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Telematics and Informatics Reports 10 (2023) 100049
Contents lists available at ScienceDirect
Telematics and Informatics Reports
journal homepage: www.elsevier.com/locate/teler
A review on fog computing: Issues, characteristics, challenges, and
potential applications
Resul Das
a
, Muhammad Muhammad Inuwa
a
,
b
,
∗
a
Department of Software Engineering, Technology Faculty, Firat University, Elazig, Türkiye.
b
Department of Software Engineering, Federal University Dutse, Jigawa State, Nigeria
Keywords:
Fog computing
Edge computing
Internet of Things (IoT)
Cyber security
Security threats
Fog computing is a paradigm that utilizes the advantages of both the cloud and the edge devices providing qual-
ity services, reducing latency, providing mobility support, multi-tenancy, and many other functions that support
modern computing systems. It is sometimes referred to as fog networking or fogging. This paper reviews and dis-
cusses cloud computing, briey highlighting the implemented paradigms before fog computing. These paradigms
include cloud, mobile cloud computing, and mobile edge computing. All the paradigms targeted improving the
quality of service between the end devices and the cloud itself. A fog computing Taxonomy is presented based
on contemporary fog computing research about security challenges, services issues, operational issues, and data
management. The standard for elucidating the taxonomy is built on the functional and vital issues in fog comput-
ing. Challenges and potential applications are identied. The review shows that security, privacy, application, and
communication challenges are prominent among scholars contributions. Potential applications in fog computing
are also identied, including healthcare applications, innovative city applications, and farm applications.
1. Introduction
Nowadays, the pointer of the trending paradigm is pointing at fog
computing. As eciency and quality of service stand as important ob-
jectives in the world of computing, security also continues to be a con-
cern. Before the fog, cloud computing was viewed as a promising ap-
plication due to its exible and scalable nature. Dierent stakeholders,
which include IBM, Microsoft, Google, Amazon, and others, enabled dif-
ferent cloud-based services to steer educational and enterprise compu-
tations concurrently. Some of these services include Software as a Ser-
vice (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service
(PaaS). But the cloud feature of being centralized became an obstacle
to latency-sensitive computations [1] . The increasing demand for cloud
computing services from IoT devices is prone to the ineciency of some
basic computing and communication requirements; these include loca-
tion awareness, mobility support, and low latency [2] . The signicant
challenges that obstruct IoT devices’ service quality today include com-
putational energy, battery durability, storage capacity, and bandwidth.
To compensate for the burden of IoT devices’ limited resources, the gi-
ant became the answer —that is, employing the services of the robust,
capable of dealing with the limitations. Cloud computing is thought to
be the hope for service delivery with exible resources at a low cost [3] .
However, latency, location awareness, geo-distribution, and security are
some issues of disturbance that led to the introduction of fog computing.
∗ Corresponding author.
E-mail addresses: rdas@rat.edu.tr , resuldas@gmail.com (R. Das), muhammad.m@fud.edu.ng (M.M. Inuwa) .
According to the OpenFog Consortium
[4] , Edge devices face two sig-
nicant challenges from all cloud services. These are; 1) The data that
the IoT devices are creating is growing exponentially, which will cause
network congestion and drive performance problems at the edge of in-
frastructure. 2) There are many tasks for which a cloud-only solution
is insucient due to factors such as performance, security, bandwidth,
reliability, and many others. Performance control is the primary con-
cern in fog computing; latency and eciency have contributed to its
success.
1.1. Main contributions
Dierent research has been presented with a signicant amount of
eort dening and describing fog computing, each contributing to a
separate area of the paradigm. Many review and survey papers have
been published to provide general and classied issues in fog comput-
ing. Fifty (50) research papers have been studied thoroughly and ref-
erenced in Table 1 . This paper aims to provide a comprehensive ap-
proach to understanding the face of fog computing and the challenges
engulng the paradigm. Additionally, the article will provide a basic un-
derstanding and direction to researchers on the trending issues bedev-
iling the paradigm due to its being an intermediary between the cloud
and the edge. More specically, the contributions of this paper are the
following:
https://doi.org/10.1016/j.teler.2023.100049
Received 10 July 2022; Received in revised form 22 January 2023; Accepted 26 February 2023
2772-5030/© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
R. Das and M.M. Inuwa Telematics and Informatics Reports 10 (2023) 100049
Table 1
Existing review papers in literature.
Ref. Year Objectives and Topics Main Lesson Learned Contributions
[3] 2015 The denition of fog computing and related ideas
were examined. It identies several potentials and
challenges of fog computing.
Integration of SDN and NFV into the fog
paradigm will improve the fog’s services.
Fog computing concepts, applications, and
challenges in design and implementation.
[2] 2016 analyze fog computing applications concerning
real-time systems and broaden their motivation and
advantages. A discussion of security and privacy
issues are also included.
One factor that makes fog computing
susceptible to network attack is its
environment.
The advantages of fog computing in dierent areas
and a thorough discussion of security issues.
[1] 2018 examined the issues in fog computing and its current
state of the art. A taxonomy was provided based on
the stated issues and their fundamental attributes.
It is clear that even with the advances in the
fog paradigm, many insucient inputs are
required to improve its services.
Discussion on fog and other paradigms dierences,
challenges, and the presentation of taxonomy based
on the stated challenges.
[8] 2017 It provides a concise overview of the fog computing
architecture, key technologies, applications,
challenges, and unresolved issues.
The advent of fog computing has improved
the services of cloud computing.
Furthermore, there will be more to cultivate
with the invention of 5G technology.
a presentation of the characteristics of fog
architecture, a comparison of fog with edge and
cloud, a discussion of key technologies, and an
exploration of some problems and open topics
[12] 2020 Provided a comprehensive study of fogedge
computing to establish a baseline for solutions
oered in studies involving IoTFogCloud
environments.
Articial intelligence and machine learning
will improve fog computing services.
The article provided a baseline for solutions oered
in research involving IoT-Fog-Cloud environments
and examined the concepts, architectures, standards,
and tools for IoT-Fog-Cloud.
[57] 2018 The article gives an insight into the state of fog
computing, including concerns and challenges.
The fog computing security threats originate
from the cloud, as it is an extension of the
cloud, so the inheritance.
The article provided a summary of the fog’s security
and privacy issues.
[17] 2015 It provides a survey on fog computing challenges
and their matching solutions in a concise fashion.
Fog’s uniqueness attracted new challenges
apart from the inherited. Additionally, SDN
can be employed to help in managing the fog
nodes.
A discussion on various security and privacy
challenges in fog computing.
[5] 2018 The architectures of fog computing are discussed
and analyzed in this paper, as well as the security
and trust problems. Furthermore, open issues,
research trends, and future topics are highlighted.
One factor that makes fog computing
vulnerable to security threats is its nature of
being distributed and open-structured.
The contribution includes a specic analysis of fog
architecture, a summary and discussion of open
security and trust challenges, and future research
work.
[20] 2018 The concepts and ideas of the Fog paradigm, as well
as associated paradigms and technologies, were
discussed.
Cloudlets and Mobile Edge Computing are
more technologies that make up the larger
Fog paradigm.
The signicant contribution consists of providing
some broad basic information and perhaps some
constructive critique.
[58] 2018 Fog computing research trends, potential
architectures, and distinctions from cloud computing
were examined. A taxonomy is proposed, as well as
specic research gaps and genuine concerns.
A fog node can be any device with
computational power, network capacity, and
storage capacity.
The fog computing research trend was examined.
Dierent architectures of fog computing were
analyzed and provided a comprehensive
architecture. A taxonomy, research gaps, some
research aws, and open issues.
[59] 2018 The article reviewed basic fog architecture,
summarized service, resource allocation approach,
challenges, and research trends. And also about
current network applications.
Even though fog computing is a feasible
option for the long-term development of the
IoT industry, there are still numerous
unresolved diculties.
The logical relationship between dierent algorithms
and approaches in fog computing was presented, as
well as an organization of resource sharing, research
challenges in fog radio access network architecture,
and an organization of resource sharing.
[60] 2018 The paper assesses advancements in fog computing
using a simple criterion that covers architectures
and algorithms. Also, many issues and research
directions are being discussed.
The use of fog computing extends beyond IoT. A comprehensive analysis of fog computing, its
diculties, the signicance of fog in new
technologies, and future research prospects.
[14] 2019 Fog computing was compared with other relevant
concepts in this paper. A taxonomy is presented, and
a summary and categorization of works on fog
computing and other paradigms, obstacles, and
future research objectives.
Fog computing is one of the potential options
for dealing with the massive amounts of data
generated by the Internet of Things.
The article presented a lesson on fog computing and
its relationship to other systems, as well as a
taxonomy and overview of fog computing issues and
future directions.
[61] 2018 The study included a taxonomy, discussions on fog
architectures, technologies, features, security and
privacy, and diverse work done on them. Future
study areas and limitations are also discussed.
fog computing oers a wide range of
applications. As they are designed with the
necessary criteria, 5G cellular networking
components, for example, may readily
function as fog nodes.
The article oered an overview of fog computing,
taxonomy and the limits of previous works, future
research paths, and unique research challenges.
[62] 2019 The goal of this work is to provide a comprehensive
overview of fog computing, including the present
state-of-the-art.
Even the broad acceptance of fog computing
still suers from missing standards, node
ownership, limited simulation tools, etc.
The most signicant contribution to this work is
providing up-to-date fog computing technology.
[63] 2019 It provided an analysis of 876 journals and
conferences relevant to fog computing and discussed
the present state of research and issues in fog
computing.
There is a need to focus more on fog
computing’s eciency rather than its
robustness.
The article discussed the distinctions between fog
and cloud computing, an examination of fog-related
publications and conferences, and the current state
of research and its challenges.
[64] 2018 The paper provided an overview of fog computing’s
denition, architecture, potentials, and twelve
proposed applications. Also, the issues and future
research potential in fog computing are examined.
Integration of machine learning into fog
computing can improve its quality of service
and scalability.
The article presented an overview of fog’s denition,
contrasted its architecture and potential with cloud
computing, identied major fog implementations,
and emphasized obstacles and future research
possibilities.
[65] 2020 The study examines and discusses fog computing, as
well as identifying needed challenges and other
concerns. However, a potential research area is
being discussed.
The future of computing and automation will
rely heavily on fog computing, which will be
critical in emerging networks such as the IoT
and big data.
The study presented an overview and synopsis of fog
computing and a description of the problems and
future research issues.
