11institutetext: Balqees Talal Hasan, 22institutetext: Dept. of Computer and
Information Engineering, Nineveh University, Mosul, Iraq , 22email:
<EMAIL_ADDRESS>33institutetext: Ali Kadhum Idrees,
44institutetext: Dept. of Information Networks, University of Babylon,
Babylon, Iraq, 44email<EMAIL_ADDRESS>
# Edge Computing for IoT

Balqees Talal Hasan and Ali Kadhum Idrees

###### Abstract

Over the past few years, The idea of edge computing has seen substantial
expansion in both academic and industrial circles. This computing approach has
garnered attention due to its integrating role in advancing various state-of-
the-art technologies such as Internet of Things (IoT) , 5G, artificial
intelligence, and augmented reality. In this chapter, we introduce computing
paradigms for IoT, offering an overview of the current cutting-edge computing
approaches that can be used with IoT. Furthermore, we go deeper into edge
computing paradigms, specifically focusing on cloudlet and mobile edge
computing. After that, we investigate the architecture of edge computing-based
IoT, its advantages, and the technologies that make Edge computing-based IoT
possible, including artificial intelligence and lightweight virtualization.
Additionally, we review real-life case studies of how edge computing is
applied in IoT-based Intelligent Systems, including areas like healthcare,
manufacturing, agriculture, and transportation. Finally, we discuss current
research obstacles and outline potential future directions for further
investigation in this domain.

##  1 Introduction

In as early as 1966, an insightful prediction emerged from Karl Steinbuch, a
pioneer in German computer science. He predicted that within a few decades,
computers would be a necessary component of almost every industrial
product.The term ”pervasive computing” was first introduced by W. Mark in
1999. It means integrating computers into everyday objects so seamlessly that
they become a natural and unnoticed part of the environment, with people
interacting with them effortlessly. In the same year (1999), The term
”Internet of Things” was coined by Kevin Ashton at a presentation at Procter &
Gamble (P&G) cloudlet_2 .
IoT is a new paradigm for attaching various physical objects to the Internet
so they can interact and make informed decisions. The technologies that fall
under this paradigm include pervasive computing, RFID, communication
technologies, sensor networks, and Internet protocols. In IoT, physical things
have the ability to intelligently collaborate and establish connections with
the Internet, operating autonomously and introducing innovative applications.
These applications span a variety of industries, such as manufacturing,
transportation,healthcare, industrial automation, and emergency response edge3
; ali1 .
IoT has become permeated our daily lives, providing crucial measuring and
data-gathering capabilities that influence our decision-making. Numerous
sensors and gadgets run continuously, producing data and enabling vital
communication over complex networks. It is challenging to execute complicated
computations on most of the IoT devices due to their limited CPU and energy
resources. In general, IoT devices collect data and transmit it to more robust
processing centers for analysisali3 . The data is subjected to extra
processing and analysis at these centers ali4 . In order to lighten the load
on resources and avoid overuse of them, edge computing has become prevalent as
a novel way to address IoT and local computing requirements edge2 . In edge
computing, small servers that are located closer to users are used to process
data. In close proximity to the devices of the consumers, these edge servers
are capable of doing complex tasks and storing enormous volumes of data. As a
result of their proximity to users, processing and storage at the network edge
becomes faster and more efficient advantages . In 2022, the worldwide edge
computing market was worth USD 11.24 billion. Experts predict that it will
experience significant expansion, with an expected yearly From 2023 to 2030,
the growth rate is 37.9% edge_1 . Edge computing is different from the usual
cloud computing approach. Instead of processing and storing data in
centralized data centers far from users, edge computing involves positioning
resources closer to users, specifically at the ”edge” of the network. This
means there are multiple computing nodes spread throughout the network, which
reduces the burden on the central data center and makes data exchange much
faster, as there is less delay in sending and receiving messages edge2 . Edge
computing allows for the intelligent collection, analysis, computation, and
processing of data at every IoT network edge. This implies that data can be
filtered, processed, and used close to the devices or data sources, where it
is generated. Edge computing makes everything faster and more effective by
pushing smart services to the edge of the network. Making decisions and
processing data locally can also help deal with significant limitations in
networks and resources, and it can address concerns related to security and
privacy too agriculture ; ali2 .
Here is how this chapter is organized: Section 2 provides a comprehensive
explanation of computing paradigms for IoT. Moving on to Section 3, a detailed
introduction to edge computing paradigms is presented. Section 4 outlines the
architecture of edge computing-based IoT. In Section 5, the focus shifts to
illustrate the advantages of edge computing-based IoT. The enabling
technologies for edge computing-based IoT are introduced in Section 6. Section
7, the chapter reviews edge computing in IoT-Based Intelligent Systems.
Section 8 illustrates the challenges and future research directions for edge
computing-based IoT. Finally, Section 9 concludes the chapter.

##  2 Computing Paradigms for IoT

This section describes the fundamental concepts underlying the three major
computing paradigms and how they are integrated with IoT: cloud computing,
edge computing, and fog computing, fgure 1 shows the architecture of the 3
tiers computing paradigms 1.

