edge computing seminar report.pdf

VISVESVARAYA TECHNOLOGICAL UNIVERSITY
BELAGAVI, KARNATAKA - 590018
A SEMINAR REPORT ON
“EDGE COMPUTING”
In Partial Fulfillment of the Requirement for the Award of the Degree of Bachelor of
Engineering in Computer Science Engineering
SUBMITTED BY
BASAVAKUMAR PATIL 2KL15EC014
ABHISHEK YALIGAR 2KL15EC002
UNDER THE GUIDANCE OF
Prof. H P RAJINI
DEPARTMENT OF ELECTRONICS & COMMUNICATION
ENGINEERING
“Accredited by NBA”
KLE Dr. M. S. SHESHGIRI COLLEGE OF
ENGINEERING AND TECHNOLOGY BELAGAVI –
590008
2018-2019

KLE Dr. M S SHESHAGIRI
COLLEGE OF ENGINEERING & TECHNOLOGY,
BELAGAVI -590008
Department of Computer Science Engineering
Certificate
This is to certify that the seminar report entitled “EDGE COMPUTING” has been
carried out by NITIN PRADHAN, USN 2KL15CS043 is a bonafide student of KLE Dr.
M S Sheshagiri College of Engineering & Technology Belgaum, in partial fulfillment for
the award of Bachelor of Engineering in Computer Science branch of the Visvesvaraya
Technological University Belagavi, during the academic year 2018-19. It is certified that
all corrections/suggestions indicated have been incorporated in the report. The seminar
report has been approved as it satisfies the academic requirements in respect of seminar
report prescribed for the said degree.
GUIDE HOD PRINCIPAL
Prof.Gambhir Halse Prof. B. A. Patil Dr. Basavaraj Katageri

DECLERATION
I, Mr. Basavakumar Patil, hereby declare that the seminar work entitled “Edge
Computing” has been independently carried out by me, under the guidance of Prof.
H P Rajini Department of Computer Science & Engineering, KLE Dr. M. S.
Sheshgiri College of Engineering and Technology, Belagavi in partial fulfillment of
requirement of award of degree of Bachelor of Engineering in Computer Science
Engineering at Visversvaraya Technological University, Belagavi.
I further declare that no part of it has been submitted for the award, degree or
diploma to any university or Institute previously.
Place: Belagavi Nitin Pradhan
Date: 25/04/2019 2KL15CS043
i

ACKNOWLEDGEMENT
Before I turn towards the seminar, I would like to add a few heartfelt words for the
people who have been part of this seminar by supporting and encouraging me.
In particular, I would like to take this opportunity to express my honor, respect, deep
gratitude and genuine regards to my Guide Gambhir Halse, Professor, Computer
Science Engineering Department for giving me all guidance required for my topic,
apart from being a constant source of inspiration and motivation.
I am grateful to Prof. B. A. PATIL, Head of Department, Computer Science
Engineering Department, for providing the necessary help and encouragement
whenever needed.
I take this opportunity to thank Dr. BASAVARAJ KATEGERI, Principal of K.L.E’S
College of Engineering and Technology, Belgaum for providing a healthy
environment in our college, which helped me in concentrating on seminar.
I also extend my sincere thanks to all the faculty member of Computer Science
engineering department for their support and encouragement.
I owe special thanks to my parents for their moral support and warm wishes, and
finally I would express my appreciation to all friends for their support which helped
me to complete my seminar successfully.
NITIN PRADHAN
2KL15CS043
ii

ABSTRACT
The proliferation of Internet of Things (IoT) and the success of rich cloud services have
pushed the horizon of a new computing paradigm, edge computing, which calls for
processing the data at the edge of the network.
Edge computing has the potential to address the concerns of response time
requirement, battery life constraint, bandwidth cost saving, as well as data safety and
privacy. In this paper, we introduce the definition of edge computing, followed by several
case studies, ranging from cloud offloading to smart home and city, as well as
collaborative edge to materialize the concept of edge computing.
Finally, we present several challenges and opportunities in the field of edge
computing, and hope this paper will gain attention from the community and inspire more
research in this direction.
iii