( continued on next page )
2
R. Das and M.M. Inuwa Telematics and Informatics Reports 10 (2023) 100049
Table 1 ( continued )
[66] 2016 The paper examines the essential features of fog
computing, current research, and the constraints and
concerns that have arisen throughout the
advancement of its architecture.
Because fog is deployed at the network’s
edge, it is more exposed to security attacks.
The study examined the fundamental characteristics
of fog and emphasized the issues and concerns in the
development architecture.
[67] 2018 The study delves into the notion of fog computing,
addresses the signicant roadblocks to adoption, and
identies open research problems.
There is a long way to go in fog computing,
as some obstacles can hinder the adoption of
the paradigm, and it may incur a high cost of
implementation.
The article explored the notion of fog computing,
the barriers to its implementation, and open
research issues.
[68] 2021 The paper categorized recently published studies,
analyzed the fog’s current state, addressed
characteristics of fog computing frameworks and
found diculties linked to their architectural design,
provided taxonomy, and emphasized research
obstacles and prospects.
The advancement of fog computing will
trigger other paradigms integrated under its
domain for eective services.
The study reviewed fog computing research,
addressed current research status, presented a
taxonomy, compared previous studies, and
emphasized research obstacles and prospects.
Our Paper This paper reviews and discusses cloud computing,
highlighting the implemented paradigms before fog
computing. Taxonomy is proposed based on
contemporary fog computing research about security
challenges, services issues, operational issues, and
data management. Challenges and potential
applications are identied.
The paper shows that security and privacy
and application and communication
challenges are very prominent among the
scholars contributions.
The article provided a brief understanding of fog
computing, proposed a taxonomy, highlighted
trending issues and challenges, and discussed some
potential applications.
1. A brief but rich insight into fog computing.
2. A taxonomy based on research’s contemporary fog computing view-
points concerning security challenges, service issues, operational is-
sues, and data management.
3. A highlight of the trending issues and challenges in fog computing.
4. The paper underlined some of the potential applications of fog com-
puting.
The paper is organized as follows. In Section 3 , the background of
fog computing is introduced, which includes a review of cloudlet, mo-
bile cloud computing, mobile-edge computing, and fog computing archi-
tecture. Related works are summarised in 4 , which reviews the recent
articles on fog computing. Section 5 discusses the security threats of fog
computing, focusing more on the most troubling and those inherited
from the cloud. Section 6 presents the application areas of fog comput-
ing, providing a brief understanding of the fog’s presence in the area. In
Section 7 , issues and challenges in fog computing are being discussed.
The taxonomy of fog computing is proposed in the section. While 8 pre-
sented potential applications, the work is concluded in 9 .
2. Search methodology
This section describes the process for gathering papers and classify-
ing them according to dierent stages in the issues, characteristics, chal-
lenges, and potential applications of fog computing. Writing a review
article that is knowledge-rich requires nding, compiling, categorizing,
and reviewing numerous pertinent papers.
2.1. Search process and data collection
Online search databases are scoured as part of the search procedure.
This article used the following databases as research sources: Scopus,
Web of Science, Science Direct, ACM Digital Library, Semantic Scholar,
Springer Link, ArXiv e-print, and IEEE Xplore Library. During the search
process, a lot of terms were utilized, including ”cloud, ”or ”cloud com-
puting, ”or ”fog, ”or ”fog networking, ”and ”edge computing ”or ”edge
networking. ”
2.2. Inclusion criteria
The nal publication selection was ltered using the inclusion and
exclusion criteria after these keywords were used in relevant internet
databases. During the ltration process, the following inclusion crite-
ria were considered when choosing articles: English-language publica-
tions, high-impact journals, and conference publications; also included
are papers that look at mathematical frameworks, strategies, or models
for resource-intensive fog computing and related subjects.
2.3. Exclusion criteria
The following exclusion criteria were also taken into consideration
in addition to the inclusion criteria mentioned above: Publications that
are duplicated in the databases. Publications lacking in-depth coverage
of fog computing. Publications that outline already analyzed proposed
techniques The most recent publication is considered in this situation.
3. Background on fog computing
Fog computing is a promising paradigm that provides computational
services at the network edge, enabling new services and applications
for the Internet’s future [5] . Compared with other paradigms, such as
cloudlets, mobile cloud computing (MCC), and mobile edge computing
(MEC), fog computing has a better placement position as it is deployed
closer to the IoT nodes. Additionally, it supports the extension of cloud-
based services. Thus, it helps provide ecient services, including signif-
icant minimization of latency [1] . However, fog computing’s existence
does not replace the cloud service; somewhat, it improves it [5] . Con-
sidering the concept of edge and cloud computing, many computing
paradigms have already started to be used in computing technology.
Several computing paradigms have previously been created in com-
putation technology, taking the concepts of Edge and Cloud computing
into account. Mobile Edge Computing (MEC) and Mobile Cloud Com-
puting (MCC) are examples of prospective cloud and edge computing
developments. MEC is widely considered a critical enabler of the cur-
rent development of cellular base stations. At the same time, MCC oers
the processing resources required to facilitate the remote execution of
ooaded mobile applications closer to end users. Fog computing, like
MEC and MCC, can also enable edge computation. In addition to the
edge network, fog computing may extend to the core network. To be
more specic, edge and core networking components may be employed
as processing infrastructure in fog computing [1] . Fig. 1 presents a com-
parative gure that provides a quick insight into the dierences and
functions of the stated paradigms.
3.1. Cloudlet
Cloudlet is usually a tiny box data center, mounted at a wireless hop’s
distance from the mobile devices in a public place such as hospitals, of-
ce buildings, and shopping malls, to ease an appropriate application
[6] . A cluster of powerful multicore computers with high-speed inter-
net access and a high-bandwidth wireless LAN for use by surrounding
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R. Das and M.M. Inuwa Telematics and Informatics Reports 10 (2023) 100049
Fig. 1. Computation domain of cloud, fog, edge, mobile cloud and mobile edge computing [1]
mobile devices make up the internal structure of a cloudlet. To assure
security in unsupervised regions, the cloudlets are housed in a tamper-
resistant box for safety reasons [7] . Cloud computing has eectively
processed data due to its powerful computational ability and sucient
storage capacity. Although cloud computing is a centralized system, this
necessitates transmitting all the requests and the data to the cloud for
processing. That brings ineciency to the cloud’s bandwidth, which in
turn becomes a problem for real-time processes due to the delay in trans-
ferring the data [8] . suggested that not all data require decision-making
and analysis. These challenges are due to the massive growth of IoT,
which needs eciency in latency, network bandwidth, reliability, and
security, depending on cloud computing, which cannot be suciently
met. In the search for a solution to the stated challenges, the cloudlet
was proposed to bring the resources closer to the edge devices to make
local processes and storage. Additionally, it would reduce the rate of
network transmission and latency [8] .
3.2. Mobile cloud computing
Mobile Cloud Computing (MCC) is another technology proposed to
provide a new framework with services to mobile subscribers by tak-
ing maximum advantage of cloud computing. Some operations and an-
alytical tasks are carried out on the edge device while the coordination
and data archiving are carried out on the cloud [8] . Mobile devices’
constraints on computational resources, storage, and energy engineered
the introduction of MCC. At the network’s edge, MCC frequently deploys
small, lightweight cloud servers known as ”cloudlets. ”A three-tier hi-
erarchical application deployment architecture for rich mobile apps is
developed using cloudlets in conjunction with mobile devices, and cloud
data centers [1] .
Mobile Cloud Computing combines all of the benets of cloud com-
puting, mobile internet, and mobile computing. Mobile cloud computing
allows the use of resources based on a request; these include network,
server, mobile application, storage, and computing resources in the mo-
bile environment. In MCC architecture, the cloud servers are placed far
away from the edge devices, making it inecient in a network environ-
ment with high computational requirements [6] .
3.3. Mobile-edge computing
Mobile-Edge Computing (MEC) is a technology introduced to allow
mobile users access to the cloud and other information technology ser-
vices within close range of the Radio Access Network (RAN). The prin-
cipal target of MEC is to minimize latency by transferring storage and
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R. Das and M.M. Inuwa Telematics and Informatics Reports 10 (2023) 100049
Fig. 2. Cloudlet Architecture.
Fig. 3. Mobile cloud computing architecture.
Fig. 4. Mobile-edge computing architecture.
computational capacity from the core network to the edge network. Mo-
bile Edge Computing is a model for enabling a business-oriented cloud
computing platform within the radio access network at the proximity of
mobile subscribers to serve delay-sensitive, context-aware applications
[1,6,9] .
MEC has been seen as one of the leading providers of the following
modern development of cellular base stations. It allows cellular base
stations and edge servers to run in tandem [1] . It also delivers real-time
RAN information (network load, user location, and network conges-
tion) to the application developers and content developers. This real-
Fig. 5. Fog computing architecture.
time network information provides context-sensitive services to mobile
subscribers, increases user satisfaction, and improves the quality of ex-
perience (QoE). MEC strengthens the edge network responsibility by
allowing the services and the computational activities to be carried out
at the edge network level to reduce the subscribers’ network latency and
bandwidth consumption. The technology allows the network operators
to let a third party handle the radio network edge; this paves the way
for the ow of new applications and edge services to the mobile sub-
scribers. MEC’s primary goal is to deliver applications and services with
low latency, and low bandwidth [10] .
3.4. The architecture and the environment of fog computing
Fog computing is not an independent paradigm but an extension of
cloud services to the edge. The fog environment comprises three layers:
the terminal layer, the fog layer, and the cloud layer, while the archi-
tecture consists of combinations of fog nodes [8] . Additionally, the fog
has the capacity to process data locally with a desirable latency [11] .
3.4.1. Terminal layer
This layer comprises dierent end devices, which are geographically
distributed. These devices take the responsibility of fetching data and
conveying it to a higher-level layer for data processing and storage. The
devices might include wearable devices, sensors, intelligent vehicles,
mobile phones, and others [8,12] .
3.4.2. Fog layer
This layer stands between the cloud and the terminal layer, situated
at the network’s edge. This layer device is known as a ”fog node, ”and
it is capable of data transmission, computation, and storage [13,14] . A
fog node can be mobile or non-mobile and placed in a xed, strategic
location. Some examples of these devices include access points, routers,
fog servers, switches, base stations, etc. The computational ability of
this layer optimizes the services of latency-sensitive applications, allow-
ing real-time processing and analysis to be realized. In addition, the
fog nodes’ proximity to the end devices allowed them to communicate
eectively. Moreover, the extension of cloud services to the fog layer al-
lowed the fog nodes to have increased computational, and storage power
[8,12] .