###  2.1 Cloud Computing

As mobile hardware evolves and improves, it will always face limitations in
terms of available resources compared to stationary hardware. Regarding
devices that people wear or carry for extended periods, prioritizing
improvements in weight, size, and battery life takes precedence over enhancing
computational power. This is a fundamental aspect of mobility rather than a
transient restriction imposed by modern mobile electronics. Therefore, there
will always be trade-offs when using computational power on mobile devices.
The resource limitations of mobile devices can be solved simply and
effectively by using cloud computing. With this approach, a mobile device can
execute an application that requires a lot of resources on a robust remote
server or a cluster of servers, allowing users to interact with the
application through a lightweight client interface over the Internet
cloudlet_3 .
The cloud computing paradigm gives end users on-demand services by utilizing a
pool of computing resources. These resources include computing power, storage,
and more, and they are all immediately available at any time cloud3 .
IoT and the cloud have had separate evolutionary processes. However, its
integration has produced a number of benefits for both parties. On the one
hand, IoT can greatly profit from the boundless capabilities of cloud
computing to overcome its own technological limitations, such as storage,
processing power, and energy requirements. The cloud can take advantage of IoT
through expanding its range of applications to handle real-world objects in a
more distributed and adaptable fashion, thus providing new services in various
real-life situations cloud1 .

###  2.2 Edge Computing

Typically, the architecture of the cloud is used to manage the massive amount
of data that IoT devices produce. However, cloud computing encounters various
challenges such as lengthy transmission time, increased bandwidth
requirements, and latency between IoT devices and the cloud. The concept of
edge computing has emerged to overcome these difficulties. This approach
enhances scalability, latency, and privacy factors while enabling real-time
predictions by processing data at the source edge_h1 ; ali9 .
In an extension of cloud computing, edge computing places computer services at
the edge of the network, where they are more accessible to end users. Edge
computing shifts services, computational data, and applications out from cloud
servers and toward the edge of a network.This enables content providers and
application developers to provide users with services that are located nearby.
Edge computing is unique in that it may be used for a variety of applications
thanks to its high bandwidth, very low latency, and fast access to network
data cloud3 ; ali10 .
In the world of IoT, both Edge computing and Cloud computing offer major
advantages due to their distinct characteristics, such as their capacity to
execute complex computations and store large amounts of data. However, when it
comes to IoT, edge computing outperforms cloud computing, despite having
somewhat limited compute capability and storage capabilities. In particular,
IoT demands fast responses rather than powerful computing and massive storage.
Edge computing fulfills the requirements of IoT applications by offering
satisfactory computing capability, sufficient storage, and fast response
times. Edge computing, on the other hand, can also leverage IoT to expand its
framework and adapt to the dynamic and distributed nature of edge computing
nodes. These edge nodes can serve as providers and may consist of either IoT
devices or devices with some residual computational capabilities edge2 .

###  2.3 Fog Computing

CISCO introduced the concept of fog computing in January 2014 fog2 . This
computing paradigm offers numerous advantages across various fields,
especially the IoT fog3 ; ali5 . According to Antunes, a senior official in
charge of advancing corporate strategy at Cisco, edge computing is a division
of fog computing. He explains that fog computing primarily focuses on managing
the location of data generation and storage. In essence, edge computing
involves processing data in proximity to its source of origin ali6 . Fog
computing, on the other hand, leverages edge processing and the necessary
network connections to transfer data from the edge to the endpointfog2 . The
fog computing system was not designed to replace cloud computing; instead, its
development aimed to fill any service gaps present in cloud computing. Fog
computing emphasizes on bringing abilities of cloud computing closer to the
edge of the network so that users can access communication and software
services faster. This approach works well for offering cloud solutions for
highly mobile technologies like vehicular ad hoc networks (VANET) and the IoT
fog1 . Fog computing serves the endpoints or edges of the network of
interconnected devices. It prioritizes the analysis of time-sensitive data
near the sources, sending only the selected and abridged data to the cloud
fog2 ; ali7 .
The concept of ”fog as a service” (FaaS) is a new service possibility brought
about by the integration of fog computing and IoT. According to this concept,
a service provider creates a network of fog nodes throughout the area covered
by its service, operating as a landlord to numerous tenants from diverse
businesses. Each fog node provides storage, networking, and local computing
capabilities. Through FaaS, customers can access services using innovative
business models. Unlike clouds, which are often managed by huge businesses
with sizable data centers, FaaS enables both small and large businesses to
create and manage public or private computing, storage, and control services
at different scales, meeting the requirements of various clients fog3 ; ali8 .

Figure 1: 3-Tiers Architecture of Computing Paradigms

##  3 Edge Computing Paradigms

Edge computing emerged due to the evolution of cloud computing, and it
provides different computing advantages. Several paradigms for operating at
the edge of the network have been established throughout the growth of edge
computing, including cloudlet and mobile edge computing. The two primary edge
computing paradigms are introduced in this section.

###  3.1 Cloudlet

In 2009, Satyanarayanan and his team first proposed cloudlet computing as a
remedy for the problems that can arise while using traditional cloud
computing. These restrictions cover things like delay, jitter, and congestion
cloudlet_3 . Cloudlets, as shown in figure 2, are essentially small data
centers, often referred to as miniature clouds,which are frequently just a hop
away from a user device cloudlet_1 . Instead of relying on a far-off ”cloud,”
a nearby cloudlet with abundant resources can be used to alleviate the limited
resources of a mobile device. To fulfill the requirement for real-time
interactive responses, a solution is to establish a wireless connection with a
nearby cloudlet that provides high-bandwidth, one-hop, and low-latency
wireless access. In this situation, the mobile device serves as a lightweight
client, with the cloudlet in close proximity handling the majority of the
complex computational operationscloudlet_3 . Cloudlet relies on technologies
like Wi-Fi, making it reliant on a strong internet connectionMEC5 .
Owners of the network infrastructure, such as Nokia and AT & T, can enable
cloudlets to be positioned in closer proximity to mobile devices, in hardware
with smaller footprints than the cloud computing’s massive data centers. As a
result of their smaller footprint, cloudlets have less computational power
than typical clouds, but they still have advantages over them including lower
latency and energy usage cloudlet_1 .
A ”data center in a box” is how cloudlets are like. It operates autonomously,
with minimal power consumption, and only needs an Internet connection and
access control for setup. This administrative simplicity correlates with a
device-based computing architecture, enabling easy implementation in a variety
of business premises such as doctor’s offices or coffee shops. From an
internal viewpoint,A cloudlet can be perceived as a cluster of computers
equipped with multiple cores, fast internal connections, and a high-bandwidth
wireless LAN. For safe implementation in unsupervised areas, the cloudlet can
be housed in a protective casing designed to resist tampering with third-party
remote monitoring of hardware integrity cloudlet_3 . To avoid any serious
implications in the event of loss or malfunction, it is crucial to emphasize
that the cloudlet should only store transient data and code, such as cached
copies. In critical circumstances like military operations, disaster-affected
areas, and even cyberattacks, cloudlets are essential. In these circumstances,
the cloudlet, which is the middle layer in a 3-tier architecture consisting of
the mobile, cloudlet, and cloud, is required to fill in and provide vital
services, making up for the cloud’s unavailability. Cloudlets also have the
benefit of reducing the dangers linked to multi-hop networks, such as the
possibility of DoS attackscloudlet_2 .

