[SOLVED] THE DISCIPLINE PROJECT Translating a Scholarly Article in Your Field of Study for a Public Audience – Humanities

The goal of this assignment is to “translate” or rewrite a scholarly article written about a current
topic in your academic discipline for an educated public audience. Your purpose is to make the
findings in that article comprehensible and relevant to an educated, non-expert reader.Matthews Discipline Project Assignment Fall 2020
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THE DISCIPLINE PROJECT
Translating a Scholarly Article in Your Field of Study for a Public Audience
(20% of final grade in the course)
Adapted from An Insider’s Guide to Academic Writing, by Miller-Cochran, Stamper, and Cochran. Bedford/St. Martin’s P,
2016.
OVERVIEW
The goal of this assignment is to “translate” or rewrite a scholarly article written about a current
topic in your academic discipline for an educated public audience. Your purpose is to make the
findings in that article comprehensible and relevant to an educated, non-expert reader.
There are three parts to this assignment:
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•
•
locate, read, and analyze a recently published scholarly article in your academic discipline
that addresses a topic of interest to you and the general public.
“translate” (rewrite) the article in a new genre appropriate for public audiences
write a reflective analysis about the choices you made as you wrote your translation
LEARNING OBJECTIVES
After completing this assignment, you should be able to
•
•
•
analyze the rhetorical features of scholarly writing in your discipline and public writing
Identify the conventions of various genres of scholarly, professional, and public writing
write with an awareness of how the rhetorical situation and rhetorical context influence the
structure, language, and reference conventions (SLRs) writers use to achieve their purpose in
writing to specific audiences.
STEP ONE: CHOOSE A SCHOLARLY ARTICLE AND READ IT CAREFULLY
Choose a scholarly article published in the last two years so that your topic is relatively current. The
topic of the article should address an issue in your field of study that would be of interest to the
general public.
After you choose your scholarly article, read it carefully so that you understand its key findings and
the basic research methodology the author(s) used to determine them.
Matthews Discipline Project Assignment Fall 2020
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STEP TWO : IDENTIFY YOUR NEW AUDIENCE AND GENRE
Next, identify a genre of public writing for your translation of the article and learn how it shapes its
information for a general, educated audience. The objective is to shift the audience from an
academic one to a public, educated one.
You may choose to write an article in a magazine dedicated to topics in your field, a newspaper
article that reports research findings, or even a feature article in one of the better newspapers,
such as The New York Times.
STEP THREE: ANALYZING YOUR TARGET AUDIENCE AND GENRE
CONVENTIONS
Closely analyze two examples of the kind of public writing genre you’re attempting to create and
identify the characteristics of that genre, or its conventions. For example, identify how the example
integrates and acknowledges its sources and how that differs from source integration and citation
in a scholarly work. Look at how the example phrases its title, how it introduces the topic, the level
of diction (word choice) it uses, and where it places the key findings of the scholarly work.
Your project will be assessed according to its ability to reproduce those genre conventions, so you
will need to consider the rhetorical choices you must make in the construction of your piece.
Consider all four elements of the rhetorical context: author, audience, topic, purpose.
STEP FOUR: WRITING THE “TRANSLATION”
At this point, you’re ready to begin translating the article into the new genre. The format, structure,
and development of your ideas are contingent upon the genre of public reporting you’re attempting
to construct. If you plan to write a feature newspaper article, for example, then the article you
produce should really look like one that would appear in a newspaper. Try to mirror how the genre
would appear in a real situation, to include complying with the way public writing acknowledges its
sources. Public writing does not use MLA or APA, but it still acknowledges sources.
STEP FIVE: WRITING THE REFLECTIVE ANALYSIS
Once your translation is complete, compose a reflective analysis about writing to a new audience
and in a new genre. As part of your analysis, compare and contrast the rhetorical choices between
the scholarly article and the genre you chose for your public audience. Focus on the following:
STRUCTURE, LANGUAGE, and REFERENCE CONVENTIONS (SLRs) as you constructed your translation.
See the sample student essay for how to do this.
Matthews Discipline Project Assignment Fall 2020
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Offer a rationale for each of your decisions that connects the features of your translation to your
larger rhetorical context. For example, if you had to translate the title of the scholarly article for a
public audience, explain why your new title is the most appropriate one for your public audience. If
you had to use signaling phrases and hyperlinks to acknowledge sources you reference, explain
why.
Finally, reflect upon what you learned by adapting the scholarly findings for a public audience. What
skills did you use that were new to you? How might you use those skills in other coursework or in
your current or future workplace?
Failure to include a good faith response in the reflective analysis results in an automatic 10-point
deduction in grade.
EVIDENCE
The Discipline Project does not require you to incorporate secondary sources, but if you feel it is
appropriate to your reader and the genre you chose, then do so. Just make sure to use the format
for acknowledging sources used in the genre of public writing. In most cases, this will be a hyperlink.
It is more important to think about how you will incorporate evidence from the scholarly article you
are translating. Public audiences appreciate a quotation or two, but you will find that you need to
rely more heavily on summary and paraphrase in translating the article for a general reader.
Summary and paraphrase still require attribution, but public writing often accomplishes this with
the signaling phrase.
FORMAT AND DOCUMENTATION
● Use the manuscript format for the genre you chose.
● Use signaling phrases and/or hyperlinks to acknowledge source material so that you comply
with the genre conventions of public writing.
This adapted assignment by the George Mason Composition Program is licensed under a Creative Commons
Attribution-NonCommercial 4.0 International (CC BY-NC 4.)
Computer Networks 130 (2018) 94–120
Contents lists available at ScienceDirect
Computer Networks
journal homepage: www.elsevier.com/locate/comnet
Potentials, trends, and prospects in edge technologies: Fog, cloudlet,
mobile edge, and micro data centers
Kashif Bilal a,b,∗, Osman Khalid b, Aiman Erbad a, Samee U. Khan c
a
Qatar University, Doha, Qatar
COMSATS Institute of Information Technology, Pakistan
c
North Dakota State University, USA
b
a r t i c l e
i n f o
Article history:
Received 21 April 2017
Revised 26 September 2017
Accepted 9 October 2017
Available online 18 October 2017
Keywords:
Edge computing
Fog computing
Internet of Things
a b s t r a c t
Advancements in smart devices, wearable gadgets, sensors, and communication paradigm have enabled
the vision of smart cities, pervasive healthcare, augmented reality and interactive multimedia, Internet of
Every Thing (IoE), and cognitive assistance, to name a few. All of these visions have one thing in common, i.e., delay sensitivity and instant response. Various new technologies designed to work at the edge
of the network, such as fog computing, cloudlets, mobile edge computing, and micro data centers have
emerged in the near past. We use the name “edge computing” for this set of emerging technologies. Edge
computing is a promising paradigm to offer the required computation and storage resources with minimal delays because of “being near” to the users or terminal devices. Edge computing aims to bring cloud
resources and services at the edge of the network, as a middle layer between end user and cloud data
centers, to offer prompt service response with minimal delay. Two major aims of edge computing can
be denoted as: (a) minimize response delay by servicing the users’ request at the network edge instead
of servicing it at far located cloud data centers, and (b) minimize downward and upward traffic volumes
in the network core. Minimization of network core traffic inherently brings energy efficiency and data
cost reductions. Downward network traffic can be minimized by servicing set of users at network edge
instead of service provider’s data centers (e.g., multimedia and shared data) Content Delivery Networks
(CDNs), and upward traffic can be minimized by processing and filtering raw data (e.g., sensors monitored data) and uploading the processed information to cloud. This survey presents a detailed overview
of potentials, trends, and challenges of edge computing. The survey illustrates a list of most significant
applications and potentials in the area of edge computing. State of the art literature on edge computing
domain is included in the survey to guide readers towards the current trends and future opportunities in
the area of edge computing.
© 2017 Published by Elsevier B.V.
1. Introduction
Cloud computing brought a technological revolution and
paradigm shift in the Information and Communication Technology (ICT) sector in the last decade. Cloud computing experienced
a massive adoption in almost every domain of human life [1–4].
Data centers, the backbone and underlying resource architecture
of cloud computing are constantly growing in size and number to
meet the increasing resource demands [2]. Technological advances
in personal gadgets and wearable computing are enabling a new
stream of real-time and pervasive applications, such as cognitive
∗
Corresponding author at: Qatar University, Computer Science and Engineering,
Doha, Qatar.
E-mail addresses: kashif@qu.edu.qa (K. Bilal), osman@ciit.net.pk (O. Khalid),
aerbad@qu.edu.qa (A. Erbad), samee.khan@ndsu.edu (S.U. Khan).
https://doi.org/10.1016/j.comnet.2017.10.002
1389-1286/© 2017 Published by Elsevier B.V.
assistance, augmented reality, traffic monitoring, vehicular tracking, and interactive video streaming [5]. Such applications demand
real-time response, which is one of the major constraints in the
cloud paradigm because of the delays from distant cloud data centers. As indicated in Fig. 1, a user’s request to the cloud has to
traverse multiple hops before reaching the cloud servers, thus increasing the response time.
The proliferation of mobile devices, which are predicted to be
more than 50 billion devices by the year 2020, will produce massive amounts of data [6]. Moreover, the ever increasing data rates
from the Internet of Things (IoT) devices will impose further challenges on the cloud computing infrastructure. IoT is an emerging
technology that extends Internet connection to devices embedded
with sensors, actuators, and RFID tags [7]. IoT devices collect sensory data from the surrounding environment with a requirement to
provide scalable infrastructure to communicate, process, and store
K. Bilal et al. / Computer Networks 130 (2018) 94–120
Fig. 1. Multiple hops between end user/devices and cloud data centers result in
delayed response.
the data [8,9]. The number of such devices will reach billions in
the coming years, with a large number of sensors monitoring and
flooding the network with dynamic real-time data. According to
Cisco Global Cloud Index [10], by the year 2019, 500 ZB of data
will be produced by people, machines, and things, and 2.3 trillion GBs of data will be produced every day in the year 2020 [11].
