Table 1 - files.transtutors.com · Web viewGuidelines – Review based Projects. Proposed System (COMPONENTS of your System): (Section 3 in a Sample. 1) Please start your writing

Guidelines – Review based Projects

Proposed System (COMPONENTS of your System): (Section 3 in a Sample 1)1. Please start your writing about previous system, what is the base of their work

(Their developed system based on what?). 2. What are the criteria you considered in collecting and analysing your literature?

Why some past results have rejected. 3. What is the important (GOAL) of doing your project?. 4. You need to give explanation on each factor (class/component) in your work.

Classify these classes, and find their relationship with the subclasses. What are the attribute for each class and subclass?

5. Justify why you consider each factor (class/component) in your classification.

Subsections in your Proposed System section: Each factor (class/component) in your classification should has subsection in

proposed section. For each subsection, give introductory about that factor (class). Justify in details, why you use this factor in your classification. What was the

problem? What the need of this factor (class), … etc. Please also make other subsections for each factor that is related to subclasses

you mentioned in in your system classification.

Describe the “Domain Scenario” that associate with your system components

e.g. Describe “surgical scenario” which is associated with both data and view classes.

Why you need to describe the domain scenarioIt will allow you to describe the “dynamic systems” that change based on the end users current tasks.e.g. Surgical scenario allow us to:

Determine what type of visualisation data should be shown at a particular point in time of surgery.

Where it should be viewed How the data may be interacted with at the step of the surgery It describe the type of surgery, and number of surgical steps that are

executed to perform surgery What is the action need to be taken in each step, with association of accuracy

and time in each step? Define each factor (class) of your classification, and also their sub-class.

Justify why each factor used for classification.

How to demonstrate the USEFULNESS of your proposed system components1. “Classify” the state of art publications that describe using the “the same

technology” with the “technique” you use in your system. Check (Classification comparison - Previous Work Comparison) section guidelines.

2. “Verify” your proposed system (Check Validation and Evaluation section) based on: “it’s goodness of fit”; e.g. how well it describes mixed reality visualisation IGS

system within literature “it’s completeness” “it’s components” and compare it to those of “image guided surgery as an

example”.

2

Classification comparison - Previous Work Comparison:1. You need to create a table to show the classification comparison for all previous

work that you collected. This table should compare all previous solution in term of factors you considered in your work. Also you need to show what is the domain of each solution they work on it, and what was their input to the system.

2. Start your writing with subsection again with each of your factor that you mentioned in your classification table and write about it including what previous author done related to this factor. The last paragraph in each subsection should give the conclusion about your classification table.

3

Validation and Evaluation:1. Here you are going to validate (show the right system built and give that meet

your goal you set), and also evaluate (Show the value of your system and the usefulness of it) your proposed system. This you done by relying you on the comparison that you done in previous section.

2. Show why you evaluate and validate the system. 3. What are the parameters that you are going to use in validating and evaluating

your system. 4. Write about previous work how they validated and evaluated their system.

Subsections in your Validation and Evaluation section:1. Start your writing about the tool has used to evaluate the model you proposed. 2. Write about the tool that you are going to use to evaluate your model.

4

Validation and Evaluation:Please check section 6 in given sample 1.

Conclusion with RecommendationsTwo paras

Reiterate the purpose of the research Summarise results/findings Acknowledge limitations of the research focusing on methodology, the model and

implementation Suggest areas of research and the future direction What needs to be done as a result of your findings focusing on the weaknesses identified

For Review Based project ONLY - Samples of Factors Have considered in previous review based projectTABLE 1: Publicly available datasets for ABSA

No

Dataset and Author

Domain & Language & Size

Format Example URL

1 Customer Digital products (EN): Text format with speaker phone[+2], radio[+2], https://

5

https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html

No

Dataset and Author


Format Example URL

review data (Hu

et al., 2004)

3945 sentences tags of aspect

terms and

polarities (-3, -

2, -1, 1, 2, 3)

infrared[+2] ##my favourite

features , although there are

many , are the speaker phone ,

the radio and the infrared .

www.cs.uic.ed

u/~liub/FBS/

sentiment-

analysis.html

2 SemEval 2014

(Pontiki et al.,

2014)

Restaurants (EN): 3841

sentences

Laptops (EN): 3845

sentences

XML tag, in

which two

attributes ("from

and "to") that

indicate its start

and end offset

in the text

<sentence id="81">

<text>Lightweight and the

screen is beautiful!</text>

<aspectTerms>

<aspectTerm term="screen"

polarity="positive" from="20"

to="26"/>

</aspectTerms>

</sentence>

http://

alt.qcri.org/

semeval2014/

task4/

3 SemEval 2015

(Pontiki et al.,

2015)

Laptop (EN): 450 reviews

(2500 sentences)

XML tag of

{E#A, polarity}

<sentence id="1004293:0">

<text>

Judging from previous posts

this used to be a good place,

but not any longer.