Figure 2: Cloudle Architecture

###  3.2 Mobile Edge Computing

The idea behind a cloudlet is to strategically place powerful computers so
that they can provide neighboring user equipment (UE) with computing and
storage capabilities. Similar to WiFi hotspots, cloudlets deliver cloud
services to mobile customers as opposed to offering internet connectivity. The
fact that mobile UEs predominantly use WiFi connections to access cloudlets
has a potential disadvantage because it requires users to alternate between
the mobile network and WiFi anytime they need cloudlet servicesMEC1 .
Another option that enables cloud computing at the edge is mobile edge
computing (MEC), which first announced in 2014 by the European
Telecommunications Standard Institute (ETSI). The MEC platform is
characterized as a system that provides IT and cloud-computing functionalities
within the Radio Access Network (RAN), positioned in close proximity to mobile
subscribers MEC3 . MEC is defined by the ETSI as having low latency, local
processing and storage resources, network awareness, and better service
quality supplied by mobile carriers. MEC makes mobile end-user services more
accessible by providing compute and storage resources. These resources are
intended to be deployed on mobile networks near end users. MEC resources can
be used in a variety of locations, including radio access networks (RANs),
indoor and outdoor base stations, and access points, which connect user
equipment to Mobile Network Operators’ (MNOs’) core networks MEC4 .
Within the radio access network, MEC provides cloud computing capabilities, as
depicted in Figure 3. Rather than routing mobile traffic from the core network
to end-users, MEC creates a direct link between users and the nearest edge
network empowered with cloud services. By deploying MEC at the base station,
it enhances computation, mitigates bottlenecks, and reduces the risk of system
failure MEC5 . MEC is implemented on a virtualized platform that takes
advantage of the most recent advancements in information-centric networks
(ICN), network functions virtualization (NFV), and software-defined networks
(SDN). A single-edge device with NFV at its core can provide computational
services to numerous mobile devices by producing several virtual machines
(VMs). These VMs can handle several tasks or perform diverse network
operations at the same time MEC3 .

Figure 3: Mobile Edge Computing Architecture

##  4 Architecture of Edge Computing-Based IoT

In the context of IoT, edge computing is primarily focused on its
implementation across various IoT scenarios, aiming to minimize decision-
making latency and network trafficadvantages2 . The edge computing-based IoT
architecture, as depicted in figure 4, consists of three distinct layers: IoT,
edge, and cloud, all of these are built on top of existing edge computing
reference designs. Our primary focus is to define the specific functions
allocated to each layer and explore the communication mechanisms established
among these layersadvantages2 ; architecture2 .

  1. 1.

IoT layer : The IoT layer encompasses a broad spectrum of devices and
equipment, such as smart cars, robots, smart machinery, handheld terminals,
instruments and meters, and other physical goods. These objects are tasked
with overseeing the functioning of services, activities, or equipment.
Furthermore, The IoT layer consists of actuators, sensors, controllers, and
gateways constructed expressly for IoT contexts, which enable the
administration of computational resources within IoT devices advantages2 .

  2. 2.

Edge layer : The main purpose of this layer is to receive, process, and send
streams of data from the Device Layer. It offers real-time services like
intelligent computing, security and privacy protection, and data analysis.
Based on the equipment’s ability to process data, Three further sub-layers are
separated from the Edge Layer: the near-edge layer, the mid-edge layer, and
the far-edge layer architecture2 .

    1. a)

Far-Edge Layer (Edge controller layer) : In this layer, data is collected from
the IoT layer by edge controllers and subsequently undergoes initial threshold
assessment or data filtering. After that, the edge layer or cloud layer
directs the control flow back to the IoT layer. After IoT device data has been
collected, it is preprocessed to determine thresholds or perform data
filtering. Consequently, the edge controllers in this layer must incorporate
algorithm libraries tailored to the environment’s configuration to
consistently improve the strategy’s efficiency. Additionally, these edge
controllers should convey the control flow back to the IoT layer via the
programmable logic controller (PLC) control or action control module after
receiving decisions from the edge controller layer or upper layers advantages2
.

    2. b)

Mid-Edge Layer (Edge gateway layer) : This layer is often made up of edge
gateways, which can connect to wired networks like industrial ethernet or
wireless networks like 5G to receive data from the edge controller layer.
Furthermore, the layer enables diverse processing capabilities and caches the
accumulated data. Moreover,The edge gateways in this layer play a crucial role
in shifting control from the upper layers, such as the cloud layer or edge
server layer, to the edge controller layer. Simultaneously, they monitor the
equipment in both the edge gateway layer and the edge controller
layeradvantages2 . The mid-edge layer has more storage and processing power
than the far-edge layer, which can only carry out basic threshold judgment or
data filtering. As a result, it can handle IoT layer data in a more thorough
manner architecture2 .