Table of content
Declaration i
Acknowledgement ii
Abstract iii
Chapter No. Contents Page No.
1. Introduction 1
2. Literature Survey 4
3. Scope and Objective 7
3.1Scope 7
3.2Objective 7
4. Design and Implementation 9
4.1Taxonomy of IoT-Based Edge Computing 9
4.2Edge Computing Architecture and Security 11
4.3Case studies on Edge Computing 13
4.4Challenges and Opportunities 16
5. Conclusions 19
6. References 20

LIST OF FIGURES
Figure No. Description Page No
1.1 Cloud computing paradigm 2
1.2.1 Typical Edge computing architecture 2
1.2.2 Edge Computing paradigm 3
4.1 Taxonomy of IoT based Edge environment 10
4.2.1 Layered model for cloud edge based IoT 11
4.2.2 Major tasks of edge computing 12
4.3.1 Example of smart car parking system 13
4.3.2 Example of content delivery network 15
ABBREVIATION
IoT- INTERNET OF THINGS

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CHAPTER 1
INTRODUCTION
Internet of Things (IoT) was first introduced to the community in 1999 for supply chain
management, and then the concept of “making a computer sense information without the
aid of human intervention” was widely adapted to other fields such as healthcare, home,
environment, and transports.
Now with IoT, we will arrive in the post-cloud era, where there will be a large
quality of data generator by things that are immersed in our daily life, and a lot of
applications will also be deployed at the edge to consume these data. By 2019, data
produced by people, machines, and things will reach 500 zettabytes, as estimated by
Cisco Global Cloud Index, however, the global data centre IP traffic will only reach 10.4
zettabytes by that time. By 2019, 45% of IoT-created data will be stored, processed,
analysed, and acted be 50 billion things connected to the Internet by 2020, as predicted by
Cisco Internet Business Solutions Group.
Some IoT applications might require very short response time, some might involve
private data, and some might produce a large quantity of data which could be a heavy load for
networks. Cloud computing is not efficient enough to support these applications. With the
push from cloud services and pull from IoT, we envision that the edge of the network is
changing from data consumer to data producer as well as data consumer. In this paper, we
attempt to contribute the concept of edge computing. We start from the analysis of why we
need edge computing, then we give our definition and vision of edge computing. Several case
studies like cloud offloading, smart home and city as well as collaborative edge are
introduced to further explain edge computing in a detailed manner, followed by some
challenges and opportunities in programmability, naming, data abstraction, service
management, privacy and security, as well as optimization metrics that are worth future.
1.1 WHAT IS EDGE COMPUTING
Data is increasingly produced at the edge of the network, therefore, it would be more efficient
to also process the data at the edge of the network. Previous work such as micro datacentres
cloudlet, and fog computing has been introduced to the community because cloud computing
I not always efficient for data processing when the data is produced at the
DEPARTEMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING Page 1

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edge of the network. In this section, we list some reasons why edge computing is more
efficient than cloud computing for some computing services, then we give our definition
and understanding of edge computing.
Cloud computing paradigm
1.2 WHY DO WE NEED EDGE COMPUTING?
Edge computing enables data-stream acceleration, including real-time data processing
without latency. It allows smart applications and devices to respond to data almost
instantaneously, as its being created, eliminating lag time. This is critical for technologies
such as self-driving cars, and has equally important benefits for business.
Edge computing is expected act as a strategic brain behind IoT. Identifying the role of
edge computing in IoT is the main research issue at present. Edge computing is utilized to
reduce the amount of data sent to the cloud and decrease service access latency. Figure
illustrates the complimentary role of edge and cloud computing in the IoT environment.