</text>

<Opinions>

<Opinion target="place"

category="RESTAURANT#GE

NERAL" polarity="negative"

from="51" to="56"/>

</Opinions>

</sentence>

http://

alt.qcri.org/

semeval2015/

task12/

Restaurant (EN): 350

reviews (2000 sentences)

XML tag of

{E#A, OTE,

polarity}

Hotel (EN): 30 reviews

(266 sentences) - no

training data

XML tag of

{E#A, OTE,

polarity}

4 SemEval 2016

(Pontiki et al.,

2016)

Laptop (EN): 530 reviews

(3308 sentences)

Mobile phone (CH): 200


Camera (CH): 200


XML tag of

{E#A, polarity}

<sentence id="1661043:4">

<text>Decor is

charming.</text> <Opinions>

<Opinion target="Decor"

category="AMBIENCE#GENE

RAL" polarity="positive"

from="0" to="5"/> </Opinions>

</sentence>

http://

alt.qcri.org/

semeval2016/

task5/

Restaurant (DU): 400


Mobile phone (DU): 270


Restaurant (FR): 455


Restaurant (RU): 405


XML tag of

{E#A, OTE,

polarity}

6

http://alt.qcri.org/semeval2016/task5/











No

Dataset and Author


Format Example URL

Restaurant (ES): 913


Restaurant (TU): 339


Hotel (AR): 2291 reviews

(3309 sentences)

5 ICWSM 2010

JDPA Sentiment

Corpus for the

Automotive

Domain

(Kessler, Eckert,

Clark, & Nicolov,

2010)

Automotive & digital

devices: 515 documents

(19,322

sentences)

XML tags

(<mentionClass

> indicate the

aspect term)

<classMention

id="StructuralSentiment_Instan

ce_40033">

<mentionClass

id="Mention.Person">Mention.

Person</mentionClass>

<hasSlotMention

id="StructuralSentiment_Instan

ce_40395" />

</classMention>

https://

verbs.colorad

o.edu/

jdpacorpus/

6 Darmstadt

Service Review

Corpus (Toprak,

Jakob, &

Gurevych, 2010)

Online university & online

service review: 118


MMAX format <markables

xmlns="www.eml.org/NameSpa

ces/OpinionExpression">

<markable id="markable_38"

span="word_118..word_119"

referent="empty"

annotation_type="holder"

mmax_level="opinionexpressio

n" isreference="true" />


span="word_123"

annotation_type="opinionexpre

ssion"

opinionholder="markable_38"


n" opiniontarget="markable_39"

strength="average"

polarity="positive"

opinionmodifier="empty" />


span="word_126"

annotation_type="target"


n" isreference="false" />


https://

www.ukp.tu-

darmstadt.de/

fileadmin/

user_upload/

Group_UKP/

data/

sentiment-

analysis/

DarmstadtSer

viceReviewCo

rpus.zip

7

https://www.ukp.tu-darmstadt.de/fileadmin/user_upload/Group_UKP/data/sentiment-analysis/DarmstadtServiceReviewCorpus.zip



https://verbs.colorado.edu/jdpacorpus/



No

Dataset and Author


Format Example URL

span="word_124"

referent="markable_37"

annotation_type="target"


n" isreference="true" />

</markables>

7 FiQA ABSA

(Maia et al.,

2018)

Financial news headlines:

529 samples;

financial microblogs: 774

annotated posts

JSON nodes

with sentiment

score ranged

from -1 to 1,

"target"

indicates

opinion target,

and "aspect"

indicates

aspect

categories

according to

different level

"1": {

"sentence": "Royal Mail

chairman Donald Brydon set to

step down",

"info": [

{

"snippets": "['set to step

down']",

"target": "Royal Mail",

"sentiment_score": "-0.374",

"aspects":

"['Corporate/Appointment']"