    3. c)

Near-Edge Layer (Edge server layer) : The edge server layer is equipped with
robust edge servers. Within this layer, advanced and crucial data processing
takes place. The edge servers leverage dedicated networks to gather data from
the edge gateway layer and generate directional decision instructions based on
this collected information. Additionally, platform administration and business
application management features are anticipated for the edge servers in the
edge server layer advantages2 .

  3. 3.

Cloud layer : This layer primarily focuses on in-depth data mining and seeks
to allocate resources optimally on a big scale, across a whole organization, a
region, or even the entire country. Data from the edge layer is sent to the
cloud layer through the use of the public network. Additionally, the edge
layer has the ability to receive feedback from cloud layer-provided business
applications, services, and model implementations advantages2 .

Figure 4: Edge Computing-based IoT Architecture

##  5 Advantages of Edge Computing-based IoT

Edge computing plays a vital role as a computing paradigm for IoT devices,
involving the utilization of cloud centers located near the IoT devices for
tasks such as filtering, preprocessing, and aggregating IoT data advantages2 .
The primary advantages of edge computing include:

  1. A)

Low Latency: The close proximity and low latency of edge computing provide a
solution to the response delay faced by User Equipment (UEs) while accessing
typical cloud services. Edge computing can drastically reduce response time,
which includes communication, processing, and propagation delays. Cloud
computing typically results in an end-to-end latency of more than 80ms (or
160ms for response delay), making it unsuitable for time-sensitive
applications such as remote surgery and virtual reality (VR), which require
near-instantaneous replies within 1ms. Edge computing, on the other hand,
benefits UEs by reducing total end-to-end delay and reaction delay due to
their close proximity to edge servers. This enhancement enables faster and
more efficient interactions for time-critical applications, meeting the
requirements for tactile speed and responsivenessadvantages .

  2. B)

Energy Saving : IoT devices often have limited energy supply due to their size
and intended usage scenarios, yet they are expected to conduct complicated
activities that are frequently power-intensive. It is difficult to design a
cost-effective system to properly power numerous distributed IoT devices since
regular battery charging or discharging is not always practicable or
possible.However, edge computing offers a solution by enabling IoT devices to
offload power-consuming computation tasks to edge servers. This not only
substantially lowers energy use but also enhances processing efficiency,
enabling billions of IoT devices to function optimally AI1 .

  3. C)

Security and Privacy : among the most important features of cloud platform
services is enhancing data security and privacy. Customers of these services
can obtain centralized data security solutions from these providers, but any
compromise of the centralizedly held data may have severe consequences. In
contrast, edge computing has the benefit of allowing local deployment of
customized security solutions. With this approach, less data transport is
necessary because the majority of processing can be done at the network edge.
As a result, there is a lower chance of data leakage during transmission, and
less data is stored on the cloud platform, lowering the security and privacy
risks advantages2 .

  4. D)

Location Awareness: Edge servers with location awareness can acquire and
handle data generated by User Equipment (UEs) based on their geographical
locations. As a result, personalized and location-specific services can be
offered to UEs, allowing edge servers to collect data directly from nearby
sources without sending it to the cloud. This allows for more efficient and
targeted service provisioning customized to specific UE needsadvantages .

  5. E)

Reduce Operational Expenses : Transmitting data directly to the cloud platform
incurs substantial operational expenses due to the demands for data
transmission, sufficient bandwidth, and low latency. Edge computing, on the
other hand, has the advantage of minimizing data uploading volume, resulting
in less data transmission, lower bandwidth consumption, and lower latency. As
a result, edge computing reduces operational costs when compared to direct
data transfer to the cloud platform advantages2 .

  6. F)

Network Context Awareness: Edge servers are able to understand the network
context through network context awareness. This includes User Equipment (UE)
information, such as allocated bandwidth and user locations, as well as real-
time network conditions, such as traffic load in a network cell and radio
access network specifics. With this invaluable knowledge, edge servers are
better equipped to adapt and accommodate to the various UEs and network
conditions, which leads to an optimum use of network resources. As a result,
edge servers can effectively handle a large amount of traffic, improving
network performance. Additionally, the availability of fine-grained
information enables the development of services that are specifically
customized to the needs of various traffic flows and individual users
advantages .

##  6 Enabling Edge computing-based IoT Technologies

Edge computing-based IoT can be implemented with the integration of several
enabling technologies. This section illustrates the relevant enabling
technologies by using artificial intelligence and lightweight virtualization
as examples.