3.4.3. Cloud layer
This layer comprises storage devices and servers with high perfor-
mance and computational power. It is responsible for executing non-
latency-sensitive jobs sent by the lower layer (fog layer) [8] . Software,
platforms, and infrastructure are available as services in the cloud. Cloud
services include, for instance, IaaS and PaaS-based server hosting pro-
vided by DigitalOcean, network storage provided by Apple iCloud, IaaS
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R. Das and M.M. Inuwa Telematics and Informatics Reports 10 (2023) 100049
and PaaS-based server hosting provided by Google App Engine, and vir-
tual IT services provided by Amazon EC2 [12] .
Fog computing is a scenario where a vast number of heterogeneous
(wireless and sometimes autonomous) ubiquitous and decentralized de-
vices communicate and potentially cooperate among themselves and
with the network to perform storage and processing tasks without the
intervention of third parties [13] . These tasks can support basic network
functions or new services and applications that run in a sandboxed en-
vironment. Users who lease part of their devices to host these services
get incentives for doing so [3] .
Delay, eciency, agility, cognition, and the advent of the Internet
of Things (IoT), which requires mobility support and geo-distribution
[13] are the driving factors behind the call for fog computing. Further-
more, privacy, fault tolerance, and reliability are dened as among the
benets of fog computing [15] . As the cloud keeps growing with numer-
ous nodes requiring a quick response, the problems of location aware-
ness, mobility support, and latency remain [7] . Implementing the Fog
computing paradigm became a solution to the stated cloud problems
[5] . The control of data privacy using the cloud computing technique
can be complex as the data has to be transmitted outside the local net-
work boundary. However, for fog, there is only a need to transmit the
data to locally connected fog nodes [16] .
3.5. Characteristics of fog computing
The identied characteristics of fog computing include heterogene-
ity of fog nodes, and fog networks, the massive scale of geo-distributed
nodes, location awareness, the requirement of mobility support, and low
latency [17] .
3.5.1. Low latency
This refers to the minimal time to respond, analyze, and execute the
computational request. The fog nodes’ proximity to the edge devices
helps lead to faster computation tasks and analysis responses.
3.5.2. Heterogeneous end-user support
End-user support is maximized as the requesting IoT device is closer
to the processing node [13,18] .
3.5.3. Multi-tenancy
Refers to the ability of a system where multiple instances access and
share a single instance of the software. These systems are referred to as
shared systems. The fog platform embraced this because it is distributed
and highly virtualized.
3.5.4. Mobility support
It is a function that allows registering and deregistering IoT devices
from one access point to another. As missing or delayed data while the
device is on the move could be damaging, mobility support is an essen-
tial requirement for mobile IoT systems [19] . Thus, requiring unmedi-
ated transmission between the fog node and the IoT device [20] .
3.5.5. Real-time interaction
Real-time refers to a system that is required to respond within a spe-
cic time frame (deadline). This type of application in fog computing in-
cludes real-time e-health, trac transmission, airlines, industry-critical
process monitoring systems, and others. In addition, for providing qual-
ity of service (QoS), fog computing chooses real-time transmission over
batch processing [13,18] .
3.5.6. Context-awareness
This function allows fog nodes to learn about their networks through
fetching information, which can help the node execute certain decisions.
3.5.7. Wide geographical distribution
The architectural design of fog computing equipped the paradigm
with the ability of wide geographical distribution to ensure the delivery
of QoS [18,20] .
3.5.8. Wireless access network
This concerns wireless sensing systems that access network services
using the Wireless Access Protocol (WAP).
3.5.9. Interoperability and federation
To improve openness and interoperability in fog computing systems,
cover open standards, equipping third-party systems with the ability to
execute models and processes using web service calls, while at the same
time the results can be spread for the use of other services [3] .
3.5.10. Real-time analytic
Fog computing’s real-time analytics refers to gathering, examining,
and acting on data as it is generated rather than delaying its transmission
to a central point for processing. Instead of relying on a centralized data
center, this is accomplished by positioning small, lightweight computer
units closer to the data source at the network’s edge. The data can be
processed locally by these devices, which transfer the essential data to
the cloud for additional analysis. As a result, data processing can be done
more quickly and eectively, with less latency and bandwidth usage
[13] .
3.5.11. Support for industrial applications
With this function in fog computing, industries can be upgraded with
eective and ecient connection and communication. Data can also be
streamed through the fog node layer. If connected to a collection of
nearby sensors and actuators, a fog node at the bottom of the hierarchy,
for instance, one found on a single machine, can assess the data, inter-
pret an anomaly, and then, if given permission, autonomously react and
correct the issue [21] .
3.5.12. Broad-based sensor networks
This is intended to monitor the environment of the fog; another ex-
ample of an inherently distributed system that calls for distributed pro-
cessing and storage resources is the Smart Grid [13] .
3.5.13. Large population of nodes
This refers to the enormous number of nodes on the fog’s network
due to broad geo-distribution, as shown by sensor networks generally
and the Smart Grid specically [13] .
3.6. Different topics in fog computing research
A distributed computing paradigm known as fog computing often
referred to as ”edge computing, ”puts networking, storage, and comput-
ing capabilities closer to the network’s edge, where data is generated
and consumed. By lowering the latency and bandwidth needs for data
transmission and enabling real-time data processing at the edge of the
network, it aims to alleviate the constraints of conventional cloud com-
puting. In fog computing, some crucial topics include:
3.6.1. Resource scheduling
The act of allocating and managing resources (such as processing
power, storage, and network bandwidth) on fog nodes to satisfy the
needs of the applications and services they host is known as resource
scheduling in fog computing [13] . Utilizing optimization methods, such
as genetic algorithms, to determine the best resource allocation for a
given collection of applications and services is one resource scheduling
method in fog computing. Another option to forecast and enhance how
resources are used in real-time is to employ machine learning techniques
like neural networks [22–24] .
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R. Das and M.M. Inuwa Telematics and Informatics Reports 10 (2023) 100049
3.6.2. Task scheduling
Task scheduling in fog computing is the process of planning when
tasks should be completed on fog nodes while taking into account vari-
ables like resource availability, task location, and application or service
requirements [25] . One task scheduling method in fog computing uses
heuristic methods, such as the Ant Colony Optimization (ACO) algo-
rithm, to determine the best schedule for a given set of jobs. Game the-
ory, like the Stackelberg game, is another method for determining the
most eective approach to scheduling jobs by simulating how fog nodes
and their users interact [26,27] .
3.6.3. Load balancing
In fog computing, load balancing refers to dividing workloads and
resources among several devices in the fog network to maintain optimal
performance and prevent the overloading of any one unit. This can be
accomplished using various methods, including IP hashing, least connec-
tions, and round-robin. In conclusion, load balancing in fog computing
is essential to ensure adequate resource use and avoid network bottle-
necks [27,28] .
3.6.4. Offloading
The process of moving data and tasks from devices (such as smart-
phones or IoT devices) to fog nodes, which are placed closer to the edge
of the network, is known as ooading in fog computing. This makes pro-
cessing quicker and less demanding on the cloud. By keeping sensitive
data inside the fog network, ooading can also increase security and
privacy. Fog computing ooading improves the network’s eectiveness
and performance as a whole [29,30] .
3.6.5. Allocation
A distributed computing technology called fog computing brings
computation and data storage closer to users. In this paradigm, numer-
ous edge devices share resources like processing power, storage capac-
ity, and network bandwidth. To ensure that the system can meet end
users’ expectations while using resources eectively, fog computing re-
lies heavily on resource allocation [27,31,32] .
3.6.6. Resource management
Resource management in fog computing is the process of managing
and regulating the resources present in the system. This entails distribut-
ing resources to various programs, keeping an eye on how they’re being
used, and making sure the system can handle the demands of the end
users. For the system to be eective and trustworthy, resource manage-
ment is essential [7,29,30] .
3.6.7. Resource estimation
The process of guring out the resources (such as processing speed,
storage capacity, and bandwidth) accessible at a specic fog node is
known as resource estimate in fog computing. Decisions on where to
deploy apps and services and how to distribute resources to satisfy the
needs of various users and devices are made using this information [14] .
3.6.8. Green fog computing
Fog computing is used in ”green fog computing ”to minimize the
impact of computing systems on the environment and to conserve en-
ergy. This can be accomplished through a variety of strategies, including
power management, energy-ecient computation, and the utilization of
renewable energy sources [14,33,34] .
3.6.9. Security
The rules and policies implemented to safeguard information and
equipment in a fog computing environment are referred to as fog com-
puting security. This entails defending against cyber threats, including
hacking and malware, securing device connection, data storage, and
processing. Implementing access limits and keeping an eye out for ques-
tionable activity are also involved. The integrity, accessibility, and con-
dentiality of data and devices inside the fog computing ecosystem are
the main objectives of fog computing security [7,29,35] .
3.6.10. Privacy
Fog computing privacy refers to safeguarding private information
and data that is processed, communicated, and stored by fog comput-
ing systems. This includes ensuring data availability, condentiality,
integrity, and compliance with pertinent rules and laws. Encryption,
access restrictions, and system activity monitoring are a few privacy
safeguards in fog computing. In general, fog computing privacy aims to
maintain data security as it travels across and is processed by the fog
computing network [7,29,36,37] .
3.6.11. Data management
Fog computing is the decentralized method of storing, processing,
and analyzing data via edge devices and gateways. This eliminates the
need to send data to centralized cloud servers for processing and enables
real-time data processing, and analysis [1,7,37] .
3.6.12. Energy management
Fog computing entails the utilization of renewable energy sources
to power these devices while also optimizing the energy usage of edge
devices and gateways. Consequently, fog computing systems use less
energy overall and leave a smaller carbon imprint [1,38–40] .
3.6.13. Quality of service
The capacity to deliver a certain degree of performance for a given
service or application is referred to as quality of service (QoS). For ap-
plications and services to be supplied with the required level of per-
formance, reliability, and security in fog computing, quality of service
(QoS) is crucial [29,41,42] .
3.6.14. Mobile fog computing
A form of fog computing called ”mobile fog computing ”is created
specically for usage in moving cars or on portable electronic devices.
This kind of fog computing enables data processing and storage closer to
the data source, which can enhance application and service performance
and security [14,43,44] .
3.6.15. Cloud-fog integration
combines cloud and fog computing to create a hybrid computing
environment. Through this integration, it is possible to access the cloud’s
processing and storage resources while using fog computing resources
that are located closer to the network’s edge [45–47] .
3.6.16. Networking
In a fog computing environment, ”networking ” refers to data ex-
change and communication between various devices and components.
