different protocol layers
and non-IP based technologies to communicate between the FCNs and the end
device.
• hidden for the end-users, thus ensuring security.
Introduction to Edge/Fog computing
Edge Gateways
•
An edge gateway processes data from edge devices, sending back relevant
data, providing network translation between networks using different
protocols and ultimately reducing bandwidth needs.
•
“gateway” name certainly fits, since it connects sensors and nodes at one
end, provides one or multiple local functions, and extends bidirectional
communications to the cloud.
Interestingly, edge devices often serve as gateways.
•
Edge Computing Architecture
Multi-access Edge Computing (MEC):
•
•
•
refers to implementing Edge Computing within the Radio Access Network to
reduce the latency.
Formally known as Mobile Edge Computing, it is an ETSI-defined network
architecture located closer to the Radio Network Controller or macro base
station.
The edge orchestrator organizes the MEC, provides network information about
load and capacity, and offers information to the end-users about their location
and network information.
Edge Computing Architecture
IoT (Internet of Things)
•
contains a large set of devices and sensors that produce a huge volume of
data.
•
exchange the data through a modern communication network and monitor and
control the infrastructure.
•
end-users at the Edge use the IoT devices and sensors.
Edge Computing Architecture
Edge Computing aims to bring computational power in close proximity of IoT sensors, smartphones,
and connected technologies.
Real-Life Use Cases for Edge Computing
Real-Life Use Cases for Edge Computing
Autonomous vehicle (AV)
Real-Life Use Cases for Edge Computing
Healthcare Devices
Real-Life Use Cases for Edge Computing
Security Solutions
Real-Life Use Cases for Edge Computing
Retail Advertising
Real-Life Use Cases for Edge Computing
Smart Speakers
Real-Life Use Cases for Edge Computing
Video Conferencing
Advantages of Edge Computing
• Increasing data security and
privacy
• Better, more responsive and robust
application performance
• Reducing operational costs
• Improving business efficiency and
reliability
• Unlimited scalability
• Conserving network and
computing resources
Disadvantages of Edge Computing
• Requires more storage as data will be placed and processed at
different and various locations.
• Data is kept on distributed locations, and security becomes a
challenging task in such an environment. It often becomes risky
to identify thefts and cybersecurity issues. Also, if some new IoT
devices are added, it can open gates for the attackers for harming
the data.
• It is known that edge computing saves many expenses in
purchasing new devices, but edge computing is also expensive. It
means the cost is too high.
• It needs advanced infrastructure for processing data in an
Challenges in Edge Computing
Front end Edge Devices
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Front end Edge Devices
• Edge devices are pieces of equipment that serve to transmit data between the
local network and the cloud.
• They are able to translate between the protocols, or languages, used by local
devices into the protocols used by the cloud where the data will be further
processed.
• Local devices use protocols like Bluetooth, wi-fi, Zigbee, and NFC while the
cloud uses protocols like AMQP, MQTT, CoAP, and HTTP.
• In order for IoT data to move between the cloud and local devices, an edge
device—like a smart gateway—translates, sorts, and securely transfers
information between the two sources.
Front end Edge Devices
Front end Edge Devices
Edge Devices
• Edge Routers: Edge routers connect multiple packet transmission networks.
They manage data traffic to an IP address so multiple devices can operate
using a single internet connection.
• WAN Devices: Wide Area Network (WAN) devices extend over large areas
and can even be global.
• Routing Switches: Routing switches perform many of the same functions as a
router but also allow connections across different devices.
• Firewalls: Firewalls monitor all network traffic for security. They contain
rules-based functionality and can be configured to block data traffic deemed
harmful by the rule set.
• Multiplexers: Multiplexers merge data from multiple data sources over a
single signal. They’re integrated access devices (IAD) that help automation
systems and advanced IoT networks perform efficiently.
Front end Edge Devices
Intelligent Edge Devices
• Sensors: Sensors measure a condition or event, trigger action, and
route data to the next destination. Sensor types are almost limitless
and include GPS, motion, optical, temperature, humidity, vibration,
and more.