###  6.1 Edge Intelligence

As the need for intelligent edge devices has grown, the industry has responded
with innovation and the adoption of intelligent edge architectures. These
innovative architectures support real-time, mission-critical applications that
work with a wide variety of devices. Any machine can qualify as intelligent if
it mimics human behaviors and skills including perception, attention,
thinking, and decision-making. Machine learning has gained a lot of traction
as a field of advancement in artificial intelligence. This has led to a surge
in the presence of intelligent devices, fueled primarily by advancements in
deep learning techniques edge_h1 .
Deep Neural Networks (DNNs) have received substantial attention in the Machine
Learning era because of their unrivaled performance across different use cases
such as computer vision, natural language processing, and image processing AI3
. Notably, deep learning has even outperformed human players in complex games
like Atari Games and the game of Go. The integration of deep learning and edge
computing holds promise for addressing challenges and opening up new
possibilities for applications. On one hand, edge computing applications
greatly benefit from the powerful processing capabilities of deep learning,
enabling them to handle intricate scenarios like video analytics and
transportation control. On the other hand, edge computing offers specialized
hardware foundations and platforms, such as the lightweight Nvidia Jetson TX2
development kit, to effectively support deep learning operations at the
edgeAI1 . Many techniques have been introduced to improve the performance of
deep learning when performed on edge computing devices, such as :

  1. A)

Model design: When machine learning researchers design DNN models for
resource-constrained devices, they commonly emphasize creating models with
fewer parameters in order to minimize memory usage and execution latency,
while still maintaining high accuracy. Several techniques are employed to
achieve this, including MobileNets, SSD, YOLO, and SqueezeNet. These methods
are aimed at optimizing DNN models for efficient performance on such
devicesAI2 .

  2. B)

Run-Time Optimizations: Depending on the particular requirements of the
application, suitable run-time optimizations can be employed to minimize the
quantity of samples that need to undergo processing. For instance, in object
detection applications, a high-resolution image can be divided into smaller
images (tiling), and a selection criterion can be used to choose images with
high activity regions. This approach allows the design of DNNs that can handle
smaller inputs, resulting in improved computational and latency efficiency.

  3. C)

Hardware:In the pursuit of accelerating deep learning inference, hardware
manufacturers are adopting various strategies. These include utilizing already
existing hardware like CPUs and GPUs, as well as developing custom
Application-Specific Integrated Circuits (ASICs) dedicated to deep learning
tasks, like Google’s Tensor Processing Unit (TPU). Additionally, there are
novel custom ASICs like ShiDianNao, which prioritize efficient memory access
to minimize latency and energy consumption. FPGA-based DNN accelerators also
show promise, as FPGAs can deliver fast computation while remaining
reconfigurableAI2 .

###  6.2 Lightweight Virtualization

Virtualization technologies are widely employed in cloud computing due to
their effective method of harnessing the cloud’s capabilities by partitioning
a physical host into smaller, more manageable virtual components. By
leveraging these technologies, cloud computing services become more user-
friendly and economically efficient. Hypervisors such as VirtualBox and VMware
are frequently used in cloud computing hardware virtualization. However, this
approach has limitations such as increased resource cost, longer startup
times, and larger attack surfaces. To solve these limitations, lightweight
virtualization technologies such as Unikernels and Containers have evolved and
are currently used in both cloud and edge computing. These lightweight
virtualization technologies offer fast deployment and high efficiency,
effectively overcoming the limitations posed by traditional hypervisor-based
virtualizationcontainer1 .Considering that the computational capabilities of
edge computing devices are less potent than data centers, the adoption of
emerging lightweight virtualization technologies offers numerous advantages.
These benefits encompass swift initialization, minimal overhead, high instance
density, and commendable energy efficiency, making them well-suited for the
edge computing environment continer2 .
Lightweight virtualization technology is critical in edge computing because it
allows the deployment of resource management, orchestration, and isolation
services without the need to account for different hardware configurations.
This technology has brought about a significant transformation in software
development and deployment practices. Container-based virtualization can be
regarded as a lightweight alternative to the traditional Hypervisor-based
virtualization container1 . Container-based virtualization offers a different
level of abstraction in terms of isolation and virtualization when compared to
hypervisors, as illustrated in figure5. Hypervisors virtualize hardware and
device drivers, resulting in increased overhead. Containers, on the other
hand, isolate processes at the OS level continer2 . Containers allow
independent applications to be isolated with their own virtual network
interfaces, process spaces, and file systems because they share the same host
machine’s operating system kernel. Containers allow for a higher number of
virtualized instances with lower image volumes, all executing on a single
machine thanks to the shared kernel feature container1 .

Figure 5:  Virtualization vs Containerization

##  7 Edge Computing in IoT-Based Intelligent Systems:Case Studies

With the rise of intelligent systems, edge computing offers the most efficient
computing and storage solutions for devices with limited computational
capabilities. This section delves into the applications of edge computing in
IoT-based intelligent systems, including healthcare, manufacturing,
agriculture, and transportation. We chose these four case studies because they
have a substantial impact on improving human life.

###  7.1 Edge Computing in IoT-Based Healthcare

The term ”geriatric care” refers to a branch of healthcare that emphasizes
meeting the special mental, physical, and social needs of the aged. Geriatric
care, which is specifically designed to meet the unique demands of elderly
individuals, seeks to enhance their general well-being and health while
successfully treating age-related illnesses and diseases. Its ultimate goal is
to give them the means to maintain their independence, preserve their well-
being, and enjoy the greatest degree of comfort as they age edge_h2 . In the
area of ”Geriatric Care,” the danger of falling is regarded as a crucial
concern. Unfortunately, many older people fall and unfortunately pass away
because they lack good balance. While the fall itself may be the primary
cause, the severity of the outcome stems from the inability to recover,
leading to deteriorating physical and cognitive health. Numerous studies back
up the idea that elderly people can potentially avoid physical consequences
like brain injuries if immediate aid is given within seven minutes of a fall.
As a result, the death and disease rates among the aging population would both
dramatically decline. In edge_h1  the authors suggest an intelligent edge-
monitoring system that utilizes cameras to detect instances of falling. Real-
time video analysis is essential for continuously capturing photos,
classifying them as either normal (sleeping) or abnormal (falling)
circumstances, and instantly sounding an alarm in emergency scenarios. Due to
the significant amount of data involved, relying solely on cloud processing
would be impractical because of the resulting delays. As a result, cameras
serve as IoT devices, gathering data and sending it to nearby edge computing
equipment for processing locally. In this approach, the Edge computing server
is equipped with Deep Learning models that have already undergone pre-training
and are specifically created to detect falls with high accuracy and low
latency. They successfully reduce transmission delays by maintaining the
computing process’ independence from the cloud. This approach not only allows
them to generate valuable insights on-site, reducing response time and latency
but also addresses privacy concerns by handling sensitive and personal data at
the edge.