IoT platforms demand low latency communication, need support
for high degree of mobility, and real-time data analytics. Although
cloud computing provides many benefits, the latency sensitive and
data intensive IoT applications appear to be a challenge for current cloud computing system. The needs for real-time response
and ever increasing data demands novel solutions. Edge computing (fogs, cloudlets, micro datacenters, and mobile edge computing) is emerging as a viable solution to these challenges, offering
real-time response and near to end cloud services. Edge computing augments cloud computing by bringing networking and computational resources on edge devices near to the end user. An edge
device can be a router, gateway, switch, or a base station, that provides an entry point into the service provider’s core network. Edge
devices are proposed to have sufficient computational and storage
resources to meet real-time and resource intensive demands of end
user. Generally, the edge computing platform comprises of a heterogeneous infrastructure of access points, switches, edge routers,
servers, and end user devices. Compared to cloud computing, the
edge provides low latency and reduced data traffic, as the applications are localized to the region where the edge is deployed.
We use the term “Edge Computing Technologies” to encompass
different emerging technologies situated at the edge of the network
to provide computational and storage resources to deliver real-time
communication with minimum latency. Examples of such technologies include Fog computing, Mobile Edge Computing (MEC), Micro
Data Centers (MDC), Cloudlet, and related technologies. The term
edge computing or edge technologies used in this article refers to
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the set of these emerging technologies. Fog computing represents
a platform that brings cloud computing to the proximity of end
users [12,13]. The term Fog computing was coined initially by Cisco
[13,14]. The main focus of fog computing is to equip the network
edge and network devices with virtualized services, in terms of
processing and storage along with offering network services. MEC
is the edge technology initiated by European Telecommunications
Standards Institute (ETSI) [15,16]. The major focus of MEC is Radio Access Networks (RANs) in 4G and 5G cellular networks. MEC
offers edge computing by proposing a collocation of computation
and processing resources at base stations. MDCs, initiated by Microsoft are small scaled version of data centers to extend the hyperspace cloud data centers [17,18]. MDCs aim to provide small size
data centers extending the offered services of the cloud near to the
end users. Concept of cloudlet, initiated by Carnegie Mellon University (CMU) is similar to MDC, as small scaled virtualized data
center to serve users near the edge in a distributed fashion [19,20].
Some similar terms, such as Nano-data centers are also used in literature for similar concepts and objects [21,51].
Different edge technologies are defined independently; however, these technologies can cooperate and work together [12].
Considering the futuristic aspects of the Internet of Everything
(IoE) [143] and recent trends in technology cooperation, such as
Content Distribution Network Interconnection [22] and Heterogeneous Networks (HetNets) [144], it can be foreseen that various
edge technologies will work in cooperation to support the overall
vision of the IoE. Edge computing enables a large number of applications including vehicular communications, smart cities, smart
grid, wireless sensor networks embedded with actuators, road traffic monitoring, pipe line monitoring, wind farms, smart traffic light
system, railway monitoring, industrial control systems, and the applications in oil and gas explorations. IDC reported that by the year
2019, 45% of the data generated by IoT will be processed, stored,
and analyzed on the edge [8]. Fig. 2 shows the some of the potential application areas of IoT and edge computing.
Edge computing technologies are in their infancy, with no
standardized definitions, architectures, and protocols. Various researchers define edge technologies from their own perspective and
models, which is expected for non-standardized technologies. A
similar trend was observed in cloud computing as well before standardization of an official definition of cloud computing by National
Institute of Science and Technology (NIST) in 2011 [23]. The lack
of a standard definition leads to misconceptions in the relation
among edge technologies, IoT, and cloud. Examples of such misconception mentioned in the literature, where authors claim that edge
computing technologies will “move” or “replace” cloud with fog or
decentralize the cloud paradigm to edges. For instance, [24] mentions that “Cloud is migrating to the edge of the network and the
traditional Cloud Computing paradigm is not enough for the storage of Big Data produced by IoT”. It needs to be clearly understood that edge computing technologies should not be considered
as a substitute of cloud paradigm, rather, as shown in Fig. 3, these
technologies will complement cloud and extend cloud services to
the edges, so that the needs of applications with real-time requirements are satisfied [25]. For the big data analytics, and lengthy, resource intensive batch jobs, the cloud is a must. Similarly, there is
also a confusion in understanding and perceiving the architecture
of edge technologies, for instance, some authors treat fog computing as micro datacenters [26,27], while others focus mainly on the
idea of strengthening and equipping networking components with
extra processing and storage capabilities [25].
In this survey, we discuss various edge computing technologies, their potentials, applications, and challenges. Specifically, we
provide a list of some potential areas in the field of edge computing (please see Fig. 4 for the taxonomy and topics discussed
in this survey). The state of the art in various edge computing
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K. Bilal et al. / Computer Networks 130 (2018) 94–120
Fig. 2. Potential application areas of edge computing.
Fig. 3. The applications designed for traditional cloud computing have usually less frequent data transfer to cloud and can afford some slow response. However, the edge
specific applications have more frequent interactions with edge servers and require a quicker response.
K. Bilal et al. / Computer Networks 130 (2018) 94–120
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Fig. 4. Edge computing potentials, applications, and challenges.
technologies are also discussed in the article. Some of the authors
have presented various aspects of edge computing in the literature.
Luan et al. [28] highlighted main features of fog computing including its concept, architecture, and design goals. However, the other
edge technologies are not covered. In a similar study, Bonomi et al.
[13] outlined key characteristics of fog computing and discussed
the role of fog computing in the IoT. Some basic applications
are also discussed in the survey. A report on edge technologies
[16] discussed and briefly compared the three technologies of edge
computing: mobile edge, cloudlets, and fog computing, with no
discussion on potential areas and applications. Stojmenovic et al.
[29] discussed motivation and advantages of fog computing, and
considered only these application areas: smart grid, smart traffic lights, and software defined networks. Yi et al. [30] discussed
basic definition of fog computing and similar concepts and discussed various application scenarios. However, in [30] the discus-
sion on existing techniques on edge computing is missing. Bonomi
et al. [13] presented a discussion on fog computing in the context of IoT. Ahmed and Ahmed [31] and Beck et al. [15] discussed
the taxonomy and key attributes of mobile edge computing. Azam
et al. presented an article focusing on IoT and Cloud of Things
(CoTs) [32]. The authors presented some of the potentials of the
fog computing specifically considering the CoTs, i.e., amalgamation
of IoTs and cloud computing. The authors presented various aspects of fog in consideration of edge computing as middleware
to cloud, without presenting in-depth details. Dastderji and Buyya
highlighted the potentials of fog computing for IoTs [33]. The authors briefly presented how fog computing may impact IoT systems to work better in a real-time environment and how it can
save unnecessary transit traffic. The authors presented generic fog
computing architecture and fog based distributed data processing
models, and discussed various components involved in the model.
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K. Bilal et al. / Computer Networks 130 (2018) 94–120
Yi et al. presented an overview of various concepts, applications,
and issues in fog computing [30]. The authors in [34] discussed
motivational scenarios of fog computing and provided some simulation results. Shi et al. [7] have discussed a few case studies of
edge computing along with challenges and opportunities. Varghese
et al. [35] presented a limited discussion on motivation, challenges,
and opportunities of edge computing, without discussing the other
edge technologies.
Most of the above mentioned surveys discussed various characteristics and applications of edge computing technologies in limited and isolated way. However, detailed study on various edge
computing technologies, their potentials, applications, challenges,
and the state of art, still needs to be addressed. Our survey attempts to address deficiencies in the existing surveys and provides a focused study on various edge computing technologies,
their challenges, potentials and applications. To the best of our
knowledge this is the first survey that provides in-depth details
pertaining to edge computing and its various trends and potential areas. Moreover, this survey also presents state of the art in
edge computing, which is missing in most of the existing surveys. Specifically, our contributions in this survey are as follows. In
Section 2, we present an introduction of edge computing technologies and some motivational scenarios, followed by details of various edge computing technologies, i.e., Fog, Cloudlets, MDCs, and
MECs. Section 3 presents a detailed study on the edge computing potential and most recent works in those areas and applications. Moreover, a detailed explanation on edge computing architectures, implementations, and evaluation mechanisms is provided.
Section 4 highlights the open research challenges in the edge computing technologies, followed by conclusions in Section 5.
2. Edge computing technologies
The edge computing is based on the idea of placing small
servers called edge servers or resource rich networking devices in
the vicinity of end users/devices (see Fig. 5). In this way, some of
the computational and data storage load is transferred from cloud
platform to the edge servers. The end users’ devices usually consist of wireless sensor networks, smart phones, wearable gadgets,
and various IoT devices that require real-time response. Deploying computation and storage resources at the edge of the network
can enable a large number of applications that require real-time
response. A few examples of such applications include, but not
limited to: (a) traffic monitoring and navigation, that involves traffic reporting and computation of routes for a specific region near
to the edge, (b) data filtering and aggregation, that performs prefiltering of content and data at edge before sending it to cloud to
reduce the data volume, and (c) augmented reality, real-time interactive video streaming, and health monitoring systems that can
produce fast responses using edge nodes, thereby improving user
experience for time-sensitive applications. In this section, first we
discuss some motivational use cases and scenarios indicating why
we should use edge computing in addition to cloud. Later, we explain various technologies within the domain of the edge computing.
2.1. Edge computing motivation
2.1.1. Reduced traffic load
The traditional User-Internet interaction model involves short
requests from user to Internet services and receiving response.
Some of the requested services, e.g., file downloading and specifically, Video on Demand (VoD) or live video streaming are comprised of very small data requests from user to the Service Provider
(SP), and large volume of data flowing from SP to users. Considering the gigantic amount of data flowing from Internet to users,
various solutions have been employed, such as CDN and caching
to minimize the data and delay from SP to user [145]. For instance, cacheable contents are cached at ISP caches or CDN networks to minimize transit network data flow and delay [146–148].