The fog nodes that gather and analyze the data must be connected to
edge devices, such as sensors and cameras, as well as the cloud or other
distant servers [1] . In fog computing, several essential technologies and
protocols allow networking. To connect edge devices to fog nodes, for
instance, low-power wireless protocols like Zigbee and LoRaWAN are
frequently employed. Additionally, data is transferred between devices
and nodes using industry-standard communication protocols, including
TCP/IP, MQTT, and CoAP. Security is a crucial component of network-
ing in fog computing. Encryption, authentication, and access control are
just a few security techniques used to safeguard data from illegal access
or manipulation. Overall, networking in fog computing is essential for a
fog computing environment’s ecient operation since it allows for the
secure and seamless movement of data between devices and components
[3] .
3.6.17. Fog device virtualization
The method of operating many virtual machines (VMs) on a single
physical fog device is known as fog device virtualization [14] . This en-
ables the separation of various applications or services and the eective
use of resources. Additionally, it enables the rollout of new services or
changes without aecting already-running services. Fog device virtual-
ization uses programs like OpenFog and OpenVirtex as examples [48] .
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3.6.18. Application and service placement
The process of choosing the best location for hosting applications
and services within a fog computing environment is referred to as ap-
plication and service placement. This entails determining the ideal edge
device or fog node to host the application or service and the resources
required to run it [14] .
3.6.19. IoT Data processing
Fog computing technology handles and analyzes data produced in
real time by IoT devices. This is known as IoT data processing. This
comprises pre-processing, ltering, and aggregation of data using fog
nodes or edge devices, as well as analysis and interpretation of the data
using machine learning techniques [1,7,14] .
3.6.20. Reliability-aware
Reliability-aware fog computing systems are created to make sure
that data and services remain accessible and available even in the case of
network outages or other interruptions. Techniques like storing data in
many locations, distributing the load, and having fail-over mechanisms
can be used to achieve this [29,49–52] .
3.6.21. Delay-aware
The idea behind delay-aware fog computing is that the fog nodes
(also known as edge devices) in a fog computing architecture are made
aware of the delay requirements of the applications they are supporting
and utilize this knowledge to decide where to process data and how to
route data between nodes. This can enhance the overall performance of
the fog computing system and lessen the overall end-to-end delay that
consumers encounter [29,53–55] .
3.6.22. Context-aware
Fog computing systems’ capacity to dynamically adapt to the vary-
ing circumstances and context of their operating environment is called
”context-aware fog computing. ”The location, time, and resources avail-
able, as well as the particular demands and specications of the appli-
cations and devices linked to the fog network, are all included [42] .
Context-aware fog computing systems use machine learning and other
cutting-edge technologies to continuously monitor and analyze the envi-
ronment and make in-the-moment changes to the resources and services
the fog network oers. As a result, apps and devices that rely on the fog
network operate more eectively and eciently using their resources.
Overall, context-aware fog computing is a crucial idea that might sig-
nicantly enhance the functionality and eectiveness of linked devices
and applications while also assisting businesses in better use of their
resources [1,42] .
3.6.23. Mission critical application execution
Since data doesn’t need to travel far to reach a centralized data
center, fog computing enables mission-critical applications to process
and make decisions more quickly. Fog computing can decrease latency,
boost dependability, and speed up response times in mission-critical
applications. Industrial control systems, driverless vehicles, and emer-
gency response systems are a few examples of mission-critical applica-
tions that can prot from fog computing [56] .
3.6.24. Proactive service discovery
In fog computing, proactive service discovery is detecting and identi-
fying resources and services in the fog network before they are required.
This decreases latency and enhances overall performance by enabling
quicker and more eective access to these resources. There are many
ways to implement proactive service discovery, including employing
machine learning algorithms to forecast future resource requirements
or continuously checking and updating the network’s resource avail-
ability. In general, proactive service discovery in fog computing enables
more eective and ecient use of resources, improving performance
and decreasing delays [1] .
4. Related works
In this section, reviews and research articles published in recent
years on Fog Computing are examined and analyzed in detail. Then,
each is presented under separate headings by examining them as
Table 1 and Table 2 .
4.1. Analysis of review papers
Fog computing has received considerable contributions from dier-
ent topics. Taxonomies are presented from dierent views, where all the
views might be valid depending on the industrial and/or researcher’s
needs. Dierent papers contributed to the services provided by the fog,
which included security, privacy, application, and communication. Fur-
thermore, this paper reviews the paradigms before the fog to provide a
clear and brief understanding and then proposes a uniquely categorized
taxonomy based on the fog’s contemporary research about security chal-
lenges, service issues, operational issues, and data management. More-
over, challenges and applications with future potential are identied.
Table 1 below provides a comparative analysis between this article
and the existing survey papers. The stated contributions of the paper
are used as comparative attributes. The selection method was rened
using a custom range from 2015 to date and concentrated on articles
that focus more on general fog computing.
4.2. Analysis of research papers
After examining current works and contributions in fog comput-
ing, several researchers have proposed a fog computing taxonomy with
diverse points of view and classications. For example, [61] catego-
rized the academics’ contributions based on the system-level designs
and frameworks of fog computing, meeting the demands of end users,
the technological aspect, security and privacy, QoS, and application.
Another taxonomy is provided based on the problems mentioned in
the articles, which include fog node setup, nodal collaboration, re-
source/service provisioning networks, service level objectives, suitable
networking systems, and security considerations [1] . This continues
with the surveys that oered a fog computing taxonomy utilizing multi-
ple parameters to construct the taxonomy. Our Taxonomy is presented
based on the contemporary fog computing research on security chal-
lenges, services issues, operational issues, and data management. The
standard for elucidating the taxonomy is built on the functional and vi-
tal issues in fog computing. Table 2 below provides the highlights of the
papers used in concluding.
5. Security threats of fog computing
Most of the fog computing applications are inuenced by their crav-
ing for functional services and or the user needs, ignoring security re-
quirements, or considering them as second thoughts [114] . Security
challenges in fog computing were not given proper attention [82] . With
the inheritance of cloud computing security challenges, fog computing
might be vulnerable to exploitation [57] . Security in the OpenFog RA is
not a one-size-ts-all architecture. Rather, it describes all of the mech-
anisms that can be applied to make a fog node secure from silicon to
software application [12] . Many scholars have been trying to propose
solutions in dierent areas of fog computing. However, the issue of secu-
rity in fog computing is still a bedeviling problem in both academic and
industrial environments [72] . Additionally, the security solution imple-
mented on cloud computing may not be eective on fog computing as
they both work from dierent layers and their architecture diers [8] .
Fog computing is prone to security attacks as it is built on a tradi-
tional network. Authentication and privacy can be troubling issues in
Fog. Fog node collaboration might pose security threats on the network
as an infected node can infect others [5] . Security exposure in fog com-
puting is termed very high for its environment lies between cloud data
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Table 2
A taxonomy of the research papers in fog computing.
A taxonomy of the research papers in Fog Computing
Ref. Aim and Objectives Used Technology Application Tools Results Domain
[69] A team of scholars has proposed an
application meant to help provide and
implement a Quality of Experience (QoE)
policy by prioritizing application
placement requests based on user
projections and computing the ability of
Fog instances in consideration of their
present condition.
Fuzzy logic rules,
and multi-constraint
single objective
optimization
technique.
iFogSim, Fog nodes,
cloud data center,
and edge devices.
The results from the simulation indicated
that the proposed policy outperformed
the other policies in goal achievement.
That remarkably enhances data
processing time, service quality, network
congestion, and resource aordability.
Context Awareness
[70] Proposed a multi-tier architectural design
named Fog Computing Architecture
Network, in which it integrates the
applications running on IoT devices and
cooperatively directs, computes, and
communicates with each other through
the smart city environment.
Fog Network
Architecture, wired
and wireless
technology
iFogSim, Fog Nodes
(FNs), and IoT
devices.
The outcome shows a decrease in latency,
improved provision of energy, and
eective application management.
Context Awareness
[71] The paper presented a fog computing
context-aware framework for the
intelligent transport system, providing
multiple intelligent systems to support
services with the ability to extend to IoT
services.
Cloud, fog, edge
computing paradigm
Fog nodes, cloud
gateways, and edge
devices (vehicles).
The result showed that the proposed
CFC-ITS is resilient in real ITS
applications.
Context Awareness
[72] The paper proposed a context-aware
framework to address the shortcomings in
accessing and controlling data and
information resources from multiple
sources.
Fog computing
paradigm, and
modeling.
Web Ontology
Language,
Description Logics,
and Protg-OWL
graphical.
The result was a positive outcome, which
is eective, exible, and implementable.
Context
Awareness/Privacy,
Security and Trust
[73] The paper proposed a general architecture
and a proof-of-concept structure using fog
computing to implement mobility
applications in the vehicular and ad-hoc
networks (VANET) environment to take
care of trac inconsistency detection and
the estimation of travel time.
Vehicular and
ad-hoc Networks
(VANET), cloud, and
fog computing.
On-Bord system
Units, Road Side
system Units, fog
nodes, and cloud
data center.
The results indicated that even with a
small data set, the applications could allot
reliable information quickly. It’s possible
to use fog nodes’ computing abilities and
communication. They can be deployed in
real-time.
Context
Awareness/Service
and Application
[74] The scholars proposed an architecture to
provide services which include city-wide
trac modeling and prediction built on a
fog computing model.
Deep learning
technique, and fog
computing.
Control plane, data
store, analytical
engine, and fog
nodes.
The results revealed that executing data
processing in fog nodes provides strong
back-haul connectivity. Also, the
forecasting system and trac modeling
behavior are more appealing when
executed in the fog than in the cloud.
Context
Awareness/Service
and Application
[75] The paper proposed the implementation
of Ubiquitous Resource Management for
Interference and Latency-Aware services
(URMILA) for managerial decisions on
dynamic resources for achieving
successful trade-os between fog and
edge resources while ensuring that IoT
service latency requirements are met.
Cloud, fog, and edge
computing
paradigm.
Centralized data
center (cloud),
Micro-data center
(fog node), and edge
devices.
A new approach for managing resources
over the cloud, fog, and edge spectrum
has been developed.
Context-Awareness
[76] The scholars presented a conceptual
architecture that accounts for context
changes by adding a node controller
feature that, when context changes are
identied, triggers relocation behavior on
dedicated processing components.
Lightweight
container
technology, dataow
programming
pattern, cloud, fog,
and edge computing.
Virtual machines,
processing elements
(PE), and
interconnections.
A fog cluster management system that
spans the cloud-edge spectrum.
Context-Awareness
[77] The IoT-BSFCAN platform is proposed for
continuously monitoring the smart
environment via smart computing devices
over cloud-enabled networks.