###  7.2 Edge Computing in IoT-Based Manufacturing

Regarding intelligent manufacturing, the growing number of terminal network
devices has presented new issues in terms of data center operation and
maintenance, scalability, and dependability. To tackle these challenges, edge
computing has advanced, which moves computation from centralized data centers
to the network’s edge. This approach enables intelligent services to be
deployed near the manufacturing units, meeting essential demands such as
highly responsive cloud services, data analytics via edge nodes, and a
privacy-policy plan manufacturing1 . In manufactoring2 , the authors
introduced a cutting-edge visual sorting method created especially for
adaptable manufacturing systems. The system utilizes a visual sorting approach
based on CNNs. To support this system, they developed a cloud-edge computing
platform, which makes it easier to quickly compute and continuously maintain
and improve services.

###  7.3 Edge Computing in IoT-Based Agricultural

The IoT is typically applied in agricultural development through a monitoring
network comprised of a considerable number of sensor nodes, As a result,
agriculture increasingly moves away from a production model that is centered
on humans and toward one that is centered on information and software
agriculture . Concerning the notion of edge computing in the context of
Agricultural IoT, numerous researchers have made contributions from diverse
perspectives. The authors of agriculture2  developed a system that meets the
demanding needs of Precision Agriculture (PA) by integrating automation, IoT
technologies, edge and cloud computing through virtualization . Three
essential layers make up a multi-tier platform that has been developed: (1) a
local layer of Cyber-Physical Systems (CPS) that is connected to agricultural
greenhouses. (2)The authors suggest a new edge computing architecture in which
control modules are placed on virtualized nodes near the access network. (3) A
cloud section outfitted with powerful computing and data analytics tools to
help farmers make smart crop management decisions. The entire system was
successfully tested in a real greenhouse located in southeast Spain. Through
specialized software that was accessible to the end farmers via the platform,
this innovation made it possible to control a closed hydroponic system in
real-time. To validate the effectiveness of the architecture, two tomato crop
cycles were conducted. The results showed remarkable benefits compared to a
traditional open crop approach. Significant water savings of over 30% were
achieved, which is particularly crucial in their semi-arid region.
Additionally, certain nutrients saw improvements of up to 80% thanks to the
system’s efficient management.

###  7.4 Edge Computing in IoT-Based Transportation

The Internet of Vehicles (IoV), a new paradigm introduced by the IoT, employs
edge computing to offer ground-breaking applications for transportation
systems. Using sensors and geofencing technologies, IoV connects various cars
with Roadside Units (RSUs) and other vehicles in an Intelligent Transportation
System (ITS). Edge cloudlets are used by IoV for service provisioning and
orchestration. Currently, substantial research on smart vehicles is being
undertaken in both academic and industrial domains IoV1 . Traffic flow
detection plays a vital role in ITS. By obtaining real-time urban road traffic
flow data, ITS can intelligently guide measures to alleviate traffic
congestion and reduce environmental pollution. InIoV2 , the YOLOv3 (You Only
Look Once) model was used by the authors to create a vehicle detecting method.
The YOLOv3 model was trained on an extensive dataset of traffic data and
subsequently pruned to achieve optimal performance on edge devices.
Additionally, by retraining the feature extractor, they improved the DeepSORT
(Deep Simple Online and Realtime Tracking) algorithm, enabling multi-object
vehicle tracking. Through the integration of vehicle detection and tracking
algorithms, they developed a counter for real-time vehicle tracking capable of
accurately detecting traffic flow. Finally, the Jetson TX2 edge device
platform received and implemented the vehicle detection network and multi-
object tracking network.

##  8 Challenges and Future research Directions

In the preceding section, we outlined four possible uses of edge computing in
IoT-based systems. To achieve the full potential of IoT, we emphasize the need
for seamless collaboration between IoT devices and edge computing. Now, in
this section, we will summarize some of the challenges faced in implementing
edge computing in IoT-based systems and propose potential solutions and
research opportunities. These include resource allocation, heterogeneity,
privacy and security, and microservices.

  * •

Resource Allocation  : Edge devices play an essential role in enabling
latency-critical services. The majority of end IoT devices often experience
resource limitations; for example, local CPU computation capabilities and
battery capacity are frequently constrained. Some workloads can be offloaded
to more powerful edge devices to bypass these constraints and meet the
performance requirements of applications. Edge computing improves IoT device
capabilities, allowing them to handle more resource-intensive applications.
However, Practically speaking, edge computing devices have a finite amount of
processing power. As a result, it is unable to handle the massive computing
tasks generated by all of the end devices in its service region. As a result,
the allocation of resources becomes highly crucial in such environments.
Traditional optimization approaches like convex optimization and Lyapunov
optimization have been used to tackle the computation offloading problem and
find the best scheme. However, these methods are limited when it comes to
making optimal decisions in dynamic environments. In contrast, modern resource
allocation algorithms, powered by artificial intelligence and deep learning,
such as deep reinforcement learning, offer more effective solutions for
achieving optimal allocation.

  * •

Heterogeneity  : The existence of numerous computing technologies in edge
computing, such as distinct hardware architectures and operating systems, has
made it difficult to develop a viable approach that can be employed with ease
in diverse scenarios. This problem can be solved by developing a programming
model for edge nodes using software-based techniques, which enables workloads
to be executed effectively on numerous hardware configurations simultaneously.