However, the advent of new technologies, gadget proliferations,
smart environments, and IoE are changing the data flow paradigm
and patterns. Futuristic vision of smart and pervasive environments
is foreseen to transmit massive volumes of data to the Internet.
Consider live streaming, specifically crowd-sourced live streaming
as an example, significant amount of data per second now flows
from the users to SP and then disseminated globally from various SPs, such as Twitch (a crowd-sourced live gaming system)
[149], YouTube Live [150], Periscope [151], and YouNow [152]. Netflix hosts a huge collection of entertainment video content. If 10%
of 8 million people in New York want to stream movies from Netflix at the same time, it would require an infrastructure capacity of 1.6 Tera bits per second (Tbps) to handle all requests in
parallel [36]. Despite remarkable improvements in bandwidth and
server- side processing, the networks may still suffer in performance with huge viewership spikes. For instance, in a recent boxing match held in Las Vegas, USA, the live video streaming payper-view servers crashed and network got congested due to sudden
rise in viewership [156]. If CDNs are not deployed within the networks, then the centrally hosted content must travel through many
networks to reach the end users. In the futuristic scenario, current CDN based content delivery model is expensive, because, data
still has to travel many hops between CDN and Internet Service
Provider (ISP), before reaching to viewers’. For instance, consider
the scenario of European football tournament final match, where
the Akamai network served 3.3 million video streams concurrently
to viewers, experiencing a peak load of 7.3 Tbps [142]. If multicast
is not enabled, which is the general case because of configuration
and security issues, then 5.7 Tbps data passing through multiple
hops between CDN and ISP results in significant energy consumptions and network cost and management. Moreover, CDNs are passive storage designs, hosting large volumes of data, with generally
no or very limited processing capabilities. On the fly transcoding of
the videos are not available in the current CDN designs. Edge computing technologies offer a feasible solution in terms of very small
delay and data filtration to fulfill the futuristic IoT, IoE, and smart
world visions. If edge locations are used as data delivery and sharing points, huge volumes of transit data between CDNs and network edge can be saved [169]. Caching at the mobile edge (base
stations/eNodeBs) may save considerable amount of backhaul network traffic. It has been shown that caching at the edge of the network considerably reduce access latency and network traffic [170].
Edge locations can perform on the fly video transcoding to create required video representation versions, minimizing the storage
requirements, minimizing access delays, and maximizing viewers’
QoE. Moreover, edge technologies may host dedicated services at
edge to provide real-time response and data filtration. For instance,
Akamai network have deployed edge computing networks to provide distributed execution of Java applications [37].
2.1.2. Minimizing the latency
The inherent cloud computing delays are challenging for applications that require real-time response, e.g., intelligent transportation systems, games, live streaming applications, and other safety
critical applications, where such delays are intolerable. It is studied
in [41] that for real-time visual guiding services, the preferred response time is between 25 ms to 50 ms. Moreover, high processing
load imposed on cloud’s central servers may cause scalability problems for the compute intensive applications and increase network
overhead, resulting in slow response time and excessive utilization
of the Internet bandwidth [42]. Inter-network data transfer leads
to increased latency and congestion. Generally, Internet comprises
K. Bilal et al. / Computer Networks 130 (2018) 94–120
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Fig. 5. Edge computing architecture.
Table 1
Effect of distance on round trip time (RTT), packet loss, throughput, and down time [37].
Distance (Server to user)
Network round trip time
Packet loss
Throughput
4 GB download time
Local: 5 million checkins per day [44]. Similarly, several sports activity logging applications, such as Nike+ [45], Runtastic [46], Runkeeper [47], and Endomondo [48] are becoming popular. These applications run on
smartphones and log daily activities of users with the help of various sensors, e.g., accelerometers, GPS, gyroscope, and temperature sensors, typically installed on smartphones. Mostly, the data
recorded by the applications is sent to the cloud in the form of
tuples, where each tuple contain several pieces of information,
such as user id, longitude, latitude, time, distance, speed, duration,
calories, weather, and other related items. For instance, a recent
study on Endomondo revealed that a single workout on the average generates 170 GPS tuples, and average number of tuples generated per month is between 2.8 and 6.3 billion [49]. With 30 million users, the number of tuples generated per second could reach
25,0 0 0 tuples/sec [49]. Considering the IoT enabled smart cities,
with thousands of sensors deployed, the numbers of tuples generated per second would be many times higher. When such high ve-
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K. Bilal et al. / Computer Networks 130 (2018) 94–120
locity real-time data streams will be sent to the centralized cloud
servers, the backbone network will get congested and the cloud
servers may get overburdened. Moreover, not all of the sensed
data is useful. For instance, sensors deployed in Large Hadron Collider (LHC) project generate around 500 EB data per day. However,
99.999% data is filtered out [49]. Edge computing can be leveraged by the application providers to locally process the data to
filter unnecessary data, and generate real-time response for the
users in the vicinity of the deployed edge. Moreover, data can be
trimmed/filtered before sending to the cloud, thereby reducing the
network traffic and processing burden from cloud servers.
2.1.4. Reduced load on end user devices
As discussed earlier, the end user devices and IoT generate huge
volumes of data on which some form of analytics needs to be performed to generate useful information. However, if end devices,
such as smartphone are subjected to such complex tasks, they may
sooner run out of resources, e.g., battery drainage. Moreover, the
devices may not be compatible due to heterogeneity of technologies. Therefore, the end devices can offload some of their high processing tasks to the nearby edge to reduce their load. Moreover,
not all the data generated from the end device may contribute in
the computation of useful information. For example, the study conducted on Endomondo sports activity tracking application revealed
that even if a jogger stops to take rest, his sensors’ stores the same
values at regular intervals [49]. Therefore, some form of data filtering can be employed on the edge to discard the redundant data
and only filtered data is sent to the cloud. Similarly, in interactive
multimedia applications, such as free-view video, client’s device is
generally used to perform complex tasks like virtual view generation, which are resource intensive and results in battery depletion [87]. Performing such resource intensive jobs at the edge of
the network, and delivering synthesized virtual view may result in
significant bandwidth and energy savings at client’s device.
2.1.5. Reducing energy consumption
Generally, the end user mobile devices and IoT are constrained
by computing capabilities, battery life, and heat dissipation. Edge
computing enables the offloading of energy consuming application
from resource constrained end user devices to the edge servers.
The majority of algorithms aim to minimize the energy consumption at the mobile device while subject to the execution delay acceptable by the offloaded application, or to find an optimal tradeoff between these two metrics. The energy consumption in using
a cloud service usually depends on the following factors [51]: (a)
energy consumption of end user device accessing the service, (b)
energy consumption of data center, including energy consumed by
internal network, storage, and servers, (c) the volume of traffic exchanged between the user and cloud, (d) the computational complexity of the task to be performed, (e) factors such as the number of users sharing a compute resource, and (f) the energy consumption of the transport network (aggregation, edge, and core
networks). Costenaro and Duer studied energy consumption due
to data transportation on the internet. The authors found out that
14% of the energy consumption in the Internet is due to the data
transportation [50]. Jalali performed a detailed analysis of energy
consumption by certain cloud-based applications, when those applications are run directly on cloud and on locally deployed fog
based nano data centers [51]. The author showed that online interactive applications generate a substantial amount of traffic and
consume more energy due to overheads arising from real-time interaction with the Cloud. The authors used various network analyzing tools to acquire traffic logs that showed the large traffic
overhead is associated with establishing/tearing down TCP sessions
very frequently and the volume of data transported to and from
the user per session (measured in tens to hundreds of KB). The
authors recommended that the fog based nano servers can complement the cloud for certain applications that can lead to energy savings, if the application or its components can be offloaded
from centralized data centers and run on nano servers. Moreover,
energy can be saved by employing intelligent client-side caching
techniques, and optimizing the synchronization frequency of contents between edge and cloud [51]. Furthermore, data caching at
edge locations reduce burden on the core network, which enable to
reduce link rates using green technologies like Adaptive Link Rate
(ALR) to make links energy proportional [4].
2.1.6. Data center computation offloading
Edge computing can also be exploited to offload computation from data centers that require limited resources to the edge
nodes. For example, the live streaming applications, like Facebook
Live, YouTube Live, and Livestream [153] allow users to perform
live broadcast. It is reported that during a period of one minute,
YouTube users upload 72 hours of new video, Facebook users share
2460,0 0 0 pieces of content, WhatsApp users share 347,222 photos,
Instagram users post 216,0 0 0 new photos, and Vine users share
8333 videos [7,52]. Usually, when a video or photo is uploaded,
e.g., to Facebook or YouTube, it is subjected to lossy compressions
to reduce the media size. Uploading the high resolution photos and
videos from user devices to the cloud occupy lots of bandwidth
and may take lot of time in areas where internet connectivity is
poor. Similar issues arise in live health monitoring applications, or
smart city applications where live streams of data from surveillance cameras and other sensors needs to be uploaded to cloud.
Edge computing can be utilized to transfer some of the compression related tasks to the edge devices near to the end users, before
uploading to the cloud. Moreover, edge can also be used to encrypt the user data instead of uploading the raw data to the cloud,
thereby ensuring security and privacy of user data in the intermediate hops.
In the next subsection, we discuss various technologies that we
covered under the domain of edge computing. We discuss characteristics, similarities, and dissimilarities of these technologies,
along with some practical examples.
2.2. Edge computing technologies
2.2.1. Fog
Fog computing represents a platform that brings cloud computing to the proximity of end users. The term “Fog” was initially introduced by Cisco and has an analogy with real-life fog [13]. As
the clouds are far above the sky, the fog is closer to the earth.
The same concept is used by fog computing, where the virtualized
fog platform is deployed closer to the end users – between cloud
and end users’ devices. Although both cloud and fog paradigms
share almost similar set of services, such as computation, storage,
and networking, yet there are some differences between the two.
