Journal Editorial Board Editor-in-ChiefProf. Thomas Byeongnam YOON, PhD.
Founding Editor-in-Chief
ICACT Transactions on the Advanced Communications Technology (TACT) Editors
Prof. Jun-Chul Chun, Kyonggi University, Korea
Dr. JongWon Kim, GIST (Gwangju Institute of Science & Technology), Korea
Dr. Xi Chen, State Grid Corparation of China, China
Prof. Arash Dana, Islamic Azad university , Central Tehran Branch, Iran
Dr. Pasquale Pace, University of Calabria - DEIS - Italy, Italy
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Prof. Rosilah Hassan, Universiti Kebangsaan Malaysia(UKM), Malaysia
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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
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Dr. Mohammed M. Kadhum, School of Computing, Goodwin Hall, Queen's University , Canada
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Dr. Gopal Chandra Manna, BSNL, India

Copyright ⓒ 2016 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
Prof. Jiann-Liang Chen, National Taiwan University of Science and Technology, Taiwan
Prof. Ruay-Shiung Chang, National Dong Hwa University, Taiwan
Dr. Vasaka Visoottiviseth, Mahidol University, Thailand
Prof. Dae-Ki Kang, Dongseo University, Korea
Dr. Yong-Sik Choi, Research Institute, IDLE co., ltd, Korea
Dr. Xuena Peng, Northeastern University, China
Dr. Ming-Shen Jian, National Formosa University, Taiwan
Dr. Soobin Lee, KAIST Institute for IT Convergence, Korea
Prof. Yongpan Liu, Tsinghua University, China
Prof. Chih-Lin HU, National Central University, Taiwan
Prof. Chen-Shie Ho, Oriental Institute of Technology, Taiwan
Dr. Hyoung-Jun Kim, ETRI, Korea
Prof. Bernard Cousin, IRISA/Universite de Rennes 1, France
Prof. Eun-young Lee, Dongduk Woman s University, Korea
Dr. Porkumaran K, NGP institute of technology India, India
Dr. Feng CHENG, Hasso Plattner Institute at University of Potsdam, Germany
Prof. El-Sayed M. El-Alfy, King Fahd University of Petroleum and Minerals, Saudi Arabia
Prof. Lin You, Hangzhou Dianzi Univ, China
Mr. Nicolai Kuntze, Fraunhofer Institute for Secure Information Technology, Germany
Dr. Min-Hong Yun, ETRI, Korea
Dr. Seong Joon Lee, Korea Electrotechnology Research Institute, korea
Dr. Kwihoon Kim, ETRI, Korea
Dr. Jin Woo HONG, Electronics and Telecommunications Research Inst., Korea
Dr. Heeseok Choi, KISTI(Korea Institute of Science and Technology Information), korea
Dr. Somkiat Kitjongthawonkul, Australian Catholic University, St Patrick's Campus, Australia
Dr. Dae Won Kim, ETRI, Korea
Dr. Ho-Jin CHOI, KAIST(Univ), Korea
Dr. Su-Cheng HAW, Multimedia University, Faculty of Information Technology, Malaysia
Dr. Myoung-Jin Kim, Soongsil University, Korea
Dr. Gyu Myoung Lee, Institut Mines-Telecom, Telecom SudParis, France
Dr. Dongkyun Kim, KISTI(Korea Institute of Science and Technology Information), Korea
Prof. Yoonhee Kim, Sookmyung Women s University, Korea
Prof. Li-Der Chou, National Central University, Taiwan
Prof. Young Woong Ko, Hallym University, Korea
Prof. Dimiter G. Velev, UNWE(University of National and World Economy), Bulgaria
Dr. Tadasuke Minagawa, Meiji University, Japan
Prof. Jun-Kyun Choi, KAIST (Univ.), Korea
Dr. Brownson ObaridoaObele, Hyundai Mobis Multimedia R&D Lab , Korea
Prof. Anisha Lal, VIT university, India
Dr. kyeong kang, University of technology sydney, faculty of engineering and IT , Australia
Prof. Chwen-Yea Lin, Tatung Institute of Commerce and Technology, Taiwan
Dr. Ting Peng, Chang'an University, China
Prof. ChaeSoo Kim, Donga University in Korea, Korea
Prof. kirankumar M. joshi, m.s.uni.of baroda, India
Dr. Chin-Feng Lin, National Taiwan Ocean University, Taiwan
Dr. Chang-shin Chung, TTA(Telecommunications Technology Association), Korea

Copyright ⓒ 2016 GiRI (Global IT Research Institute)Dr. Che-Sheng Chiu, Chunghwa Telecom Laboratories, Taiwan
Dr. Chirawat Kotchasarn, RMUTT, Thailand
Dr. Fateme Khalili, K.N.Toosi. University of Technology, IranDr. Izzeldin Ibrahim Mohamed Abdelaziz, Universiti Teknologi Malaysia , Malaysia
Dr. Kamrul Hasan Talukder, Khulna University, Bangladesh
Prof. HwaSung Kim, Kwangwoon University, Korea
Prof. Jongsub Moon, CIST, Korea University, Korea
Prof. Juinn-Horng Deng, Yuan Ze University, Taiwan
Dr. Yen-Wen Lin, National Taichung University, Taiwan
Prof. Junhui Zhao, Beijing Jiaotong University, China
Dr. JaeGwan Kim, SamsungThales co, Korea
Prof. Davar PISHVA, Ph.D., Asia Pacific University, Japan
Ms. Hela Mliki, National School of Engineers of Sfax, Tunisia
Prof. Amirmansour Nabavinejad, Ph.D., Sepahan Institute of Higher Education, Iran

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Evalution Procedure

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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Anonymity:
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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Journal Procedure
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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Journal Submission Guide
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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Volume. 5 Issue. 2

6J

1 The Current State of Mobile Apps Development of Higher Education in Taiwan

780

Hsu-Chen CHENG *, Tsuei-Ping KUNG *, Chia-Ming LI *, Yu-Jou SUN ** Wisdom Garden Research Center, Taiwan

2 Investigation of Different Ethernet Wiring and Different Frame Size to Enhance the787

Performance of LAN
Ashraf M. Khalaf*, Mostafa S. Abd El Salam**, Khalil A. Ahmed**Faculty of Engineering, Department of Electricity, Minia University, Minia, Egypt**Egyptian Electricity Holding Company, Cairo, Egypt

3 Internet of Things: Security and Privacy Issues and Possible Solution

797

Davar PISHVA
Ritsumeikan Asia Pacific University, 1-1 Jumonjibaru, Beppu, Oita 874-8577 Japan

4 CampusSense - A Smart Vehicle Parking Monitoring and Management System using ANPR

809

Cameras and Android Phones
Mohammed Y Aalsalem, Wazir Zada Khan
Farasan Networking Res. Lab, Faculty of Computer Science & Information System, Jazan University,
Kingdom of Saudi

5 An Effective Speedup Metric Considering I/O Constraint in Large-scale Parallel Computer

816

Systems
Guilin Cai*, Wei Hu*, Guangming Liu*,**, Qiong Li*, Xiaofeng Wang*, Wenrui Dong**College of Computer, National University of Defense Technology, Changsha, China**National Supercomputer Center in Tianjin, Tianjin, China

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

780

The Current State of Mobile Apps Developmentof Higher Education in Taiwan
Hsu-Chen CHENG *, Tsuei-Ping KUNG *, Chia-Ming LI *, Yu-Jou SUN ** Wisdom Garden Research Center, Taiwan{michael, evelyn, achilles.lee, rora}@wisdomgarden.com
1
Abstract—Mobile apps have had a large impact on manyindustries including higher education for many years since it
emerged. The goal of this research is to deepen our
understanding of the state of mobile apps development at higher
education institutions in Taiwan, and it focuses on three major
issues: (a) how many institutions are there in Taiwan adopting
mobile apps; (b) what are the popular mobile services in higher
education; (c) and whether mobile apps play a more importantrole than mobile webs in delivering the mobile service on
campus.
The research process was broken down into few phases. In
the beginning, this research searched all the institution-related
mobile apps on Apple App Store and Google Play, anddetermined whether it was owned by an institution based on thedecision tree. Next, the classification of mobile services was
proposed for analyzing the content of every official mobile app.
Last, the institutions which had both mobile apps as well as
mobile webs were sorted out, and the difference of mobile
services between the two deliveries was compared.
The results indicates that less than half of the institutions in
Taiwan have their own mobile apps, and the most popularmobile services on apps are general information as well as
library services. And it also shows that the services delivered via
mobile apps are more abundant than via mobile webs.
Keyword—Mobile App, Development, Higher Education in
Taiwan, Classification of Mobile App Services, Mobile Web

I. INTRODUCTIONI

N this research, the state of mobile apps development
consists of the following three issues: (a) the mobile apps
usage rate; (b) the popular mobile service; and (c) the main
access to mobile service. Hence, the introduction would begiven by the issues above.
A. The Mobile Apps Usage Rate
In recent years, mobile apps are more and more importantin higher education. The NMC Horizon Report by 2012indicated that mobile apps were the key technology thathigher education would adopt within a year [1]. Besides, therelevant surveys showed that there were upward trends of———————————————————————
Manuscript received April 8, 2015. This work is a follow-up of the invitedjournal to the accepted conference paper of the 17th International Conference
on Advanced Communication Technology.
Hsu-Chen Cheng is with Wisdom Garden Research Center, New Taipei
City, 23444 Taiwan. (Corresponding author, phone: +886-2-7730-8100; fax:
+886-2-2923-2589; e-mail: michael@wisdomgarden.com)

using mobile apps at higher education institutions in the
United States and in Taiwan (see Fig. 1.), and the proportion
got to 83% [2] and 56.7% [3] in 2014 respectively.
CCP survey in U.S.

ISAC survey in Taiwan

100%
78%

83%

80%
60%
60%
40%

53.2%

56.7%

2013

2014

42%
30.2%
23%

20%
0%
2010

2011

2012

Fig. 1. The trends of adopting mobile apps at higher education institutions in
U.S. and in Taiwan

Evidently, implementing mobile apps is an inevitabletendency for higher education in the future, and there would
be more and more institutions starting to adopt it. In otherwords, the percentage of institutions adopting mobile apps in2015 would be larger than in 2014.
As a result, it is necessary to investigate how many
institutions provide its own mobile apps in 2015. The concern
would be addressed specifically in Taiwan.
B. The Popular Mobile Service
The EDUCAUSE survey [4] showed that American
universities and colleges tended to place high priority on
student- and public-facing mobile services, and the top threeof them were primary web presence, learning/course
management services as well as library services.
In addition, when it came to the “killer mobile app” forhigher education [4], most people regarded LMS (Learning
Management System) and student services as top priority.
Moreover, university libraries were moving toward themobile web to deliver information access at the early stage ofmobile enablement [5], so the library service was animportant category for mobile services.
In this case, the research concerns what the most popular
service is in higher education.
Tsuei-Ping Kung is with Wisdom Garden Research Center, New Taipei
City, 23444 Taiwan. (phone: +886-2-7730-8100; fax: +886-2-2923-2589; email: evelyn@wisdomgarden.com)
Chia-Ming Li is with Wisdom Garden Research Center, New Taipei City,
23444 Taiwan. (phone: +886-2-7730-8100; fax: +886-2-2923-2589; e-mail:achilles.lee@wisdomgarden.com)
Yu-Jou Sun is with Wisdom Garden Research Center, New Taipei City,
23444 Taiwan. (phone: +886-2-7730-8100; fax: +886-2-2923-2589; e-mail:rora @wisdomgarden.com)

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

C. Mobile Apps vs. Mobile Webs
A mobile app is a program which is developed for small
handheld devices [6] and installed directly onto it [7]. A
mobile web is a website which is also developed specificallyfor mobile devices [8] but accessed through the mobile
browser [7].
In most cases, mobile webs are more affordable thanmobile app development [9], because it can be released in any
form and any time without an approval by the app store or
marketplace [7]. Moreover, mobile webs could be accessedby all types of platforms [10], and it is more flexible in thelight of updating and changing content [9]. From the point ofview of institutions, as a result, they prefer mobile webs
rather than mobile apps.
However, students prefer to use mobile apps when they
have the most mobile activities in their daily affairs (e.g.
weather) as well as course-related tasks (e.g. access mycourse schedule) [11]. In addition, they consider that mobile
apps have better performance in effectiveness of speed andease of use than mobile webs [11]. Therefore, which accessto provide is a major concern for institutions.
In light of these concerns, the assumptions of the research
are listed below: (a) the mobile apps usage rate would belarger than the counterpart of the ISAC survey in 2014 [3]which is the researchers’ previous study; (b) LMS and libraryservices are the most popular categories of mobile services inhigher education in Taiwan; and (c) mobile apps are the main
access to mobile service base on the trend of using mobile
apps in higher education.
II. RESEARCH DESIGN AND METHODS
The research was conducted during the period fromJanuary 25, 2015, through April 7, 2015. The content analysis
was undertaken in this research and composed of five phases,
which was summarized as follows:
A. Collecting Mobile Apps Related to Institutions
The related survey [3] suggested that the institutions in
Taiwan preferred to publish their mobile apps for distribution
via Android (100%) and iOS (70.8%). As a result, the search
for mobile apps related to all the higher education institutionsin Taiwan [12] would be limited to the two platforms, Google
Play and Apple App Store, for this research.
Then, the researcher typed every institution’s Chinesename as well as its English abbreviation sequentially in the
search bar on the two platforms. Finally, around 670 apps
associated with all the institutions in Taiwan were founded
during the period from February 6, 2015, through February
10, 2015.
B. Determining Whether a Mobile Apps Is Official
The first issue of this paper is how many institutions are
there in Taiwan adopting mobile apps. Therefore, it isnecessary to justify whether an institution has its own mobile
apps or not, and then the decision tree (see Fig. 2.) isdeveloped for determining whether a mobile app is owned byan institution. The criteria was summarized as follows:First, official mobile apps of institutions should bemaintained properly, and its renewal should not be so long
ago from now. As a result, if the mobile app was not updated
in the past three years, it would not be regarded as an official

781

one.
Second, the use of an institution-owned mobile app
should be widespread on campus. If the installs of a mobile
app did not exceed 500 times, it would be regarded as nonofficial.

Fig. 2. The decision tree for determining whether a mobile app is official

Third, the official mobile apps should be introduced in thename of an institution or a corporation, or it would beconsidered as non-official. In the case of a corporation being
the developer of the mobiles app, it was assumed that theinstitution outsourced their mobile apps to the corporation.
Forth, the services provided through an official appsshould be institution-wide. If the mobile app’s description did
so, it would be regarded as official and would be downloaded
to the researcher’s mobile device.
Finally, the research got 177 official mobile apps
associated with higher education institutions in Taiwan basedon the decision tree.
C. Proposing the Classification of Mobile Services
In order to analyze what the content of every mobile app is,
it is necessary to propose the classification of mobile services.
According to the category of the killer apps for higher
education [4], the research drafted the classification of mobile
services and then made use of it to examine the content ofofficial apps in the analysis of pretest.
In the process of the pretest, the classification was adjusted
and refined gradually and it became more mutually exclusive
as well as collectively exhaustive. The revision of theclassification of mobile services in higher education isshowed on TABLEⅠ.
D. Analyzing the Content of Official Mobile Apps
Then, the researchers examined the content of every
official mobile apps based on the classification (see TABLEⅠ) and recorded what services it provides. The frequency
data was record by mobile apps initially, but it was converted
into the statistics by institutions later.
For instance, the app 01, app 02 as well as app 03 belonged
to the institution A, and the frequency of their services in sum
was below: category A got three times, category B got two
times, and category C got nothing. Then this researchconcluded that the institution A provided the service of
category A and B, but not category C.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016
TABLEⅠTHE CLASSIFICATION OF MOBILE SERVICES IN HIGHER EDUCATION

Categories

ExamplesAdministration

Making Complaints
Punch in/out

General Information

DirectoryCampus Maps
Events Calendar

Personal Information

Push Notification
Financial Services Inquiry
Social Network

LMS

Learning Management System
Courses/Grades Inquiry

e-Learning

e-Books
Videos/Images

Productivity

Cloud
e-MailStudent Services

Reservations
Emergency Call

Library Services

Library Catalog
Renew Materials

Alumni Services

Events Information

782

Third, what service the mobile web provided was analyzed.
If the icon of a service on the mobile web was linked to a
standard website instead of a page with responsive design(RD), then it was concluded that the mobile web did notprovide this kind of service.
Finally, the services between mobile webs and mobile appsof the 16 institutions were compared.
III. RESULTS
A number of interesting findings emerged from thisprocess, but this section focused on the three issues: (a) themobile apps usage rate; (b) the popular mobile service; and(c) the main access to mobile services.
A. The Mobile Apps Usage Rate
Around 43% of institutions, which was 68 of 159, adopted
mobile apps (see TABLE Ⅲ) in Taiwan and they seemingly
preferred to deploy their mobiles apps for iOS (82%) rather
than Android (75%).

PS. Each category is including but not limited to above examples.

E. Comparing Mobile Webs to Mobile Apps
The third issue of this research is to discuss whose service
is more diversified, mobile apps or mobile webs. The research
design of the section is as follows:
At the beginning, the researcher visited every institution’s
primary websites presence via a mobile browser anddetermined whether it was a mobile web or not (see TABLE
Ⅱ). Then it was filtered out that the institutions having botha mobile web and mobile apps, which amounted to 30
institutions.
Next, the mobile webs of these 30 institutions werecategorized into two types, the general type and the special
type. This research focused on the mobile webs belonging to
the special type which included 16 institutions.
TABLE ⅡTHE CRITERIA FOR ANALYZING MOBILE WEB

IssuesCriteria

Does the institution havea mobile web?