Cloud, and fog
computing
paradigm.
Fog nodes, cloud
servers, and edge
devices.
The illustrative result shows that the
proposed IoT-BSFCAN system
outperforms the other alternative
solutions in terms of ecient execution.
Context-Awareness
[78] The paper proposes MobMBAR, a
mobility-aware task scheduling, and
allocation approach. It distributes
healthcare activities between cloud and
fog devices in a dynamically balanced
manner.
Cloud, fog
computing
paradigm, hand o,
WiFi,
Cloud data center,
fog nodes, and
sensors.
The proposed solution ensures QoS by
executing healthcare tasks on time and
allowing the execution of heterogeneous
healthcare tasks with varying processing
speeds, data sizes, and numbers.
Context-Awareness/
Services and
Applications
[79] the paper proposed an energy- and
performance-aware vehicular fog
distributed computing approach to
eciently process IoT jobs using a
cluster-enabled capacity-based load
balancing approach.
Cloud, and fog
computing
paradigm.
vehicles, the
state-of-the-art NS2
simulator, fog nodes,
and cloud data
center.
The proposed scheme achieves balanced
network energy usage, decreased network
latency, and increased network eciency,
according to the results.
Context-Awareness/
Services and
Applications
( continued on next page )
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Table 2 ( continued )
A taxonomy of the research papers in Fog Computing
Ref. Aim and Objectives Used Technology Application Tools Results Domain
[80] Implementation of fog computing
approach to tackling DDOS attacks on the
Industrial Internet of Things (IIoT).
Cloud, fog, network
function
virtualization (NFV),
and Modbus
protocol.
Industrial devices,
IIoT controllers,
serial
communication
devices, onsite
cameras, and trac
simulators.
The Fog computing approach has been
implemented successfully on a testbed to
handle the DDoS attack on IIoT, to
improve the real-time response and device
computational capabilities of the IIoT.
Privacy, Security
and Trust
[81] The scholars proposed a protocol for
authentication between cloud, fog, and
edge devices using key initiation.
The 3-layered
architecture of
cloud-fog-edge, and
encryption
techniques.
Scyther simulator,
cloud data center,
fog nodes, and edge
devices.
The security proposal claimed to provide
maximum protection against the relevant
attacks. However, the proposed protocol
is more eective in computation and
communication.
Privacy, Security
and Trust
[82] The scholar proposed a security
architecture model handling privacy
preservation in fog computing, utilizing
the support of device-to-device
communication, and introduced three
lightweight anonymous authentication
protocols (LAAPs).
Cloud, fog,
lightweight
cryptography,
one-way function,
and exclusive OR
operations.
Edge devices,
Network Access
Devices (Fog nodes),
and Centralized
cloud server.
The proposed system is claimed to provide
eective security and be realizable on IoT
devices with limited resources.
Privacy, Security
and Trust
[83] A system is proposed in which DDoS
attack trac is created with various tools
and then routed through the fog defender
to the cloud, and identies and lters
DDoS attack trac directed at the cloud.
Cloud computing
and Fog computing.
Cloud data center,
fog nodes, Linux,
and windows
systems.
Recorded success as the Cloud server only
forwarded valid requests. Response times
and resource usage in the cloud have
increased.
Privacy, Security
and Trust
[84] The paper proposed a method for
detecting DDoS attacks and eciently
provisioning resources in a cloud
environment using fog computing and an
ecient algorithm to service cloud
requests by intermediate fog servers
eectively.
Cloud and fog
computing
Cloud server and fog
servers
The majority of requests are delivered by
the intermediate fog layer, which detects
and mitigates harmful trac.
Privacy, Security
and Trust
[85] The paper centered on the extension of
the Attribute Based Access Control
(ABAC) model. A new prediction model
was proposed. It describes the
authentication process of fog computing
access control’s uncertainty problem.
Decision Tree,
Random Forest, and
Logistic Regression.
Machine learning.
MATLAB,
sci-kit-learn package,
and Python.
The model outperforms other algorithms
in terms of accuracy while having a
reduced computational cost.
Privacy, Security
and Trust
[86] Focusing on the randomness of HTTP GET
requests for every source IP address, a
unique approach for detecting
application-layer DDoS attacks is
suggested.
Hyper Text Transfer
Protocol (HTTP),
and time series.
Support Vector
Machine (SVM),
Adaptive
Autoregressive
(AAR), NS-2 network
simulator and Linux
platform.
The results suggest that this method may
eciently detect application-layer DDoS
assaults.
Privacy, Security
and Trust
[87] The scholars implemented a technique
using fog computing to improve medical
data security.
Cloud, fog
computing, and
Advance Encryption
Standard (AES).
Fog nodes, edge
devices, and cloud
data centers.
The implementation result shows that
medical data security can be improved
using fog computing.
Privacy, Security
and Trust
[36] To safeguard IoT data, this article
presents a decentralized access control
system based on blockchain and fog
computing technology.
Fog, blockchain,
mixed and nonlinear
spatiotemporal
chaotic systems, and
the least signicant
bit.
IoT devices, and
edge nodes.
The technique’s experiments showed that
this could eectively secure the privacy of
IoT data.
Privacy, Security
and Trust
[88] Using fog computing technology, the
researchers suggested the FONICA (Fog
Node as an Intermediate Certication
Authority) certicate verication
technique.
Cloud and Fog
computing.
Fog nodes and edge
devices.
When compared to other recently
presented systems, the proposed system
yields ecient results based on storage
and transmission overhead characteristics.
Privacy, Security
and Trust
[89] The paper focused on preserving privacy
in vehicular crowdsensing. Designed a
two-tier architecture for fog computing
that could be used in fog-based vehicular
crowd sensing systems and proposed a
secure approach for data collection that
could maintain both privacy and access
control and data traceability.
Fog computing, and
Cloud computing
paradigm.
Cloud data centers,
data requesters, fog
buses, Participating
vehicles, and trust
authorities.
According to the paper, the simulation of
the proposed approach showed that the
approach is ecient in both
communication and computation.
Privacy, Security
and Trust/Service
and Application
[90] To safeguard data and manage mobility,
the paper provided a data protection
paradigm for fog computing by
implementing a Region-Based
Trust-Aware (RBTA) and a Fog-based
Privacy-aware Role-Based Access Control
(FPRBAC).
Cloud, and fog
computing
paradigm.
Cloud data center,
fog nodes, and edge
devices.
The model’s viability and eciency were
proven by the results of the experiments.
Privacy, Security
and Trust/Service
and Application
( continued on next page )
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Table 2 ( continued )
A taxonomy of the research papers in Fog Computing
Ref. Aim and Objectives Used Technology Application Tools Results Domain
[91] Designed an IoT architecture based on
cloud and fog computing, and then
proposed a system for how to place the
IoT data into the cloud and fog data
centers of the stated architecture.
Cloud, and fog
computing
paradigm.
Cloud and fog
computing data
centers, and IoT
nodes.
Designed an IoT architecture based on
cloud and fog computing and then
proposed a system for placing the IoT
data into the cloud and fog data centers of
the stated architecture.
Service and
Application
[92] The purpose of the study is to provide a
resource provisioning scheme for
partitioning a given workload between
multiple computing layers that are subject
to reliability and real-time requirements.
Virtual Machine,
1-to-2 mapping, and
fog computing.
gEDF scheduler,
Compositional
Schedulability
Analysis (CSA),
AUTOSAR-based
application.
The software tool can allow the designers
to decide on the minimum number of
local servers that can be harmonized with
the fog nodes for the execution of
real-time jobs. It can also help the
designer select the appropriate bandwidth
size linking the factory and the cloud data
center.
Service and
Application
[93] The paper focused on task scheduling of
fog-based IoT applications to minimize
term service delays and computation costs
under resource and deadline limitations.
Double Deep
Q-Learning (DDQL)
algorithm, Task
Scheduling Scheme,
and First come, rst
serve (FCFS).
Cloud, fog
Computing, virtual
Machine, Fog Nodes,
Cloud Data Centers,
IoT devices, Keras,
and SimPy.
Based on the experimental results, the
proposed algorithm trades between
transmission, propagation, waiting, and
execution delays of tasks by allocating
each incoming task to more suitable VMs.
Service and
Application
[94] Dual energy sources were employed to
power the fog nodes, with solar power as
the primary source and grid power as
backup. An analytic framework for
integrating green energy sources was
presented to support IoT and fog
computing-based systems operation. Also,
LOTEC (Lyapunov Optimization on Time
and Energy Cost) was developed.
Cloud, fog systems,
Lyapunov
optimization, and
discrete event
simulation.
SimPy, cloud
servers, fog servers,
and edge devices.
The proposed algorithm appears to be
promising based on the simulation
performance.
Service and
Application
[95] The scholars proposed an Energy-Ecient
Cluster Routing Method for Cross-Layer
Sensing. The algorithm projects fog nodes
onto the sensing layer using a
sensing-event-driven mechanism and
creates a strong virtual control node. The
Particles Swarm Optimization algorithm
was introduced to select a group of
optimal nodes to serve as cluster heads.
Fog, clustering
technique, PSO
algorithm, and
Wireless Sensor
Networks.
Fog nodes, and
sensors.
The proposed ECCM algorithm performed
wonderfully. As a result, the proposed
ECCM algorithm’s ecacy and reliability
have been demonstrated.
Service and
Application
[96] In fog computing, a mixed-task approach
was introduced to solve the joint
computation and communication resource
allocation challenge.
Fog computing,
Logistic regression,
Split-spectrum, and
Quasi-static.
Fog nodes and end
users (devices)
The suggested approach could provide a
considerable increase in energy eciency,
according to the ndings.
Service and
Application
[97] A tree-based fog computing (TBFC) model
is proposed for distributing processes and
data to servers and fog nodes in the IoT,
with the goal of lowering node total
electricity consumption.
Tree-based fog
computing model
and tree-based cloud
computing model.
Edge devices
(sensors and
actuators), fog
nodes, and cloud
servers.
The overall electric energy consumption
of nodes in the TBFC model is lower than
in the cloud computing model, according
to the assessment.
Service and
Application
[98] A mobile caching network with
energy-ecient edge nodes called
CachinMobile was proposed by leveraging
social networking and device-to-device
communication.
Cloud Computing,
Fog Computing,
Wired
communication, and
Wireless
Communication.
Edge node, fog
servers, base station,
routers, and cloud
servers.
The proposed paradigm is claimed to have
signicantly improved energy eciency
while maintaining the quality of service.