The fog’s deployment targets a specific geographic region. Moreover, the fog is specifically designed for applications that require
real-time response with less latency, e.g., interactive and IoT applications. Alternatively, the cloud is centralized and being mostly
far from the user, it suffers from some performance limitations in
terms of latency and response time for real-time applications. The
deployment of IoT in a two tiered architecture with cloud at one
end and IoT devices at other end does not fulfill the requirements
of low latency, mobility of the “things”, and location awareness
[53]. Therefore, as indicated in Fig. 6, a multi-tiered architecture is
required in which the first part consists of IoT application deployed
on “thing” which is an end user device, e.g., a vehicle. The second
part of the architecture is fog, connected with end users through
a router, access point, wireless access network, or an LTE base station. The final part of the 3-tier architecture is cloud’s data center
K. Bilal et al. / Computer Networks 130 (2018) 94–120
101
Fig. 6. 3-tier architecture consisting of cloud, fog, and IoT end devices layers.
(e.g. Amazon EC2 [154]). By having the 3-tier architecture, the fog
allows IoT applications and services to be operated from edge of
the network as well as from end devices, such as gateways, routers,
access points, set top boxes, Road Side Units (RSUs), and Machine
to Machine (M2M) gateways [33,34,53]. Moreover, such configuration allows fog to perform real-time monitoring, actuation, data
analysis with reduced latency, improved QoS, and saving of bandwidth as data are processed at the edge of the network. Due to
dense geographic coverage and distributed operations, fog computing promotes fault tolerance, reliability, and maintains scalability of the system. Fog can also perform preprocessing of data before sending it to cloud. This can further reduce the load on cloud
network. In future applications, the fog computing is expected to
deliver high quality data/video streaming to moving vehicles, mobile nodes, and public places through access points deployed for
instance, along highways and malls [9,33].
Fog computing is a novel paradigm and faces various challenges
apart from the issues it inherits from cloud computing. These challenges include management of heterogeneous devices, architectural
issues, security, mobility, and privacy issues. Fog comprises of heterogeneous devices, with different types of data collected. Interoperability among heterogeneous devices is a challenging task. If
the number of connected devices exceeds, this may raise scalability issues for the fog. Moreover, for proper management of resources and load balancing, an efficient resource scheduler is required. Designing such resource scheduler for heterogeneous devices and data is a challenging task. It is also critical to perform
proper monitoring and management of devices, especially those
running real-time applications. Moreover, the monitoring of traffic and billing mechanism is a must requirement. One of the major
challenges in fog computing is to devise a fair billing model for
the services offered. Fog services are offered using various pricing
schemes and models, and the end users expect high QoS with minimum price. The billing model must be fair and balanced to attract
more subscribers and generate high revenue. Due to unavailability
of any standard billing model for fog, it is still an open research
issue. Fog computing involves setup of expensive devices and networking. It is important to perform pre-deployment testing of fog
platform using some simulation tool. However, there is no such
standard simulation model/tool available at the moment for fog
computing, which makes it an open research issue as well. Finally,
the protection against malicious attackers and security threats is
also a key research challenge for fog platform.
2.2.2. Cloudlets
Cloudlets are developed by a team at CMU [19,20]. Like fog
computing, cloudlet also represents the middle tier of the 3-tier architecture: mobile device – cloudlet – cloud. Cloudlets are viewed
as “data center in a box” with a purpose to bring cloud services
closer to the mobile users. Internally, a cloudlet consists of a cluster of resource-rich multicore computers with high-speed internet
connectivity and a high bandwidth wireless LAN for use by nearby
mobile devices. For safety purposes, the cloudlets are enclosed in
a tamper-resistant box for ensuring security in unmonitored areas
[19].
Despite significant technological improvement, mobile devices,
such as smart phones are still resource deficient when compared
to other stationary devices like laptops and servers. This is primarily because of their smaller size, less memory, and shorter battery
life. On the other side, there is a significant increase in development of various mobile applications. Most of the emerging applications, such as augmented reality, interactive media, speech recognition, natural language processing, require greater number of resources for processing with minimum latencies [19,34,52]. To meet
such demands, cloudlets are designed with virtualization features
to specifically provide computational resources to the mobile users.
The mobile device, acting as a thin client, can offload computational tasks through a wireless network to a cloudlet, deployed one
hop away. However, a cloudlet’s presence in mobile device’s proximity is necessary, as the end-to-end response time with executing
applications must be smaller and predictable. If a device goes out
of the range of cloudlet, then it should gracefully switch to the distant cloud, or in worst case, solely rely on its own resources. The
cloudlet’s simplicity in management makes it trivial to be deployed
at a business premises or near an experimental field where sensory
devices are producing lot of data that requires processing. An example application can be mobile phone based language translation
application. The VM running at cloudlet receives captured speech
from mobile device, performs speech recognition and translation,
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Fig. 7. Mobile edge computing.
and returns the output to the mobile device. The launched VM can
be cloned to exploit parallelism in cloudlet. A basic difference between cloud and cloudlet is that a cloudlet contains only the soft
state of data or code, whereas a cloud can contain both soft and
hard state. Therefore, a cloudlet’s failure does not result in data
loss of mobile devices.
Satyanarayanan et al. [19] developed a cloudlet prototype,
named Kimberly. The cloudlet infrastructure is setup on a desktop computer running Maemo 4.0 Linux, whereas the mobile device used in the prototype is Nokia N810. The mobile user utilizes
VM technology to instantiate a service on nearby cloudlet and uses
wireless LAN to interact with cloudlet. More technical details about
the configuration and setup of Kimberly cloudlet can be found in
[19]. Ha et al. [54] implemented a prototype for assisting people
with cognitive decline using Google Glass and cloudlet technologies. The technology sends the captured image and other sensing
information from a Google Glass to a cloudlet to perform real-time
scene interpretation. The proposed system architecture is multitier to address the concerns related to limited battery and computation powers of mobile devices while performing the computational tasks on a connected cloudlet. The system gracefully degrades the services in case of network failures and if the device
goes out of the range. Ye et al. [55] proposed a bus-based fog computing model in which the fog servers are deployed in buses. The
roadside cloudlets can offload some portions of their computation
tasks in case of overloading to a bus’s fog servers. The authors proposed an optimal allocation strategy based on genetic algorithm
using which, the cloudlets offload their tasks to fog servers deployed on busses. In addition, the bus servers can also offer the
computational offloading for mobile devices in bus without any
disruption and with improved QoS.
2.2.3. Micro datacenters
Microsoft Research under the supervision of Victor Bahl has introduced the concept of micro datacenters as an extension of today’s hyper-scale cloud data centers [17,18]. Analogous to Cloudlets,
Micro datacenters are also designed to meet demands of applications that require lower latency or that face constraints in terms of
battery life or computations. A micro data center, shipped in one
enclosure, is a self-contained, secure computing environment that
includes all necessary computation, storage, and networking equipment to run customer applications. A micro datacenter can have a
size range from 1–100 kW to meet the scalability and latency demands considering the IT load, and can also scale if more capacity
is needed in the future.
Micro data centers have a number of applications in domains
where real-time or near real-time data processing is required. Examples include, but not limited to, industrial automation, environmental monitoring, oil and gas exploration, construction sites, or
any other applications where the sheer volumes of data requires
on-site and real-time processing. Some of the micro datacenter’s
current implementations include Cisco’s UCS [56], VCE’s V-Blocks
[57], or Dell’s Active Systems [58]. These are pre-built systems that
can be rapidly deployed and reconfigured. The company Schneider
Electric offers micro datacenter solutions, such as Smart Bunker
and Smart Data Safe [59]. Smart Bunker is designed to host 85 VMs
within a 42 U rack assembly. The company also offers smaller micro datacenter’s solutions with 23 U size deployed in single rack
enclosure. Elliptical Mobile Solutions offer R.A.S.E.R. DX and HD
systems [60]. The Elliptical Mobile has also created a complete
stand-alone VPLEX system in conjunction with EMC, Microsoft, and
AVNET [60]. Huawei is another important player in micro datacenters, whose MicroDC30 0 0L 24 U systems can be used in environments of less than 100 users in an unattended, lights-out operations mode [61].
2.2.4. Mobile edge computing
MEC is designed to bring cloud computing capabilities and IT
services environment at the edge of cellular networks [31]. The
MEC offers lower latency, proximity, context and location awareness, and higher bandwidth. As reflected in Fig. 7, MEC servers
are deployed at cellular base stations enabling flexible and rapid
deployment of new applications and services for customers. MEC
can be envisioned as cloud servers running at the edge of mobile
networks and performing specific tasks that cannot be achieved
with traditional cloud network infrastructure. Instead of forwarding all traffic to the remote cloud, the MEC shifts traffic targeted
for the centralized cloud to the MEC servers. In this way, the
MEC servers running applications and performing related processing tasks closer to the cellular customers reduce network congestion and response time of applications. Either the request is processed directly on MEC server sending quick response to the ender
user, or, in some cases, the request may be forwarded to remote
cloud.
In September 2014, the ETSI announced an industry specification for MEC [62]. The group of researchers are developing system
architecture and standardizing a number of APIs essential for MEC
[62]. In 2013, Nokia introduced MEC as a step towards automated
driving. Usually, communications between cars and a central cloud
has an end-to-end latency more than 100 ms. Base stations with
distributed MEC cloudlets have shown an end-to-end latency of
K. Bilal et al. / Computer Networks 130 (2018) 94–120
lower than 20 ms. Nokia introduced MEC and geo service application to the LTE base stations that resulted in faster communications
[63]. With connected driving via LTE, cars can now communicate
almost in real-time over a larger distance and beyond the line of
sight. This allows the cars to slow down in advance when there is
an emergency situation.