}

]

https://

sites.google.c

om/view/fiqa/

home

8 Target-

dependent

Twitter

sentiment

classification

dataset (Dong et

al., 2014)

Twitter comments:

training data has 6,248

tweets, and testing data

has 692 tweets

http://goo.gl/

5Enpu7

TABLE 2: Application of the CNN model in the consumer review domain

No Study Domain Dataset & Language Model Performance

Opinion target extraction

1 Poria, Cambria, et al. (2016)

12 electronic products

Hu and Liu (2004)

English Deep CNN + Amazon WE + POS + LP

Precision: 82 .65 - 92.75%Recall: 85.02 - 88.32%F1: 84.87 - 90.44%

Laptop SemEval '14 English Precision: 86.72%

8

http://goo.gl/5Enpu7

http://goo.gl/5Enpu7

https://sites.google.com/view/fiqa/home



Recall: 78.35%F1: 82.32%

Restaurant SemEval '14 English Precision: 88.27%Recall: 86.10%F1: 87.17%

2 Feng et al. (2018)

Mobile phone

PM from Amazon, Jingdong, and Lynx

Chinese Deep CNN + WE + POS + dependent syntactic- (explicit aspects)

Precision: 77.75%Recall: 72.61%F1: 75.09%

Aspect category extraction

3Toh & Su (2016)

Restaurant SemEval '16 English CNN + WE +head word + name list + word cluster

F1: 75.10%

Laptop SemEval '16 English F1: 59.83%

4Ruder et al. (2016)

Mobile phone

SemEval '16 Dutch CNN + concatenated vectors

F1: 45.55%

Hotel SemEval '16 Arabic F1: 52.11%

5 Gu et al. (2017)

Smartphone PM from Amazon

English Multiple CNNs for each aspect

F1: 72.67 - 83.74%

Shirt PM from Taobao

Chinese F1: 92.26 - 97.34%

6 Wu et al. (2016)


English Multi-task CNN + word2vec/Wikipedia

F1:71.6-81.2%

Sentiment polarity

7 Gu et al. (2017)


English Single CNN Acc: 84.87% (binary)

Shirt PM from Taobao

Chinese Acc: 98.26% (binary)

8 Ruder et al. (2016)

Hotel SemEval '16 Arabic CNN + aspect tokens Acc: 82.72%

Mobile phone

SemEval '16 Dutch CNN + aspect tokens Acc: 83.33%

9 Du et al. (2016)

Electronics PM from Amazon

English Aspect specific sentiment WE + CNN

Acc: 92.08% (binary)

Movies and TV

English Acc: 92.05% (binary)

CDs and vinyl


Clothing, shoes and jewellery


9

10 Wu et al. (2016)


English Multi-task CNN+word2vec/Wikipedia

Acc: 84.1% (binary)

11 Xu et al. (2017)

Laptop PM from Yelp English CNN + CRF Acc: 70.90% (binary, lower than SVM model)

Restaurant PM from Yelp English CNN + CRF Acc: 68.34% (binary, lower than SVM model)

12Akhtar, Kumar, et al. (2016)

12 personal electronic products

PM (Akhtar, Ekbal, & Bhattacharyya, 2016)

Hindi CNN + SVM Acc: 65.96% (3-way)

TABLE 3: Summary of model comparison

CNN RNN RecNNAdvantages Ability to extract

meaningful local

patterns (n-grams)

Non-linear dynamics

Fast computation

Distributed hidden state that can

store past computations

Ability to produce a fixed size

vector that takes into account the

weighted combination of all words

and summarizes the sequence

Do not require large dataset

Require fewer parameters

Simpler architecture

Ability to learn tree-like

structures

Ability to construct

representations for any

new word

Disadvantages High demand for data

Fixed size of hidden

layer

Failure to capture

long-term

dependencies

Chooses the last hidden state to

represent the sentence which may

lead to incorrect prediction

Requires parsers/

parameters which can

be slow and lead to

inaccuracies

Models are still in their

infancy

Implication for

ABSA

Useful if the sentence

is supposed to have

one opinion/target of

fixed length

Not effective for

parsing longer

sentences

Useful to capture the semantic

meaning

Not useful if the sentence is

represented by a key phrase

The application and

training regime still

require further research

10

TABLE 4: Performance in opinion target extraction using the SemEval 2014 dataset (restaurant and laptop domains). The best outcomes are highlighted in blue

Domain No Study Model Performance (F1)