Service and
Application
[40] The scholars proposed an energy-ecient
caching and node association algorithm
for cache-aided fog networks.
Caching technology
and node-to-node
communication.
Fog nodes, and
access points.
The technique outperforms the standard
caching strategy in terms of energy
eciency when the modulation modes
and caching are implemented
simultaneously.
Service and
Application
[99] This paper develops a three-layer
ooading architecture for the intelligent
Internet of Vehicles (IoV) to reduce total
energy consumption while meeting users’
delay constraints. The formulated
problem is divided into two sections: 1)
ow redirection and 2) ooading
decision, and then solved using a deep
reinforcement learning-based scheme.
Three-layer network
model, Fog
computing, and deep
reinforcement
learning-based
scheme.
Fog nodes (vehicles),
cloudlets, and
roadside units
(RSUs).
The success of the approaches is
demonstrated by performance assessment
based on real-world footprints, whereby
the energy usage may be reduced by
roughly 60 percent compared to the
default algorithm.
Service and
Application
[100] The paper covered some of the
fundamental issues that a system architect
can think about when developing,
implementing, and deploying an
end-to-end healthcare framework that
incorporates IoT nodes and cloud
computing backend resources and takes
advantage of the Fog computing
approach.
Cloud, and fog
computing
paradigm.
mobile devices,
wearable and
sensors, Spark IoT
Platform Core, cloud
data center, and fog
nodes.
Because of the large number of networked
devices, comprehensive solutions are
required to eectively tackle the
diculties of massive data transfer across
network nodes.
Service and
Application
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Table 2 ( continued )
A taxonomy of the research papers in Fog Computing
Ref. Aim and Objectives Used Technology Application Tools Results Domain
[101] The study suggests a smart home
approach based on fog computing to
provide an optimal healthcare
environment for domesticated animals.
Cloud, fog systems,
wireless
communication
protocol, SSL,
credential mapping,
and CNN.
Cloud data centre,
fog nodes, sensors,
IoT devices.
According to the comparison results, the
proposed model outperformed other
state-of-the-art approaches for veterinary
healthcare provisioning.
Service and
Application
[102] The paper built an end-to-end Internet of
Things application that uses advanced
machine learning and data analytics
technology to control calves in real-time
and detect defective livestock early.
Cloud, fog
computing
paradigm, and
Message Queue
Telemetry Transport.
Radio-based Long
Range Pedometer
(sensor), fog nodes,
and cloud data
centre.
The ndings show that lameness can be
detected three days earlier than its
appearance. With fog computing, a
reduction in data transferred to the cloud
is recorded.
Service and
Application
[103] By utilizing dierent parameters, a
mechanism for task ooading to fog
nodes and cloud data centers was
presented.
Cloud, and fog
computing paradigm
(theoretically)
Cloud data centre,
fog nodes, and edge
devices
(theoretically)
The proposed technique proved ecient
theoretically.
Service and
Application
[104] An ooading standard depending on the
work’s computational requirements is
presented, where those with high
computational requirements are ooaded
to the fog. At the same time, those with
less are handled locally.
Cloud and fog
computing.
Cloud node, fog
node, and mobile
device.
In average latency, the load is
appropriately distributed between mobile
devices and the fog node at minimal
points.
Service and
Application
[105] Designed and proposed a platform called
Fog-as-a-Service, proposing a new
knowledge-centered and service-based
service model and granting the denition
of self-adaptive and composition-friendly
services, which can be implemented on
either the edge device or the cloud.
AWS Cloud platform,
AMI Linux. And a
Raspberry Pi 3
Single Board.
The testbed consists
of a root controller,
2 SPF platforms, a
Wireless Sensor
Network, and
Programmable IoT
Gateways.
The model oers signicant advantages
for self-adaptive and composition-friendly
fog services. An information-centered and
valuable service model presented by the
Fog-as-a-Service platform provides
aerodynamic development and
management.
Services and
Applications
[106] A proposal for secure computational
ooad in the IoT, fog, and cloud
environments to reduce latency and
energy consumption.
Cloud, fog, and
load-input data ratio
(LDR) to set the
dierences between
jobs with high
computational
intensity and those
without.
Smart gateway, IoT
mobile devices, Fog
nodes, a hybrid
cloud server, and
Network Simulator
(NS3.26) with Java
programming.
The Scholars’ Ooading Scheme proposal
shows signicant improvement compared
to the reviewed ooading schemes. It
executes ooading with minimal latency
and energy consumption. It also claimed
to be secure and eective in balancing the
tradeo between latency and energy
consumption.
Services and
Applications
[107] The scholars introduced Quantum
Computing-inspired (QCi), an optimized
technique for load scheduling in a fog
computing environment targeting
real-time IoT applications. Additionally,
the QCi-Neural Network Model was
integrated to predict and determine the
node with optimal computation to enable
real-time service delivery.
Fog computing,
Quantum
Computing-inspired
(QCi) and
QCi-Neural Network
Model.
Fog nodes, personal
computer and
iFogSim toolkit.
The result shows that the system
presented high eciency in predicting the
optimum fog node for tasks depending on
the availability of the respective node.
Services and
Applications
[16] The scholars give a comparison of cloud
and fog computing based on the reliable
performance and latency of
cyber-physical interfaces, using Industry 4
to execute real-time embedded machine
learning engineering applications.
Cloud, Fog
computing
paradigm, and
Amazon Web
Services (AWS).
Cloud server, fog
nodes,
PMML-encoded
machine learning
models, Jmeter,
wireless router,
OpenScoring engine,
Raspberry Pi3,
Python, Cylon BMS,
and OPC Driver.
The results showed that the fog computing
paradigm is more consistent, reliable, and
secure than the cloud paradigm.
Services and
Applications
[108] The paper proposed a fog-assisted health
monitoring system in consideration of the
services fog computing provides. These
include latency, network usage, and
power consumption.
Cloud, and fog
computing paradigm
WiFi, medi-
cal/environmental
sensors, actuators,
cloud server, fog
server, and an
application running
on the Raspberry Pi
Zero W board from
Adafruit.
The result indicated that the proposed
could help minimize data trac and
improve the system’s security as it can
hold private and sensitive data within the
organization.
Services and
Applications
[109] Experimented on the signal quality and
comfortability of electrocardiogram
(ECG), optimizing and balancing the
quality of signals and the comfort of
usage by the patient.
Fog computing
paradigm, knitting,
and
electrocardiogram.
Fog nodes, elastic
fabrics, and E7.2
STOLL CMS 530
computerized at.
The result shows that the material with
70% nylon ber coated silver and 30%
cotton provided well-balanced user
comfort and signal quality with less air
resistance.
Services and
Applications
[110] The aim was to nd the Fog Node with
the shortest delay, which was unknown at
the time, while also ensuring that
switching costs between Fog Nodes were
as low as possible.
Multi-armed bandits,
Block-based
technique, and
greedy selection
technique.
Python, fog node,
access points, and
edge devices.
The two methods considerably increase
fog node selection eciency, according to
the ndings.
Services and
Applications
( continued on next page )
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R. Das and M.M. Inuwa Telematics and Informatics Reports 10 (2023) 100049
Table 2 ( continued )
A taxonomy of the research papers in Fog Computing
Ref. Aim and Objectives Used Technology Application Tools Results Domain
[111] For interconnected Fog–Cloud
environments, a prot-aware application
placement strategy was suggested. It is
formulated using a constraint Integer
Linear Programming model that
maximizes benets while maintaining
QoS when applications are placed on
computing instances. In addition, it
compensates customers for any breaches
of the Service Level Agreement (SLA) and
prices instances based on their willingness
to reduce service delivery time.
Cloud, fog system,
Integer Linear
Programming,
Application Protocol,
SNM Protocol, and
cybersecurity
frameworks.
Cloud datacentres,
fog nodes, iFogSim,
and edge devices.
The proposed policy increases gross and
net prot, wait time, and QoS satisfaction
rate. They also showed that heuristic
policy implementation gets you closer to
the best solution in the shortest time, and
their results justify fog instance pricing.
Services and
Applications
[112] In a real-world test-bed, the paper looked
at the impact of fog computing on
response time and Internet trac for a
WBSN-based (Wireless Body Sensor
Network) healthcare application.
Cloud, fog
computing
paradigm, and
delay-sensitive
application.
Cloud datacentres,
fog servers, sensors,
and mobile devices.
According to the ndings, the fog-based
architecture decreases response time and
Internet trac by 46 percent and 77
percent, respectively, compared to the
cloud.
Services and
Applications
[113] The paper presented how to secure a
prover’s position location privacy using
cryptographic protocols in the bounded
retrieval model.
Cryptography Bounded-retrieval
model, and a
personal computer.
The result indicated that the proposed
protocol is implementable.
Services and
Applications/
context-awareness
centers and the devices. The study shows that the security topic of dis-
cussion in fog computing is bending more on authentication, secured
data exchange, denial of service attacks, and privacy issues [1] . Limited
network visibility, ineective way of attack detection, absence of user
selective data collection, virtualization issues, multitenancy issues, and
malicious fog nod issues are some of the security and privacy challenges
in fog computing [115] . According to [82] , mutual authentication, se-
cure key exchange, and anonymity must be appropriately addressed to
provide adequate security and privacy in fog computing tiers. Addition-
ally, [72] stated that access control is one of the popular preventive
measures used in the cyberspace domain to protect against unautho-
rized access and reduce the eect of security breaks. These will guar-
antee a good quality of service in dierent IoT services [5] . added that
having the fog node as the rst processor that most of the time data en-
counter made it necessary for the nodes to have hardware root of trust
implemented to avert incessant rate of attacks.
5.1. Forgery
It is a kind of attack where the attacker tries to emulate a particular
node and fool the end-users into executing actions of his/her choice.
Unfortunately, that can aect the networks quality of service as it might
slow the performance through the excess use of bandwidth, storage, and
energy [57] .
5.2. Tampering
It is a type of attack where the attackers create a delay in data trans-
mission or drop the data, or even sometimes modify it. Due to the mobil-
ity nature of fog computing, this kind of attack becomes more dicult
to gure out the attack immediately, as the network itself can cause a
delay and failure in the data transmission [90] .
5.3. Spam
Spam is often used to describe undesired, trash communications de-
livered to an Internet user’s mailbox [116] . This attack is where the
attacker fools the victim with unwanted data to seize the opportunity to
use the victims network resource and expose privacy. Spam is seen as a
signicant danger to the Internet and society. Internet users are exposed
to security risks when they receive spam communications, and children
are exposed to unlawful and inappropriate information. Furthermore,
spam communications use necessary storage, bandwidth, and produc-
tivity resources.