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EDGE COMPUTING
CHAPTER 2
LITERATURE SURVEY
1. Edge Computing: Vision and Challenges (2016)
The proliferation of Internet of Things (IoT) and the success of rich cloud services
have pushed the horizon of a new computing paradigm, edge computing, which
calls for processing the data at the edge of the network. Edge computing has the
potential to address the concerns of response time requirement, battery life
constraint, bandwidth cost saving, as well as data safety and privacy. In this paper,
we introduce the definition of edge computing, followed by several case studies,
ranging from cloud offloading to smart home and city, as well as collaborative
edge to materialize the concept of edge computing. Finally, we present several
challenges and opportunities in the field of edge computing, and hope this paper
will gain attention from the community and inspire more research in this direction.
2. Secure Edge Computing in IoT Systems: Review and Case Studies (2018)
The architectures for efficient and secure network system designs, such as Internet of
Things (IoT) and big data analytics, are growing at a faster pace than ever before.
Edge computing for an IoT system is data processing that is done at or near the
collectors of data in an IoT system. In this paper, we aim to briefly review the
concepts, features, security, applications of IoT empowered edge computing as well
as its security aspects in our data-driven world. We focus on clarifying different
aspects that should be taken into consideration while creating a scalable, reliable,
secure and distributed edge computing system. We also summarize the basic ideas
regarding security risk mitigation techniques. Then, we explore the presented
challenges and opportunities in the field of edge computing. Finally, we review two
case studies, smart parking and content delivery network (CDN), and analyse
different methods in which IoT systems can be used to carry out daily tasks.
3. The Role of Edge Computing in Internet of Things (2018)
Remarkable advancements in embedded systems-on-a-chip have significantly
increased the number of commercial devices that possess sufficient resources to run
full-fledged operating systems. This change has extended the potential of the IoT.

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Many early IoT devices could only collect and send data for analysis. However, the
increasing computing capacity of today’s devices allow them to perform complex
computations on-site, resulting in edge computing. Edge computing extends cloud
computing capabilities by bringing services close to the edge of a network and thus
supports a new variety of services and applications. In this work, we investigate,
highlight, and report on recent advances in edge computing technologies with respect
to measuring their impact on IoT. We establish a taxonomy of edge computing by
classifying and categorizing existing literature, and by doing so, we reveal the salient
and supportive features of different edge computing paradigms for IoT. Moreover, we
present the key requirements for the successful deployment of edge computing in IoT
and discuss a few indispensable scenarios of edge computing in IoT. Several open
research challenges are also outlined.
4. Internet of Things: A Survey on Enabling Technologies, Protocols, and
Applications (2015)
Internet of Things (IoT) with emphasis on enabling technologies, protocols, and
application issues. The IoT is enabled by the latest developments in RFID, smart
sensors, communication technologies, and Internet protocols. The basic premise is
to have smart sensors collaborate directly without human involvement to deliver a
new class of applications. The current revolution in Internet, mobile, and machine-
to-machine (M2M) technologies can be seen as the first phase of the IoT. In the
coming years, the IoT is expected to bridge diverse technologies to enable new
applications by connecting physical objects together in support of intelligent
decision making.
This paper starts by providing a horizontal overview of the IoT. Then, we give
an overview of some technical details that pertain to the IoT enabling technologies,
protocols, and applications. Compared to other survey papers in the field, our
objective is to provide a more thorough summary of the most relevant protocols and
application issues to enable researchers and application developers to get up to speed
quickly on how the different protocols fit together to deliver desired functionalities
without having to go through RFCs and the standards specifications.
We also provide an overview of some of the key IoT challenges presented in
the recent literature and provide a summary of related research work. Moreover, we
explore the relation between the IoT and other emerging technologies including big
data analytics and cloud and fog computing. We also present the need for better

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horizontal integration among IoT services. Finally, we present detailed service
use-cases to illustrate how the different protocols presented in the paper fit
together to deliver desired IoT services.
5. Efficient Next Generation Emergency Communications over Multi-
Access Edge Computing (2017)
This becomes even more evident in light of the forthcoming 5G networks, which
are envisioned to support an amalgam of diverse applications and services with
heterogeneous performance requirements, including mission-critical IoT
communication, massive machine-type communication, and gigabit mobile
connectivity. Emergency service operators face an enormous challenge in order to
synchronize their model of operation with the 5G paradigm. This article studies
the challenges that next generation emergency services need to overcome in order
to fulfill the requirements for rich-content, real-time, location-specific
communications. The concept for next generation emergency communications as
described in the project EMYNOS is presented, along with a vision of how this
concept can fulfill the 5G requirements for ultra-reliable and ultra-low-latency
emergency communications.
6. Mobile Edge Computing Potential in Making Cities Smarter (2017)
This article proposes an approach to enhance users’ experience of video streaming
in the context of smart cities. The proposed approach relies on the concept of
MEC as a key factor in enhancing QoS. It sustains QoS by ensuring that
applications/services follow the mobility of users, realizing the “Follow Me Edge”
concept. The proposed scheme enforces an autonomic creation of MEC services to
allow anywhere anytime data access with optimum QoE and reduced latency.
Considering its application in smart city scenarios, the proposed scheme
represents an important solution for reducing core network traffic and ensuring
ultra-short latency through a smart MEC architecture capable of achieving the 1
ms latency dream for the upcoming 5G mobile systems.