Restaurant

1 Poria, Cambria, et al. (2016)

Deep CNN + Amazon WE + POS + LP 87.17

2 Li et al. (2018) LTSMs + Truncated History-Attention (THA) andSelective Transformation Network (STN)

85.61

3 W. Wang et al. (2017)

Coupled Multi-layer Attentions (CMLA) based on GRU 85.29

4 W. Wang et al. (2016)

RecNN + CRF + POS + Name list + Sentiment Lexicon 84.93

5 Toh & Wang (2014) CRF + POS + dependency tree based features 84.01

6 Ye et al. (2017) Dependency-Tree Based CNN + POS + chunk 83.97

7 Xue et al. (2017) Multi-task learning neural network combines BiLSTM and CNN layers

83.65

8 X. Wang et al. (2016)

Uni-directional Elman RNN 82.12

9 Liu et al. (2015) Bi-Elman-RNN + POS + chunk + Amazon WE 82.06

10 Yuan et al. (2017) LSTM + Local Context + Senna WE 80.62

11 Tay, Tuan, et al. (2017)

Holo DyMemNN 79.73

12 Nguyen & Shirai (2015)

PhaseRecNN + CRF + multiple global functions 62.21

Laptop 1 Poria, Cambria, et al. (2016)

Deep CNN + Amazon WE + POS + LP 82.32

2 Li et al. (2018) LTSMs + Truncated History-Attention (THA) andSelective Transformation Network (STN)

79.52

3 Li & Lam (2017) Memory Interaction Network (MIN) based on LSTM with extended memory

77.58

4 W. Wang et al. (2016)

RecNN + CRF + POS + Name list + Sentiment Lexicon 78.42

5 W. Wang et al. Coupled Multi-layer Attentions (CMLA) based on GRU 77.8

11

(2017)

6 Ye et al. (2017) Dependency-Tree Based CNN + POS + chunk 75.66

7 X. Wang et al. (2016)

Uni-directional Elman RNN 75.45

8 Liu et al. (2015) LSTM-RNN+ POS + chunk + Amazon WE 75

9 Yuan et al. (2017) BiLSTM + Local Context + Senna WE 74.78

10 Chernyshevich (2014)

CRF + NER + POS + parsing + semantic analysis + additional reviews

74.55

11 Tay, Tuan, et al. (2017)

Holo DyMemNN 74.03

Figure 2 Cloud Service Models

12

Table 5 – RBAC Security Concerns – Data Based Surveys from 2008-2017

Table 1: Analysis of surveys on SDN comparing architecture, benefits and use cases.

13

Survey Paper Data Security

Data Leakage

Network Security

Data Locality

Data Integrity

Data Segregation

Access(Data/Remote)

Data Confidentiality

Web App Security

Data Breaches

Availability

Li and Kantola, 2008 Kumbhar et al, 2012 Jules and Opera, 2013 He et al, 2014 Han et al, 2014 Razzaq et al, 2014 Ortega, 2014 PCI-DSS, 2016 Yu et al, 2016 Alizadeh et al, 2016 Yan and Shi, 2017 Xu et al, 2017 Saa et al, 2017 Modi and Acha, 2017