3. Edge computing: the state-of-the-art
Edge computing is envisioned to assist in a number of domains
with localized setup and configurations. In this section, we provide
a detailed study of various edge potentials, and recent literature review in edge computing applications. We also discuss various edge
architectures, and at the end, we present various implementations
and simulation methodologies of edge computing as discussed in
the literature. Table 2 presents a summary of state of art in edge
computing in various domains.
3.1. Edge computing potentials and applications
Mission critical and latency sensitive applications mandate immediate response, and cannot afford communication delays incurred due to distant cloud and shared Internet medium. Some of
the examples of real-time applications are, emergency and healthcare services, multi-player gaming, interactive multimedia, and
augmented reality applications, etc. Services, such as visual guiding, demand a response time of 25 ms–50 ms, which cannot be
achieved from cloud [64]. Ha et al. [38] evaluated the response
time of face recognition applications under various network conditions. The study demonstrated that response time may increase
to 4.02 s under worst network conditions compared to 620 ms required for a human subject. Such studies clearly demonstrate the
needs of edge computing for real-time applications.
Besides minimal latency, serving users at nearby edge also
brings related advantages. Some of the benefits are: (a) minimized
core network traffic, (b) energy efficiency, and (c) data cost reduction. When users are served from edge for the applications that
can be offloaded from cloud, the high volumes of TCP sessions’
traffic is reduced on the core network, consequently reducing network traffic, congestion, and latency, and data transit energy [51].
Minimizing core network traffic is important, specifically, in terms
of multimedia applications and IoTs, where huge volumes of data
transfer from service provider to device (e.g., video streaming) and
from device to service provider (e.g., sensor network monitored
data and crowd-sourced video). Sharing and filtration of data can
be performed at the edge of the network to minimize core network
traffic. Edge based IoT solutions are reported to gain around 40%
energy efficiency when IoT devices are served from edge locations
instead of cloud [25]. Below, we present how edge computing can
help to achieve least response time, minimize network core traffic,
which results in achieving energy efficiency and reduced data cost.
We provide a detailed discussion on a number of edge computing
potential applications. Moreover, we also present the state-of-theart in those areas to demonstrate how edge computing can benefit
ICT sector in various ways, what are the possible applications, and
future research areas.
3.1.1. Internet of things (IoT) and edge computing
IoT not only encompasses intelligent or M2M devices, but also
covers the “dumb” and non-communicable devices, such as objects with Bar Code or RFID tags [65]. Such scenarios lead to
IoE paradigm with trillions of interconnected devices, e.g., in case
of smart cities (see Fig. 8), producing large streams of big data.
With currently more than 9 billion devices connected, future connectivity is predicted to surpass approximately 50 billion devices
103
[7,34,66]. Wireless Aggregated readings from sensors produce enormous amounts of data. For instance, Large Hadron Collider (LHC) in
Switzerland uses data from around 150 million sensors, which generate around 500 Exabyte data per day. However, 99.999% data is
filtered out and still, only 0.001% of the data produces 25 PB data
annually [49]. Boeing 787, fully integrated with IoT sensors, will
produce over half a TB of data per flight, said by Virgin Atlantic
[67]. Similarly, the self-driving cars by Google generate nearly 1 GB
of data every second [68], and the data requires real-time processing for making correct decisions. Wireless Sensor Networks
(WSNs), the core components of IoT, are designed to operate at
very low power to save battery life. The sensory nodes have small
memory, processing power, and low bandwidth. Due to resource
deficiency, the nodes cannot perform various compute intensive
tasks related to data analysis and reporting. Efficient and real-time
communication, processing, storage, and information retrieval of
such massive volumes of connected devices is a challenge that can
only be served by extensive distribution of processing and storage capability nearest to these devices. Edge technologies are foreseen to be one of the key players in future IoT and IoE paradigms
[65]. Moreover, sending huge volumes of sensory data to cloud can
cause increased congestion. In this scenario, the edge devices can
undertake the task of data processing and analysis. Moreover, the
data can be filtered and compressed by edge devices before sending to cloud to conserve bandwidth and minimize data flow. Considering the futuristic vision of IoT and IoE, with billions of connected objects, retrieved data needs to be processed and filtered.
Being resource constrained, most of the IoT devices can be envisioned to rely on nearest edge nodes, for processing, filtering, and
in some cases data storage. In addition, the actuators serving as
edge devices can control physical actions, like open, close, move,
etc. by acting in a closed loop system.
Edge computing has numerous applications in smart building
control where the IoT devices acting as “things” embedded with
sensors and network connectivity perform various monitoring and
actuation tasks. Smart buildings are usually installed with numerous IoT based heterogeneous sensors that perform measurement
of temperature, vibration, humidity, or various gas levels present
in the building. Edge devices can process information from heterogeneous sources to deduce valuable information about the building’s current health. Moreover, the edge devices can also make decisions on available data to operate (or actuate) sensors for specific
tasks, for instance to lower temperature, inject fresh air, or open
ventilators. By making use of edge computing, a building’s security
can also be improved by performing real-time video processing of
surveillance cameras and activate warning alarms, or door locks. It
is reported in [69] that there will be an increase in the combined
global market for Internet of Things in Buildings (BIoT), rising from
$25.65 billion in 2015 to $75.5 billion by 2021, and a combined
annual growth rate in BIoT will be about 20.7%. Gooee, a company
producing enterprise level IoT solutions for smart lighting has developed ‘Full-Stack’ operating system to allow manufacturers create IoT enabled lighting solutions [70]. Gooee has partnered with
PointGrab [71], a provider of edge-analytics sensing solution. PointGrab performs real-time analytics using edge on the obtained data
from buildings and applies its deep-learning and sensing technology to the building ecosystem. The company allows data capture
about how and where occupants use building by utilizing its CogniPoint embedded-analytics sensors and edge-computing platform
[72]. Intel has been actively involved in IoT enabled smart building
solutions and offers a range of products, including, system on chips
for secure edge computing, IoT gateways, analytics platforms, security management solutions [73]. Intel partnered with AVOB [74] for
“Energy Saver” project, a small and medium sized building energy
management solution to provide monitoring and remote control
for smart energy management [73]. Intel’s BMP integrated with
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K. Bilal et al. / Computer Networks 130 (2018) 94–120
Table 2
Edge computing applied in various areas.
Area/Application
Reference
Idea presented
Internet of Things
[75]
Datta et al. proposed an IoT architecture for connected vehicles and utilized fog computing as a platform for
providing IoT services to connected vehicles.
Tawalbeh et al. used cloudlets for big data analytics in a mobile cloud computing environment.
Brzoza-Woch et al. presented a fog-enabled embedded system for environmental monitoring.
Companies like PointGrab and Gooee partnered to provide IoT enabled lighting solutions with the help of
real-time edge computing
Intel partnered with AVOB to develop edge enabled remote control and monitoring for IoT based smart energy
management
Ha et al. presented an architecture and prototype implementation of a cognitive assistance system using cloudlets
and Google glass
Simoens et al. proposed GigaSight, to store crowd-sourced videos in a local cloudlet for efficient uploading,
downloading, and processing [91].
Chen et al. presented the architecture and implementation details of a wearable cognitive assistance application
using cloudlets
Cai et al. proposed to use cloudlets to assist multi-player gaming to share the received video frames aiming to
minimize the server transmission bandwidth usage
Méndez et al. used ElectroEncephaloGram (EEG) headsets, smartphones, and fog computing to stream data
captured from brain and send it to fog server for processing.
Soyata et al. used cloudlets for real-time face recognition at airports named MOCHA using Mobile-Cloudlet-Cloud
architecture.
Valancius et al. proposed to employ nano data centers to minimize the energy consumption and latency for VoD
Jalalai et al. identified scenarios in which running applications on nano servers used in fog are more efficient than
running the same applications on centralized data centers
Gai et al. proposed Dynamic Energy-aware Cloudlet-based Mobile cloud computing (DECM) model to minimize
additional energy wastage in MCC scenario
Sun and Ansari proposed Green Cloudlet Network (GCN) architecture for MCC aimed for process offloading
between User Equipment (UE) and software clone at cloudlet with minimal delay and energy consumption
Sarkar et al. presented a mathematical model for fog computing paradigm by mathematically quantifying power
consumption, service latency, CO2 emission, and computational cost
Presented a generic fog model for smart living comprising of 3 major components: Fog Edge Node (FEN), Fog
Server (FS), and Foglet as a middleware program agent
Li et al. proposed to use smart agents to mitigate lack of intelligence and reasoning in current smart objects in IoT
and smart environments using swarm intelligence. They proposed Rainbow, an architecture for smart
multi-agent system using fog computing.
Sneppe and Namiot proposed to use mobile edge computing to share data among interoperating services of smart
city
Taleb et al. presented Follow-Me-Edge (FME) to enable emerging services for smart living using mobile edge
computing
Naranjo et al. introduced the concept of SmartLocalGrid (SLG) for communication between two micro-grids that
allows communication among multiple devices efficiently to enable data processing and real-time decision
locally without cloud support.
Masip-Bruin et al. proposed fog enabled solution for Chronic Obstructive Pulmonary Disease (COPD) patients’
assistance that enable patients to roam and move freely with the automated provision of breathing and oxygen
supply
Fratu et al. employed fog computing to eWALL EU project to achieve real-time response for Mild Dementia (MD)
and COPD patients.
Cao et al. proposed FAST, a distributed analytics based fall monitoring system using fog computing for stroke
mitigation.
Ha et al. presented architecture and prototype implementation of a cognitive assistance system using cloudlets and
Google glass
Chen et al. presented the architecture and implementation details of a wearable cognitive assistance application
using cloudlets
Quwaider and Jararweh proposed cloudlet based architecture for collection and processing of data from Body Area
Networks (BANs). Authors employed cloudlets to minimize packet-to-cloud energy and packet delay
Amraoui and Sethom proposed cloudlet based pervasive healthcare monitoring system for chronic diseases using
BANs
Althebyan et al. presented a largescale e-health system using edge technologies. The authors proposed wearable
textile based sensor, strategically distributed in clothing to continuously monitor patients’ health condition
Intharawijitr et al. analyzed fog computing in 5G mobile networks paradigm for communication and computation
latencies
Peng et al. presented the suitability and benefits of using edge computing paradigm in 5G networks and proposed
Fog- Radio Access Networks (F-RAN) to mitigate the shortcomings of Cloud Radio Access Networks (CRAN).