Yes, if the layout of the website on themobile browser was a responsive design.
No, if the layout of the website was like thepage of standard websites on computer.

What the type dose themobile web belong to?

General type, if the layout of the pagewould be automatically adjusted according
to a device’s screen size.
Special type, if there were additional
modular icons on the page in addition to thefeatures of the general type.

Does a mobile web
provide some kind ofservice?

Yes, if the icon of a service was linked to apage with responsive design
No, if the icon of a service was linked to a
standard website.

TABLE Ⅲ
THE MOBILE APPS USAGE RATE
For all
For institutions
institutions
adopting mobile apps
MobileFor iOSFor Android
apps
All institutions

42.8%

82.4%

75.0%

Universities & Colleges

47.9%

85.3%

88.2%

Technical Colleges

38.6%

79.4%

61.8%

Large institutions

77.8%

92.9%

92.9%

Medium institutions

49.4%

77.5%

77.5%

Small institutions

23.3%

85.7%

50.0%

Public institutions

45.1%

91.3%

73.9%

Private institutions

41.7%

77.8%

75.6%

According to the results, institution’s size did matter inreference to the mobile apps usage rate. Large institutions
were more likely than medium and small institutions to adopt
mobile apps with the rate at 78 percent, as opposed to 49% of
medium institutions as well as 23% of small institutions.
Generally speaking, institutions in Taiwan tended to
publish their mobile apps on iOS instead of Android, andsmall institutions was the most evident example of thatviewpoint because of its greatest difference of usage rate
between the two platforms among all types of institutions.
However, there was a result in the opposite direction. In
relation to the type of institutions by education system,
universities and colleges slightly favored Android.
Interestingly, such a few types of institutions as large
institutions and medium institutions had the same usage rate
between iOS and Android, but it did not mean that every
institution deployed their apps for the two platforms
simultaneously.
Approximately two thirds of institutions (68%) owned one
or two mobiles apps, leading those who had three or four(19%) as well as five and more (13%). The average for thosesurveyed was 2.6 units, which indicated that only a third of
institutions (32%) above the average (see TABLE Ⅳ).

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

TABLE Ⅳ
THE DISTRIBUTION OF MOBILE APPS AN INSTITUTION OWNED
Cumulated pct. of
Number of official
Pct. of institutions
institutions
app(s)(n=68)(n=68)
One

33.8%

33.8%

Two

33.8%

67.6%

Three

10.3%

77.9%

Four

8.8%

86.8%

Five

4.4%

91.2%

Six

2.9%

94.1%

Seven

2.9%

97.1%

Eight and more

2.9%

100.0%

B. The Popular Mobile Service
The most popular mobile service provided by institutions
through their mobile apps was general information (75%),
leading library services (62%), personal information (60%),
LMS (54%), e-learning (52%), student services (50%),
productivity (16%), alumni services (9%), and administration(6%) (see TABLE V). The number in the parentheses referredto the percentage of institutions which offered a certain kindof mobile service and was named “the supply rate” in thisresearch. Consequently, the result showed that six of nine
mobile services were offered by more than half of institutions
while the others were furnished by less than 20% of
institutions. In researcher’s opinion, higher education
institutions in Taiwan had a strong preference in some mobile
services.
TABLE V.
THE STATISTICS ABOUT MOBILE SERVICES

783

example, public institutions gave top priority to libraryservices (74%) while private institutions put greater emphasison general information (84%) (see TABLE VI). Second, eLearning was one of the top three mobile services for private
institutions but not for public institutions. Third, an ANOVA
test showed that private institutions were more likely thanpublic institutions to provide general information as well as
e-Learning.
TABLE VI.
THE STATISTICS ABOUT MOBILE SERVICES BY CONTROL OF SCHOOL
Pct. of publicPct. of private
institutions
institutions(n=23)(n=45)
General Information

56.5%

84.4%

e-Learning

21.7%

66.7%

Personal Information

60.9%

60.0%

LMS

47.8%

57.8%

Library Services

73.9%

55.6%

Student Services

52.2%

48.9%

Productivity

21.7%

13.3%

Alumni Services

8.7%

8.9%

Administration

4.3%

6.7%

How many mobile services an institution offered was
examined in this research. Less than half of institutions (47%)
supplied three or fewer mobile services (see TABLE VII),and the average for those surveyed was 4 units. There was an
obvious gap between six units (18%) and seven units (10%),which displayed that providing more than seven units ofmobile services was a high threshold for higher education in
Taiwan.
TABLE VII.
THE DISTRIBUTION OF MOBILE SERVICES AN INSTITUTION PROVIDED
Cumulated pct. of
Number ofPct. of institutions
institutions
unit(s)(n=68)(n=68)

Pct. of institutions(n=68)

Pct. ofmobile apps
(n=177)

General Information

75.0%

57.1%

Library Services

61.8%

42.4%

One

11.8%

11.8%

Personal Information

60.3%

40.1%

Two

14.7%

26.5%

LMS

54.4%

37.3%

Three

20.6%

47.1%

e-Learning

51.5%

36.2%

Four

13.2%

60.3%

Student Services

50.0%

33.3%

Five

11.8%

72.1%

Productivity

16.2%

14.7%

Six

17.6%

89.7%

Alumni Services

8.8%

5.1%

Seven

5.9%

95.6%

Administration

5.9%

3.4%

Eight

4.4%

100.0%

When it came to the most prevalent mobile service ininstitutions’ mobile apps, the ranking of mobile services was
identical to the order by the supply rate. However, only oneof them, general information (57%), existed in more than halfof institutions’ mobile apps. Less than half of mobile apps
were equipped with the following mobile services, including
library services (42%), personal information (40%), LMS(37%), e-learning (36%), student services (33%),
productivity (15%), alumni services (5%), and administration(3%) (see TABLE V).
Besides, it was observed that institutions’ preference formobile services seemed to alter by control of school. For

Moreover, the researcher wondered how many mobile
services an institution provided though a mobile app. The
researcher set a value called efficiency and its formula was“the number of mobile services of the institution” divided by“the number of that institution’s mobile apps. The value ofefficiency presented the mean of mobile services per mobile
app of an institution. The larger the value was, the more
services a mobile app provided.
The institutions with value of efficiency between
1.00~1.99 accounted for 41 percent of the total, and those
whose value of efficiency exceed 4.0 merely accounted 9
percent (see TABLE VIII). Interesting, there was roughly

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

10% of institutions with value of efficiency less than 1.00,
which showed that their mobile services were overlapping
largely. Briefly, the average for those surveyed was 2 units.
TABLE VIII.
THE DISTRIBUTION OF THE VALUE OF EFFICIENCY
Value ofefficiency

Pct. of institutions(n=68)

5.00~5.99

2.9%

4.00~4.99

5.9%

3.00~3.99

19.1%

2.00~2.99

19.1%

1.00~1.99

41.2%

0.00~0.99

11.8%

There was a significant difference in the value of efficiency
with respect to control of school: private institutions weremore likely than public institutions to provide more mobile
services with less mobile apps.
Next, the mobile services were focused again. In thisresearch, two numbers, “the multi-app rate” and “the service
multiple”, were calculated in order to understand the degreeof diversity or redundancy of a mobile service.
The multi-app rate was the proportion of institutions
offering a certain kind of mobile service via two or more apps.
The larger the multi-app rate was, the larger degree ofdiversity or redundancy a mobile service was. Among themobile services, productivity had the highest multi-app rate(91%), leading e-Learning (69%), student services (68%) andso on (see TABLE IX).
TABLE IX.
THE DEGREE OF DIVERSITY OR REDUNDANCY OF MOBILE SERVICES
The multi-app
rate

The service
multiple

Productivity

90.9%

2.36

e-Learning

68.6%

1.83

Student Services

67.6%

1.74

General Information

64.7%

1.98

Library Services

64.3%

1.79

LMS

62.2%

1.78

Personal Information

56.1%

1.73

Administration

50.0%

1.50

Alumni Services

50.0%

1.50

The service multiple was the division gained from thenumber of mobile apps furnishing a certain mobile service
divided by the number of institutions providing the same
mobile service. It displayed how many mobile apps was used
to support a mobile service by an institution. The larger theservice multiple was, the larger degree of diversity or
redundancy a mobile service was. Among the mobile services,
productivity also had the highest value (2.36), followed bygeneral information (1.98), e-Learning (1.83), etc. (seeTABLE IX).
The supply rate mentioned above stood for the degree ofwidespread of a mobile service because it presented “howmany institutions offer the mobile service”. On the other hand,784

the multi-app rate and the service multiple represented “thedegree of diversity or redundancy of a mobile service”.
From the aspect of the supply rate, personal information
was one of the top three mobile services, trailing behind
general information and library services, yet it came inseventh place with respect to the degree of diversity or
redundancy of mobile services. It showed that personal
information was offered by most intuitions via just one or two
mobile apps (see TABLE X).
However, the result of productivity was in the opposite
direction: productivity had a quite low ranking by the supply
rate but became the top one by the degree of diversity or
redundancy. That was, once productivity was offered by an
institution, it would be supported by relatively more mobile
apps (see TABLE X).
TABLE X.
THE RANKING OF MOBILE SERVICES BY THREE CRITERIA
By supply
By multiBy service
rate
app rate
multiple
General Information

1

4

2

Library Services

2

5

4

Personal Information

3

7

7

LMS

4

6

5

e-Learning

5

2

3

Student Services

6

3

6

Productivity

7

1

1

Alumni Services

8

8

8

Administration

9

8

8

C. Mobile Apps vs. Mobile Webs
Around 33% of institutions in Taiwan adopted mobile
webs (see TABLE XI). Again, the institutions’ size had muchthings to do with the mobile webs usage rate. For example,
large institutions (56%) were more likely than medium (40%)
and small institutions (17%) to adopt mobile webs.
Besides, among all types of institutions, the mobile webs
usage rate was always lower than the mobile apps usage rate
(see TABLE XI), which indicated that the institutions in
Taiwan tended to focus their mobile enablement on apps
instead of webs.
TABLE XI.
THE USAGE RATE: MOBILE WEBS VS. MOBILE APPS
Pct. of institutions
Pct. of institutions
adopting
adopting
mobile webs
mobile apps
(n=159)(n=159)All institutions
32.7%
42.8%
Universities & Colleges

32.4%

47.9%

Technical Colleges

33.0%

38.6%

Large institutions

55.6%

77.8%

Medium institutions

39.5%

49.4%

Small institutions

16.7%

23.3%

Public institutions

37.3%

45.1%

Private institutions

38.1%

41.7%

For those institutions who adopted mobile apps and mobile
webs simultaneously, a certain kind of mobile service might
be provided by their mobile apps but not mobile webs, andCopyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016vice versa. The four possible situations were described in Fig.
3. A mobile service was placed in the corresponding quadrant
according to the situation the majority of institutions
belonged to. Frist, many institutions offered general
information (88%) and library services (31%) via both theirapps and webs. Second, three mobile services was provided
via apps but not webs, including personal information (63%),
LMS (56%) and e-Learning (38%). Third, most of institutions
did not provide some kinds of mobile services via neither
mobile apps nor mobile webs, such as alumni services (88%),
administration (81%), productivity (69%), and student
services (44%). Apparently, there was no mobile service
offered via mobile webs but not mobile apps.
Adopting
Mobile Webs

General Information (87.5%)
Library Services (31.3%)

Not Adopting
Mobile Apps

Adopting
Mobile Apps

Alumni Services (87.5%)
Administration (81.3%)
Productivity (68.8%)
Student Services (43.8%)

Personal Information (62.5%)
LMS (56.3%)
e-Learning (37.5%)

785

assumption. There is a way to explain such a result. The
criteria of this research for determining whether a mobile app
is official is strict that the percentage of adopting mobile apps
declined. For example, a mobile app whose developer is aperson would be regarded as non-official even though its
copyright belongs to an institution or a corporation.
TABLE XII.
CONTENT ANALYSIS ACROSS MOBILE APP AND MOBILE WEB
App doesWeb does(n=16)App doesn’t
Web does(n=16)App doesWeb doesn’t(n=16)

Administration

0.0%

0.0%

18.8%

General Information

87.5%

12.5%

0.0%

Personal Information

0.0%

0.0%

62.5%

LMS

0.0%

0.0%

56.3%

e-Learning

25.0%

12.5%

37.5%

Productivity

12.5%

0.0%

18.8%

Student Services

12.5%

6.3%

37.5%

Library Services

31.3%

18.8%

25.0%

Alumni Services

0.0%

37.5%

0.0%

Others

12.5%

12.5%

12.5%

Does It Provide?

PS. The number is the percentage of institutions.
Not Adopting
Mobile Webs

The most popular mobile services provided by institutioninTaiwan via official apps are as follows: generalFig. 3. The situation the majority of institutions belonged to by mobile
information, library services, personal information and LMS.
services
In addition, more than half of the institutions offer these
Furthermore, the detail of the percentage by mobile services. Hence, the second assumption is supported.
services was viewed. Among the services provided by both
The services delivered via mobile apps are more abundantmobile apps and mobile webs, only general information than via mobile webs, which indicates that mobile app is theexceeded 80% of institutions while the other night of ten were main tool for delivering mobile services for higher education
below 35%, even four of them were zero (see TABLE XII). in Taiwan. As results, it is concluded that the third
It showed that there was no room for mobile webs to replace assumption is supported and serving users takes priority overmobile apps.
cutting cost in the consideration of the institutions in Taiwan.
Besides, there were four mobile services that mobile apps
TABLE XIII.
provided but mobile webs did not, including personal
THE OUTCOMES OF THE RESEARCH
information (62.5%), LMS (56.3%), administration (18.8%)
Assumptions
Results
The usage rate of the institutions
and productivity (18.8%). It specified that the mobile services
The percentage of this paper isof this research is larger than thedelivered via mobile apps were more abundant than via
42.8% as opposed to 56.7% of thecounterpart of the ISAC survey in
ISAC 2014 survey.
mobile webs. Hence, the conclusion of this part was that the2014.
main access to mobile service for institutions in Taiwan was
In terms of frequency, library
LMS and library services are theservices and LMS are at top 2 andmobile apps instead of mobile webs.
most popular categories of mobile
IV. CONCLUSIONSThe last two of the assumptions of the research aresupported while the first one is not. There is an explanation
for the result. Besides, four limitations of the research would
be summarized.
A. Findings
The result indicates that the mobile apps usage rate ofhigher education institutions in Taiwan is 42.8%, as opposed
to 56.7% of the ISAC 2014 survey, which rejects the firstservices in higher education in
Taiwan.

top 4 respectively, and both ofthem are provided by more thanhalf of institutions in Taiwan.

The main access to mobile
services for institution in Taiwan
is mobile app instead of mobileweb.

The services delivered via mobile
apps are more abundant than via
mobile webs

B. LimitationsFirst, if the name or the developer of an official app was
irrelevant to the institution’s name then researchers could not
find it out, which may affected the usage rate.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

Second, there was no information about the installs of anapp on Apple App Store. If the installs of an app were lessthan 500 times then the researcher could not cross it out.
Third, it was impossible to analyze the content of somemobile services on the mobile app or the mobile web due tono user accounts to log in. If the researcher could not ensurethat the institution did provide the service, then it didn’t.
Finally, a concise method of analysis was taken because of
the restriction of time. For mobile apps, the researcher
regarded the app on iOS and Android as the same in case thatits name, developer and user interface are the same. The
analysis result of one of them would be applied to the other.
For mobile webs, analyzing the institutions belonging to thespecial type of 16 were focused on instead of the general typeof 30.
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2012 Higher Education Edition, The New Media Consortium,
Austin, Texas, 2012.
[2] C. Green and K. C. Green, The 2014 National Survey of Computing
and Information Technology in US Higher Education, The Campus
Computing Project, Los Angeles, California, 2014.
[3] D. M. Hwang and C. H. Cheng, The 2014 National Survey ofInformation Techonology in Taiwan Higer Education, Information
Service Association of Chinese Colleges (ISAC), Taipei, Taiwan,
2014.
[4] G. Dobbin, E. Dahlstrom and M. C. Sheehan, Mobile IT in Higher
Education, 2011 (Research Report), EDUCAUSE Center for Applied
Research, Louisville, Colorado, 2011.
[5] A. Aldrich, Universities and Libraries Move to the Mobile Web., Jun.
24, 2010. [Online]. Available:
http://www.educause.edu/ero/article/universities-and-libraries-movemobile-web. [Accessed Jan. 1, 2015].
[6] P. Viswanathan, What is a Mobile Application?, About.com,[Online]. Available:
http://mobiledevices.about.com/od/glossary/g/What-Is-A-MobileApplication.htm. [Accessed Mar. 25, 2015].
[7] M. Hostad and S. Owczarek, Mobile Apps, Mobile Web, and Other
Cool Tools, Nov. 9, 2012. [Online]. Available:
https://registrar.wisc.edu/documents/UMACRAO_WACRAO_Mobile.pdf. [Accessed Jan. 12, 2015].