Stud

ies

Fram

ewor

k

Architecture aspects Features of SDN Use cases

Prot

ocol

Mod

el

Net

wor

k Sc

ope

Prog

ram

mab

ility

Dyn

amic

mod

ifica

tion

Gra

nula

rity

Scal

abili

ty

Loa

d ba

lanc

ing

Inte

rope

rabi

lity

Mon

itori

ng

&

mea

suri

ngU

tility

optim

izat

ion

Net

wor

k

man

agem

ent

Mayora,

2017

SDN

orchestrati

on

OpenFlo

w,

PCEP

C Cloud and

Datacenter

, Inter-DC

Kobo et

al, 2017

SDWSN OpenFlo

w

C

Gudipati

, 2013

SoftRAN Distribut

ed

C RAN

Thyagat

uru,

2016

SDON Distribut

ed

C Optical

Networks

Fu et al.,

2017

SDN-5G OpenFlo

w

C RAN

Xu et

al., 2018

SoftSpace OpenFlo

w

D Satellite

network

Sun et

al., 2017

Hybrid

EV-

charging

network

C VANET

Li et al.,

2018

SDN-

based QoS

guaranteed

technique

OpenFlo

w

C Cloud

Akyildiz

et al.,

2015

SoftAir OpenFlo

w

C 5G-

wireless

Huo

et al.,

2016

SDN -

Caching

and

computing

OpenFlo

w

C Green

Wireless

Networks

Benzekk

i et al.,

2016

Cloud-

based

models

OpenFlo

w

C Cloud

14

Xu et

al., 2017

MP-

SDWN

OpenFlo

w

C WN

Liu et al,

2018

Fronthaul-

aware

software-

defined

network

C WSN

Abdulqa

dder et

al., 2018

SecSDN

Cloud

OpenFlo

w

D Cloud

Amiri et

al., 2018

Cloud

gaming

datacenter

OpenFlo

w

C Cloud and

data center

Table 2: Summary of SDN control plane.

Studies APIs Controller design aspects Development Tools

Nor

th

boun

d

Inte

rfac

e

Sout

h

boun

d

Inte

rfac

e

Stat

e co

nsis

tenc

y

Plac

emen

t

Scal

abili

ty

Flex

ibili

ty

mod

ular

ity

Ava

ilabi

lity

Secu

rity

&

depe

ndab

ility

Sim

ulat

ors

Tes

tbed

s

Deb

ugge

rs

Karakus

&

Durresi,

2017

15

Wang et

al., 2017

Huang et

al., 2017

Islam et

al., 2018

Kobo et

al., 2017

Alvizu et

al., 2017

Cox et

al., 2017

Jmal &

Fourati,

2017

Bannour

et al,

2018

Chahal et

al., 2017

Hu et al.,

2018

Haque et

al., 2016

Masoudi

et al.,

2016

Sandhya

et al.,

2017

Huque et

al., 2015

Dai et al.,

2017

Table 5: Summary of SDN relationship with NFV and NV.

16

References Model Architecture/

Frameworks

Merging

Technique

Services Challenges

Cox et al.,

2017

SDN+NV FlowVisor,

FlowN,

AutoSlice,

SoftAir, and

V-Cell

Virtual Edge

and SD-WAN,

dynamic

interconnects,

virtual core

and

aggregation,

and data center

optimization

Isolation, multi-

tenancy,

extended/stretched

networks, VLAN,

VPN, tunnel

Performance

issues with control

plane, basic

monitoring, and

analysis,

troubleshooting

several virtual

networks

Zhang et al.,

2017

SDN+NFV SoftCell and

CloudMAC

OpenBox and

Slick

On-demand

networking,

content-centric

networking

Resource

management,

distributed

management,

programmability,

interface, and

interoperability

Bizanis and

Kuipers, 2016

SDN+NV+NFV MobileFlow - 5th Gen networks Performance

issues, scalability,

management and

resource

configuration

Table 7: Summary of the SDN-based IoT networks co-related with there applications.

Studies Applications Edge

netwrok

Access network Core

Network

Data center

network

Dong et al. Rule-caching in

mobile access

networks

Boussard et al. SDN LAN

Wang et al.

&Yuan et al.

Traffic

engineering in the

data center

17

Diaz-Monte et

al.

SDN-based

federated muti-

cloud networking

Ding et al. Cloud-based

SDWN

Hakiri et al. &

Usman et al.

Scalable IoT

communications

Li et al. Energy efficient

networks

Liu et al. Device-to-device

communication in

LTE network

Zeng et al. SDN based WSN

18

Table 4: Analysis of present technique

Stud

y

Sam

ple

size

Use

r T

ype

Loc

atio

n

Con

text

e-health source

Secu

re

Cos

t Eff

icie

nt

Tim

e E

ffic

ient

Dig

ital g

ap b

etw

een

skill

ed a

nd u

nski

lled

Tra

ditio

nal

Web

site

Soci

al m

edia

Inte

rnet

Tel

ecom

mun

icat

ions

(mob

ile)

19

(Quinn et al., 2017)

54Univers

ity student

United

Kingdom

Tracking online health

information seeking behavior

✔ ✔ N/A

✔ - ❌

(Razmak & Belanger, 2017)

323Physicians and patients

Canada

IT indicators of e-health usage ✔ ✔ ✔ ✔ ✔ ✔ ✔ - ✔

(Keijser et al., 2016)

- - - Physician leadership - - ✔ - - - -

(Maeen & Zykov, 2015)

138

Contacts of

social network

Various

country

UserAttitudes ✔ ✔ ✔ ✔ - ✔ ❌

(Rosis & Barsanti, 2016)