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7. Edge Analytics in the Internet of Things (2015)
High-data-rate sensors are becoming ubiquitous in the Internet of Things. GigaSight is
an Internet-scale repository of crowd-sourced video content that enforces privacy
preferences and access controls. The architecture is a federated system of VM-based
cloudlets that perform video analytics at the edge of the Internet.

EDGE COMPUTING
CHAPTER 3
SCOPE AND OBJECTIVE
3.1 Scope
Push from Cloud Services: Putting all the computing tasks on the cloud has been proved
to be an efficient way for data processing since the computing power on the cloud
outclasses the capability of the things at the edge. However, compared to the fast
developing data processing speed, the bandwidth of the network has come to a standstill.
With the growing quantity of data generated at the edge, speed of data transportation is
becoming the bottleneck for the cloud based computing paradigm.
Pull From IoT: Almost all kinds of electrical devices will become part of IoT, and they
will play the role of data producers as well as consumers, such as air quality sensors, LED
bars, streetlights and even an Internet-connected microwave oven. It is safe to infer that
the number of things at the edge of the network will develop to more than billions in a
few years. Thus, raw data produced by them will be enormous, making conventional
cloud computing not efficient enough to handle all these data. This means most of the
data produced by IoT will never be transmitted to the cloud, instead it will be consumed at
the edge of the network.
Change from Data Consumer to Producer: In the cloud computing paradigm, the end
devices at the edge usually play as data consumer, for example, watching a YouTube
video on your smart phone. However, people are also producing data nowadays from their
mobile devices. The change from data consumer to data producer/consumer requires more
function placement at the edge.
3.1 Objective
The different objectives for edge computing in the context of IoT are as follows:
Latency Minimization: High latency has become a crucial problem for IoT-based smart
applications. An alternative platform, such as edge computing, that can guarantee timely
delivery of service is required to fulfil the quality of service (QoS) requirements of delay-
sensitive IoT applications (e.g., smart transportation and online gaming).

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Network Management: A number of phenomena, such as inadequate virtualization
support, lack of seamless connectivity, and inefficient congestion control, degrade the
overall network performance. Therefore, efficient usage of network resources in edge
computing is vital for IoT.
Cost Optimization: The use of an adequate platform for enabling edge computing
necessitates extensive infrastructure deployment that involves substantial upfront
investment and operational expenses. Most of these expenses are related to network node
placement, which requires deliberate planning and optimization to minimize the overall
cost. Deployment of an optimal number of nodes at appropriate positions can significantly
reduce capital, and optimal arrangement of edge nodes can minimize operational costs.
Energy Management: Energy management is also an important objective of IoT-based
edge computing. Subscribers need to have strict control over power management. Energy-
efficient IoT devices and applications are desirable in edge computing. According to a
study, one trillion IoT nodes need sensing platforms that support various applications using
power harvesting to ensure scalability, reduce costs, and avoid frequent battery
replacement.
Data Management: The large number of IoT devices at present are expected to generate
large amounts of data that need to be managed in a timely manner. Efficient and effective
data management mechanisms are desirable in edge computing. Transmission and
aggregation of IoT-generated data are important concerns in data management.
Resource Management: Optimal management of computational resources is crucial in
obtaining service-level objectives. Appropriate resource management includes
coordination of resources, estimation of available resources, and proper allocation of
workload.