786

his current role, he served as a campus CIO and an associate professor,
accumulating rich knowledge and experiences in campus IT operation, IT
management, and student teaching.
He is also a regular speaker at many important educational technology
conferences and a project initiator for developing innovative educational
products. So far his products have been used by over 30 universities andenterprises in Greater China, and are awarded several patents in USA,
mainland China, and Taiwan.
Tsuei-Ping Kung earned her MA degree in learning,
design and technology in Stanford University, CA,
USA, in 2008. She is the MANAGINGCONSULTANT of WisdomGarden in Taipei and has
been working in the field of educational technology fornearly 10 years. At her current role, she is responsible
for formulating institutional strategies, reengineeringbusiness processes, implementing academic ERP, andconducting campus IT research of higher education.
Prior to that, she served as a consultant in IBM, aiming to apply multiple
technologies and blended learning methods to enterprise learning. With her
professionalism, many of the top 100 enterprises had been successfullytransformed to vigorous learning enterprises.
Chia-Ming Li graduated with a degree in history in2009 and earned his MBA in 2013 both from National
Chengchi University (NCCU), Taipei, Taiwan. Now
he is the RESEARCHER of WisdomGarden in Taipei,
and takes responsibility for industry study ofeducational technology in higher education.
Furthermore, he is in charge of collecting, organizing
and carrying out complex data analysis in support ofmanagement as well as customer requests, and also
involved in reporting statistical findings to developing data-driven strategies
with senior managers.
Prior to the current position, Mr. Li served as a second lieutenant while
fulfilling his compulsory military service in 2010. Besides, Mr. Li had anoverseas internship for two months at Sinyi Realty in Shanghai, China, when
he was an MBA student in 2012.
Yu-Jou Sun earned her MBA in 2014 from Feng Chia
University, Taichung City, Taiwan, and Double
Bachelor Commerce Degrees in 2012 in financial andeconomic law as well as finance from Chihlee Instituteof Technology, New Taipei City, Taiwan. Now she is
the ASSISTANT RESEARCHER of WisdomGarden
in Taipei, and responsible for industry study ofeducational technology in higher education. Prior tothe current position, she had an overseas internship fortwo months at ACE group International Management Consulting Co. in
Dongguan, China, when she was an undergraduate in 2011.

[8] J. Brewer, Mobile Site vs. Mobile App: What You Need to Know
About Going Mobile, BROLIK PRODUCTIONS, INC, Mar. 2, 2011.
[Online]. Available: http://brolik.com/blog/mobile-site-vs-mobileapp/. [Accessed Apr. 2, 2015].
[9] I. Bizness Apps, Mobile Apps VS Mobile Websites, Mar. 21, 2013.
[Online]. Available: http://www.slideshare.net/biznessapps/mobileapps-vs-mobile-websites-17466120?qid=2e38e3df-6648-4ba5-8a4ce110ee165156&v=qf1&b=&from_search=2. [Accessed Feb. 5,2015].
[10] J. Summerfield, Mobile Website vs. Mobile App (Application):Whichis Best for Your Organization?, Human Service Solutions, [Online].
Available: http://www.hswsolutions.com/services/mobile-webdevelopment/mobile-website-vs-apps/. [Accessed Jan. 16, 2015].
[11] K. Bowen and M. D. Pistilli, Student Preferences for Mobile App
Usage, EDUCAUSE Center for Applied Research, Louisville,
Colorado, 2012.
[12] Bureau of Statistics, Directory of Colleges and Universities in
Taiwan by 2014-2015, Ministry of Education, Taiwan, Taipei,
Taiwan, 2014.
Hsu-Chen CHENG became a Member (M) of IEEEin 2000. He earned his Ph.D. in information
management in National Taiwan University, Taipei,
Taiwan, in 2005. He is the PRESIDENT of
WisdomGarden in Taipei and has been working in thefield of educational technology in higher education for
more than 20 years with a focus on campus IT research,
implementation of credit systems, teachers’ IT literary,and ICT integration in teaching and learning. Prior to

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

787

Investigation of Different Ethernet Wiring andDifferent Frame Size to Enhance the
Performance of LAN
Ashraf M. Khalaf*, Mostafa S. Abd El Salam**, Khalil A. Ahmed**Faculty of Engineering, Department of Electricity, Minia University, Minia, Egypt**Egyptian Electricity Holding Company, Cairo, Egypt
ashkhalaf@yahoo.com,mostafashokry0@gmail.com, khalilaa47@gmail.com

Abstract—A computer network that covers only a small
area networks abbreviated Local Area Network LAN, is usedin campus computer networks, buildings, offices, in homes,
schools or smaller.Currently, most LANs based on the IEEE
802.3 Ethernet technology using devices such as hubs andswitches, which have a data transfer speed of 10, 100, or 1000
Mega bit /s (Mbps). In this paper, we are investigating thedifferent Ethernet wiring standard and different frame size.
Keyword—frame
performance,Switch, Hub.

size,10BaseT,100BaseT,LAN

I. INTRODUCTION
A LAN is a computer network limited to a small area suchas an office building, university, or even a residential home.
Most mid to large-sized businesses today use LANs, whichmakes it easy for employees to share information. Currently,
the most common type of LANs are Ethernet-based.
The Ethernet standard comprise several wiring andsignaling variants of the OSI physical layer in use with
Ethernet. The original 10BASE5 Ethernet used coaxial cable
as a shared medium. Later the coaxial cables were replacedwith twisted pair and fiber optics links in conjunction withhubs or switches.
System communicating over Ethernet divide a stream ofdata into shorter piece called frames. Each frame contains
source and destination addresses and error-checking data sothat damaged data can be detected and re-transmitted. As per
the OSI model. Ethernet provides services up to andincluding the data link layer.
Collisions happen when two stations attempt to transmit at
the same time. They corrupt transmitted fata and require
stations to transmit. The lost data and retransmissions reduce
throughput. In the worst case where multiple active hosts———————————————————————
Manuscript received at October 11, 2014. This work was self-supported,and a follow-up of the invited journal to the accepted conference paper of the
17th International Conference on Advanced Communication Technology,
and with no Grants.
Ashraf M. Khalaf is with the Faculty of Engineering, Department ofElectrical Engineering. (Ashraf M. Khalaf, Phone: +20 86 2355261; fax: +20
86 2346674; e-mail: ashkhalaf@yahoo.com).
Mostafa S. Abd El Salam, was with Egyptian Electricity Holding
Company, Houston, Ministry of Electricity and Renewable Energy (Phone:+20227271045; e-mail: mostafashokry0@gmail.com).
Khalil A. Ahmed is with the Faculty of Engineering, Department ofElectrical Engineering. (E-mail: khalilaa47@gmail.com).

connected with maximum allowed cable length attempt totransmit many short frames, excessive collisions can reduce
throughput dramatically.
LAN is found in many business environments thatlinks aworkgroup of task-related personal computers (PCs), forexample, engineering workstations or accounting PCs. Oneof the computers is given a large capacity disk drive andbecome a server toall other PCs. Software can be stored on
this server and used by the wholeclients of the group.
In the implementation of LAN, we use different types ofdevices such as repeater, switches, hubs, connectors anddifferent cables. Currently, most LANs based on the IEEE
802.3 Ethernet technology using devices such as hubs andswitches, which have a data transfer speed of 10, 100, or 1000
Mega bit/s (Mbps).
In the work done in [1], they are measuring the LAN
performance. Their work depends on variation of the time ofsimulation and the number of hubs and making the frame size
fixed value of (46, 2000 bytes)with segmentation (1500
bytes).
In the work done in [2], they are evaluating theperformance of the LAN by varying the frame size between(1500, 1024 and 512) only and the variation of the Ethernet
wiring standard.
In this paper, we evaluate and test the performance ofLANs under different conditions of Ethernet wiring(10BaseT and 100BaseT) and different frame size (1500,
1024, 512, 128 and 64 bytes). The collision count, utilization,
data traffic received and data traffic sent is calculated in eachcase of conditions for hub. For switch the parameters that will
be measured is data traffic sent, data traffic received andfiltered traffic. Simulations are performed by using Riverbed
Modeler Academic edition. In our work we are seeking tosimulate 1000BaseT as another type of Ethernet cables withdata transmission speed 1000Mbps but the problem that thesimulation tool that we used contains 10BaseT, 100BaseTand 10Gig but not contains 1000BaseT as an Ethernet cable.
A network station wishing to transmit will first check thecable plant to ensure that no other station is currently
transmitting (CARRIER SENSE) since the communications
medium us one cable, therefore, it does allow multiple
stations access to it with all being able to transmit and receiveon the same cable (MULTIPLE ACCESS). Error detection is
implemented throughout the use of station “listening” while
it is transmitting its data. Two or more stations transmitting
cause a collision (COLLISION DETECTION), jam signal istransmitted to network by a jam signal is transmitted to

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

network by the transmitting stations that detected thecollision, to ensure that all stations know of the collision. All
stations will “back off” for a random time. Detection andretransmission is accomplished in microseconds.

Fig. 1. The Ethernet frame format

Figure 1 demonstrate the minimum and maximum value of
Ethernet frame size in bytes. We can see that the maximum
value of the frame size at the case of Ethernet is 15018 bytes
while the minimum value of the frame size is 64 bytes.
If the data packet size is larger than the maximum size so
we will use the segmentation where one of them will be themaximum size and the other segment will be the remaining
bits.
If the data packet size is smaller than the minimum value ofthe frame size, there are padding bits will be added to theframe size to make its value reach to the minimum value offrame size. [3]II. LAN COMPONENTS
A. Network media
Network media, sometimes called Network medium, is thephysical channel that used for transmission in network. There
are two types of mediums used in the implementation ofcomputer networks. One is guided medium and another isunguided medium.
Guided Media (wired)
In guided medium electrical/optical signals are passed
through a solid medium such as Copper UnshieldedTwisted
Pair (UTP), Copper shielded Twisted Pair (STP), Copper
co-axial cables and fiber optics cables.in guided mediums,
the signals are confined within the wire and do not propagate
outside of the cables.
Unguided Media (wireless)In unguided medium the data is transmitted by sending
electromagnetic signals through free space and hence thesignals are not guided in any specific direction. All unguided
transmission mediums are classified as wireless transmission.
10BaseT cables
It is a twisted pair Ethernet wiring standard for LAN
implementation that support 10Mbps data rate. The
maximum transmission length is 100 meters.
100BaseT cables
It is another twisted pair Ethernet wiring standard for LAN
implementation that supports 100Mbps data rate. The
100BaseT Ethernet wiring standard is the most commonlyused in LAN creation due to its highspeed, robustness andlow cost. It is alsocalled fastEthernet because it is ten times
faster than 10BaseT [4]- [5].

788

TABLE ICOMPARISON BETWEEN GUIDED CABLESMediaFrequency
range

Typicalattenuation

Repeaters

Twisted pair

0 - 3.5 kHz

0.2 dB/Km
At 1KHz

2 km

Coaxial cable

0 -500MHz

7 dB/km
At 10MHz

1 - 9 km

Optical fiber

186 -370THz

0.2 to 0.5
dB/km

40km

Table I shows the comparison between the guided cables(Twisted pair, Co-axial cables and optical fiber).
B. Hub
Hub is the simplest component in any local area network(LAN). Any data packet coming from one port is sent to allother ports it is then up to the receiving computer to decide if
the packet is for it or not.Since every packet is sent out toevery computer on the network there is a lot of wasted
transmission, so the network can be easily become bogged
down.Hubs are typically used on small networks where theamount of data going across the network is not very high.
C. Switch
Switch has multiple ports. When the packet comesthrough a switch it is read to determine which computer tosend the data to. This leads to increase the efficiency and theperformance of the device because the packets are not going
to computers that do not require them [6].
The switch can determine the address of the sender and thereceiver according to its MAC address table, where it’s atable in each switch which store the MAC addresstransmission easy when any device need to send data several
times.

III. SIMULATION SOFTWARE AND PARAMETERS
The simulation will be done by using Riverbed Modeler
Academic Edition 17.5 [7].Riverbed Modeler Academic
edition is a high-level event based network level simulation
tool, it contains a huge library of accurate models ofcommercial available fixed network hardware and protocols.
Riverbed Modeler Academic Edition can be used as a
research tool or as a network design/analysis tool. It consistsof high level user interface, which is constructed from C andC++ source code with a huge library of Riverbed Modeler
specific functions. Modelling in Riverbed is divided into
three main domains. The first one is Network domain that is
responsible for networks, sub networks, network topologies,
geographical coordinates and mobility. The second one isNode domain that includes single networknodes such asrouters, workstations, mobile devices. The last model called
Process domain that represent single module and source code
inside network nodes such as data traffic source model and IP
protocol. For this work we will create an office LAN which
consists of hubs, switch, twenty Ethernet stations,10 devices
per each hub, under 10baseT (for scenario 1) and 100baseT(for scenario 2) Ethernet wiring standard.
A. Riverbed Modeler Academic Edition 17.5
Riverbed Modeler is software that is specialized for
network research and development.This release replaced

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

OPNET IT guru academic edition “Optimized Network
Engineering Tool”. I used that software to implement theoffice LAN because it offers relatively much powerful visualor graphical support for the users.

789

B. Scenario 2

B. Parameters of nodes
Traffic Generation Parameters
Start time in seconds will be constant (5.0), ON State Timein second is constant (1000), OFF state Time is (0).
Packet Generation Arguments
Packet size in bytes will be varied according to the frame
size in each case which will be (1500,1024,512,128,64),
segmentation size in bytes will be No segmentation.
C. Performance parameters
For Hub
Utilization, Collision count, Traffic forwarded (bits/sec)
and Traffic received (bits/sec).

Fig. 3. Office LAN under 100BaseT wiring standard

Figure 3 illustrates scenario 2 which contains connection of
20 Ethernet stations to hubs, each hub connected to 10
Ethernet stations, and the hubs connected to Ethernet switch.
100BaseT Ethernet wiring standard will be used in that
scenario.

For Switch
Traffic forwarded (bits/sec), Traffic received (bits/sec),
Traffic filtered (bits/sec).

V. SIMULATION RESULTS

D. Running time parameters
The simulation are performed for 4 min and we make the timeof simulation is constant for all the scenarios that we made.

After we made the simulation we took the results thatmeasure and evaluate the performance of LAN under
different Ethernet wiring standard with different frame size
as following:
A. Number of collision counts at Hub 1

IV. SIMULATION SCENARIOSIn our simulation we used two different scenarios for
implementation of LANs with two different wiring Ethernet
standard. At each scenario we changed the frame size tocalculate some parameters of the network, then we evaluate
the performance of the network.
A. Scenario 1

Fig. 2. Office LAN under 10BaseT wiring standard

Figure 2 illustrates scenario 1 which contains connection of
20 Ethernet stations to hubs, each hub connected to 10
Ethernet stations, and the hubs connected to Ethernet switch.
10BaseT Ethernet wiring standard will be used in that
scenario.

TABLE IINUMBER OF COLLISION COUNTS AT HUB1 (AVG.)
Time duration

4 minutes
Collision countDevices

Hub 1standards

10BaseT (scenario1)

100BaseT (scenario2)

1500 bytes

3,456.19

24.24

1024 bytes

1,558.101

14.051512 bytes

333.037.753

128 bytes

26.292

5.589

64 bytes

13.54

4.7

Table II shows the comparison between the collision countnumber at hub 1 under 10BaseT (scenario 1) and 100BaseT(scenario 2) for 1500, 1024, 512, 128 and 64 bytes of frame
size.This table shows that the value of the collision count at
the case of using 10BaseT Ethernet cables is larger than valueof the collision count at the case of using 100BaseT
regardless the value of frame size is.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

790

512 bytes

0.321

0.032

128 bytes

0.092

0.009

64 bytes

0.053

0.005

Table III shows the comparison between the utilization of
hub 1 under 10BaseT (scenario1) and 100BaseT (scenario2)
for 1500, 1024, 512, 128 and 64 bytes of frame size.This
table shows that the value of the utilization at the case ofusing 10BaseT cables is larger than the value of theutilization when using 100BaseT cables regardless the valueof the frame size that is used because the value of utilization
proportional directly with the value of the collision count.
Fig. 4. Comparison between numbers of collision count at Hub1 under
different frame size at 10BaseT Ethernet wiring standard.

Fig. 7. Comparison
betweenutilizationat
Hub1
different frame size at 10BaseT Ethernet wiring standard.

under

Fig. 5. Comparison between numbers of collision count at Hub1 under
different frame size at 100BaseT Ethernet wiring standard

Fig. 8. Comparison between utilization at Hub1 under different frame size
at 100BaseT Ethernet wiring standard
Fig. 6. Graphs for number of collision count at Hub1

Figures 4, 5 and 6 show that the number of collision counts
in 10BaseT is more than 100BaseT for all frame
sizesregardless the value of the frame size which is used.
B. Utilization of Hub
TABLE IIIUTILIZATION OF HUB1 (AVG.)
Time duration

4 minutes
Utilization

Devices

Hub 1standards

10BaseT (scenario1)

100BaseT (scenario2)

1500 bytes

0.883

0.091

1024 bytes

0.627

0.062

Fig. 9. Graphs for Utilization of Hub1

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791

Figures 7, 8 and 9 demonstrate that the utilization in
10BaseT is more than 100BaseT for all frame sizesregardlessthe value of the frame size that is used.
C. Traffic forwarded (bits/sec) at Hub 1
TABLE IV
TRAFFIC FORWARDED (BITS/SEC) TO HUB 1 (AVG.)