Nunna et al. presented various use cases for potential context-aware collaboration systems using 5G technology
with MEC.
Zhang et al. presented a multi-tiered architecture for delay sensitive cloud Data Service Subscribers (DSS).
[76]
[77]
[71]
[73]
Multimedia and edge
computing
[54]
[91]
[5]
[93]
[94]
[98]
Energy efficiency and edge
[21]
[99]
[100]
[101]
[66]
Smart living
[102]
[40]
[103]
[104]
[24]
Health care
[113]
[6]
[112]
[54]
[5]
[114]
[115]
[116]
Communication efficiency and
edge computing
[117]
[118]
[119]
Edge computing architectures
and resource management
[120]
[121]
[122]
[26]
[123]
Eui-Nam et al. presented an architecture of a smart gateway with fog computing.
Yin et al. proposed Tentacle, a dynamic and on the fly resource provisioning algorithm to procure edge servers for
online service providers.
Azam and Hu presented a service oriented strategy to effectively and efficiently manage resources in fog
computing
IoT devices are classified based on a device’s nature and mobility to efficiently perform resource allocation. A
detailed pricing model was also discussed
(continued on next page)
K. Bilal et al. / Computer Networks 130 (2018) 94–120
105
Table 2 (continued)
Area/Application
Reference
Idea presented
[124]
Nippon Telegraph and Telephone Corporation developed an edge accelerated web platform (EAWP). The EAWP
enables the web applications to run on edge servers.
Zhu et al. proposed the concept of fog boxes to improve the website experience. The users connect with the
internet via edge servers (fog boxes) using HTTP
Aazam et al. proposed a service oriented model for fair management of IoT resources using fog computing that
allows fair pricing, distribution, and management of resources in IoT
Zeng et al. proposed a fog computing supported software-defined embedded system consisting of edge devices
(cellular base stations) equipped with computation and storage resources and embedded client systems are
general purpose hardware
Cirani et al. proposed an architecture of Fog nodes as an IoT hub using Constrained Application Protocol (CoAP)
protocol
[125]
[27]
[126]
Edge computing
Implementation and
Simulation
[129]
[128]
[25]
[130]
[132]
[133]
[134]
[53]
[135]
Butterfield evaluated Google’s Go language for IoT and fog scenario.
Sarkar and Misra presented theoretical modeling and mathematical formulation of fog computing architecture
considering its various components
Gupta et al. presented iFogSim, a simulation environment focusing on evaluation of resource management
strategies for fog computing
Orsini et al. proposed a mobile edge computing based programming framework CloudAware that allowed the
users to offload their compute-intensive tasks from smartphones to the edge servers.
Cisco’s ParStream is a platform that allows handling of massive volumes of high-velocity data to provide real-time
analytics at the edge
Vortex fog computing provides platform independent interoperable solutions for intelligent data sharing and
analytics platform for business critical IoT applications
Cisco Data in Motion (DMo) technology allows data management and analysis of large volumes of data coming
through IoT at the edge
Cisco IOx is a combination of Cisco IOS, a network operating system, and Linux. The IOx allows hosting
capabilities for fog applications, and allows management of network components, such as routers, switches, and
compute modules
Fig. 8. IoT/Edge enabled smart city.
Candi PowerTools is a secure management platform that connects
to various building systems and sensors to access data, performs
data filtering, protocols translation, and secure transfer of data to
cloud or to on-premise deployed edge servers [73].
Datta et al. proposed an IoT architecture for connected vehicles
and utilized fog computing as a platform for providing IoT services
to connected vehicles [75]. The architecture consists of: (a) smart
phones and sensors fitted on vehicles acting as data source, (b) ac-
cess points as RSUs, and (c) cloud system. The vehicular sensors
utilize sensor markup language to report the sensory data. The
data is communicated to RSUs that are connected with fog computing platform having a discovery module. The connected vehicles utilize discovery module to look for application and services
provided by the RSUs. The fog platform is deployed at the middle
nodes that are placed at the edge of the network. The vehicles are
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K. Bilal et al. / Computer Networks 130 (2018) 94–120
able to connect with various fog services with low latency, due to
wide geographic distribution of fog platform.
Edge computing can provide solutions for big data processing,
where the big data represents the large volumes of data generated by IoT devices or sensor networks. With edge computing, ondemand elastic resources can be provisioned for locally processing the big data without sending data to cloud and suffering from
drawback of higher latency and bandwidth consumption. A combination of edge and cloud computing can address big data acquisition, aggregation, and preprocessing, reducing the data transportation and storage on cloud. For instance, for large scale environmental monitoring system, the local data can be collected
and processed at the regional fog nodes to provide timely feedback to end users, especially in emergency scenarios. In addition,
the detailed and thorough analysis, and computational intensive
tasks can be performed on the remote cloud. Tawalbeh et al. used
cloudlets for big data analytics in a MCC environment [76]. The
authors proposed a master-cloudlet management system for intercloudlet communications. The authors implemented the proposed
model and reported considerable gains in energy efficiency and latency. Authors in [77] presented a fog-enabled embedded system
for environmental monitoring. A smart levee monitoring system is
proposed for flood warning. The authors proposed to use fog infrastructure to process and filter raw data, before sending it to
cloud, and to support rescue teams in emergency situation without connecting to the Internet. The authors designed an embedded
system using WSANs and fog for flood risk assessment. The basic
idea is to collect the sensed data from WSN and transmit it to fog
to analyze the data using an analysis and forecasting application.
To conclude, there are several practical applications of edge
computing for IoT, including smart homes and big data processing.
The major of focus of most IoT applications is energy conservation,
such as implementing IoT based smart lighting [70,73,74]. The IoT
generated big data needs to be processed to extract useful patterns
[71]. The edge computing is also finding its applications in IoT enabled connected vehicles [75]. Tawalbeh et al. used cloudlets for
big data analytics in a MCC environment [76]. In [77] authors utilized IoT and edge for environmental monitoring.
3.1.2. Multimedia and edge computing
Multimedia, specifically, video content is one of the major consumers of overall Internet bandwidth. It has been reported that in
2015, video data comprised around 70% of the total Internet traffic.
These figures are predicted to rise to 82% in 2020 [78]. In future
IoT scenarios, many multimedia generating gadgets, such as closed
circuit TV and visual sensor networks will generate massive volumes of multimedia data [65]. As multimedia requires more bandwidth, processing, and storage, so handling such huge volumes in
terms of communication, processing, and storage is a real challenge. Edge computing is envisioned to aid in such scenarios to
minimize the overall end-to-end bandwidth usage, distribution, efficient processing, and storage for multimedia [79–81]. Multimedia delivery also incurs high costs. CDNs, like CloudFront charge
substantially, when considering Tbps data delivery. It has been reported that YouTube Live and Twitch surpassed 1 Tbps mark during peak hours in 2014 [82]. In another study on Twitch trace analysis, it has been reported that Twitch surpassed 1.5 Tbps video
content delivery to viewers across the globe [5]. It also needs to be
considered that Internet connectivity and data rates are increasing
every term, which means higher data access by users. In 2014 state
of the Internet report, Akamai reported an average global bandwidth of 4.5 Mbps, with 59% users having more than 4 Mbps connectivity, among which 13% and 10% had 10 Mbps or higher and
15 Mbps or higher Internet connectivity, respectively [83]. Whereas,
in 2016, the average bandwidth globally raised to 6.8 Mbps, with
73% connections having more than 4 Mbps. Among these 73% con-
nections, 35% had more than 10 Mbps, 21% had more than 15 Mbps,
and 21% had more than 25 Mbps Internet connectivity [84]. It can
be seen that in 2016, way more users have 4 K ready Internet connectivity as compared to 2014. Such large volumes of data are
charged heavily, e.g., Amazon CloudFront CDN charges $0.085 per
GB for first 10 TB and $0.26 Per GB for higher usage of data transmission [85]. It was observed in 2015 captured logs, Twitch delivered video content at more than 1.5 Tbps [86]. Although, Twitch
is owned by Amazon, so the payment matters may be internal.
However, if one calculates the total cost required to transmit 1.5
Tera bits (192 Giga Bytes) using Amazon CloudFront with minimum charges, i.e., $0.02 /GB, then it will be $3.84 /s leading to
$13,800 /h. Fog computing may be used to cache the popular content at edge and serve the local community from edge or from
minimum possible hops, as CDNs are still many hops away from
the users. Moreover, live content can also be disseminated from
network edge, offering higher bandwidths to viewers. Specifically,
in terms of interactive multimedia, which is strictly delay sensitive,
e.g., multi-view and free-view video [87], switched view delivery
and virtual view synthesis can be performed at edge with minimal
delay. If we consider per bit energy consumption across the Internet hops, then it may be realized that even four hops (generally
considered as average from CDN to users) may inhibit excessive
amount of energy usage and Green House Gases (GHG) emissions.
2013 NSF workshop report predicted that “it will soon be possible to find a camera on every human body, in every room, on every
street, and in every vehicle” [88]. Video surveillance plays a significant role in effective urban planning and management administratively, as well as for law enforcement departments. It is estimated
that in 2013, that there was one surveillance camera for 11 persons
in the United Kingdom [89]. Considering the futuristic scenario of
such massive number of cameras and their streams uploaded to
the Internet mandates feasible solutions for communication, processing, and storage. Surveillance information may come in a heterogeneous form from multiple sensors. Target tracking and object
assessment in such surveillance environment requires information
fusion and collective processing. Efficient extraction of information from various streams, analysis, and understanding requires resource, which can be provisioned from Cloud computing. However,
long response time and delays prohibit using cloud computing for
mission critical, sensitive surveillance, and tracking systems. Edge
computing, however, offer the resources, as well as real-time response for such applications.