EDGE COMPUTING
CHAPTER 4
DESIGN AND IMPLEMENTATION
4.1 Taxonomy of IoT-Based Edge Computing
Taxonomy of IoT-based edge computing that considers particular features, such as
wireless network technologies, computing nodes, computing paradigms, service level
objectives, major enablers, data types, applications, and attributes.
Network Technologies IoT devices send collected data to a locally available edge server
for processing. These devices communicate with edge computing platforms through
either wireless networking technologies, such as WiFi and cellular networking (e.g., 3G,
4G, and 5G), or wired technologies, such as Ethernet. These network technologies vary in
terms of data rate, transmission range, and number ofsupported devices. Wireless
networks provide flexibility and mobility to users who execute their applications on the
edge server. However, wireless network technologies are not as reliable as wired
technologies.
Computing Nodes IoT devices have limited processing capabilities, which make them
unsuitable for computation-intensive tasks. However, resource-constrained IoT devices can
augment their capabilities by leveraging the resources of edge servers. The edge computing
paradigm relies on different computational devices to provide services to IoT users. These
computational devices are the core element of IoT-based edge computing. Computing nodes
include servers, base stations (BS), routers, and vehicles that can provide resources and
various services to IoT devices. The use of these devices is specific to the computing
paradigm.
Computing Paradigms Various computing paradigms are used in IoT to provide different
services depending on diverse application requirements. These paradigms can be categorized
into cloud computing, edge computing (i.e., MEC, fog, and cloudlet), mobile ad hoc cloud
(MAC), and hybrid platforms. Cloud computing is a centralized computing infrastructure that
aims to provide interruption-free access to powerful cloud servers. These servers can rapidly
process large amounts of data upon receipt from remote IoT devices and send back the results.
However, real-time delay-sensitive applications cannot afford long delays induced by a wide
area network. Continuous transmission of voluminous

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raw data through unreliable wireless links may also be ineffective. By contrast, edge
computing is a decentralized computing platform that brings cloud computing capabilities
near IoT devices, that is, the network edge. An important type of edge computing platform is
MEC, which brings cloud computing capabilities to the edge of a cellular network [10].
Computational and storage services in MEC are provided at the BS. Unlike MEC, fog
computing employs local fog nodes (i.e., local network devices such as a router or switch)
available within a limited geographic region to provide computational services. Fog
computing is considered a premier technology following the success of IoT. Cloudlet is
another form of edge computing, in which delay-sensitive and computation-intensive tasks
from IoT devices are performed on a server deployed in the local area network. Unlike cloud
and edge computing platforms that rely on infrastructure deployment, MAC capitalizes the
shared resources of available mobile devices within local proximity to process computation-
intensive tasks. Cloud and edge computing are used together in hybrid computing. Such
infrastructure is usually adopted when we require the large computing resources of cloud
computing but cannot tolerate the latency of the cloud. Variants of edge computing can be
employed in such applications to overcome the latency problems of cloud computing.

EDGE COMPUTING
4.2 EDGE COMPUTING ARCHITECTURE AND SECURITY
Edge Computing is a distributed architecture, simply defined as the processing of data
when it is collected. It has been emerged to minimize both bandwidth and time response
in an IoT system. The use of an edge computing technique is required when the latency is
required to be optimized to avoid network saturation as well as when the data processing
burden is high at a centralized infrastructure. An extended version of edge computing is
fog computing, which is an architecture that makes use of edge gadgets to accomplish a
considerable amount of computation, storage, communication regionally, which
undoubtedly possesses input and output from the real world referred to as transduction.
Fog nodes determine whether to process the data locally from several data sources or send
the data out to cloud.
The tasks of edge computing, which people carry out in a daily manner. There are three
basic elements: input, processing, and output as summarized based.
• Data sources: As the input, any endpoint which records and collects data from clients or
its environments is described as a data source.
• Artificial intelligence: As the processing function, it is the main facet after data
collected to uncover practical observations, locate patterns and trends, produce
individualized recommendations, and improve the performance based on machine
learning or data analytics models.

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• Actionable insights: The results from the previous stage succeed only when an
individual can act and make any informed selection. Thus, within this stage, the insights
appear in a transparent manner in type of control panels, visualizations, alerts and so on,
which motivates communication between machines and humans, therefore generating a
beneficial feedback loop.
Privacy and Security
An organization should oversee and ensure privacy and security of their IoT framework.
Multiple terminologies used in privacy-preserving management are enumerated in the
following:
• Pseudonymity: where the pseudonym is used as an ID to ensure that an individual can
utilize the source (e.g. pseudonym) without revealing the source’s real identity. However,
a user could still be responsible for usage.
• Unobservability: assuring that an individual could utilize a resource or service without
other third parties and having the ability to observe that the resource or service is being
used.
• Unlinkability: ensuring that a third party (e.g., an attacker) cannot identify whether two
objects are linked to each other or not.
• Anonymity: an individual may make use of a resource without revealing his identity.
• Confidentiality: assuring only the data proprietor and an individual can access the personal
information in the edge computing. It protects against unapproved parties’ access to the data
when the individual’s data is transferred and also collected in edge or core network
framework, as well as when the data is kept or handled in edge or cloud nodes.