4 minutes

Time duration

Traffic Forwarded (bps)
DevicesStandards

Hub 1
10BaseT (scenario1)

100BaseT (scenario2)

1500 bytes

8,815,602

9,100,506

1024 bytes

6,259,192

6,263,042

512 bytes

3,209,531

3,214,534

128 bytes

919,988918,550

64 bytes

538,601

538,967

Fig. 12. Graphs of traffic forwarded at Hub1 (bit/sec)

Figures 10, 11 and 12 show that at some points both curves
overlap to each other; it means that traffic forwarded to hub1
is approximately same at these points.
D. Traffic received (bits/sec) at Hub 1

Table IV shows the comparison between the traffic
forwarded to Hub 1 under 10BaseT (scenario1) and
100BaseT (scenario2) for 1500, 1024, 512, 128 and 64 bytes
of frame size.

TABLE V
TRAFFIC RECEIVED (BITS/SEC) TO HUB 1 (AVG.)

4 minutes

Time duration

Traffic Received (bps)Fig. 10. Comparison between traffic forwarded (bps) at Hub1 under
different frame size at 10BaseT Ethernet wiring standard

Devices

Hub 1Standards

10BaseT (scenario1)

100BaseT (scenario2)

1500 bytes

8,815,602

9,100,506

1024 bytes

6,259,192

6,263,042

512 bytes

3,209,531

3,214,534

128 bytes

919,988918,550

64 bytes

538,601

538,967

Table V shows the comparison between the traffic received
to Hub 1 under 10BaseT (scenario1) and 100BaseT(scenario2) for 1500, 1024, 512, 128 and 64 bytes of frame
size.
Tables V and IV shows that the value of the traffic sent inbits per second and the value of the traffic received in bits per
second at the case of hub is equivalent because hub doesn’t
understand addressing the data which the hub receive isbroadcasted to all device in the network so the amount ofreceived data is the same as the amount of sent data.

Fig. 11. Comparison between traffic forwarded (bps) at Hub1 under
different frame size at 100BaseT Ethernet wiring standard

Fig. 13. Comparison between traffic received (bps) at Hub1 under different
frame size at 10BaseT Ethernet wiring standard

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

Fig. 14. Comparison between traffic received (bps) at Hub1 under different
frame size at 100BaseT Ethernet wiring standard.

Fig. 15. Graphs of traffic received at hub1 (bit/sec)

792

Fig. 16. Comparison between numbers of collision count at Hub2 under
different frame size at 10BaseT Ethernet wiring standard

Fig. 17. Comparison between numbers of collision count at Hub2 under
different frame size at 100BaseT Ethernet wiring standard

Figures 13, 14 and 15 demonstrate that at some points bothcurves overlap to each other; it means that traffic received tohub1 is approximately same at these points.
E. Number of collision counts at Hub 2
TABLE VI
NUMBER OF COLLISION COUNTS AT HUB 2 (AVG.)

4 minutes

Time duration
Collision countDevicesHub 2Standards

10BaseT (scenario1)

100BaseT (scenario2)

1500 bytes

3,435.919

27.70

1024 bytes

1,572.666

14.877512 bytes

340.3

7.626

128 bytes

29.4

5.29

64 bytes

12.4

5.13

Table VI illustrates the comparison between the collisioncount number at hub 2 under 10BaseT (scenario 1) and
100BaseT (scenario 2) for 1500, 1024, 512, 128 and 64 bytes
of frame size.The table VI illustrates that the value of thecollision count at the case of using 10BaseT Ethernet cable islarger than the amount of collision count when using
100BaseT cables regardless the value of the frame size that isused.

Fig. 18. Graphs for number of collision count at Hub2

Figures 16, 17 and 18show that the number of collisioncount in 10BaseT is more than 100BaseT for all frame
sizesregardless the value of the frame size that is used.
F. Utilization of Hub 2
TABLE VIIUTILIZATION OF HUB 2 (AVG.)
Time duration

4 minutes
Utilization

DevicesHub 2Standards
1500 bytes

10BaseT (scenario1)

100BaseT (scenario2)

0.8836

0.09101024 bytes

0.62880.0626512 bytes

0.32170.0321

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016
128 bytes

0.092

0.009

64 bytes

0.053

0.005

Table 7 shows the comparison between the utilization at
hub 2 under 10BaseT (scenario 1) and 100BaseT (scenario 2)
for 1500, 1024, 512, 128 and 64 bytes of frame size. The tabledemonstrates that the value of collision count at the case ofusing 10BaseT Ethernet cable is larger than its value whenusing 100BaseT cables regardless the frame size which usedbecause the value of collision count proportional directlywith the value of collision count.

793

G. Traffic forwarded (bits/sec) at Hub2
TABLE VIII
TRAFFIC FORWARDED (BITS/SEC) TO HUB 2 (AVG.)

4 minutes

Time duration

Traffic Forwarded (bps)
DevicesHub 2Standards

10BaseT (scenario1)

100BaseT (scenario2)

1500 bytes

8,813,873

9,105,746.133

1024 bytes

6,276,727

6,266,542.4

512 bytes

3,214,211

3,212,418.33

128 bytes

920,963.466

918,889.6

64 bytes

538,082.4

539,042.4

Table VIII shows the comparison between the traffic
forwarded to Hub 2 under 10BaseT (scenario1) and
100BaseT (scenario2) for 1500, 1024, 512, 128 and 64 bytes
of frame size.

Fig. 19. Comparison between utilization at Hub2 under different frame
size at 10BaseT Ethernet wiring standard

Fig. 22. Comparison between traffic forwarded (bps) at Hub2 under
different frame size at 10BaseT Ethernet wiring standard.

Fig. 20. Comparison between utilization at Hub2 under different frame
size at 100BaseT Ethernet wiring standard

Fig. 23. Comparison between traffic forwarded (bps) at Hub2 under
different frame size at 100BaseT Ethernet wiring standard.

Fig. 21. Graphs for Utilization of Hub2

Figures 19, 20 and 21 illustrate that the utilization in
10BaseT is more than 100BaseT for all frame sizesregardlessthe value of the frame size which is used.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

794

Fig. 24. Graphs of traffic forwarded to hub2 (bit/sec)

Figures 22, 23 and 24 demonstrate that at some points bothcurves overlap to each other, it means that traffic forwarded
to hub2 is approximately same at these points.

Fig. 26. Comparison between traffic received (bps) at Hub2 under different
frame size at 100BaseT Ethernet wiring standard.

H. Traffic received (bits/sec) at hub 2
TABLE IX
TRAFFIC RECEIVED (BITS/ SEC) TO HUB 2 (AVG.)

Time duration

4 minutes
Traffic Received (bps)

DevicesHub 2Standards

10BaseT (scenario1)

100BaseT (scenario2)

1500 bytes

8,813,873

9,105,746.133

1024 bytes

6,276,727

6,266,542.4

512 bytes

3,214,211

3,212,418.33

128 bytes

920,963.466

918,889.6

64 bytes

538,082.4

539,042.4

Table IX shows the comparison between the traffic
received to Hub 2 under 10BaseT (scenario1) and 100BaseT(scenario2) for 1500, 1024, 512, 128 and 64 bytes of frame
size.

Fig. 27. Graphs of traffic received to hub2 (bit/sec)

Figures 25, 26 and 27 show that at some points bothcurves overlap to each other; it means that traffic received athub2 is approximately same at these points.
I. Traffic forwarded (bits/sec) at switch

Tables VIII and IX shows that the value of the data traffic
sent in bits per second and the data traffic received in bits per
second for hub are the same because hub is broadcasting all
the incoming data to all the devices in the same network
without filtering the traffic.

Fig. 25. Comparison between traffic received (bps) at Hub2 under different
frame size at 10BaseT Ethernet wiring standard

TABLE XTRAFFIC FORWARDED (BITS/SEC) TO SWITCH (AVG.)

4 minutes

Time duration

Traffic Forwarded (bps)
Devices

SwitchStandards

10BaseT (scenario1)

100BaseT (scenario2)

1500 bytes

6,091,588.533

6,281,372.066

1024 bytes

4,312,105

4,327,995

512 bytes

2,212,381.533

2,220,971.6

128 bytes

634,480

633,561.133

64 bytes

370,812372,297

Table X shows the comparison between the traffic
forwarded to switch under 10BaseT (scenario1) and
100BaseT (scenario2) for 1500, 1024, 512, 128 and 64 bytes
of frame size.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016795

(scenario2) for 1500, 1024, 512, 128 and 64 bytes of frame
size.
Tables X and XI shows that the amount of the data traffic
sent in bits per second and the amount of data traffic received
in bits per second at the case of switch is not the same as thecase of hub. The amount of data traffic received is larger than
the amount of data traffic sent because there are some filtereddata. At the case of switch not all the incoming data is
broadcasted because it knows the address of the sender andreceiver by using the MAC address table.

Fig. 28. Comparison between traffic forwarded (bps) at Switch under
different frame size at 10BaseT Ethernet wiring standard

Fig. 31. Comparison between traffic received (bps) at switch under different
frame size at 10BaseT Ethernet wiring standard

Fig. 29. Comparison between traffic forwarded (bps) at Switch under
different frame size at 100BaseT Ethernet wiring standard

Fig. 32. Comparison between traffic received (bps) at switch under different
frame size at 100BaseT Ethernet wiring standard
Fig. 30. Graphs of traffic forwarded to switch (bit/sec)

J. Traffic received (bits/sec) at switch
TABLE XI
TRAFFIC RECEIVED (BITS/SEC) TO SWITCH (AVG.)

4 minutes

Time duration

Traffic Received (bps)
Devices

SwitchStandards

10BaseT (scenario1)

100BaseT (scenario2)

1500 bytes

11,559,450

11,924,876.13

1024 bytes

8,223,810

8,201,585

512 bytes

4,211,356

4,205,994.33

128 bytes

1,206,466.8

1,203,874.466

64 bytes

705,867

705,708

Table XI shows the comparison between the traffic
received by switch under 10BaseT (scenario1) and 100BaseT

Fig. 33. Graphs of traffic received at switch (bit/sec)

K. Traffic filtered (bits/sec) by switch
TABLE XII

Traffic received (bits/sec) to switch (Avg.)
Time duration

4 minutes

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

Traffic Filtered (bps)
Devices

SwitchStandards

10BaseT (scenario1)

100BaseT (scenario2)

1500 bytes

5,467,861.67

5,643,504.04

1024 bytes

3,911,705

3,873,590

512 bytes

1,998,974.8

1,985,022.7

128 bytes

571,986.8

570,313.33

64 bytes

335,055

333,411

Table XII shows the comparison between the traffic
received by switch under 10BaseT (scenario1) and 100BaseT(scenario2) for 1500, 1024, 512, 128 and 64 bytes of frame
size.From the table we can see that the amount of filteredtraffic at the case of using frame size 1500 bytes is larger
when using 100BaseT than 10BaseT which indicates better
performance, but when we decrease the frame size to 512,
128 and 64 the amount of filtered traffic at the case of using
10BaseT is larger than the amount of filtered traffic whenusing 100BaseT which indicated better performance forswitch.

796that the case of 10BaseT because it filters more
traffic.When the frame size is 1024 bytes, filtered traffic
will be approximately the same for both 10BaseT and
100BaseT.When frame size is further reduced to 512, 128
and 64 the results show that the performance of 10BaseT
becomes better than 100BaseT because the switch filtered
more traffic than 100BaseT.
 If we have LAN in which high traffic is not required andthe frame size will be fixed to 512, 128 or 64
bytes,10BaseT will give us good result and performs
better as compared to 100BaseT Ethernet wiring standard.
 At the case of small frame size we will not able to transfer
more traffic per seconds (traffic receiving and forwarding
is less) so we cannot use them in heavy traffic (refer tables
X and XI).
REFERENCES
Mohammad Wazid Roshan Singh Sachan, R.H. Goudar “Analysis of a
LAN under Different Ethernet Wiring Standards with Variation in Time
and Components”, UACEE International jornal of Advance incomputer Networks and its security – volume 2: Issue 3 [ISSN 2250 –
3757].
[2] Mohammad Wazid, Roshan Singh Sachan, R.H. Goudar “Performance
Evaluation of a LAN under Different Ethernet Wiring Standards withDifferent Frame Size”, International Journal of Computer Applications(0975 – 8887) Volume 43– No.13, April 2012.
[3] Charles Spurgeon and Chuck Toporek, “Ethernet: The Definitive
Guide”, 1st Edition 2000: O’Reilly & Associates[4] A. Forouzan, “Data Communication & Networking” 4th Edition 2006:
Tata McGraw Hill.
[5] William Stallings, “Data and Computer Communications” 8th Edition
2006: Pearson Education[6] Todd Lammle, “Cisco Certified Network Associate Study Guide” 2nd
Edition 2000: SYBEX, Inc., Alameda, CA[7] Xinjie Chang, “Network simulations with OPNET”, IEEE Conference
Publications of Simulation Conference Proceedings, 1999 winter[8] S. Kabir, S. Khatun, M. K. Abdullah, M.A. Mahdi and S. B. A. Anas,“Throughput Analysis of an Enhanced CSMA/CD Based Single
Channel Fast Ethernet Optical LAN”, International Conference on
Advance Communication Technology (ICACT) 2005.
[9] R. M. Daud, H. M. Elsayed and H. H. Amer, “Performance of Fast andGigabit Ethernet in Networked Control System”, 46th IEEE
International Midwest Symposium on Circuit and System 2003.
[1]

Fig. 34. Comparison between the traffic filtered by switch under different
frame size and wiring standard

Figure 34 shows that the initially filtered traffic is better incase of 100BaseT for 1500 bytes frame size than 10BaseT.
For 1024, 512, 128 and 64 bytes frame size the switch under
10BaseT filtered more traffic than 100BaseT, it means that theperformance of 10BaseT Ethernet wiring standard is become
better at the case of low traffic data.

VI. CONCLUSION
Computer Simulation are outperformed by using Riverbed
Modeler Academic edition tool. The performance of LANs is
tested and investigated under different conditions of Ethernet
wiring and different frame size. The result remarks andobservations from our simulations outcomes are:
 The number of collision counts in 10BaseT is always
more than 100BaseT for all the frame sizes because of thenature of 10BaseT [8], [9].
 Hubs are more utilized in case of 10BaseT because of thelarge value of collision count so the more retransmission
attempts will be required.
 The performance of a switch is better for 100BaseT
wiring standard at the case of 1500 bytes frame size thanAshraf A.M. Khalaf (M’98) received his B.Sc. and
M.Sc. degrees in electrical engineering from Minia
University, Egypt, in 1989 and 1994 respectively. He
received his Ph.D in electrical engineering from
Graduate School of Natural Science and Technology,
Kanazawa University, Japan, in March 2000. He iscurrently works as an associate professor atelectronics and communications engineering
Department, Minia University, Egypt.

Mostafa S.A. Mokademwas born in Aswan at 1989.
He got his B.Sc. degree from faculty of engineering,department of communication and electronics at 2011.
He works as an engineer in the Egyptian Electricity
Holding company, Ministry of Electricity andrenewable Energy.
He is currently a master course student for M.Sc.
degree in Electrical Engineering (Communication andElectronics)-FacultyofEngineering,
Minia
University, El-Minia, Egypt.
Khalil A. Ahmadis a professor in the department ofelectrical engineering, faculty of engineering, Minia
University, Minia, Egypt.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

9797

Internet of Things:
Security and Privacy Issues and Possible Solution
Davar PISHVA
Ritsumeikan Asia Pacific University, 1-1 Jumonjibaru, Beppu, Oita 874-8577 Japandpishva@apu.ac.jp
Abstract—In this paper, the author demonstrates how
commonly used communication scheme and information
retrieval which are carried out via Internet using numerous
types of smart devices, can turn the Internet into a very
dangerous platform because of its built-in nature of making its
users’ identity easily traceable and discusses somecountermeasures that can be used to prevent existing andprojected security breaches. It initially provides some
introductory information on technology advancement,
penetration of the Internet to all aspects of our life and its
associated conveniences. It then discusses vulnerability ofinternet services, presents some typical attack cases that are
carried out via smart home appliances, and explains security
implementation challenges on such devices from technical, social,
and practical aspects. Finally, it proposes an appropriate
security model, demonstrates relevant counter measures fornumerous attack scenarios, and explains why and how
numerous stake holders are needed to get together for its
commercial implementation.
Keyword—Internet of Things, IoT, Smart, Social Cybernetics,
Social Networking, Facebook, Online Advertising, Security,
Privacy, Network Tracing Tools.