Most of the captured videos and pictures are stored locally.
However, crowd-sourced based video streaming is gaining popularity. Twitch is estimated to serve around 50 million users every month with 150 billion minutes of live video [90]. Simoens
et al. proposed GigaSight, to store crowd-sourced videos in a local
cloudlet for efficient uploading, downloading, and processing [91].
Processing videos captured in a small geography at a local cloudlet
enables searching and processing of related videos easily and efficiently. For instance, if some kid or dog is lost in some theme park
or concert, recent videos from same event uploaded to the local
cloudlet in recent times may be searched to find the missing.
With the emergence of wearable computing and gadgets, cognitive assistance based applications are becoming a reality. One
of the major requirements of cognitive assistance applications is
real-time response. Human subjects take from minimum 370 ms
to maximum 620 ms to respond an unknown face [92]. The edge
computing can be used for real-time cognitive assistance by integrating image capturing, sensing, and processing to deliver response instantly. More than 20 Million Americans suffer from some
form of cognitive impairment, for whom, edge computing offers
hope and a feasible platform. Ha et al. presented architecture and
prototype implementation of a cognitive assistance system using
cloudlets and Google glass in [54]. The authors detailed the archi-
K. Bilal et al. / Computer Networks 130 (2018) 94–120
tectural requirements of cognitive assistance systems and implementation details. Considering the high delay, cloud platforms cannot be used for task offloading, therefore, authors used cloudlet for
efficient communication and processing. Similarly, Chen et al. presented the architecture and implementation details of a wearable
cognitive assistance application using cloudlets [5]. The application
is designed for cognitive assistance in four different tasks, i.e., free
hand sketching, 2D Lego models assembling, context-relevant recommendation of YouTube tutorials, and playing a ping-pong game.
Constraints on network bandwidth, delay, and jitter in cloud
gaming seriously impact users’ Quality of Experience (QoE). Cai
et al. proposed to use cloudlets to assist multi-player gaming to
share the received video frames aiming to minimize the server
transmission bandwidth usage [93]. Game server sends the encoded video to Adhoc-cloudlet, which in turn transmit the video
to the group of connected players. Classification of brain state is
a heavily computational intensive and delay sensitive real-time
task. Méndez et al. [94] used ElectroEncephaloGram (EEG) headsets, smartphones, and fog computing to stream data captured
from brain and send it to fog server for processing. EEG can be
used to determine ones’ brain states in real time. Based on the
processed data, authors demonstrated the effectiveness of work by
playing an online Brain Computer Interaction (BCI) “EEG Tractor
Beam” game among different users in USA and Taiwan. Near to
end users, fog servers successfully processed the data streams and
classification/calibration is performed at the cloud servers. Previously, various BCI related projects, such as HeadIT [95], BrainMap
[96], and PhysioNet [97] employed physiological signal processing. However, none of these projects were able to interact with
their clients in real-time. Edge computing enables the real-time
signal processing and enabled client interaction to perform various tasks. Such usage of edge computing resources can be foreseen to bring realistic applications. Soyata et al. used cloudlets for
real-time face recognition at airports named MOCHA using MobileCloudlet-Cloud architecture [98]. Cloudlets were employed to minimize the response time. Experimental results demonstrated that
inclusion of cloudlets considerably enhanced the performance and
response time of MOCHA.
To summarize, edge computing finds its place in numerous multimedia applications, especially in real-time processing of
crowdsourced video streams [91] and cognitive assistance applications [54]. The existing works have utilized cloudlet based architectures for cognitive assistance [5] and multi-player gaming
[93] to minimize latency and response time required for such applications. Similarly, edge has been utilized to perform real-time
processing of EEG data acquired from brain [94] and real-time face
recognition applications [98] in a bid to reduce the response time
and latency.
3.1.3. Energy efficiency and edge
Energy efficiency is one of the mandatory and key concerns today because of environmental impacts, energy demand, and cost
[1]. The ICT sector is one of the major energy consumer, estimated
to consume more than 271 billion KWh of energy in data centers
in 2010 [3]. Network infrastructure is also one of major energy
consumer, estimated to consume around 15.6 billion KWh energy
in 2010 [2]. ICT sector is also attributed as a major Green House
Gases (GHG) contributor, emitting around 2% of global GHG emissions [4]. The GHG emissions by cloud datacenters are estimated to
be 1034 t in 2020 [1], which clearly raise the environmental concerns and calls for appropriate solutions. In recent years, several
proposals have been presented to employ edge computing for improving energy efficiency of cloud services and end user devices.
MEC enables offloading of compute intensive and energy consuming applications from mobile devices to edge servers, thereby reducing the energy consumption of end devices. The majority of
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algorithms optimize the tradeoffs between energy consumption
at mobile devices and execution delays caused by the offloading
of the application. Gao et al. performed various experiments and
showed that cloudlets can reduce an energy consumption by up to
42% in a mobile device [164]. Zhang et al. demonstrated that MEC
can improve energy efficiency in heterogeneous networks by computation offloading [165]. In [166], the authors have investigated
the energy-efficient resource allocation problem for computation
offloading. Sardellitti et al. performed the joint optimization of
radio and computational resources for multi-cell mobile-edge computing [167]. The major aim of the authors was to minimize energy
consumption under latency and power budget constraints in [167].
The tradeoff between power consumption and transmission delay
in the fog-cloud computing system is investigated in [168].
Jalalai et al. identified scenarios in which running applications
on nano servers used in fog are more efficient than running the
same applications on centralized data centers [51,99]. The authors
proposed new energy models for shared and unshared network
equipment to measure the energy in different scenarios. Nano
servers were implemented using Raspberry Pi computers and were
measured for traffic and power consumption. The energy consumption of data requests to nano servers were compared with data requests to centralized data centers using energy consumption models. The results indicated that energy can be saved on transport
network, when the frequently used contents are pushed to the
nano servers near to the requesting user, thus resulting in less traffic on backbone. The authors concluded that for efficient content
storage and energy saving, the application architecture could be a
hybrid of both fog and cloud.
Gai et al. explored the impact of edge computing considering energy and delay in Mobile Cloud Computing (MCC) [100].
The authors proposed Dynamic Energy-aware Cloudlet-based Mobile cloud computing (DECM) model to minimize additional energy
wastage in MCC scenario. The authors proposed a web service at
cloudlet layer to search and allocate appropriate cloud resources
using dynamic computing for the request, considering energy and
latency constraints. Sun and Ansari [101] proposed Green Cloudlet
Network (GCN) architecture for MCC. GCN aims at process offloading between User Equipment (UE) and software clone at cloudlet
with minimal delay and energy consumption. The GCN architecture also used SDN technology and proposed Cloudlet Network File
System (CNFS) to protect data integrity. Sarkar et al. presented
a mathematical model for fog computing paradigm by mathematically quantifying power consumption, service latency, CO2
emission, and computational cost [66]. The performance of proposed model is evaluated by considering large number of Internetconnected devices demanding real-time service. The model is evaluated using a case study of devices generating traffic from hundred most populated cities, being served by eight geographically
distributed data centers. The experimental results indicated that
with the increase in the number of applications demanding realtime service, the fog computing platform outperforms the traditional cloud computing. The authors further observed that with
50% devices requiring real-time services, the service latency of
fog computing decreases by 50%. However, an interesting observation by the authors was that the environments where there
are less percentage of applications that demand low latency services, the fog computing appeared to be an overhead over traditional cloud computing. The evaluation parameters utilized by
the authors were power consumption and service latency. Power
consumption further included consumption due to data forwarding, computation, storage, and data migration. Whereas the service
latency was subdivided into transmission latency and processing
latency.
In summary, edge computing has been investigated as a motivation for improving energy efficiency of cloud applications. The
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K. Bilal et al. / Computer Networks 130 (2018) 94–120
Fig. 9. Smart home using edge computing.
existing literature demonstrated that cloudlets and MEC can reduce the energy consumption for some cloud-based applications
through computation offloading [164–166]. The joint optimization
of radio and computational resources for multi-cell MEC helps
minimizing energy consumption under latency and power budge
constraints [167,168]. If properly deployed, the edge computing can
augment cloud computing to reduce energy and response time of
various cloud applications [51,99]. The delay and energy consumption are investigated together in various proposals to find a balance between the two to improve overall energy efficiency with
minimal delay [100,101,66].
3.1.4. Smart living
Communication (delay sensitive) and interaction among smart
objects, such as sensors, controllers, and actuators, is a pivotal and
common phenomenon in all domains of smart living and pervasive environments [102]. Smart objects and Cloud computing interaction model, used in various smart solutions, such as Cognitive Gateway, depict various limitations and deficiencies in cloud
interaction, specifically unpredictable delay and jitter [102]. Edge
technologies offer the solution for these problems that hinder the
visions and performance of smart living solutions. Advancement in
smart devices and sensors are leading to fulfill the smart living visions. Smart Energy, Health, Offices, Protection, Entertainment, and
Surroundings (EHOPES) represent the fundamental components of
smart living. Fig. 9 reflects the use of edge computing in smart
homes. Authors in [102] presented a generic fog model for smart
living. The authors represented the fog architecture comprising of
3 major components.
• Fog Edge Node (FEN) is hardware component of fog architecture, which lies near or in close proximity to the smart
objects, such as a smart phone, PCs, access points, set top
boxes, located at one-hop proximity. FENs act as the endpoint of this fog architecture. FEN may perform basic processing, storage, and information filtration. The significance
of FEN lies in providing various access methods (wired or
wireless) to smart objects such as sensors and actuators, e.g.,
Bluetooth, ZigBee, Wi-Fi, etc.