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• Integrity: assuring the proper and steady transmission of data to the accredited
individual without unauthorized modification of the data. Privacy of individuals can be
impacted due to the lack of integrity measures.
• Availability: ensuring the accredited party manages to access the edge services in any
regions based on individual’s needs. This also implies that an individual’s data held in
edge or cloud nodes along with the cipher text format can be handled under various
practical needs.
• Access control and authentication: access control imitates a linking point of all privacy
and security demands by the access control technique. Authentication ensures that the
identification of an individual is accredited.
4.3 CASE STUDIES OF EDGE COMPUTING
Two case studies are presented in this section to illustrate the edge computing vision
comprehensively. First, we analyse a smart parking system that lessens traffic when an
individual is navigating for a car parking space. Second, we explore utilizing the CDN to
minimize the latency of transmitted data as well as enhance Internet content availability.
Smart Parking System
Consider a system that allows individuals to promptly find an available parking place on
one click of a key on a smart device. This system will significantly decrease the time
devoted to looking for a parking slot and inhibit vehicle parking violations.

EDGE COMPUTING
The smart parking system is usually powered via RFID, ultrasonic detector, and infrared
sensing units.
1) Flow of Execution: A common flow of execution works
as follows for a smart parking system.
1) Log in to the smartphone parking application.
2) Choose the parking area near the customer’s location.
3) Browse between randomly available parking slots, then select a preferable slot.
4) Select the desired timeframe to park the vehicle.
5) Pay off the parking fee for a chosen timeframe.
6) When a customer parks the car via navigation and confirms his parking, the time
countdown starts.
7) On departure, the customer can pay any additional charge if he exceeds the allowed time.
2) Benefits: Smart parking may minimize traffic for an automobile navigating for a slot,
can be useful for many people and decrease vehicles emissions, making for an even more
environmentally friendly city. It can also boost accessibility for businesses and grocery
stores by enhanced optimization of available parking slots.
3) Future Scope:
• The system may be adjusted to integrate future self-driving automobiles and assure real-
time communication between several vehicles such that an individual possesses no
interactive burden with the system.
• More efficient parking algorithms could be established for the optimal consumption of
resources, such as availability
of slots and parking durations. For example, a deep learning model can be trained for real-
time space allocation.
Content Delivery Network
A CDN is one of the most promising solutions to address the issue of massive web traffic,
by distributing many servers efficiently in different geographical locations, thereby
delivering web content in a faster way. The CDN is a special case of edge computing.
Today, a lot of Internet websites, such as Facebook, eBay, and Netflix, leverage the CDN
architecture to efficiently provide web content.
1) Architecture: Consider an application with a large number of users in large geographical
areas. Providing requests to every user from a central location can easily result insubstantial
network latency. If such an application is service essential, service level agreement (SLA)
violations can also occur. CDN addresses precisely the issue in this use case. The origin
server is connected to several exchange points (IXP). These servers named as Point of
Presence (POPs). They are distributed throughout different geographical areas. POPs play

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a significant role because the caching system relies on the performance of these servers.
The CDN architecture enhances the reliability of the entire system. If one POP server is
down, traffic will be re-routed to other PoPs. Individuals can be delivered services with
their nearest POP server.
2) Advantages: There are many advantages for consumers under the CDN architecture.
• Website security improvement: a CDN with the help of distributed denial of service
(DDoS) mitigation can enhance and maintain the website security from DDoS attacks that
can severely interrupt and degrade the service accessibility.
• Faster website page loading: a CDN can be utilized to provide static web content, which
decreases the webpage load time.
• Botnet and spam defence: a CDN can be set up with firewall policies which obstruct
unwanted spamming and botnet probing against the system.
• Enhancing global content availability: a CDN can manage massive traffic and hold up
against failure as compared with central services.
• Handling website traffic spikes: a CDN provides better load balancing between servers
and offers fast horizontal scaling.