I. INTRODUCTION

The 21st century has been regarded by many as theinformation era and penetration of Internet into all
aspects of our life is a new dimension along which
technologies continue to grow. The advancement intechnology has been changing the way of our life and digital
information has now become a social infrastructure. We are
surrounded by technologies and there are computer
technologies in our cars, phones, watches, entertainment
systems, and home appliances. The idea is to make use ofexisting electronics in the devices and, in conjunction withsome specialized software, create an intelligent network withaccess to the Internet.
Since the expansion of the Internet in 1990s, network
infrastructure has become an indispensable part of social life
and industrial activity for mankind. The idea of using existing
electronics in smart home appliances and connecting them tothe Internet is a new dimension along which technologies———————————————————————
Manuscript received on January 8, 2016. This work is a follow up of an
accepted conference paper for the International Conference on Advanced
Communication Technology.
Davar Pishva is a professor in ICT at Ritsumeikan Asia Pacific University(APU) Japan ( +81-977-78-1261, fax: +81-977-78-1261, e-mail: dpishva@
apu.ac.jp).

continue to grow, and in recent years mankind has witnessed
an upsurge of usage of devices such as smart phone, smart
television, home health-care device, etc. Their build-in
internet-controlled function has made them quite attractive to
many segments of consumers and smart phone has become acommon gadget for social networking. Commercial
advertising has also greatly benefitted from Internet services
and social networking is also being used for online advertisingand business transaction.
With adoptions of cloud computing, mobile applicationsand virtualized enterprise architectures have led to atremendous expansion of applications that are connected toInternet resources [1]. Just to mention a few examples, we use
Internet for various sorts of communication like VoIP andemail, multimedia services like Online Music and Online
Movie, business transaction like e-Banking and e-Business,
administrative work like e-Governance and e-Administration,
networking activities such as Online Advertising and Social
Networking. Furthermore, along with the development ofInternet, e-Commerce has become an efficient marketing tool
for many companies and Social Networking with Facebook isan emerging market which has recently become the mostvisited website in the world [2][3].
Traditionally social networking was done in person and inplaces like schools, communities, neighborhoods, workplacesetc. and hence limited in size. Although the use of social
networking for business and advertisement is not something
new, its traditional approach had limited size as it required
face-to-face or mouth-to-mouth interactions. Since theexpansion of the Internet, however, social networking has
grown exponentially and according to Statista [4],Facebook’s registered accounts have surpassed one billion
users as of March 2015. According to the same source,
approximately 347 million people are active on LinkedIn, 300
million uses Google+ and another 288 million use twitter.
There are of course many other social networking sites, but
none of them even existed at the beginning of the 21st century.
In fact, when Facebook was launched in 2004, many people
considered it as a place for kids to share their pictures andemotions. Today, however, businesses and marketers lovesocial media and indeed, 90 percent of marketers are usingsocial media for business [5]. Seventy percent have used
Facebook to successfully gain new customers and 34 percent
have used Twitter to successfully generate leads [6].
In Japan, many audio visual equipment can already beconnected to the Internet, enabling people to enjoy network
based services, such as Video on Demand (VOD), Music on

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

Demand (MOD), remote update, e-Commerce, remotecontrol, and other similar services. Network connectivity islikely to be equipped in all AV equipment in the near future.
Such developments have been leading mankind to a new eraof technology, the era of the “Internet of Things” (hereafter:
IoT), where all the appliances are getting tiny and controllable
via the Internet, thus enabling people to enjoy network based
services like Video on Demand (VOD), Music on Demand(MOD), remote update, e-Commerce, remote control, andother similar services.
Furthermore, researchers around the world have come up
with an abundance of resourceful ideas on how to effectively
use microprocessors and Internet in other everyday household

798

appliances. ‘Smart’ is the new buzzword that we can hearthese days; for example, in ‘smart’ homes, ‘smart’ kitchens,‘smart’ ovens, ‘smart’ refrigerators, etc. [7]. Table 1 indicates
functional classification of smart home appliances [8][9].
There is also a tremendous business potential for thembecause of foreseen future demand by elderly people, where
the number of people over the age of 65 is expected to double
to 70 million by 2030 [10]. According to a study conducted byInternational Data Corporation, 212 billion “things” will be
installed based on IoT with an estimated market value of $8.9
trillion in 2020 [11]. Those “things” will be nothing special
but daily used appliances ranging from watch, light bulb tosmart television, refrigerator and so on.

TABLE IFUNCTIONAL CLASSIFICATION OF SMART HOME APPLIANCES

No
1
2
3
4
5
6
7
8

FunctionContent Retrieval
Content Storage/Usage
Communication/Messaging
Remote Surveillance
Remote Control
Remote MaintenanceInstrument Linkage
Networked Game

Example of Product or Usage
Broadband TV, Microwave Oven, HDD Recorder (for TV program, etc.)
HDD Recorder (for TV program, etc.), MP3 Player
VoIP , IP-TV Phone, All kinds of Emails System, Healthcare System
Security Camera, Gas/Fire Sensors, Refrigerator, Lighting Fixture, Door Lock
Air Conditioner, Lighting Fixture, TV, TV Program Recording
Firmware Update, Trouble Report
Networked AV Equipment
Family Type Game Machine

Commercial advertising has also greatly benefitted fromInternet services and online advertising can even beconsidered as the foundation of web economy. However, thesystem of online advertising is quite unique. Unlike
conventional forms of advertising, the system of online
advertising allows its target to receive something in return inexchange for viewing the advertisement. Besides getting
information from the advertisement itself, the target of online
advertising is usually allowed to use the advertisement hostwebsite’s service. For example, as shown in Figure 1,
video2mp3.net, a website that offers free file conversion from
YouTube video to mp3 format, has numerous advertisements
displayed on its page [12]. It even goes as far as setting ahidden advertisement that will only appear when the user
clicks the “High quality” option. Nevertheless, the users are
not required to pay for the service. They are instead offered a
premium service where they can get exclusive access to thewebsite without advertisements popping up along the way. In
this way, online advertising has defined a new term of “freeservice”, where four parties – the advertisers, the ad network,

Fig. 1. A view of Video2mp3.net. On the right side is one of its many
advertisements.

the ad hosts, and the users - are involved within it [13].
In other words, today's business activities depend highly on
information systems and every enterprise has its own
information for its business. In an industrialized country like
Japan, most enterprises use information technology toestablish their management governance. This helps them
improve their efficiency and cost performance. As it is called'IT governance', information systems have significant impact
on the operations. Information assets have thus become
valuable commodities for business and information systems
are the key factors to ensure the growths of enterprises. Hence,
it is essential to control the design process, development cycleand effective utilization of information systems. Nonetheless,as information and its value continue to increase, so does the
management complexity, vulnerability and attractiveness tomalicious attacks. Security threats can come in the form ofunauthorized accesses, computer viruses, or cyber-attacks.
II. VULNERABILITY OF INTERNET SERVICES
For various reasons, however, today’s networks arevulnerable to numerous risks, such as information leakage,
privacy infringement and data corruption. One of the mainunderlying factors is operating nature of the communication
protocol used in the Internet domain and availability of many
free software that can carry out most of these attacks. The
Internet protocol suite which is commonly known as TCP/IP(Transmission Control Protocol and Internet Protocol), isused for most Internet applications. IP serving as its primary
component carries out the task of delivering packets fromsource host to destination host solely based on the IP
addresses contained in the packet headers. In order to achieve
proper operation of such transaction worldwide, this requires
source and destination to have unique IP address and included

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

it in the packet headers of their information packets. Since
every IP address is associated with a unique entity, identity ofIP address holders can be traced using their IP addresses
contained in the packet headers. This section briefly discusses
vulnerabilities of some Internet services.
A.

Vulnerabilities of e-Commerce
It is a known fact that privacy is implicated in e-Commerce
because of the risk involved in disclosing personal
information such as email addresses or credit card
information, which is required for most electronictransactions. Specific privacy concerns in this realm include
use of customers’ information by companies for electronic
surveillance (e.g., ‘cookies’), email solicitation (e.g., ‘spam’),or data transfer (e.g., when customer database information issold to third parties or stolen) resulting in identity or credit
card theft [12][13]. Online advertisement which is a major
component of e-Commerce uses cookies as a mean to identify
users and deliver targeted advertising by tracking their
movements in the website. Such cookies are called tracking
cookies and an ad network company like Google uses thistype of cookies for delivering ads that are relevant to the user's
interests, controlling the number of times the user sees a given
ad, and "measure the effectiveness of ad campaigns" (Google
Policies & Principles, 2012).
The problem with tracking cookies is that when the user
visits multiple websites with the same ad provider, the samecookies from the ad provider will be used. This means that thead provider will be able to track the user’s activity innumerous sites just by compiling the information via tracking
the cookies without the user’s knowledge. The result of thisaction means the loss of anonymity to the users, which is ablatant breach of information security as well as privacy.
Although the ad network are obliged to comply with privacy
act and use the information only for marketing strategy, users
cannot be sure if it is not used for other purposes. In fact,
personal information is being freely traded without theconsent of their owners for money making purposes. Personal
information related to interest and habit of prospect customers
are an essential component for delivering online advertisingand can be considered as the lifeline of many websites.
It is only recently that people and governments started to
pay attention to this previously un-scrutinized golden goose.
The Wall Street Journal’s investigation in 2010 revealed anarray of cookies and surveillance technologies, with real timefunction and deactivation resistance, are being used tomonitor the users’ personal information. The investigation
also found out that online tracking is not a small business. The
top 50 websites kept close tabs on their users by installing, on
the average, 64 different pieces of surveillance technology[14]. As such approaches could unconsciously victimize bothtechnical
andnon-technicalusers,
anonymouscommunication is becoming more and more important on
Internet environment since it can protect people’s right toonline privacy and reduce the possibility of getting
recognized and thus victimized.

799

social-engineering technique is also being widely exploited to
victimize users. Phishing is a good example of social
engineering intrusion technique in which a hacker just needs
to tempt the innocent users to fill in only their Facebook ID
and password. The aftermaths of releasing such information
are detrimental since huge amount of private information suchas user’s address, birthday, job, education history, hobbies,
friends, relationship and a bunch of other sensitive
information could be accessed from the Facebook account.
Although Facebook filters all URLs which link its users to
an external website and warns them of fraudulent websites,
the approach does not always work. For example, after
clicking to the link: http://anhhot-duthi.ucoz.net/, which is a
fraudulent website created by a Vietnamese hacker [2][3],Facebook will warn the user about the vulnerability of the sitethrough a dialog box shown in Figure 2. This, however, does
not always happen since hackers keep on creating new
fraudulent web pages in order to penetrate through loopholesof Facebook’s security. Furthermore, oftentimes,
non-technical people may unconsciously press the “Continue”
button instead of the “Cancel” button.
Now let us see what happens when either Facebook’s

Fig. 2. Vulnerability warning of Facebook.

security does not detect the above mentioned fraudulent
website or a user clicks the “Continue” button. As shown in
Figure 3, the control would transfer to a phishing site that has
the appearance of Yahoo Vietnam website, containing
Facebook Logo and a login form which resembles that of theofficial website. Although a technical user could easily
display HyperText Markup Language (HTML) view of thepage to determine where the information would be sent, someinnocents users may just fill up the form and press the submit
button. As indicated in the highlighted section of Figure 3,
information content of the form would simply be sent tohttp://allforms. mailjol.net/, a site which provides freeForm-to-Mail service. In other words, filling out the form andpressing “submit” button, will transfer ID and password ofFacebook user directly to the email address of the attacker[2][3].

B. Vulnerabilities of Social Networking
In recent years, because of dramatic increase in the use ofsocial networking platforms by many non-technical people,
Fig. 3. An example of a phishing website http://anhhot-duthi.ucoz.net/.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

800

After obtaining the first victim’s Facebook account, theattacker can easily exploit more users in a targeted manner bytaking advantage of the victim’s personal information andFacebook’s internal working mechanism. Facebook has abuilt-in feature called “important friends” the function ofwhich is to internally keep track of people with whom aFacebook user communicates frequently and shares somecommonality (e.g., same high school, hometown, fan page,etc.). In a targeted phishing Facebook, since the phishing linkis being sent from Facebook account of an important friend,
i.e., trustable and authentic source, attacker may easily
persuade the recipient friend to click the link and supply therequested information. The chain reaction of such approach
will enable the attacker to easily victimize many Facebook
users in a short period of time.
According to the 2013 Data Breach Investigations Report[15], cyber threat derived from social-engineering technique
is increasing dramatically as shown in Figure 4. Although its
percentage is still low compared to “Malware” and “Hacking”,
threat caused by social-engineering intrusion has increased bymore than 4 times within one year. Considering the rapid
development of social networks, it can be foreseen that social
engineering intrusion will continue to increase in the coming
years, thus necessitating appropriate countermeasures.
C.

Vulnerabilities of Smart Home Appliances
Connecting smart home appliances to the Internet can also
makes us vulnerable to malicious attacks. An intruder cansteal private information such as contact info, shopping or
eating preferences, lifestyle and relaxation habits, or credit

Fig. 4 Threat action categories in 2011 and 2012 (Verizon Enterprise, 2013).

card information used to pay for such services. With the rapid
growth of online content in the last decade, advertisers
became more aware that demographic information would
allow more targeted approach in the advertisement. As
database-mining techniques become more and moresophisticated, advertisers find more effective strategies, e.g.
contextual targeting, in order to attract the attention of moretargeted audiences as opposed to the basic targeting
techniques such as time, frequency, demography, etc. [14].
They can also use smart appliances as launching pads to carryout malicious attacks into other systems. Table 2 shows a listof common attacks that can be carried out through smart home
appliance and the next section discusses some specific attack
cases[8][9].

TABLE IIA LIST OF COMMON ATTACKS

No
1
2
3
4
5
6
7
8
9

Common Threat
User Impersonation
Device Impersonation
Service Interruption
Data Alteration
Worm/Virus Infection
Phishing/Pharming
Data Wiretapping
Firmware Alteration
OS/Software Vulnerability

Example of an Attack
Impersonation using password
Impersonation of a device using its faulty certificate
Distributed Denial of Service (DDOS)
Data alteration of transmitted or stored data
Infiltration and/or damaging of a computer system
Impersonation of users’ destination
Information leakage through wiretapping
Replacing of firmware at will
Launching of worms and attacks using such vulnerabilities
attacking IoT networks too. Table 3 shows threat likelihood
level of a given smart home appliance type for a particular
III. TYPICAL ATTACK CASES
attack based on past and present security-related incidents,
The author has previously shown how the nature of Internet where H, M and L indicate high, medium, and low level threatProtocol could accidentally put its user’s identity into high likelihood, respectively and ‘-’ entries implies no supportingrisk of being revealed due to the existence of private data available. The rest of the section briefly discusses certain
information behind the IP address in the packet header, which classical techniques that have recently been employed to carry
can be easily extracted and observed by various IP tracer and some of these attacks on the smart home appliance system notdeep packet inspection tools [2][3]. In addition, the use of only to steal personal information but also abuse the devices
sniffing tools such as Wireshark or other network monitoring and make them serve cyber criminals’ numerous illegal
applications, though not new, turn out to be very efficient for purposes.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

801

TABLE IIITHREAT LIKELIHOOD LEVEL OF A GIVEN SMART HOME APPLIANCE

Common
1
2
3
4
5
6
7
8
9
ThreatUser
DeviceService
Data Worm/Virus Phishing
Data
Firmware OS/Software
Function
Impersonatio Impersonatio Interruptio Alteration Infection Pharming Wiretapping Alteration VulnerabilitnnnyContent
1
H
H
M
L
M
L
L
L
RetrievalContent
2
L
L
M
L
L
Storage/Usage
Communication
3
H
H
M
L
M
M
L
L
/Messaging
Remote
4
H
H
L
L
L
L
L
L
Surveillance
Remote
5
H
H
H
H
L
L
L
L
Control
Remote
6
H
H
H
M
L
L
L
L
L
MaintenanceInstrument
7
M
M
M
L
L
L
L
L
Linkage
Networked
8
H
H
H
M
M
L
L
L
Game

No

A. Man-In-The-Middle Attack
In March 2014, a Vulnerability Research Firm named
ReVuln, published a video which describes how to employ
man-in-the-middle attack to penetrate into the Philips Smart
Television through the wireless network that the device
connects to. Consequently, the cybercriminal could steal thecookies from the built-in web browser of the television andgenerate a session hijacking attack to gain access to victim’s
personal pages [16].
After observing the attack video, one can easily say thatTV’s configuration for connecting to wireless network
through a default hard-coded password is not appropriate.
Though it may be convenient for the users, it is quite
dangerous if the cybercriminal is also within the range of
wireless router. It could even cause more serious aftermath
since remote control TV application can easily be
downloaded from the Internet. Through such application the
hacker could obtain the TV’s configuration files and control
the TV if he knew the IP address of the television.
B. Denial-of-Service (DOS) Attack
DOS attack is not a new technique and the main attacking
mechanism is that a huge amount of packets are generated andsent simultaneously to a targeted appliance. As a consequence,
the appliance is either brought down causing permanent crash,or reset to factory setting automatically and making it lose its
configuration, stored data and applications.
This kind of attack has recently been reported [17]. A
hacker named, Hemanth Joseph, shared on his blog a very
simple way to carry a DOS attack on a Pebble Smart Watch.
The attacker just needs to know the victim’s phone number,
Facebook ID, or any other way to interact with the Watch’s IP
address. Considering that the watch has a function of showing
messages received from Facebook, tablet or phone on its
screen without character limitation, the attacker can keep on
sending many lengthy messages so as to cause a DOS attackon the watch. As a consequence, the Smart Watch could be

Fig. 5 After DOS attack, the Watch’s screen is full of white straight lines, alldata and applications are erased because of reset to factory setting (Hemanth
Joseph, August 2014)brought down, reset to factory setting, and lose all of its data
as shown in Figure 5
In addition to the IoT network of home appliances, cybercriminals can also easily penetrate into internet-based-control
public appliances. A study published in August 2014 bysecurity researchers from the University of Michigan
demonstrates how a series of vital security vulnerabilities intraffic light systems in the US could allow adversaries to quite
easily take control of the whole network of at least 100 traffic
signals from a single point of access [18].
By carefully examining the above cases, one can easily
trace the main reason behind such vulnerability to the fact thatthose appliances make use of unencrypted wireless radio
signals thus can be monitored and compromised bycybercriminals.
C. Thingbot
Thingbot is an abbreviation similar to the word botnet,
which itself is a combination of the words “robot” and“network”. In a similar manner, thingbot is comprised of thewords “thing” and “robot”.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

In order to create a huge botnet network, many computers
are compromised and abused by malware to launch
cyber-attacks without awareness of internet users. In a very
similar manner, thingbot composed of smart home appliancesand other devices in IoT network, can be infected and easily
turned into slaves by the attackers because of lack of proper
security. After knowing the real IP addresses of suchcompromised devices, it becomes easy for the hacker togenerate cyber-attacks such as spamming, or executing
Distributed Denial of Service (DDOS) by manipulating them
via standards-based network protocols such as Internet Relay
Chat (IRC) and Hypertext Transfer Protocol (HTTP), [19].
Although no serious DDOS attack originating from IoT
network has yet been reported, it is predictable that DDOS
attack scheme from IoT will be on its upward trend in a near
future as mentioned in a warning press from Kaspersky blog[20]. Just to do a small calculation as a reference, let us
assume that only 0.01% of the IoT network is compromisedby 2020. This will make around 20 million appliances
vulnerable to cyber-attacks. Even granting that most of the
IoT will only transmit relatively small amounts of data,considering their enormous size, the DDOS attack will besevere enough and should have no difficulty in bringing downa server, or any single host. Moreover, unlike DOS attack
which is generated in a pinpointed manner from a single
computer or server to flood a target, DDOS attack has anintegrated effect of a huge number of compromised devices.
Once it occurs, blocking becomes extremely difficult sinceeach compromised element has its own unique IP address.
D. Some Specific Attack Cases in JapanA DVD/HDD video recorder in Japan, which implemented
a proxy server and was acessible without authentication under
its default configuration, was used as an open proxy serverbase for spamming [21], as shown in Figure 6.