1700

GP and patients Italy

Health-related purposes;

online findings by discussing with physician

✔ ✔ ✔N/A

✔ ✔ ✔

(Obasola et al., 2015a)

-

Pregnant

women and

children

AfricaPregnant

women and children health

✔ ✔ - ✔ ✔ - ✔ ✔ -

(Suri, Majid, Chang, & Foo, 2016)

1062

College-going adults

Singapore

Domain-specific skills

ofhealth literacy

✔ ✔N/A

- - ✔

20

(Tu et al., 2017)

159

Overweight/obese

adolescents and their

parents

Canada

Modification of weight

outcomes✔ ✔ ✔ ✔ ✔ ✔ N/A

(Carrà et al., 2016)

654

People of 18 to

24 years

Great Milan

Alcohol risk alertness ✔ ✔ ✔ ✔ ✔ ✔ ✔

Risk Assessment for Natural disaster bases on their consequences and likelihood

Types of Disaster Consequences Likelihood of

Disaster

Risk Environmental effects

Earthquake Serious Rare Medium Landslide, avalanche, soil

liquefactions.

Tsunami Serious Rare Medium Flooding, erosion, loss of life.

Cyclone/Tornado Serious Possible High Flooding, landslides, erosion,

loss of life.

Prolonged

Flooding

Medium Unlikely Medium Landslides, erosion,

destruction of life.

Heat wave Low Possible Medium Fire, depletion of water

resources, deterioration of soil,

and snow melt.

Landslip Low Unlikely Low Damage life disrupt water

mains, power lines and mining

plants

Bushfire Medium Almost

certain

High Destruction of ground cover,

erosion, flooding, tainted soil.

Severe Storms Medium Almost

certain

High Loss of life, damage

infrastructure.

21

Table 6: Offloading models, parameters, findings and limitations

Author/Date

Concept/theoretical Model Parametersconsidered

Findings Limitation/Gaps

(Ahn et al., 2018)

Game theoretic approach+computation offloading Model

Network bandwidth, speed, energy gain, requested size, time balance.

Guarantee of more utility from residual energy in compare to adequate energy

Issues remain with the periodic initialization of this progressive handicap as the policy of system.

(Guo et al., 2017)

Computation offloading system model

Execution time, energy consumption, speed, invocation time, CPU processing, velocities.

Efficient utilization of the time duration over the transmission and execution.

Complexity resides on the non-negligible task execution time.

(Li, Huang, Zhou, He, & Ming, 2017)

Per frame data deploy task scheduling method

Node number, local, computation partitioning

Multi frame data in fluctuating network is more efficient compare to single frame data.

The over load in data frame, data transfer are not addressed properly.

(Qiu et al., 2017)

Multidimensional Dynamic Programming Data Allocation (MDPDA) algorithm.

Cost, latency, energy uses, data chunks, processing time.

Gives and optimal solution to allocate data chunk to individual chip memory.

Absence of cache memory creates high penalty in CMP system

(Rashidi & Sharifian, 2017)

Adaptive neuro-fuzzy inference system

Transmission time, Response time, service rate, task offload.

result shows the best performance is about 30% and the minimum is 8.93% at worst case of the algorithms.

Due to overhead update is high, this algorithm fails to execute online.

(Tseng, Cho, Chang, Li, & Shih, 2017)

(MOTM) algorithm+(METC) algorithm.

Number of tasks, Data size of task.Battery capacity, Bandwidth,Memory, CPU of mobile

Minimizing total execution as well as the decreasing energy consumption of mobile devices.

Is limit only to the computation and the network type not the storage and the security of application is not considered.

22

device.

Table 7: Performance matrix of different Offloading Technique.

Study Architecture/Models

Performance

Reliability Energy efficiency

Runtime Efficiency

Stability Availability cost utilization

(Ahn et al., 2018)

Computation offloading

yes yes Yes No No N-A

yes

(Armstrong, Djemame, & Kavanagh, 2017)

Energy efficient cloud architecture

Yes Yes Yes yes Yes Yes Yes

(Bangui et al., 2017)

Multi-Criteria Decision Analysis (MCDA)

yes No No No Yes N-A

N-A

(Chabbouh, Ben Rejeb, et al., 2017)

joint service offloading

N-A No Yes N-A N-A N-A

N-A

(Xing Chen et al., 2017)