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4.4 CHALLENGES AND OPPORTUNITIES
We have described five potential applications of edge computing in the last section. To
realize the vision of edge computing, we argue that the systems and network community
need to work together. In this section, we will further summarize these challenges in
detail and bring forward some potential solutions and opportunities worth further
research, including programmability, naming, data abstraction, service management,
privacy and security and optimization metrics.
Programmability
In cloud computing, users program their code and deploy them on the cloud. The cloud
provider is in charge to decide where the computing is conducted in a cloud. Users have zero
or partial knowledge of how the application runs. This is one of the benefits of cloud
computing that the infrastructure is transparent to the user. Usually, the program is written in
one programing language and compiled for a certain target platform, since the program only
runs in the cloud. However, in the edge computing, computation is offloaded from the cloud,
and the edge nodes are most likely heterogeneous platforms. In this case, the runtime of these
nodes differ from each other, and the programmer faces huge difficulties to write an
application that may be deployed in the edge computing paradigm. To address the
programmability of edge computing, we propose the concept of computing stream that is
defined as a serial of functions/computing applied on the data along the data propagation
path. The functions/computing could be entire or partial functionalities of an application, and
the computing can occur anywhere on the path as long as the application defines where the
computing should be conducted. The computing stream is software defined computing flow
such that data can be processed in distributed and efficient fashion on data generating
devices, edge nodes, and the cloud environment. As defined in edge computing, a lot of
computing can be done at the edge instead of the centric cloud. In this case, the computing
stream can help the user to determine what functions/computing should be done and how the
data is propagated after the computing happened at the edge. The function/computing
distribution metric could be latency-driven, energy cost, TCO, and hardware/ software
specified limitations. The detailed cost model is discussed in Section IV-F. By deploying a
computing stream, we expect that data is computed as close as possible to the data source,
and the data transmission cost can be reduced.
Naming
In edge computing, one important assumption is that the number of things is tremendously
large. At the top of the edge nodes, there are a lot of applications running, and each
application has its own structure about how the service is provided. Similar to all computer

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systems, the naming scheme in edge computing is very important for programing, addressing,
things identification, and data communication. However, an efficient naming mechanism for
the edge computing paradigm has not been built and standardized yet. Edge practitioners
usually needs to learn various communication and network protocols in order to communicate
with the heterogeneous things in their system. The naming scheme for edge computing needs
to handle the mobility of things, highly dynamic network topology, privacy and security
protection, as well as the scalability targeting the tremendously large amount of unreliable
things. Traditional naming mechanisms such as DNS and uniform resource identifier satisfy
most of the current networks very well. However, they are not flexible enough to serve the
dynamic edge network since sometimes most of the things at edge could be highly mobile
and resource constrained. Moreover, for some resource constrained things at the edge of the
network, IP based naming scheme could be too heavy to support considering its complexity
and overhead. New naming mechanisms such as named data networking (NDN) [27] and
Mobility First [28] could also be applied to edge computing. NDN provide a hierarchically
structured name for content/data centric network, and it is human friendly for service
management and provides good scalability for edge. However, it would need extra proxy in
order to fit into other communication protocols such as Bluetooth or ZigBee, and so on.
Another issue associated with NDN is security, since it is very hard to isolate things hardware
information with service providers. MobileFirst can separate name from network address in
order to provide better mobility support, and it would be very efficient if applied to edge
services where things are of highly mobility. Nerveless, a global unique identification (GUID)
needs to be used for naming is MobileFirst, and this is not required in related fixed
information aggregation service at the edge of the network such as home environment.
Another disadvantage of MobileFirst for edge is the difficulty in service management since
GUID is not human friendly.
Data Abstraction
Various applications can run on the edgesOS consuming data or providing service by
communicating through the air position indicators from the service management layer.
Data abstraction has been well discussed and researched in the wireless sensor network
and cloud computing paradigm. However, in edge computing, this issue becomes more
challenging. With IoT, there would be a huge number of data generators in the network,
and here we take a smart home environment as an
example. In a smart home, almost all of the things will report data to the edgeOS, not to
mention the large number of things deployed all around the home. However, most of the