Fig. 6 A spamming incident

In another incident, a music player, which was infectedwith a virus in the factory, corrupted its user’s computer uponconnection [22], as depicted in Figure 7.
In an example of privacy violation, a poorly implemented‘referrer’ feature in a cellular phone constantly transmitted
previously accessed page information even when the page
was reached via direct addressing (i.e., non-hyperlink access).
The browser flaw caused private information, which may had

802

Fig. 7 A PC Infection Incident

been required to access a previous page (e.g., user name,password), to be revealed to the next link. It also revealed theuser’s favorite sites by transmitting information on apreviously accessed page [23]. A Japanese researcher has also
successfully exploited buffer overflow vulnerabilities in
embedded home routers and managed to remotely gain
complete access to peripheral devices [24].
IV. SECURITY IMPLEMENTATION CHALLENGES
Considering the above mentioned attacks aimed at smart
appliances, it is apparent that the attack techniques are notnew. However, what have been changed are the attack targets,
which are the smart home appliances and devices in IoT
network. In most cases, the attackers make complete use of
the nature of Internet Protocol to have the access to those
appliances; and oftentimes, the devices do not havefull-functional display or screen so it is really hard for thevictims to even detect that they are being attacked and abused
internally. Furthermore, different from human-controlled
computers, most of smart appliances (such as LED light bulbs
smart system, smart refrigerators and smart meters) can easilybe accessed due to their 24 hours around-the-clock
availability on the Internet. Last but not least, because eachappliance is designed to serve only a specific purpose,
marketability factors such as low cost, portability, tinier size,
etc. make built-in full cryptography capability infeasible inmost of such appliances.
Therefore, implementing security on these devices presents
more challenges than traditional computer security due to thelimited resources (e.g., toy CPUs that cannot handle
computationally expensive cryptographic computations andbattery power that prohibits long-lasting or high-peakcomputations). Moreover, because security of a network
depends on its weakest link, security of networked smart
home appliances would rely on the security of its mostprimitive home appliance e.g., a coffee maker or a toaster.
The problem is further aggravated by the fact that home
appliance users cannot be considered as “skilled”
administrators, but are instead technology-unaware people inmany cases.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

Fig. 8.

803

A heterogeneous home network and its service Providers

As such, continuous growth of diverse smart home
appliances and development of numerous networking
technologies make management of home network security
and their associated services complex to both users andservice providers, as can be seen from Figure 8.
V. APPROPRIATE SECURITY MODEL
Though it is essential to make the smart home appliancesystem more secure from aforementioned cyber-attacks, it isobvious that equipping each of them with its own built-in
security function against cyber-attacks in the same mannerthat has been done on personal computers and web servers is
not the right approach. In a previous work, the author together
with Mr. Hoang proposed implementation of TOR-based
anonymous communication into the IoT network as aneffective alternative way to help smart home appliance users

protect their privacy and make the smart home appliancesystem more secure from aforementioned cyber-attacks [2][3].
Although such approach can be effective, particularly fortechnology-aware and expert users, in this paper the author
would like to emphasize the concept of universal home
gateways and involvement of network operators in theirsecurity challenges [8][9] so as to effectively protect
technology-unaware people who are the main users of suchdevices.
In order to come up with a model that can deal with thesecurity requirements of smart appliances, a functionallycategorized list of which shown in Table 1 along with a list ofcommon attacks in Table 2 and likelihood of the attacks in
Table 3, this section examines a proposed countermeasures
shown in Table 4 in a conventional manner for each of thecommon attack and demonstrates how to tailor them for smart
appliances via universal home gateways.

TABLE IVSUMMARY OF PROPOSED COUNTERMEASURES

No
1
2
3
4
5
6
7
8
9
A.

Common Threat
User Impersonation
Device Impersonation
Service Interruption
Data Alteration
Worm/Virus Infection
Phishing/Pharming
Data Wiretapping
Firmware Alteration
OS/Software Vulnerability

Proposed Countermeasure
Introduce a certificate mechanism through memory card like devices.
Control through network and access mechanism to outside world.
Introduce access control and certificate mechanism.
Use virus protection software and prepare to handle new vulnerabilities.
Consider using SSL to assure genuineness of displayed sites.
Protect communication via IPSEC, SSL/TLS.
Use physical access control for update procedure.
Educate R&D people on security and conduct product test.

Counter Measures
This subsection examines in some details thecountermeasures for common attacks listed in Table 4 andrecommends how to tailor them for smart appliances.
1) User/Device Impersonation: In this scenario, a
malicious attacker tries to make an unauthorized access to theappliance and possibly perform some configuration changes
on the system. Since the risk level of such an attack is high forsmart appliances, a certification mechanism based onstandard and public-key infrastructures (PKI) must be usedamong the entities involved as shown in Figure 9.
Following is a list of required certification mechanisms:

A standard certification mechanism between user andserver.
An easy-to-use certification mechanism (e.g., through amemory card like device, a built-in speech recognizer,
or biometrics recognition method) between thefollowing entities:
o Appliance ⇔ Usero Appliance ⇔ PC
o Appliance ⇔ Server
o Appliance ⇔ Appliance
An SSL server certificate between the appliance andserver.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

Fig. 9.

Use of Certification Mechanism2) Service Interruption: Smart home appliance can also
be used as a launching pad for a distributed denial of service(DDOS) attack. The risk posed by this threat is medium tohigh across the various types of appliances and the following
methods are recommended as countermeasures:
 Provide a suitable access control for external accesses
based on an authentication mechanism.
 The appliance should neither implement redirection
nor allow internet access via user input. It should also
thoroughly examine the input.
 To prevent attack against a currently accessed site, theappliance should not hard code access address andshould make it easily changeable.
 Redundancy and other usual prevention methods
against service interruption should be implemented.
3) Data Alteration: This threat involves misuse of thelink between the appliance and PC to alter data or system
configuration in an unauthorized manner. The risk posed bythis threat is high for remote controlled appliances, medium
for networked games and remotely maintained appliances,and low for the remaining items. We can cope with thesethreats by: Implementing certification mechanisms for all
appliances.
 Implementing access control based on theauthentication method.
Presently most of the network media players that areavailable in the market do not have an authentication andaccess control system.
4) Infection by Worm or Virus: Worm/virus infected
appliances can become a secondary source of damage forexternal access and poses a low to medium risk. We can
employ the following methods to safeguard against these
infections: Virus protection software, which is practical even fromresource and operational points of view.
 Use of firewall or intrusion detections system (IDS) to
deter infection.
Use of distributed patch programs for establishedvulnerability information.
5) Phishing/Pharming: In this scenario, a malicious entity
tries to guide the appliance user into a different server for
either marketing purposes or try to deceive the user to steal
his/her
personal
information.
Except
for
the

804

Communication/Messaging appliance, the risk of such an
attack is relatively low. Nonetheless an SSL certificate
mechanism can be used to prevent its occurrence. For server
certificate verification, however, a mechanism wherein theuser could clearly verify would be necessary. Furthermore, adesign which prevents user information from being sent to theserver is required.
6) Data Wiretapping: Wiretapping is a covert means ofmonitoring communication, the risk of which is quite low forsmart appliances. The following methods can be considered
as countermeasures:
 Use IPSEC, TLS or SSL for the communication.
 Use server certification mechanism from access points
to the appliance. Or, facilitate the router’s VPN or
NAPT settings.
7) Overwriting Firmware: This involves unauthorized
external access to the appliance and overwriting of its
firmware. Appliances which are remotely maintained aresusceptible to this attack. In order to prevent the risk of suchattack, the appliance should require some form of direct user
operation for its firmware update.
8) OS/Software Vulnerabilities; In this attack, amalicious user takes advantage of OS/software vulnerabilities
and freely executes any code externally. The attacker can also
misuse back doors that are left open for R&D andmaintenance purposes for their malicious purpose. Although
the risk of such an attack is relatively low for the smart
appliances, the following methods can be employed tocounteract the threat: Educate system designers and R&D people on securedevelopment system.
 Familiarize R&D people on the vulnerabilities of backdoors that are left open.
 Conduct an exhaustive test on projected vulnerable
components during test phase of the product.
B.

Discussion
As can be observed from the above, security requirementsof smart appliances depend on their functions. To address
such functionally dependent security requirements, one has toconsider whether a given smart appliance is to be utilized on astand-alone basis, or several of them are to be used in aninterconnected manner in a family area network (FAN)
environment. Although it is simpler to meet the security
requirements of a single appliance, in reality however, several
appliances will be used in an interconnected manner.
Therefore, security of the FAN and its underlying
technologies, e.g., dedicated wiring, existing power or phonewiring, or wireless, must also be considered. Similarly, while
it is ideal to address security requirements of smart appliances
by local means and without any need for a background online
system, it is not clear what such a security infrastructure will
look like at present. Hence, it becomes essential to also
employ PKI in this field. Furthermore, because applianceusers are technology-unaware people, network operators
appear to be in an excellent position to offer the required
security services. They handle various network technologies,
have experience with PKI and direct access to the users, andare capable of managing large-scale infrastructures.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

Implementation Guidelines
It seems that no single vendor/manufacturer may be able tosolve the problems faced. Nevertheless, the best way toproceed is to develop the security model around smart home
appliances and network components that conform to certain
standards. There are standards bodies which specify how tobuild these devices and meet their various requirements. It isessential to conform to these standards when building,managing and providing services for smart home appliancesin the future. Such an approach will encourage more
vendors/manufacturers to conform to these standards, and thestandards themselves will evolve as needs arise.
The following subsections briefly explain some of theexisting standards that define how to build and manage
universally deployable smart home appliances. It also
specifies implementation guidelines to set up a prototype
system using an open architecture and a modular development
scheme.
1) Association of Home Appliance Manufacturers: The
Association of Home Appliance Manufacturers (AHAM) has
completed an ANSI standard for generic object models for allof the major white goods in the home, including refrigerators,
washing machines, dryers, dishwashers, microwave ovens,room air conditioners and ranges [25]. The AHAM Standard
for Connected Home Appliances-Object Modeling (CHA-1)
was also created to provide interoperability with higher-levelprotocols such as Universal Plug and Play (UPnP), Versatile
Home Network (VHN) and others. It enables both appliancemanufacturers and third-party developers to create newvalue-added services and features that will allow remote
operation and monitoring of appliances from anywhere in thehome or even when you are away from home.
2) ECHONET Consortium: ECHONET develops
specifications of home network for networked household
appliances, facilities and sensors. ECHONET Consortium has
formulated the ECHONET Specification, which can be used
to centrally monitor and control smart home appliances thatare connected through an ECHONET compatible network
interface and a controller [26]. The ECHONET specifications
can ensure interoperability between devices of different
vendors and realize easy home network at low cost. Also,
ECHONET promotes the development of attractive service

805

C.

and application systems using the ECHONET ™
specifications.
ECHONET routers have conversion functions toaccommodate home appliances that use different transmission
media. The ECHONET, which had previously only supported
transmission media such as power lines, low-power radio
frequency, and infrared radiation, has come to support othertransmission media such as Bluetooth and Ethernet since therelease of its version 3.00. Therefore, when there are multiple
transmission media in the same domain, the installation of an
ECHONET router specified by the ECHONET Specification
will allow seamless connection between different types oftransmission media.
The ECHONET consortium has over 100 members and its
major sponsors are "Hitachi, Ltd.," "Matsushita ElectricIndustrial Co., Ltd.," "Mitsubishi Electric Corporation,""Sharp Corporation," "Tokyo Electric Power Company,
Inc.," and "Toshiba Corporation." Smart home appliancesFig. 10. OSGi and other Standardized Technologies

built by major Japanese companies are ECHONET
compliant.
3) Open Services Gateway Initiative Alliance: The OSGi
Alliance is an open forum. Their mission is to specify, create,advance, and promote an open service platform for thedelivery and management of multiple applications andservices to many types of networked devices. Their
specifications were initially targeted at residential gateways
for the connection of home devices to an external network or
for interconnections between the different protocols that areused in devices at homes but have recently been extended toaccommodate vehicle, mobile and other environments [28].
About half of its members are based in North America, athird are based in Europe, and the rest are based in the
Asia/Pacific region. A number of products developed bymember companies are based on the OSGi Service Platform
specification [29].
The OSGi specification is not a new protocol technologythat replaces existing ones; but rather assumes that multiple
protocols could be used within the target device. Its
relationship to other standards is schematically shown in
Figure 10, where OSGi maps existing local network standards
to broadband networks and provides portal services.
Figure 11 shows OSGi architecture which consists of OSGi
Framework and a set of bundles [28].
The OSGi Framework provides the basic functionality forexecuting OSGi bundles which are software components that
contain algorithms and protocols for controlling a device.
When a bundle is required, it can be downloaded from aserver on the network and then executed. This feature makes it
possible to download and use the latest and most optimal
bundles and allows customization of gateway functions foreach user. Since only the bundles that are needed are
downloaded and stored, little memory space is required.

Fig. 11.

Structure of OSGi Gateway

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 20164) Next Generation Gateways: Even with the existenceof standardized smart home appliances and standard
specifications on how to network them together, a major
challenge still falls on capabilities of gateways that connect allof these devices to the network, control the devices, andprovide portal capability for using services offered by theexternal networks, including the Internet. The OSGi
specification can be used to implement such gateway
capabilities through software component technologies that
use the Java language.
There are vendors like ProSyst [30] Bosch Group that use
OSGi specification in their modular and open service
platform. Companies like Motorola integrate ProSyst’sService Gateway software in their advanced residential
products and there are forums like Home Gateway Initiative[31] which define the next generation of residential gateway.
There are also forums like UPnP [32] which provides
architectural framework for self-configuring, self-describing
devices.
The next logical step is to develop an experimental, secured
service application for a home network consisting of open
standard compliant home appliances and network
components that are manufactured by different vendors.
Although when using off-the-shelf products, one may need to
employ several gateways and a computer as a service gateway,
the above described OSGi specification maybe used to answer
such problems. The next generation gateway should enable
adoption of a uniform approach regardless of underlying
technology and manufacturer.
D.