• Actuators: Actuators act as the physical connectivity bridge between
electronic devices like sensors and the physical movement required
on a machine. Actuators take the sensor signals and instructions from
intelligent edge devices and trigger actions using electric, air, or
hydraulic power.
• IoT Gateways: IoT gateways connect multiple sensors and other
devices to cloud computing platforms for analytics, computing,
processing, and storage.
• M2M Devices: M2M devices connect equipment or machines to
transfer data and facilitate automation.
Front end Edge Devices
How does Edge Devices Work?
a combination of sensors, actuators, routers, switches, and edge computing devices
can be controlled and accessed locally or over WAN to provide visibility and the
capability to act over long distances.
• If the data is informational or transactional, the device may send it
directly to the cloud for storage. Or, based on parameters, it may send
it to an edge device for processing and instructions and then forward
the instructions to an actuator to trigger a response.
• Devices like embedded or added sensors act as
the device layer, acquiring data and sending it to
the edge computing device or a cloud
computing platform.
Edge Gateways
•
•
An edge gateway processes data from edge
devices, sending back relevant data,
providing network translation between
networks using different protocols and
ultimately reducing bandwidth needs.
“gateway” name certainly fits, since it
connects sensors and nodes at one end,
provides one or multiple local functions,
and extends bidirectional communications
to the cloud.
•
Interestingly, edge devices
often serve as gateways.
Edge Gateways
Edge Gateways
Industrial Edge Computing
Requirements for Industrial Edge Computing
Industrial Edge Computing
Edge ML for Industrial Automation
• Edge AI is the class of ML architecture in which the AI algorithms process the
data on the edge of the network (the place where data is generated, i.e., locally)
instead of sending it to the cloud.
• The nature of the edge architecture makes it a perfect fit for reducing the
inefficiencies in the existing systems.
• Operational Efficiency- Significantly reduces latency, enhancing the real-time
decision-making capabilities.
• Enhanced Security- It increases the level of security in terms of data
privacy through local processing. Data is no longer shared in a centralized cloud.
• Decentralization of Workloads- Decentralization of the Processing makes all the
distributed systems efficient and self-sustained.
Edge ML for Industrial Automation
•Data Ingestion and Storage of the Data : The real-time sensor data from the monitoring system of
machines into the pipeline. The stream of the data will be stored locally and securely as no cloud processing is
involved here.
•Processing: This stage of the pipeline will process the data according to the need of the pre-trained models.
•Analysis: Here the data will be analyzed by the models, and they will give the results.
•Results: This stage will compile the results. And after these Results, the Response can be taken by the
stakeholders.
Use cases of Edge AI
• Predictive Maintenance refers to the ability to pre-emptively detect the failure of
machines using machine learning predictive algorithms.
• Predictive Maintenance has been in the industry for some time, but it has also
been difficult to implement.
• Condition-based Monitoring: Challenges in fetching the data from their machines,
processes, and system. Whether the streams are sent to the cloud and Then
processing is done.
• If some initial filtering can be done, then only useful data streams can be utilized
in the cloud or locally, this can be achieved with edge ai near the data generation
streams.
• Precision monitoring and controlling system: Use a large amount of data Machine
learning algorithms.
• Edge Computing is a perfect fit for it as it can collect, aggregate, and filter the
data used by the AI/ML algorithms.
Case Studies
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UAV‐based LoRaWAN flying gateway for the internet of flying things
LoRa Based IoT Platform for Remote Monitoring of Large-Scale Agriculture Farms in Chile
Edge Computing Application, Architecture, and Challenges in Ubiquitous Power Internet of Things
An internet of radiation sensor system (IoRSS) to detect radioactive sources out of regulatory control
LoRaWAN for Smart Campus: Deployment and Long-Term Operation Analysis
Forest 4.0: Digitalization of forest using the Internet of Things (IoT)
Dr. Om Prakash Sahu
Assistant Professor (SG-2)
SENSE, VIT University
09827889143
Email Id : omprakash.sahu@vit.ac.in