5.4. Sybil
In a Sybil attack, an attacker generates many false identities, disrupts
the reputation system of network service, and exploits it disproportion-
ately to achieve a more signicant impact. The impersonating node is
referred to as the malicious node or Sybil attacker, while the identity
spoofed node is referred to as the Sybil node [117] . The harsh impact
of a Sybil attack is that it creates bogus crowdsensing reports since the
ndings produced by these reports are untrustworthy [57] .
5.5. Eavesdropping
This type of attack is sometimes called snooping or sning, where
the attacker monitors and steals the victims data over the network. Due
to the open nature of the channel, data transmission in wireless mode is
vulnerable to eavesdropping. As a result, the eavesdropper can decode
important private information [118] .
5.6. Denial of service
This attack denies the rightful users the right to access the net-
work. The attack preoccupies the network resources, which leads to the
poor performance of fog computing. Because fog nodes have limited re-
sources, they struggle to manage a high volume of concurrent queries.
Fog productivity might suer signicantly in this situation. Denial-of-
Service (DoS) attacks can be crucial in causing signicant service inter-
ruptions in fog computing. Fog nodes may be kept active for extended
periods by issuing many unrelated service requests simultaneously. As a
result, resources for hosting helpful services are no longer available [1] .
5.7. Collusion
This type of attack aims to cheat and or mislead a legitimate group
from accessing their rightful resources by cooperating with multiple
numbers of groups. Additionally, this attack can expose the victims se-
curity key for the attacker to access les and other documents [57] .
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5.8. Man-in-the-Middle
As the name implies, the man-in-the-middle attack occurs when A
and B nodes communicate, and another node is in between them, ac-
tively observing, recording, and exercising control of the data in com-
munication transparently [119] . This attack will only require minimal
resources in Fog devices, such as low CPU and memory usage. As a
result, standard anomaly detection algorithms are unlikely to detect a
man-in-the-middle threat if no discernible characteristics of the attack
are acquired from the Fog [2] .
5.9. Impersonation
It is a type of attack in which the attacker successfully acquires the
identity of a legitimate fog node and executes the services as if it comes
from the genuine node. This type of attack has become a severe problem
in cloud systems due to the vast quantities of system resources, dierent
deployment patterns, and the dispersion of user audits and activities
across numerous virtual servers with diverse settings [120] .
5.10. Identity privacy
This type of privacy ensures the safety of the identity information
of the parties in communication. On the other hand, fog authentication,
which requires the users basic information, including name, address,
phone number, etc., might expose the user to a particular threat [57] .
5.11. Data privacy
They are also referred to as information privacy. It is responsible
for dening how the information can be handled appropriately. Poor
data privacy on fog networks can expose the user’s preferences and ide-
ologies to nodes that are not trusted. This can be controlled using the
proposed scheme of [121] , where a lightweight privacy-preserving ag-
gregation technique in fog computing is presented. The system is built on
the divide-and-conquer strategy. The suggested privacy method neces-
sitates data encryption, consisting primarily of lightweight symmetric
cryptographic operations, and encrypted data division depends on the
data-chosen owner’s level of privacy.
5.12. Usage privacy
This refers to the model or layout in which the user uses fog services,
ensuring the privacy of the user’s status and operational deals on the
network. One nave option would be for the fog client to create false
jobs and ooad them to several fog nodes, concealing its actual duties
amid the dummy ones. This method, however, will raise the fog client’s
payment while also wasting resources and energy. Another option would
be to devise a clever way of dividing the program to ensure that the
ooaded resource usages do not reveal sensitive information [17] .
5.13. Location privacy
It refers to the user’s ability to control their past and present location
access. As the fog supports mobility, the edge devices keep using the
current or saved location, where such exposure might make the user
vulnerable to some threats. One method for preserving location privacy
is identity obfuscation, which means that even if the fog node detects
the presence of a fog client nearby, the fog client cannot be identied
[5,17] .
6. Application areas of fog computing
Fog computing’s target is to decentralize the computational pro-
cesses and provide eciency to the IoT nodes. The Fog provides its
services at the LAN network layer [5] . Thus, exposing self to security,
trust, and privacy challenges as IoT devices and data keep growing ex-
ponentially. Many IT executives and Chief Information Ocers rejected
cloud computing due to its security and privacy risk [17] . Many contri-
butions were made in the security area of fog computing, those include
[80] , which implemented a fog computing approach to tackle DDOS
attacks in an Industrial Internet of Things (IIoT). The approach was im-
plemented successfully on a testbed to handle the DDoS attack in IIoT,
to improve the real-time response and device computational capabilities
in the IIoT. Also, [81] proposed a protocol for authentication between
cloud, fog, and edge devices using key initiation. The security proposal
is implemented in ve phases simulated using an informal method with
nine dierent forms of attacks, and it is claimed to provide maximum
protection to the relevant attacks. However, the proposed protocol is
more eective in computation and communication.
6.1. Healthcare management
The benet of having multiple servers in a fog environment with the
ability to run a particular application became the advantage of deliver-
ing improved healthcare services. Fog computing provides a healthcare
system with many advantages, including reducing energy consumption
and minimizing delays and data trac. In addition, analyzing and stor-
ing data locally at the fog’s layer enhances security as it stores vital and
private data within the organization [108] .
6.2. Traffic light systems
Fog computing and vehicular networks replaced conventional phys-
ical trac light systems with virtual trac light systems, which are
more cost-eective. In addition, fog computing provides an easy way
to reduce the trac crowd using trac signals adjustment based on the
trac situation or through vehicular communications guiding drivers
to non-crowded routes [122] .
6.3. Medical wearables:
The use of these devices by healthcare providers is becoming per-
vasive. It is used in delivering telemedicine, monitoring the condition
of patients, and directing on-site sta in surgery procedures [20] . Fog
computing does not only reduce medical expenses but has become a
cornerstone of modern healthcare systems [109] .
6.4. Connected vehicles:
With the help of context awareness, fog computing provides a secure
and ecient real-time interaction between cars, trac lights, and access
points. Thus, providing vehicle-to-vehicle communication due to the fog
nodes’ proximity with the IoT devices [20] . The vehicles in VANET’s
environments require services with minimal latency and short-distance
domestic connections through fog computing [73] .
6.5. Smart grids:
Smart grids help manage the connected devices’ operational activ-
ities through machine-to-machine and human-to-machine communica-
tion [20] , leading to quality of service maximization, minimizing the
rate of failure, improving the ecient use of energy, and optimizing
security[ 138 ].
6.6. Smart homes and cities
The main goal of smart-city applications is to enhance the opera-
tion of the city ows and grant real-time feedback to problems that may
emerge in users’ operational activities. Nevertheless, the available so-
lutions to smart cities using the cloud approach reach many require-
ments, however, with so many shortcomings that include latency, mo-
bility support, scalability, and localization. Although the intervention of
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fog computing extends cloud computing services close to the edge de-
vices, which leads to the decrease in latency, improves the QoS, context-
awareness, load balancing, and ecient distribution of data [ 73,139 ].
6.7. Latency management
Latency management refers to the time taken to respond, analyze,
and execute the computational request. The fog nodes’ proximity with
the edge devices helps in leading to faster computation tasks and anal-
ysis responses. All possible techniques need to be employed to manage
latency, including monitoring and adopting network design that could
do away with and force the drop of latency [123] . In addition, initi-
ating structured nodal cooperation can help provide eective latency
management. The distribution of tasks and the computation between
the fog and the clients are built on achieving minimal communication,
and computational latency [1] .
6.8. Data management
As the number of IoT nodes keeps increasing also, the volume of data
keeps increasing rapidly. That made cloud computing and other previ-
ous paradigms ineective in implementing the proper way of managing
the data, making fog suitable as it is placed close to the edge devices
[18] .
6.9. Network management
Management of networks in fog computing encompasses Software-
Dened Network (SDN)/Network Function Virtualization (NFV) sup-
port, decongestion of core-network, and guaranteeing reliable network
connectivity. SDN’s popular function is equipping NFV. Therefore, SDN
is regarded to be among the signicant backbone of virtualized net-
works. NFV is a notion of network architecture that virtualizes the con-
ventional functions to enable its implementation of the software. some
of these functions include; Network Address Translation (NAT), Domain
Name Service (DNS), caching, intrusion detection, and the rest [1] .
6.10. Computation management
Commitment towards managing the fog computing computational
resources is an essential aspect. Resource management encompasses the
estimation of resources, allocation of workload, and coordination of re-
sources. Estimating resources tackles allocating computational resources
based on some given policies to guide proper allocation and reach the
desired QoS. The workload allocation helps in maximizing the ratio of
resource utilization and reducing the long computation period. While
in resource coordination, large-scale applications are distributed to bal-
ance computational ability because of the fog nodes’ limitations and
variation in computational power and storage [1] .
It is a vital issue in the fog computing paradigm which includes mo-
bility support, and situation awareness [124] . These include; The pro-
posed application meant to help provide and implement the Quality of
Experienced (QoE) policy by prioritizing application placement requests
based on user projection and computing the ability of Fog instances in
consideration of their present condition. Also, it can ease the placement
of applications to the appropriate fog instances, which will maximize
the user QoE concerning service delivery, utility access, and resource
consumption [69] . Proposal of multi-tier architectural design Named
Fog Computing Architecture Network integrates the applications run-
ning on the IoT devices and cooperatively direct, compute and commu-
nicate with each other through the innovative city environment [70] .
These contributions are referenced in Table 3 .
7. Issues and challenges
Fog computing faces structural challenges due to its heterogeneous
nature. As the edge and or the core network can be used as a fog node,
some nodes might not be designed to serve a general-purpose computa-
tion. Therefore, integrating the general-purpose function from its tradi-
tional role might be an issue. Furthermore, in the face of service orienta-
tion, the evolution of large-scale applications might be an issue because
of the limited resources of some fog nodes. Therefore there is a need
for a programming platform that will help in the development of dis-
tributed applications. The execution of a security system for data-centric
integrity can signicantly aect the fog’s QoS. Additionally, access au-
thentication on services and maintenance for preserving the privacy of
such an extensive distribution network might prove dicult [1] .
Even with the overwhelming features of fog computing, it still faces
a shortcoming in device-to-device (D2D) communications [82] . Accord-
ing to [12] , many studies on fog computing concentrated on IoT to Fog
and Fog to Cloud solutions. However, there is a need to include research
on fog-to-fog communication, which is an open issue in fog computing.