A New Security Architecture
Considering the abovementioned security requirements
and the various associated challenges, the most effective way
to address security issues of the smart appliances are to: Engage a network operator to build dedicated but
nonproprietary home gateways and become thepreferred trusted third party.
 Motivateinternet-enabledsmart
appliance
manufacturers to develop device drivers andapplication software that can run on such universal
home gateways to control and operate the appliances.
This idea is schematically shown in Figure 12, where auniversal home gateway, managed by a network operator,
functions as an entry point to the networked appliances. In thisarchitecture, all transactions with the smart appliances,whether local or remote, are done via universal home
gateways.
1) Basic Usage Scenarios: There are three basic usage
scenarios here; one is access of local services by a user from806

within a FAN (e.g., watch a movie using a video recorderlocated in another room), the other is downloading remote
services, and the third one is control of smart appliancesinterconnected in a FAN environment, by a remote user (e.g.,turn on air conditioner from the office).
From within the FAN, users can be authenticated through acommon-password-based, log-on approach. Each user’s
access control information (e.g., no adult movies for kids
below 17, or no movies for school-going children after 11 p.m.
etc.), which is stored in the universal home gateway, can beused for access granting. To access a remote service site fromwithin the FAN, the universal home gateway authenticates theuser through an authentication server, establishes the user’s
access control privilege, and initiates a secure communication
between the remote service provider and the user for thetransfer of the requested service. Remotely accessing home
appliances is the counterpart of remote service access where
the universal home gateway checks and validates a remoteuser’s access control privileges and allows securecommunication for legitimate data transfer.
2) Desired Features of the Universal Home Gateway: In
general, universal home gateways, in addition to having thegeneral functions of supporting the underlying FAN
technology, and acting as a gateway between a telecom
operator’s network and the FAN and as an access point to thesmart appliances for digital service providers and remoteusers, are envisioned to have the following security functions: Authenticate a user from within FAN through either acommon-password-based log-on approach or by theplugging of a memory card like device, containing
user’s access control information, into the system. A
more user-friendly approach would require a built-in
speech recognizer or biometrics recognition method.
 Act as a security server and maintain each user’s access
control information (security attributes and the basic
key for communication with remote services).
 Provide secure communication and deal with security
issues on behalf of the appliance users. Enable remotemanagement
services
throughproviders,intermediaries, and network operators. Allow network
operators to use universal home gateways toauthenticate their users, or bill them on behalf of a third
party e.g., service providers.
 Detect connection of a new appliance to the FAN andprompt users to insert its manufacturer supplied
driver/application software. Such a plug and play typeauto configuration mode, however, should be manually
selectable (i.e., be enabled when needed) in order toFig. 12. Smart Appliance Security via Network Operator

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016provide an added security for wireless FAN andprevent arbitrary connection from the neighborhood.
 Automatically disable, via software selectable functions,an appliance when it is detached from the FAN andauto configure when a previously detached appliance isre-connected.
 Be equipped with firewall and virus protection software
Such functionalities could meet security measures whichwere discussed earlier. Moreover, because security
requirements are handled via central universal home gateways,
it could offset resource limitations of individual smart
appliances.
3) Existing and Envisioned Technologies: Although at
present there are some vendors who market their own brand ofsmart appliances, which are equipped with a central controllerthat links the appliances together in a FAN environment andoffer limited security and exclusive digital services [33][34],history shows that such propriety approaches are prone to fail.
Similarly, while some researchers, e.g., those at the
University of Illinois [35], have done ingenious work in
coming up with protocols and schemes that could provide
security for smart home appliances, a universally deployable,user-friendly system, which is acceptable to a wide range ofusers, is the key issue.
VI. CONCLUSION
This paper demonstrated how commonly usedcommunication scheme and information retrieval which arecarried out via Internet using numerous types of smart devices,
can turn the Internet into a very dangerous platform. After
providing some introductory information and necessary
background knowledge, it examined a number of security
incidents that are carried out via smart devices, identified
existing challenges and showed that the security requirementsof smart appliances depend on their functions. The security
requirements of the appliances, categorized based on theirfunctions, were then identified and appropriate solution foreach category was proposed. PKI was identified as an
essential security component and easy-to-use authentication
mechanisms were recommended. It considered compliancewith existing standards and liaise with appropriate fora todownstream new requirements important when trying toaddress security requirements of smart home appliances. It
argued that successive security implementation involves
cooperation of manufacturers, network operators and service
providers. An architecture wherein security issues aremanaged through universal home gateways by network
operators in a product based fashion is proposed andmanufacturers and service providers are recommended toadapt the technology, in order to offset resource limitations ofindividual smart appliances and make their security issues
straightforward to ordinary users.
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Davar Pishva is a professor in ICT at the College of
Asia Pacific Studies, Ritsumeikan Asia Pacific
University (APU) Japan. In teaching, he has been
focusing on information security, technology
management, VBA for modelers, structured decision
making and carries out his lectures in an appliedmanner. In research, his current interests include
biometrics; e-learning, environmentally sound and
ICT enhanced technologies. Dr. Pishva received his
PhD degree in System Engineering from Mie University, Japan. He is a
Senior Member of IEEE, a member of IEICE (Institute of Electronics
Information & Communication Engineers), and University & College
Management Association.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

809

CampusSense - A Smart Vehicle Parking
Monitoring and Management System using ANPR
Cameras and Android Phones
Mohammed Y Aalsalem, Wazir Zada Khan
Farasan Networking Res. Lab, Faculty of Computer Science & Information System, Jazan University, Kingdom of Saudi
Arabia{aalsalem.m, wazirzadakhan}@jazanu.edu.sa
Abstract-- Vehicle parking monitoring and management has
become a big challenge for educational institutions withincreasing enrollments, high percentage of vehicle ownershipand decreasing parking supply which in result triggering
blockage of vehicle, congestion, wastage of time and money. In
university campuses particularly in Kingdom of Saudi Arabia,
vehicle parking monitoring and management problem is getting
worse and more frustrating due to the fact that majority ofstudents, faculty and staff members own cars and drive
through them to the university campuses. These common
problems include finding out people (evidence) who areresponsible for the damages (hitting, scraping, scratching anddents) to other cars and the blockage of car due to wrong car
parking which takes much time to locate the owner of the car.
Moreover, locating or forgetting their car park location
another difficulty that is often faced by the students, faculty
and staff members. The existing cameras located at the parking
lots are only for video surveillance and cannot help in suchsituations as there is a lack of proper vehicle parking
monitoring and management system. To cope with above
mentioned problems and to ensure a better parking experienceby accommodating increasing number of vehicles in a proper
convenient manner, we propose a smart vehicle parking
monitoring and management system called CampusSense. In
CampusSense, Automatic Number Plate Recognition (ANPR)
cameras and android based mobile application is developed toefficiently monitor, manage and protect the parking facilities atuniversity campuses. Parking problems around the university
campus faced by the students, faculty and staff members areanalyzed by conducting a survey.

The problem is getting more severe day by day due to thefact that a the number of student enrolments is increasing
year by year and a huge percentage of students and faculty
own cars with the limited number parking lots. Blocking theother parked vehicles in the parking lots by people whileparking their cars improperly is an important issue in vehicle
parking. Due to this, finding the responsible persons andremain stuck and frustrated for the blocked vehicle owners
until they get the vehicle out of the parking lot. The security
guards at the parking lots are unable to help in this regard
because of the lack of any monitoring and management
enforcement systems and policies. Due to this, it takes muchtime in pursuing the responsible person which consequently
results in the wastage of precious time of students as well as
faculty and staff members. Another critical problem (thatarises due to the reserved and limited number of vehicle
parking lots) is that students (for whom no reserved parking
is available) may damage other parked vehicles while
improper and wrong vehicle parking. The damaged vehicle
owners remain unsuccessful in finding out the responsible
persons for damaging their vehicles and no one can help out
in this concern because there is no proper monitoring systemthat can keep record of the in and out information (i.e.
entrance and exit) of the vehicles and parking information(like parking location, parking duration) of vehicles.
Moreover, in case of suspected vehicles (involved in any
criminal activity) are unable to trace out by current system
as there is no record or way to identify them. Another most
Keywords—Smart Vehicle Parking Monitoring and common problem faced by students, faculty and staff
Management, Android based Mobile Phones, License Plat members is they often forget where they have parked theirvehicles in the parking lots. So, finding out a vehicle in such
Recognition, Locating Vehicle, Mobile Sensing.
a scenario without any automated management system isresults in anger, exasperation and wastage of time and it is
I. INTRODUCTION
ehicle parking monitoring and management is also difficult and time consuming task.
Smart phones are now days the key computing andchallenging problem due to the growing number ofcommunication
device and these mobile phones are
vehicles at university campuses and also for catching theequippedwitharich set of embedded sensors. These
responsible persons for damaging the vehicles (like scratches,
specialized
sensors
including ambient light sensor,
dents, scraps etc.) of other people’s inside a campus who
accelerometer,
digitalcompass,
gyroscope, GPS, proximity
remain anonymous and also result in confusion, annoyancesensor and general purpose sensors like microphone andand wastage of time.
camera. These sensors collectively enable new applications———————————————————————
Manuscript received Feb. 13, 2016. This work is a follow-up the invitedacross a wide variety of domains like homecare, healthcare,journal to the accepted conference paper of the 17th Internationalsocial networks, safety, environmental monitoring,
Conference on Advanced Communication Technology (ICACT2015).
ecommerce and transportation [1-2]. Mobile sensing
Mohammed Y Aalsalem is currently Dean Faculty of Computer Science
provides the opportunity to track dynamic information about
and Information System, Jazan University, KSA and Founder & Director of
Farasan Networking Res. Lab. (Corresponding author, Phone:environmental impacts and develop maps and understand+966173230004; fax: +966173210850; e-mail: aalsalem.m @patterns of human movement, traffic, and air pollution [3].
jazanu.edu.sa).
Using these extraordinary monitoring capabilities of smart
Wazir Zada Khan is currently with Faculty of Computer Science andphone sensors a wide range of mobile application are
Information System, Jazan University, KSA and Member & Co-Founder ofFarasan Networking Res. Lab. (e-mail: wazirzadakhan@jazanu.edu.sa).
developed for traffic monitoring for example monitoringV

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

road and traffic conditions, detecting road bumps, honks,
potholes etc. Such traffic monitoring systems include [4-9].
The current vehicle parking monitoring and management
system at the Jazan University is fully manual which only
allows the authorized vehicles that are registered by having
the entrance sticker. The whole university area including
entrance and exit gates, academic area, administrative andparking zones are all under video surveillance. But this canonly serve for video capturing and storing and are notconnected to any proper monitoring and management
system.
In this paper we have proposed a smart vehicle parking
monitoring and Management system called CampusSense to
overcome the above mentioned problems which encountered
while vehicle parking in the parking area of the University
campus. We have also review the contribution in [10] andafter further investigation the CampusSense system bydiscussing the design and the system features in more details.
CampusSense consist of hardware (ANPR Cameras) andsoftware components (Mobile Application and Management
System) and can assist the security department to handle theparking problems more effectively such as locating the car if
a person forgets its exact parking location or to locate andpursue the liable person for damaging or blocking some ones
car while wrong car parking in the parking lot.
A quantitative questionnaire based survey is also
conducted to investigate the problems encountered by thestudents, faculty and staff members. The results of thesurvey provide a confirmation of the above mentioned
problems that the students, faculty and staff members arefacing and thus our proposed system fulfills all therequirements that need to be addressed by providing
appropriate solutions of these problems.
The proposed CampusSense system aims to provide anappropriate solution for all the problems which have been
identified by analyzing the conducted survey. The aims andobjectives of the proposed systems are as follows:
•

•

•

•

•

To implement a vehicle parking monitoring andmanagement system that will automate the existing
parking management system by keeping all the in/out
information and parking information of vehicles atuniversity campus.
To facilitate the security department in assuring thesafety and satisfaction of the students, faculty and staff
members while parking at the university campus.
To develop a mobile application which will facilitate in
reducing the frustration and annoyance of those who
often forget the exact parking location of their cars.
To assist the security department in finding out the carowners who have blocked other cars by parking cars
improperly.
To help the security department for tracing out thesuspected or abundant (long term parked) vehicles atuniversity campus.

810

The rest of the paper is organized as follows: Section II
describes the existing parking management systems. Section
III presents the design & working of the proposed system. In
Section IV the survey statistics are discussed. Finally
Section V concludes the paper.
II. RELATED WORKIn this section, first we have covered the Sensor and RFID
based vehicle parking monitoring and management systems
and secondly the mobile sensing applications related totraffic monitoring.
In the literature the available vehicle parking monitoringand management systems are either sensor based [11-13] or
RFID [14-16] based and they mostly address the issue offinding a vacant parking location in the parking lot. Thesesystems are only helpful in determining the occupancy statusof parking space but are unable to figure out the solutions forthe above mentioned problems like the information about
responsible persons who either block or damage other cars
while parking their own. So of the parking problem
encountered at universities campuses are studied in [17].
The parking management systems based on sensors havea problem as mostly sensors are unable to detect obstacles
that are not visible because of their flatness to the groundlevel and thus they cannot distinguish pedestrians or objects
from the vehicles of interest, in result have more false
positives. Another challenge in Sensor and RFID based
systems is that they are prone to many attacks [18] likedenial of service attacks (DoS)[19], selective forwarding
attack[20-21], node replication attack[22-24], Sybil
attack[25-26], wormhole attack[27], black hole attack[27]and Signal or Radio Jamming attack [29-30] etc. RFID
based systems are also suspect to many attacks like [31-32].
Many mobile sensing based systems are proposed for trafficmonitoring for example monitoring road and traffic
conditions, detecting road bumps, honks, potholes etc, thesesystems include Nericell [4], VTrack [5], TrafficSense [6],Mobile Millennium [7], TARIFA [8] and Road Bump
Monitor [9]. TABLE I shows the comparison ofCampusSense with other mobile sensing system.
Prashanth Mohan et al. have proposed two systems
Nericell [4] and TrafficSense [6] to detect potholes, bumps,
braking, and honking using phones sensors likeaccelerometer, microphone, GSM radio, and/or GPS. The
authors have used the idea of triggered sensing, where
dissimilar sensors are used in tandem to conserve energy.
The data is gathered through GPS-tagged cellular tower
measurements during several drives over the course of 4
weeks. GPS-tagged accelerometer data measurements arealso separately gathered on drives on some of the sameroutes over the course of 6 days. Cellular tower
measurements are also gathered over the course of a few
days in the Seattle area. The system could be used toannotate traditional traffic maps with information such as thebumpiness of roads, and the noisiness and level of chaos intraffic, for the benefit of the traffic police, the road works

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

811

TABLE ICOMPARISON OF CAMPUSSENSE WITH OTHER MOBILE SENSING BASED APPLICATION
Systems

Smart Mobile
Phone Used

NeriCell [11]Nokia N95,
HTC Typhoon

V-Track [12]TrafficSense [13]Road Bump
Monitor [45]CampusSense

Software
Used
Perl, windows
mobile5.0,Windows
mobile 2003,
Python, C#

Mode ofCommunication

Not Mentioned

Wifi, Bluetooth

HPiPAQ nw6965,
HTC Typhoon

Windows
mobile5.0, Windows
Mobile 2003

Bluetooth, Wifi,
GSM

Microphone,
Camera,GPS Sensor,
Accelerometer,
GSM Radio

Monitoring of Roads and Traffic
Conditions

HPiPAQ nw6965,
Samsung
SGHi780,
HTC
Adrantoge
7510,7501
Any Android
based smart
mobile phone

MS Windows
Mobile 5.0,
6.1,
Windows 7,
C#,
C/C++
Android
Studio, C#,SQL ServerBluetooth,
GPRS, EDGE,
3G Radio

Microphone,
Camera,GPS Sensor,
Accelerometer

Road Bumps Detection

3G/4G, Wifi

GPS, Camera

Locate the vehicle using map, retrieve
vehicle owner information form
management system

iPhone, Nokia
N95

GSM, GPRS,
UNTS, EDGE,
Wifi, Bluetooth

department, and ordinary users. Nericell uses honk detection
to identify noisy and chaotic traffic conditions like that at an
unregulated intersection. TrafficSense monitor road andtraffic conditions in complex varied road conditions (e.g.,potholed roads), chaotic traffic (e.g., a lot of braking andhonking), and a heterogeneous mix of vehicles (2-wheelers,
3-wheelers, cars, buses, etc.) called TrafficSense. The
effectiveness of the sensing functions in Nericell andTrafficSense are evaluated based on experiments conductedon the roads of Bangalore, with promising results.
Arvind Thiagarajan et al [5] have proposed a systemcalled VTrack. VTrack performs map matching, whichassociates each position sample with the most likely point on
the road map, and produces travel time estimates for eachtraversed road segment. VTrack provides real-time estimates
recent to within several seconds to users. It also compiles a
database of historic travel delays on road segments. In
VTrack data was gathered using iPhone 3G application, andfrom GPS and WiFi radios embedded in-car. It is shown that
VTrack can tolerate significant noise and outages in theselocation estimates, and still successfully identify delay-prone
segments, and provide accurate enough delays for delayaware routing algorithms.
R. Herring et al. [7] have proposed a system called
Mobile Millennium to estimate traffic on all major highways
in and around the target area, as well as on major arterial
roads. The Mobile Millennium architecture consists of aphysical component: GPS-enabled smart phones onboard
vehicles (driving public), and three cyber components: acellular network operator (network provider), cellular phonedata aggregation, traffic service provision and estimation. A
back end server aggregates data from a large number ofmobile devices and pushes the data to UC Berkeley
estimation engine for data assimilation, which combines thecell phone data with other information such as loop detectors
to produce the best estimate of the current state of traffic.

Phones Sensor
Used
Microphone,
GSM Radio,GPS,
Accelerometer,
CameraGPS

Applications

Bump, Breaking and Honking Detection

Detecting and Visualizing hotspots, Real
time Route planning

Georgios Adam et al [8] have proposed a system called
TARIFA (Traffic and Abnormalities Road Instructor For
Anyone) that estimates road traffic as well as roadabnormalities, and makes the collected information available
to anyone that has Internet access. This system is capable ofspotting potholes and can also provide information for the
traffic using the GPS receiver. The architecture of thesystem consists of two independent parts. A smart phonethat is equipped with an accelerometer and a GPS receiver isplaced inside a car. There is also a local database for the
temporary storage of data. A heuristic algorithm is used todetect potholes and other surface abnormalities.
T. Das et al. have propose a system called Road Bump
Monitor [9] which is an application of PRISM platform todetect and locate road bumps automatically without any user
involvement. The sensed (accelerometer) data is processed
locally on the phones to extract the desired information (thelocation of road bumps), before it is shipped back to theserver. Then the road bump monitoring application was
opportunistically deployed on the phones and the bumps
detected by the application were compared with the manual
recording of road bumps.
The existing car parking management system at theuniversity is fully manual which only allows the authorized
vehicles that are registered by having the entrance sticker.
Security department provides three types of stickers forentering into the University, which are for students, faculty
and staff members. The whole university area including
Entrance and exit gates, academic zone, administrative andparking zones are all under video surveillance. But this canonly serve for video capturing and storing and are notconnected to any proper management and monitoring
systems. In case of any acute incident, a sequential video
search is required which is time consuming and unfruitfulprocess. There are reserved (by name) parking lots for mostof the staff members and dedicated areas (on first come firstserve basis) are available for students and the faculty
members.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016812

III. CAMPUSSENSE
This section presents a smart vehicle parking monitoringand management system called CampusSense. The
CampusSense is consist of both hardware and software
components.
••

The hardware component will consist of Automatic
Number Plate Recognition (ANPR) cameras which aremounted on the entrance and exit gates and parking lotsof the campus.
The software components will include a parking
management system and a mobile application.