• Fog Server (FS) represents the fog instances placed inside a
micro-data center or cloudlet, representing a powerful virtualized server, offering inter-play between FEN and cloud. FS
can offer processing power storage, and be used to take collective decisions based on information from various servers
of FEN in smart environments. FS sits between FEN and
cloud servers and offers required processing, storage, collective control, and updating of information. Smart objects may
communicate directly to FS bypassing PEN in various models depending on the smart objects capabilities and requirements.
• Foglet is a middleware program agent, installed on fog nodes
(FEN and FS) for dynamic and scalable services. Foglet offers
provision for catering the heterogeneity in smart objects, applications, network management, and protocols. High level
of privacy and security may be achieved using customized
Foglets employing security techniques between FEN and FS.
FEN lies in close proximity of smart objects and client, and
has negligible privacy concerns, thus using custom privacy
and security procedures to diminish the eavesdropping or
leakage of information between FEN and FS, or even FEN and
cloud.
Smart living and new IoT paradigms call for smart interactions
among objects and ability to take decision based on presumed information. Authors in [40] proposed to use smart agents to mitigate lack of intelligence and reasoning in current smart objects in
IoT and smart environments using swarm intelligence. They proposed Rainbow, an architecture for smart multi-agent system using fog computing. The Rainbow is a three layered architecture,
having the physical “things” (smart objects), such as sensors and
actuator constituting first layer. The intermediate layer, a middleware represented these things as Virtual Object (VOs), acting as an
K. Bilal et al. / Computer Networks 130 (2018) 94–120
intelligent agent exposing an abstract representation of smart object or thing. The VOs are coupled in computations nodes, named
as Gateways. Different VOs or agents in a gateway may work together to achieve some high level goal. The gateways perform required processing and only the fine-grained agent process is sent
to cloud server (which constitute the third layer of Rainbow architecture), leading to filtered and processed information to be executed on cloud servers. The authors detailed the design of three
smart city applications, i.e., cyber physical system (CPS) for catering noise pollution, CPS for drainage network, and smart streets.
Sneppe and Namiot proposed to use mobile edge computing to
share data among interoperating services of smart city [103]. As
various smart city services use data from multiple sources, therefore, an edge based storage to store and receive local data enhance
service efficiency and minimizes latency and core traffic. Taleb
et al. presented Follow-Me-Edge (FME) to enable emerging services
for smart living using mobile edge computing [104]. The FME is an
extended version of Follow-Me-Cloud (FMC) concept [105,106] for
edge computing to enable low latency services. The idea is to enable service to keep track of user and always service user from
nearest edge service. The authors discussed the FME service using
case studies, where a user watching a video while riding a bus is
served by an edge location say Edge A. As the user is mobile, and
gradually moves near to Edge B, the video and related streaming
virtual function are migrated from Edge A to Edge B, so that the
user may be served from Edge B. The authors presented the FME
architecture and performed simulation based evaluation to depict
live migration latency.
With the evolution of intelligent transportation system (ITS), a
large number of sensors are deployed in city premises that collect traffic data on daily basis [107,108]. The live streams captured
through video cameras require real-time processing and minimal
latency, and therefore, the information cannot be sent to the traditional cloud as the response time will be higher. Edge devices embedded with traffic lights constitute smart traffic lights that receive
real-time traffic information and coordinate among each other to
create a dynamic green wave or to send warning signals in case
of any road emergencies [155]. For instance, a camera mounted
on a signal can detect flashing lights of an approaching ambulance and switch the street lights to allow free movement of ambulance through the intersection. Edge connected wireless access
points can allow vehicle to vehicle, vehicle to access point, and
access point to access point communications and numerous other
applications, thus allowing information transfer and sharing among
moving vehicles with minimum latency. For instance, traffic light
system in Chicago, USA, is controlled with the help of smart sensors and edge computing [109]. Traffic volume data is collected
from individual traffic lights. The IoT enabled smart traffic application computes real-time traffic congestion at network edge and
automatically alter the timings of traffic signals, thereby allowing
the smooth flow of vehicles.
A potential application of edge computing is smart grid. Smart
grid constitutes smart meters, smart appliances, renewable energy
sources, and energy efficient resources, as reflected in Fig. 10. The
energy load balancing and distribution applications running on
smart grid require real-time processing and actuation capabilities.
The data generated by grid sensors and devices is processed at
the edge servers, and filtered out to be consumed locally or sent
to the higher tiers for visualization, reporting, and transactional
analysis. In this way, the edge computing reduces the amount of
traffic that would be otherwise sent to cloud for analysis if the
edge layer is not present. The long term reporting and business
intelligent analytics are provided by cloud computing. Smart meters installation in households of USA has witnessed exponential
growth from 6% in 2008 to 89% in 2012. It is estimated that in
2019, various homes and small businesses will be having around
109
19 million smart meters [110]. With 50 0,0 0 0 smart devices, Austin
energy gathered around 100 Terabytes of data. Smart meters send
power usage updates every 15 minutes. With millions of smart meters, this will result in huge data, demanding substantial storage
and bandwidth resources. Considering the smart grid paradigm,
with power devices connected, and exchanging information will
further aggravate the needs. Authors in [24] presented an approach
to use fog computing for smart grids. The authors introduced the
concept of SmartLocalGrid (SLG) for communication between two
micro-grids. SLG allows communication among multiple devices
efficiently to enable data processing and real-time decision locally
without cloud support. Use of fog computing to mitigate the limited bandwidth capacity of Power Line Communication (PLC) is discussed in [111]. The authors proposed a distributed data aggregation and processing of consumer smart meters using fog computing. The simulation results depicted a great improvement in latency and response time when and intermediate fog layer is used
for smart grid.
To summarize, edge computing finds its applications in energy,
health, offices, protection (security), entertainment, and surroundings – the factors that constitute smart living. As we saw above,
the recent works proposed: (a) fog enabled models for smart living to reduce latency and response time [102,104], (b) multi-agent
based architectures to induce intelligence in smart living objects
[40], (c) data sharing models for interoperating services in a smart
city, (d) smart traffic control systems for controlling traffic lights
using edge computing [109], and (e) models for smart power distribution using edge computing in smart grids [24,111].
3.1.5. HealthCare
Edge computing paradigms are foreseen to play significant role
in eHealth care solutions and smart health [112]. Pervasive health
monitoring applications are widely growing area of biomedical research offering various novel healthcare solutions, where most of
the solutions are rooted in cloud computing. However, real-world
user experience for these cloud based smart healthcare applications is unsatisfactory and poor because of the long delays and response times between application and cloud [112]. Edge computing
technologies portrays great potential as a viable solution for pervasive healthcare applications to elevate the user experience and
minimize delay [112]. A recent analysis of an eHealth application
shows that around 25,0 0 0 tuples of health data flows every second, which will increase with the proliferation of IoT and smart
city implementations to millions of tuples [33]. Several solutions
for Chronic Obstructive Pulmonary Disease (COPD) patients’ assistance are proposed using Fog computing, which enable patients to
roam and move freely with the automated provision of breathing and oxygen supply [113,6]. Such assistance system will save
patients from health deterioration and hospital expenses. Patients
with COPD require assistance with the amount of oxygen required
in various stages, such as during rest or walking. COPD breath assistance system employs the idea of constant patient state monitoring using BAN sensors. The required amount of oxygen depends
on the arterial blood gas measurements. The extracted information
is sent to fog instances, which calculate the exact amount of oxygen required by patient. The actuators on oxygen supply devices
and cylinders react to the processed information and start supplying the required amount of oxygen. The oxygen supply does not
depend only on the patient’s physical conditions, rather it encompasses various parameters, such as patient condition, air pollution,
and air quality. Fratu et al. employed fog computing to eWALL EU
project to achieve real-time response for Mild Dementia (MD) and
COPD patients [6]. eWALL offers a prefabricated system with various sensors to monitor various vital signs and habits of MD and
COPD patients [6].
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K. Bilal et al. / Computer Networks 130 (2018) 94–120
Fig. 10. Smart grid using edge computing.
Ambient Assisted Living (AAL) may play a key role to elevate
senior citizens’ life style and independence [6]. Real-time processing of information gathered from sensors is one of the key attributes of AAL system. Previously, such real-time processing for
delay-sensitive applications was inconvenient and difficult. Edge
technologies, like fog computing now offer the required processing
capabilities in real-time for AAL systems. Cao et al. [112] propose
FAST, a distributed analytics based fall monitoring system using fog
computing for stroke mitigation [112]. Around one third of stroke
mortalities may be prevented if stroke related risk factors, such as
falling may be efficiently mitigated. Authors proposed new fall detection algorithms based on analysis techniques for non-linear time
series and acceleration magnitude values, along with filtering techniques.
With the emergence of wearable computing and gadgets, cognitive assistance based application are becoming a reality. One of
the major requirements of cognitive assistance applications is realtime response. Human subjects take from up to maximum 620 ms
to respond an unknown face [92]. The edge computing can be used
for real-time cognitive assistance by integrating image capturing,
sensing, and processing to deliver response instantly. More than
20 million Americans suffer from some form of cognitive impairment, for whom, edge computing offers hope and a feasible platform. Ha et al. presented an architecture and prototype implementation of a cognitive assistance system using cloudlets and Google
glass in [54]. The authors detailed the architectural requirements
of cognitive assistance systems and implementation. Considering
the high delay, cloud platforms cannot be used for task offloading,
therefore, authors used cloudlet for efficient communication and
processing. Similarly, Chen et al. presented an architecture and implementation details of a wearable cognitive assistance application
using cloudlets [5]. The application is designed for cognitive assistance in four different tasks.
Quwaider and Jararweh proposed cloudlet based architecture
for collection and processing of data from Body Area Networks
(BANs) [114]. Authors employed cloudlets to minimize packet-tocloud energy and packet delay. The authors simulated the proposed architecture using CloudSim simulator to illustrate energy
efficiency and low latency. Amraoui and Sethom proposed cloudlet
based pervasive healthc…
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