EDGE COMPUTING
things at the edge of the network, only periodically report sensed data to the gateway. For
example, the thermometer could report the temperature every minute, but this data will
most likely only be consumed by the real user several times a day. Another example could
be a security camera in the home which might keep recording and sending the video to the
gateway, but the data will just be stored in the database for a certain time with nobody
actually consuming it, and then be flushed by the latest video.
Service Management
In terms of service management at the edge of the network, we argue that the following four
fundamental features should be supported to guarantee a reliable system, including
differentiation, extensibility, isolation, and reliability. Differentiation: With the fast growth of
IoT deployment, we expected multiple services will be deployed at the edge of the network,
such as Smart Home. These services will have different priorities. For example, critical
services such as things diagnosis and failure alarm should be processed earlier than ordinary
service. Health related service, for example, fall detection or heart failure detection should
also have a higher priority compared with other service such as entertainment.
Optimization Metrics
In edge computing, we have multiple layers with different computation capability. Workload
allocation becomes a big issue. We need to decide which layer to handle the workload or how
many tasks to assign at each part. There are multiple allocation strategies to complete a
workload, for instances, evenly distribute the workload on each layer or complete as much as
possible on each layer. The extreme cases are fully operated on endpoint or fully operated on
cloud. To choose an optimal allocation strategy, we discuss several optimization metrics in
this section, including latency, bandwidth, energy and cost.
Latency: Latency is one of the most important metrics to evaluate the performance,
especially in interaction applications/services
Bandwidth: From latency’s point of view, high bandwidth can reduce transmission time,
especially for large data.
Energy: Battery is the most precious resource for things at the edge of the network. For the
endpoint layer, offloading workload to the edge can be treated as an energy free method.
Cost: From the service providers’ perspective, e.g., YouTube, Amazon, etc., edge
computing provides them less latency and energy consumption, potential increased
throughput and improved user experience.

EDGE COMPUTING
CHAPTER 5
CONCLUSION
Nowadays, more and more services are pushed from the cloud to the edge of the network
because processing data at the edge can ensure shorter response time and better reliability.
Moreover, bandwidth could also be saved if a larger portion of data could be handled at
the edge rather than uploaded to the cloud. The burgeoning of IoT and the universalized
mobile devices changed the role of edge in the computing paradigm from data consumer
to data producer/consumer. It would be more efficient to process or massage data at the
edge of the network.
In this paper, we came up with our understanding of edge computing, with the
rationale that computing should happen at the proximity of data sources. In this article,
we investigated, highlighted, and reported recent premier advances in edge computing
technologies (e.g., fog computing, MEC, and cloudlets) with respect to measuring their
effect on IoT. Then, we categorized edge computing literature by devising a taxonomy,
which was used to uncover the premium features of edge computing that can be
beneficial to the IoT paradigm. We outlined a few key requirements for the deployment of
edge computing in IoT and discussed indispensable scenarios of edge computing in IoT.
Furthermore, several open research challenges to the successful deployment of edge
computing in IoT are identified and discussed.
We conclude that although the deployment of edge computing in IoT provides
numerous benefits, the convergence of these two computing paradigms brings about new
issues that should be resolved in the future.

EDGE COMPUTING
CHAPTER 6
REFERENCES
[1] Edge Computing: Vision and Challenges,Weisong Shi, Fellow, IEEE, Jie Cao,
Student Member, IEEE, Quan Zhang, Student Member, IEEE, Youhuizi Li, and Lanyu
Xu 2016
[2] Secure Edge Computing in IoT Systems: Review and Case Studies, Mohammed
Alrowaily Department of Electrical Engineering University of South Florida, Zhuo Lu
Department of Electrical IEEE,2018
[3] The Role of Edge Computing in Internet of Things , Najmul Hassan, Saira Gillani,
Ejaz Ahmed IEEE, Ibrar Yaqoob, and Muhammad Imran, IEEE,2018
[4] Ala Al-Fuqaha, Senior Member, IEEE, Mohsen Guizani, Fellow, IEEE, Mehdi
Mohammadi, Student Member, IEEE, Mohammed Aledhari, Student Member, IEEE, and
Moussa Ayyash, Senior Member, IEEE,2015
[5] E. K. Markakis et al., “Efficient Next Generation Emergency Communications over
Multi-Access Edge Computing,” IEEE Commune. Mag., vol. 55, no. 11, Nov. 2017
[6] T. Taleb et al., “Mobile Edge Computing Potential in Making Cities Smarter,” IEEE
Commune. Mag., vol. 55, no. 3, Mar.2017
[7] M. Satyanarayanan et al., “Edge Analytics in the Internet of Things,” IEEE Pervasive
Computing, vol. 14, no. 2, 2015
.

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