Some examples were fog-to-fog selection, task prediction from historical
data, fog-to-fog resource utilization, scalability, robustness, and mobil-
ity of the fog nodes. Other topics that have been discovered but need to
be understood include the performance of fog ecosystems, robustness,
storage, energy savings, use of distributed fog through a combination
of device-to-device (D2D) and cellular network, mobility and node se-
lection, trust and security issues at all levels of the fog paradigm. New
technologies are topics that researchers have addressed for a long time.
Integrating IoT and fog services has started improving the quality
of services in health industries. These improve operational eciency,
optimize power consumption, and minimize the cost of operation. With
fog e-Health applications, the quality of service on patient management
can be improved, and some medical complications that could even lead
to the patient’s death can be minimized. Moreover, the generated data
from the applications can research and prevent epidemics and or pan-
demic diseases [108] . It is being highlighted that healthcare industries
will witness the deployment of robust solutions using fog computing for
quality of service delivery. However, the challenges it faces include; se-
curing sensitive data, system failure management, the conguration of
multiple systems, and the management of diverse systems having nu-
merous dimensions [12] .
In the smart city infrastructure, management of information and
communication resources needs to increase to perform real-time analy-
sis of big data close to the user and guarantee the security of the infras-
tructure and the generated information. The infrastructure has not yet
been fully resolved in terms of reliability, robustness, and durability to
process large amounts of data on the edge of the network to improve
node failure, service quality, and the performance of the smart city due
to slow convergence or malicious attack, trac congestion and the re-
liability and availability of services [12] . With the function of FC of
delivering application awareness to satisfy crowd-sourcing/sensing ap-
plications, FC needs to make some arrangements to set the resources
that could provide mobility services. However, the challenge here is
that the mobile node may experience some issues due to the variation
of computation, storage, bandwidth, and latency [8] .
8. Future research directions
Fog computing is a platform between the cloud and the edge nodes
to provide computing, storage, and network services between the two
[5] . Thus, many crucial application services are developed to serve a
generic or particular service. Transportation, placement, storage, and
ecient and eective computation of a large amount of data is the most
crucial issue in cloud, and edge computing [91] . Thus, serving as a future
research direction.
8.1. Healthcare applications
Before the introduction of fog computing, healthcare management
was already a success in cloud computing. However, any network failure
or inadequate bandwidth in a Cloud-based architecture results in a long
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R. Das and M.M. Inuwa Telematics and Informatics Reports 10 (2023) 100049
Table 3
A taxonomy of the research papers in fog computing.
Taxonomy Description References
Services and
Applications
Proposed an application, protocol, and service to improve the quality of service in fog
computing.
[16,40,71,73,74,78,79,89–100,102–113,125]
Context-Awareness Proposed a system that works based on the collected data about the environment and
analyzes it to guide the response of the proposed system.
[69,70,72–79,113,126]
Security, Privacy,
and Trust
Advances in securing the paradigm, provision of privacy protection, detection, and
mitigation of attacks.
[36,72,80–90]
response time. It is unsuitable for healthcare applications because it af-
fects the patient’s quality of life, even leading to death. It may be argued
that cloud computing does not always oer a high enough standard of
quality of service for healthcare applications [100,112,127] . The World
Health Organization predicts that by 2035, there will be a distressing
shortage of 12.9 million healthcare workers worldwide. To address the
shortage of healthcare workers, energy-ecient, low-cost, and scalable
healthcare technologies are needed to promote disease prevention, and
treatment [128] .
The fog is now used to optimize traditional services by improving the
response time, access control, privacy, etc., and enriching the environ-
ment with device proximity to the user and the patient [20] . If fog com-
puting’s latest features are eectively applied to time-sensitive health-
care applications, they can speed up the discovery of early warning
of emergencies, allowing for better decision-making. In time-sensitive
applications, fog computing outperforms cloud computing in provid-
ing easy access to data and making intelligent decisions in emergencies
[129] . Fog computing functions can be exploited to minimize the patient
data retrieval latency [40] .
8.2. Smart city applications
In comparison to cloud-based models, fog computing oers improved
real-time response. Due to the weak communication abilities of Wireless
Sensor Networks (WSNs), transmission from intelligent devices (e.g.,
sensors) to the cloud becomes a bottleneck, particularly for cloud-based
latency-sensitive IoT applications. This bottleneck has the potential to
undermine application eciency and stie future growth. Fog com-
puting strategies control resource use, minimize costs, increase system
eciency, and link IoT devices more eectively in sustainable smart
cities. Integrating multiple IoT technologies creates many opportunities
for management, growth, and governance of consumer services. Cities
are essential places that portray the life standard of humans and are
full of routine activities, social amenities, businesses, industries, health
services, and governmental and non-governmental infrastructures. With
these characteristics, it becomes a critical stage that shows human de-
velopment. Thus, making it easier for the fog to be adopted and imple-
mented for the development of society. The increased demand for new
technology like smartphones, IoT, and big data analytics leads to the
introduction of smart cities [12] .
8.3. Farm applications
The agricultural sector is gradually adopting the Internet and certain
networking technologies to enhance the services they provide to end-
users. It is widely acknowledged that the Internet has aws, especially
when dealing with large networked devices (IoT) or stakeholders [130] .
But it isn’t falling behind in fog computing, as the fog is making its way
into production control, keeping up the standard of farm production,
keeping an eye on the health of crops and livestock in real-time, and
making the market bigger. Fog computing farm applications can also
be used to predict climate state, livestock management, and crop man-
agement [12] . It can also be used to improve pest detection systems
through the use of image alignment analysis [131] . The use of smart
agriculture can be used to choose an appropriate time for disease treat-
ment and help the farmer monitor the farm without being there [130] .
According to [102,107] , people worldwide have been concerned about
the welfare of domesticated animals. Domestic pets are being infected
with lethal diseases due to poor treatment. As a solution, a smart home
based on fog computing could be used to give domesticated animals the
best possible healthcare environment.
It’s important to remember that fog computing is still developing and
that much more research is required. Overall, fog computing has a lot
of interesting research areas, and in the coming years, we may nd out
a lot more. These could consist of the following:
• Resource Allocation and Optimization: In fog computing settings,
practical algorithms and strategies are required for resource alloca-
tion. For fog computing systems to work better and be more reliable,
research in this area may focus on making techniques for dynamic
resource allocation and optimization [132,133] .
• Privacy and Security: Fog computing systems are vulnerable to
several security risks, including denial-of-service assaults and data
leaks. The aim of this research could be to nd solutions to maintain
security and privacy in fog computing environments [17,89] .
• Edge Intelligence: By using fog computing, machine learning and
articial intelligence (AI) algorithms can be implemented nearer to
the data source at the network’s edge. The performance and eec-
tiveness of fog computing systems can be enhanced through research
into edge intelligence, and analytics [134,135] .
• Real-time and distributed applications: IoT and 5G systems, for ex-
ample, can be deployed with the help of fog computing’s distributed
and real-time capabilities. Research in this eld might concentrate
on creating techniques for real-time and distributed computing in
fog environments while also guring out new use cases and applica-
tions for fog computing [136] .
• Augmented reality (AR): is a type of technology that improves how
the user sees the outside environment by superimposing digital infor-
mation on top of it. Smartphones or head-mounted displays (HMDs)
can be used to accomplish this. In recent years, the growing interest
in combining augmented reality (AR) and fog computing to produce
more responsive and immersive experiences has been observed. The
combination of AR and fog computing provides numerous advan-
tages, including real-time processing, minimal latency, and scalabil-
ity. Particularly in the context of smart cities and industrial appli-
cations, this technology has the potential to produce more immer-
sive and responsive experiences [137] . Research in augmented real-
ity with fog computing might concentrate on enhancing the latency
and responsiveness of augmented reality apps and creating new aug-
mented reality use cases that use fog computing’s capabilities. Such
application cases include intelligent transportation systems, smart
cities, and industrial automation. Additionally, security and privacy
concerns unique to AR should be considered while designing and
implementing fog-based AR systems.
9. Conclusion
Fog computing is a new paradigm that receives wide acceptance
and recognition due to its signicant contribution to modern computing
technology. This paper reviews and discusses cloud computing, briey
highlighting the implemented paradigms before fog computing. These
paradigms include cloudlet, mobile cloud computing, and mobile edge
16
R. Das and M.M. Inuwa Telematics and Informatics Reports 10 (2023) 100049
computing. All the paradigms targeted improving the quality of service
between the end devices and the cloud itself. The Taxonomy is pre-
sented based on contemporary fog computing research about security
challenges, services issues, operational issues, and data management.
The standard for elucidating the taxonomy is built on the functional and
vital issues in fog computing. Challenges and potential applications are
identied. The paper shows that security, privacy, application, and com-
munication challenges are prominent among the scholars contributions.
Potential applications in fog computing are also identied, including
healthcare applications, innovative city applications, and farm applica-
tions.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
No data was used for the research described in the article.
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Resul Das is a professor and chair in the Department of
Software Engineering, Technology Faculty, Firat University,
where he has been a faculty member since 2011. He grad-
uated with B.Sc. and M.Sc. degrees from the Department of
Computer Science at Firat University in 1999 and 2002 respec-
tively. Then he completed his Ph.D. degree at the Department
of Electrical-Electronics Engineering at the same university in
2008. He served as both lecturer and network administrator at
the Department of Informatics at Firat University from 2000
to 2011. In addition, he is the CCNA and CCNP instructor and
the coordinator of the
Cisco Networking Academy Program
since 2002 at this university. He worked between September
2017 and June 2018 as a visiting professor at the Department
of Computing Science at the University of Alberta, Edmonton,
Canada supported by the TÜB İ TAK-BIDEB 2219 Post-Doctoral
Fellowship. He has many journal papers and international con-
ference proceedings. he served as Associate Editor for the
Journal of IEEE Access and the Turkish Journal of Electrical
Engineering and Computer Science from 2018 to 2021. He en-
tered the 2% of the "World’s Most Inuential Scientists" list
published by Stanford University researchers in 2020, 2021,
and 2022. His
current research areas include computer net-
works and security, cyber-security, software design methods,
software testing, IoT/M2M applications, graph visualization,
knowledge discovery, and data fusion.
M. Muhammad Inuwa obtained his B.Sc. degree from the De-
partment of Computer Science at Bayero University in Kano in
2010. Then he completed his Master’s degree at Firat Univer-
sity’s Department of Software Engineering in 2018, and cur-
rently, he is a Ph.D. student in the same department. Addi-
tionally, he works as a lecturer in the Department of Software
Engineering, Federal University Dutse, Jigawa State, Nigeria.
His current research areas include fog computing, communi-
cation networks, cyber-security, and internet of things.
20