Context-aware computation offloading

Yes Yes No Yes Yes N-A

Yes

(Goudarzi et al., 2017)

Hybrid multi-site computation offloading

N/A Yes Yes No Yes yes Yes

(Rashidi & Sharifian, 2017)

Distributed task allocation architecture

No No Yes No Yes N-A

Yes

23

Table 8: Performance matrix of the Scheduling Algorithms

Authors Bandwidth Make Span

Latency Resources Utility

Completion Time

Execution Time

Success Rate

Cost

(Xing Chen et al., 2017)

Elmougy et al., 2017)

(Jakóbik, 2017)

(Li et al., 2017)

(Shah-Mansouri et al., 2017)

(Shaw, 2017)

(Soltani et al., 2017)

(S. Zhang, Pan, Wu, Liu, & Meng, 2017b)

(Wu et al., 2017)

Psychological Factor Groups

Cognitive Skills Health and Well being Emotional

24

Journal No Author Name Neg

otia

ting

stru

ctur

e

Com

mun

icat

ion

and

lear

ning

Aca

dem

ic p

erfo

rman

ce

Soci

al C

onne

cted

ness

Qua

lity

of L

ife

Faci

ng d

iscr

imin

atio

n an

d so

cial

isol

atio

n

Phys

ical

Hea

lth

Livi

ng C

ondi

tion

Atti

tude

Mot

ivat

ion

Self

Este

em

Com

mun

icat

ion

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[2] J29 Assari, 2018 ✔ ✔ ✔ ✔ ✔ ✔ ✔

[3] J49Balasundaram, et al., 2016

✔ ✔

[4] J28 Chang, et al., 2017 ✔ ✔ ✔ ✔ ✔ ✔

[5] J32 Chen, et al., 2015 ✔ ✔ ✔ ✔ ✔

[6] J11 Cheng,et al., 2017 ✔ ✔ ✔ ✔ ✔

[7] J26 Chsarei, 2015 ✔ ✔ ✔ ✔

[8] J45 Cortina, et al., 2016 ✔ ✔ ✔ ✔ ✔ ✔ ✔

[9] J6Demirbas, et al., 2016

✔ ✔ ✔ ✔

[10] J38 Fadda, et al., 2015 ✔ ✔ ✔ ✔ ✔ ✔ ✔

[11] J36Fawzy and Hamed, 2017

✔ ✔ ✔ ✔ ✔ ✔ ✔

[12] J13 Ferrer, et al., 2016 ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔

[13] J48 Gu, et al., 2015 ✔ ✔ ✔

[14] J21 Guettal, et al., 2015 ✔ ✔ ✔ ✔ ✔ ✔

25

[15] J39 Hashim, et al., 2015 ✔ ✔ ✔ ✔ ✔

[16] J24 He, et al., 2018 ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔

[17] J30 Khanna, et al.,2018 ✔ ✔ ✔ ✔ ✔

[18] J46 Lee, et al, 2018 ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔

[19] J37 Li, et al., 2015 ✔ ✔ ✔ ✔ ✔

[20] J23 Li, et al., 2017 ✔ ✔ ✔ ✔ ✔ ✔

[21] J40 Liang, 2015 ✔ ✔ ✔ ✔ ✔ ✔

[22] J8 Mckenna, et al., 2017 ✔ ✔ ✔ ✔ ✔ ✔ ✔

[23] J16 Mesidor, 2016 ✔ ✔ ✔ ✔ ✔ ✔

[24] J50Molano and Jones, 2017

✔ ✔

[25] J47 Oginni, et al, 2018 ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔

[26] J34Rokhman and Ahamed, 2015

✔ ✔ ✔ ✔ ✔ ✔

[27] J35 Schotte, et al, 2018 ✔ ✔ ✔ ✔ ✔ ✔

[28] J33Shafaei and Razak, 2018

✔ ✔ ✔ ✔ ✔ ✔

[29] J4 Stevenson, 2016 ✔ ✔ ✔

[30] J41Surette and Shier, 2016

✔ ✔ ✔ ✔ ✔

[31] J5 Surette, et al., 2017 ✔ ✔ ✔ ✔ ✔ ✔

[32] J25 Vela, et al., 2015 ✔ ✔ ✔ ✔ ✔ ✔

[33] J44Woodford and Kulick, 2015

✔ ✔ ✔ ✔

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27

28

29

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SAMPLES OF FRAMEWORKS

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