  * •

Privacy and Security :At the network’s edge, the primary services that require
assurance are the protection of usage privacy and data security. When IoT is
used in a home, the usage data collected can be used to infer important
private information. For instance, examining the patterns of energy or water
use can show whether the home is vacant or occupied. This presents a
significant challenge in providing services while safeguarding privacy. Having
a reliable architecture is crucial before users can feel confident in
embracing new technologies. ”Privacy by design” can be considered as a
reliable approach to enhance security in edge computing. It involves
incorporating privacy features directly into the design, taking preventive
measures instead of just reacting after privacy breaches, and ensuring data
privacy throughout its entire lifecycle.

  * •

Microservices : Recently, both edge and cloud services have been changing from
monolithic, standalone systems to loosely coupled, independent microservices.
When running complex computations such as deep learning, there are several
software requirements, and it’s important to find a way to separate different
deep learning services when using shared resources. Currently, the
microservice framework, which can be used to host complex services on the
edge, is still developing and in its early stages. However, it shows great
potential for efficiently introducing services in the future.

##  9 Conclusion

As IoT continues to grow, edge computing is increasingly regarded as a
promising and viable solution to address the complexities of managing numerous
sensors and devices, along with the demands for resources they require. Edge
computing, in contrast to standard cloud computing, involves placing data
processing and storage to the edge of the network, bringing them closer to the
end users. Thus, by dispersing compute nodes across the network, it is
possible to reduce message exchange latency and relieve the computational load
on the centralized data center. In conclusion, our chapter has explored the
computing paradigms for IoT, edge computing paradigms like cloudlet and MEC,
the architecture of edge computing-based IoT, the benefits it offers, and the
enabling technologies such as artificial intelligence and lightweight
virtualization. Additionally, it presented case studies showcasing how edge
computing is applied in intelligent systems based on IoT and highlighted the
issues with current research and suggested future directions for further
exploration in this field.

## References

  * (1) Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., & Jue, J. P. (2019). All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture. https://doi.org/10.1016/j.sysarc.2019.02.009
  * (2) Elazhary, H. (2019). Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions. Journal of network and computer applications, 128, 105-140.
  * (3) Satyanarayanan, M., Bahl, P., Caceres, R., & Davies, N. (2009). The case for vm-based cloudlets in mobile computing. IEEE pervasive Computing, 8(4), 14-23.
  * (4) Alessio, B., De Donato, W., Persico, V., & Pescapé, A. (2014). On the integration of cloud computing and internet of things. Proc. Future internet of things and cloud (FiCloud), 23-30.
  * (5) Naveen, S., Kounte, M. R., & Ahmed, M. R. (2021). Low latency deep learning inference model for distributed intelligent IoT edge clusters. IEEE Access, 9, 160607-160621.
  * (6) Edge Computing Market Size, Share & Trends Analysis Report By Component (Hardware, Software, Services, Edge-managed Platforms), By Application, By Industry Vertical, By Region, And Segment Forecasts, 2023 - 2030. Accessed: July 15, 2023. [Online]. Available: https://www.grandviewresearch.com/industry-analysis/edge-computing-market
  * (7) Paulauskaite-Taraseviciene, A., Siaulys, J., Sutiene, K., Petravicius, T., Navickas, S., Oliandra, M., … & Balciunas, J. (2023, April). Geriatric Care Management System Powered by the IoT and Computer Vision Techniques. In Healthcare (Vol. 11, No. 8, p. 1152). MDPI.
  * (8) Khan, W. Z., Ahmed, E., Hakak, S., Yaqoob, I., & Ahmed, A. (2019). Edge computing: A survey. Future Generation Computer Systems, 97, 219 235.
  * (9) Yu, W., Liang, F., He, X., Hatcher, W. G., Lu, C., Lin, J., & Yang, X. (2017). A survey on the edge computing for the Internet of Things. IEEE access, 6, 6900-6919.
  * (10) Lorenzo, B., Garcia-Rois, J., Li, X., Gonzalez-Castano, J., & Fang, Y. (2018). A robust dynamic edge network architecture for the internet of things. IEEE network, 32(1), 8-15.
  * (11) Delfin, S., Sivasanker, N. P., Raj, N., & Anand, A. (2019, March). Fog computing: A new era of cloud computing. In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1106-1111). IEEE.
  * (12) Atlam, H. F., Walters, R. J., & Wills, G. B. (2018). Fog computing and the Internet of Things: A review. Big Data and Cognitive Computing, 2(2), 10.
  * (13) Alwakeel, A. M. (2021). An overview of fog computing and edge computing security and privacy issues. Sensors, 21(24), 8226.
  * (14) Zhang, X., Cao, Z., & Dong, W. (2020). Overview of edge computing in the agricultural internet of things: key technologies, applications, challenges. Ieee Access, 8, 141748-141761.
  * (15) Zamora-Izquierdo, M. A., Santa, J., Martínez, J. A., Martínez, V., & Skarmeta, A. F. (2019). Smart farming IoT platform based on edge and cloud computing. Biosystems engineering, 177, 4-17.
  * (16) Mach, P., & Becvar, Z. (2017). Mobile edge computing: A survey on architecture and computation offloading. IEEE communications surveys & tutorials, 19(3), 1628-1656.
  * (17) Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017). Mobile edge computing: Survey and research outlook. arXiv preprint arXiv:1701.01090, 1-37.
  * (18) Haibeh, L. A., Yagoub, M. C., & Jarray, A. (2022). A survey on mobile edge computing infrastructure: Design, resource management, and optimization approaches. IEEE Access, 10, 27591-27610.
  * (19) Abbas, N., Zhang, Y., Taherkordi, A., & Skeie, T. (2017). Mobile edge computing: A survey. IEEE Internet of Things Journal, 5(1), 450-465.
  * (20) Chen, S., & Zhou, M. (2021). Evolving container to unikernel for edge computing and applications in process industry. Processes, 9(2), 351.
  * (21) Morabito, R., & Beijar, N. (2016, June). Enabling data processing at the network edge through lightweight virtualization technologies. In 2016 IEEE International Conference on Sensing, Communication and Networking (SECON Workshops) (pp. 1-6). IEEE.
  * (22) Chen, B., Wan, J., Celesti, A., Li, D., Abbas, H., & Zhang, Q. (2018). Edge computing in IoT-based manufacturing. IEEE Communications Magazine, 56(9), 103-109.
  * (23) Rafique, W., Qi, L., Yaqoob, I., Imran, M., Rasool, R. U., & Dou, W. (2020). Complementing IoT services through software defined networking and edge computing: A comprehensive survey. IEEE Communications Surveys & Tutorials, 22(3), 1761-1804.
  * (24) Chen, C., Liu, B., Wan, S., Qiao, P., & Pei, Q. (2020). An edge traffic flow detection scheme based on deeplearning in an intelligent transportation system. IEEE Transactions on Intelligent Transportation Systems, 22(3), 1840-1852.
  * (25) Wang, Y., Hong, K., Zou, J., Peng, T., & Yang, H. (2019). A CNN-based visual sorting system with cloud-edge computing for flexible manufacturing systems. IEEE Transactions on Industrial Informatics, 16(7), 4726-4735.
  * (26) Hassan, N., Yau, K. L. A., & Wu, C. (2019). Edge computing in 5G: A review. IEEE Access, 7, 127276-127289.
  * (27) Fazeldehkordi, E., & Grønli, T. M. (2022). A survey of security architectures for edge computing-based IoT. IoT, 3(3), 332-365.
  * (28) Sha, K., Yang, T. A., Wei, W., & Davari, S. (2020). A survey of edge computing-based designs for IoT security. Digital Communications and Networks, 6(2), 195-202
  * (29) Qiu, T., Chi, J., Zhou, X., Ning, Z., Atiquzzaman, M., & Wu, D. O. (2020). Edge computing in industrial internet of things: Architecture, advances and challenges. IEEE Communications Surveys & Tutorials, 22(4),2462-2488.
  * (30) Wang, F., Zhang, M., Wang, X., Ma, X., & Liu, J. (2020). Deep learning for edge computing applications: A state-of-the-art survey. IEEE Access, 8,58322-58336.
  * (31) Chen, J., & Ran, X. (2019). Deep learning with edge computing: A review. Proceedings of the IEEE, 107(8), 1655-1674.
  * (32) Marchisio, A., Hanif, M. A., Khalid, F., Plastiras, G., Kyrkou, C., Theocharides, T., & Shafique, M. (2019, July). Deep learning for edge computing: Current trends, cross-layer optimizations, and open research challenges. In 2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI) (pp. 553-559). IEEE.
  * (33) Idrees, A. K., Alhussaini, R., & Salman, M. A. (2020). Energy-efficient two-layer data transmission reduction protocol in periodic sensor networks of IoTs. Personal and Ubiquitous Computing 27 (2), 139–158.
  * (34) Shawqi Jaber, A., & Kadhum Idrees, A. (2020). Adaptive rate energy‐saving data collecting technique for health monitoring in wireless body sensor networks. International Journal of Communication Systems, 33(17), e4589.
  * (35) Alhussaini, R., Idrees, A. K., & Salman, M. A. (2018, September). Data transmission protocol for reducing the energy consumption in wireless sensor networks. In International conference on new trends in information and communications technology applications (pp. 35-49). Cham: Springer International Publishing.
  * (36) Idrees, A. K., Abou Jaoude, C., & Al-Qurabat, A. K. M. (2020, October). Data reduction and cleaning approach for energy-saving in wireless sensors networks of IoT. In 2020 16th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (pp. 1-6). IEEE.
  * (37) Idrees, A. K., & Khlief, M. S. (2023) Lossless EEG Data Compression using Clustering and Encoding for Fog Computing based IoMT Networks, International Journal of Computer Applications in Technology 72 (1), 77–83.
  * (38) Idrees, A. K., & Khlief, M. S. (2023). A new lossless electroencephalogram compression technique for fog computing‐based IoHT networks. International Journal of Communication Systems, e5572, https://doi.org/10.1002/dac.5572.
  * (39) Idrees, A. K., & Khlief, M. S. (2023). Efficient compression technique for reducing transmitted EEG data without loss in IoMT networks based on fog computing. The Journal of Supercomputing, 79(8), 9047-9072.
  * (40) Idrees, A. K., Ali–Yahiya, T., Idrees, S., & Couturier, R. (2022, September). DaTOS: Data Transmission Optimization Scheme in Tactile Internet-based Fog Computing Applications. In 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) (pp. 01-06). IEEE.
  * (41) Idrees, A. K., Idrees, S. K., Couturier, R., & Ali-Yahiya, T. (2022). An edge-fog computing-enabled lossless EEG data compression with epileptic seizure detection in IoMT networks. IEEE Internet of Things Journal, 9(15), 13327-13337.
  * (42) Idrees, A. K., & Jawad, L. W. (2023). Energy-efficient Data Processing Protocol in edge-based IoT networks. Annals of Telecommunications, 1-16, https://doi.org/10.1007/s12243-023-00957-8.