CampusSense leverage ANPR cameras for capturing thelicense number plates of the vehicles instead of Sensor or
RFID based existing vehicle parking management systems.
These ANPR cameras are more advantageous than othertechnologies. ANPR cameras are known with different
names like ALPR (Automatic License Plate Recognition),
LPR (License Plate Recognition), CPR (Car Plate
Recognition) and AVI (Automatic Vehicle Identification). It
provides faster traffic management at parking areas, ability
to automate access control systems with a setup of ticket freesystems providing new and more effective law enforcement.
ANPR cameras are suitable in all weather conditions and canbe mounted at higher mounting locations to assure a wider
field view of the whole parking lot.

Fig. 3. Capture the vehicle license plate using camera at exit and store it in
the database.

The vehicle license plate number will be capture using
ANPR cameras on entering/exiting the gates and at the
parking lots and will be store to vehicle management system.
Fig. 1 and Fig. 3 show the capturing of vehicle license plateusing camera at entrance/exit and store it in the database. Fig.
2. Shows the capturing of vehicle license plate using camera
at parking lots or when change the place then store it in the
database. A mobile application has two features. First, it is
responsible for locating the car if a person forgets its exact
parking location. This feature is for general purpose and canguide all the persons who are parking in the parking zones of
the University. Second, it can assist the mobile security units
to locate and pursue the liable person for damaging or
blocking some ones car while wrong car parking in theparking lot. This feature is specifically built for security
purposes that can aid the security department to ensure thesafety of the students, faculty and staff members while car
parking. Fig. 5 shows the working of the mobile application
to retrieve the required information.

Fig. 1. Capture the vehicle license plate using camera at entrance andstore it in the database.

The authorized vehicles will be registered in the parking
management system along with their owner information.
The information about the parking zones and parking lots
will already be stored in the system with the other related
information (e.g. which camera is monitoring the parking lot
with their physical locations (x & y coordinates).

Fig. 4. Flow Chart for the working of ANPR Cameras integrated withvehicle management system.

Fig. 2. Capture the vehicle license plate using camera at parking lots orwhen change the place then store it in the database.

The working of the ANPR cameras can be easily
comprehended by the flow chart in Fig. 4. It shows that the
ANPR cameras that are mounted on the entrance and exit
gates of university and placed as well in all the parking lots
will first capture the License plate number while
entering/exiting/parking of the vehicles and then store it in
the database. The ANPR cameras are integrated with the
Vehicle Parking Management System which contains the

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

records of entrance and exit timing information of theauthorized vehicles along with the parking location and theirowner’s information. In case when any parked car isdamaged by the some other car, the information about thevehicle and liable person for damaging the parked car in theparking lot can be found by the system records. This can bedone by searching out the information about the timing andparking location of the cars nearby the damaged car/vehicle.

813

The screen shots of the proposed prototype mobile
application are shown below in Fig. 7. These screen shotsshow the two main features of the prototype application i.e.
locating the exact parking location of the car (Find my Car)and finding out the owner of the vehicle who has blocked
another car (Who is this Car).

Fig. 7. Screen shots of the prototype mobile application.

The Proposed CampusSense has following benefits:
Fig. 5. Working of the mobile application to retrieve the required
information.

The working of a mobile application can be shown in theform of a flow chart as in Fig. 6. This mobile application can
assist the security manger to handle two problematic
situations during car parking in the university campus. In
case if any car is blocked by wrong car parking then thisapplication can locate the owner of the car who has blocked
the other parked car. This application can also help the
persons who often forget the exact location of their parkedcars. This mobile application keeps the personal information
about the vehicle owners as a secret and only the security
department personnel are able to see that information. For
developing the prototype mobile application Android Studiois used and for parking management system the database isdeveloped in SQL Server and the user interface is design &developed in C#.

•

Helps the security department to find out the
responsible for the damages (hitting, scraping,scratching and dents) to other cars.

•

Helps the security department to locate the ownerof the car which cause blockage.

•

Helps the students, faculty and staff to locate theircars on forgetting their car park location.

•

Helps in finding out the owner of those cars that areparked from many days.

•

The security department can retrieve all the historyof any particular car.

IV. SURVEY STATISTICS
A survey from 26th to 30th of October 2014 was
conducted by filling out a quantitative questionnaire. A totalof 88 persons participated in the survey out of which 53
were students and 35 were faculty and staff members. A
number of questions were asked from the participants. Fig. 8(a) shows the percentages of the different age groups of thestaff/ faculty members and Fig. 8 (b) shows the percentagesof the different age groups of students the. The survey
results show that majority of the participants from staff andfaculty were above 26 years old (91%) and on other side
majority of students were between 22 to 25 years old (66%).
9%

(a)
0%

(b)
34%

91%
Age 18-21
Age 22-25
Age 26 & Above66%
Age 18-21
Age 22-25
Age 26 & Above

Fig. 8. Percentages of different age groups of the participants.

Fig. 6. Flow Chart for the working of the mobile application.

Fig. 9 (a) shows the percentages of different modes oftransportation for reaching the university campus used by
the staff/ faculty members and Fig. 9 (b) shows the

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

percentages of different modes of transportation for reaching
the university campus used by the students. The survey
results show that majority of the participants from staff andfaculty come to campus by their own vehicles (88%) and onother side majority of students who own cars were (81%).
3%
9%
0%

0%

17%

By Taxi
Others

Yes

2%
81%

With Friend
By Own car

By Taxi
OthersFig. 9. Percentages of modes of different transportation.

Fig. 10 (a) shows the percentages of the staff/ faculty
members whose vehicles were damaged by other vehicles
while wrong parking at university campus and Fig. 10 (b)shows the percentages of student whose vehicles weredamaged during parking. The survey results show thatmajority of the student vehicles were damaged during
parking (65%) as camper to faculty/ staff (29%). The reason
was no reserve parking for students at university campus.

29%

65%
35%

71%
Yes

No

Yes

No

Fig. 10. Percentages of vehicle dames during parking.

Fig. 11 (a) shows the percentages of staff/ faculty
members whom are able to find the responsible persons fordamaging their vehicles in the parking lot and Fig. 11 (b)shows the percentages of the students whom are able to findthe responsible persons for damaging their vehicles in theparking lot. The survey results show that majority of theparticipants were unable to find the responsible persons whodamaged their vehicles (i.e. 71% faculty/ staff and 65%
students).

3%
97%

89%
Yes

No

85%
Yes

No

No

Fig. 13 (a) shows the percentages of staff/ faculty
members whose vehicles were blocked and they have waiteduntil clearance of blockage and Fig. 13 (b) shows thepercentages of time taken to clear the blockage vehicles by
the students. The survey results show that majority of thestudents waited more than 30 min to clear the blockage(67%) as compared to faculty/ staff member (50%).

17%

2%

Yes

No

Fig. 11. Percentages of persons who have found responsible persons fordamaging other cars.

Fig. 12 (a) shows the percentages of those staff/ faculty
members those whom cars are blocked by others cars thatare parked wrong and Fig. 12 (b) shows the percentages of
those student whom vehicles are blocked by others vehicles
that were parked wrong. The survey results show thatmajority of the students vehicles were the blockage (85%) ascompared to faculty/ staff member (58%). The reason is thereserve parking for staff/ faculty members at university
campus.

31%

33%
50%

67%

Less 10 min
B/w 10 to 20 min
More then 30 min

Less 10 min
B/w 10 to 20 min
More then 30 min

Fig. 13. Percentages of time consumed due to car blockages.

Fig. 14 (a) shows the percentages of staff/ faculty
members those who have found difficulties in finding theirvehicles in the matter of forgetting the location where theyparked their vehicles and Fig. 14 (b) shows the percentagesof those students who forget their vehicles in the parking lot.
The survey results show that majority of the students forget
their vehicles most of the time (65%) as compared tofaculty/ staff member (35%). The reason is the reserve
parking for staff/ faculty members at university campus.

35%

65%
35%

65%
Yes

11%

15%

42%

Fig. 12. Percentages of car blockage.

88%

With Friend
By Own car

58%

814

No

Yes

No

Fig. 14. Percentages of participants forgetting the location of their parked
cars.

V. CONCLUSION AND FUTURE WORK
Parking problems at university campuses continue toprevail and have become a major concerning issue. In thispaper we have proposed a smart Vehicle Parking Monitoringand Management System called CampusSense for Jazan
University whose students, faculty and staff members arefacing parking problems while parking their cars in parking
lots of the University. The key concern of our proposedsystem is to automate the existing manual parking
management system with efficient and effective use of theparking lots. This system contributes to reduce thefrustration and annoyance of the students, faculty and staff
members while car parking in the University parking zones.
We have also conducted a survey by distributing thequestionnaire to the students, faculty and staff members. The
results of the survey confirm the car parking problems facedby the participants. Our future research includes the real-

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016
[20]

time implementation of our proposed system with additionalfeatures like searching for vacant parking spaces in an
effective manner and also automating the mobile application
by adding some OCR techniques to take vehicle number
plates form camera images instead of entering it manually.

[21]

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Dr. Mohammed Y Aalsalem is currently Dean
Faculty of Computer Science and Information
System, Jazan University, Kingdom of Saudi
Arabia. He received his PhD in Computer
Science from Sydney University. His researchinterests include real time communication,
network security, distributed systems, andwireless systems. In particular, he is currently
leading in a research group developing flood
warning system using real time sensors. He is
Program Committee of the International Conference on Computer
Applications in Industry and Engineering, CAINE2011. He is regularreviewer for many international journals such as King Saud University
Journal (CCIS-KSU Journal).
Dr. Wazir Zada Khan is currently with
Faculty of Computer Science and Information
System, Jazan University, Kingdom of Saudi
Arabia. He received his PhD from Electricaland Electronic Engineering Department,
Universiti
Teknologi
Petronas(UTP),
Malaysia and his MS in Computer Sciencefrom Comsats Institute of Information
Technology, Pakistan. His research interests
include network and system security, sensor
networks, wireless and ad hoc networks. His subjects of interest include
Sensor Networks, Wireless Networks, Network Security and Digital
Image Processing, Computer Vision.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

816

An Effective Speedup Metric Considering I/O
Constraint in Large-scale Parallel Computer
Systems
Guilin Cai*, Wei Hu*, Guangming Liu*,**, Qiong Li*, Xiaofeng Wang*, Wenrui Dong**College of Computer, National University of Defense Technology, Changsha, China**National Supercomputer Center in Tianjin, Tianjin, China
cc_cai@163.com, { huwei, liugm}@nscc-tj.gov.cn, qiong_joan_li@yahoo.com.cn, xf_wang@nudt.edu.cn,
dongwr@nscc-tj.gov.cn

Abstract—With supercomputer system scaling up, theperformance gap between compute and storage systemincreases dramatically. The traditional speedup only measures
the performance of compute system. In this paper, we firstly
propose the speedup metric taking into account the I/O
constraint. The new metric unifies the computing and I/O
performance, and evaluates practical speedup of parallel
application under the limitation of I/O system. Furthermore,this paper classifies and analyzes existing parallel systems
according to the proposed speedup metric, and makessuggestions on system design and application optimization.
Based on the storage speedup, we also generalize these results
into a general storage speedup by considering not only speedup
but also costup. Finally, we provide the analysis of these new
speedup metrics by case studies. The storage speedup reflects
the degree of parallel application scalability affected by
performance of storage system. The results indicate that the
proposed speedups for parallel applications are effective
metrics.
Keyword—storage speedup,
scalability, system classificationW

generalstoragespeedup,

I. INTRODUCTION

ith the scaling up of supercomputers, the system
performance advances considerably. In the latest
TOP500 supercomputing list in 2015, Tianhe-2 consisting of
3,120,000 cores reaches the performance of 33.86 petaflop/s[1]. Planed exascale supercomputers (1018 floating point———————————————————————
Manuscript received February 21, 2016. This work is a follow-up of theinvited journal to the accepted conference paper of the 18th International
Conference on Advanced Communication Technology.
G. Cai is with the College of Computer, National University of Defense
Technology, Changsha, 410073, China (e-mail: cc_cai@163.com).
W. Hu is with the College of Computer, National University of Defense
Technology, Changsha, 410073, China (corresponding author, phone:+86-22-65375500; fax: +86-22-65375501; e-mail: huwei@nscc-tj.gov.cn).
G. Liu is with the College of Computer, National University of Defense
Technology, Changsha, 410073, China (e-mail: liugm@nscc-tj.gov.cn).
Q. Li is with the College of Computer, National University of Defense
Technology, Changsha, 410073, China (e-mail: qiong_joan_li@yahoo.com.
cn).
X. Wang is with the College of Computer, National University of Defense
Technology, Changsha, 410073, China (e-mail: xf_wang@nudt. edu.cn).
W. Dong is with the College of Computer, National University of Defense
Technology, Changsha, 410073, China (e-mail: dongwr@nscc-tj.gov.cn).

operations per second or 103 petaflop/s) are promising tocome in this decade [2].
Since the 1980s, the average growth of processor
performance could reach 60%, while the read and write
bandwidth of disk which is the main storage device only
increased by 10% to 20% per year. In addition, thecomputing system scales much faster than I/O system. Bothof above reasons exacerbated the mismatch between
application requirements and I/O performance. For
I/O-intensive applications, the performance is not only
determined by compute system, but also greatly affected by
I/O system.
Due to the increasing scale of the supercomputer systemand parallel application, the I/O performance and storage
capacity requirements of the supercomputer increase rapidly.
The concerns about the storage scalability are not only theperformance can be obtained but also the cost-effectiveness
related to investment. The larger the system is, the moreinvestments improving system performance needs. How toevaluate and promote the effectiveness of the investments ofthe storage system is another important problem need to beaddressed.
In this research, we analyze and quantify the effects ofstorage bottleneck of supercomputers. Our theory provides
the new metric on how to quantify the impact of I/O system
on application performance, and we also present the new
method to evaluate the cost-effectiveness of the storage
system of supercomputer. The main contributions of ourwork lie in the following aspects.
 This paper introduces a new speedup metric calledstorage speedup taking into account the I/O
performance constraint. The new metric unifies thecomputing and I/O performance, and evaluates practical
speedup of parallel application under the limitation of
I/O system.
 The existing parallel systems are classified and analyzed
according to the storage speedup, and the suggestions
are acquired on system design and application
optimization.
 Based on the storage speedup, a general storage speedup
is generalized to evaluate the cost-effectiveness of thestorage system by considering not only speedup but also
costup.
 Through the case studies, the effectiveness of these new

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

speedup metrics is validated.
The rest of paper is organized as follows. Section 2
reviews the related work. Section 3 defines the storage
speedup, classifies the existing systems according to it andpresents the general storage speedup. Section 4 uses somecase studies to analyze the I/O architecture insupercomputing system to validate the effectiveness of the
new metrics. Finally, in section 5 we conclude this paper anddiscuss some future works.
II. RELATED WORKThe essentiality of supercomputer is to reduce theexecution time of applications by parallel computing, while
speedup has been almost exclusively used for measuring
scalability in parallel computing [3].
In 1967, Amdahl advocated a speedup model for a
fixed-size problem [4]:S Amdahl =1
f + (1 − f ) P

where P is the number of processors and f represents theserial ratio of the program. Obviously, the equation reveals a
pessimistic view on the usefulness of large scale parallel
computers since the maximum speedup cannot exceed 1/f.
In 1988, Gustafson introduced a scaled speedup for a
fix-time problem [5], which scales up the workload with theincreasing number of processors to preserve the execution
time:
SGustafson = f + (1 − f ) P

This speedup proves the scalability of the parallel
computers and overcomes the shortcomings of Amdahl’s
speedup model.
Sun and Ni [6] presented a memory-bounded speedup
model which scales up the workload according to thememory capacity of the system, demonstrates the relationshipamong memory capacity, parallel workload and speedup.
Culler [3] proposed a speedup model taking thecommunication and synchronization overhead into account.
Researchers can improve the system performance dependingon this model by reducing the overhead of communicationand synchronization such as overlapping communication andcomputing, load balancing and so on.
Yang [7], [8] studied the reliability and power issues ofsupercomputers, introduced the reliability and power related
speedup. They provided a new perspective to solve reliability
and power related issues.
In this work, we develop our storage speedup metric toanalyze and quantify the effects of I/O performance forparallel applications in supercomputing.

III. STORAGE SPEEDUP
Suppose a parallel system consisting of P nodes of single
core processor, denoted by N1... NP. Let a parallel program Q
be executed on the system with one process per node,
resulting in a total of P processes. If a processor has morethan one core or one node has more than one processor, P
denotes the number of cores in the system with cores being
treated as nodes. Hereby, there will be one process runningon each core. In the following paper, it is assumed that there
will be one process running on one node to simply the model.
A. Definition for Storage Speedup
As shown in Figure 1, for a given program Q which has
I/O workloads can be divided into two kinds of phases: I/Ophases and compute phases. The compute phases are denoted
as shaded parts compared with the I/O phases denoted as thewhite parts. The single process execution case is shown in
Figure 1(a), where 0 represents the process and T1 denotes theexecution time. The execution case of parallel system isshown in Figure 1(b), where P represents the number ofprocesses and TPS denotes the practical execution time ofparallel program. The I/O workloads mainly consist ofreading the raw data, storing intermediate data, writing thefinal results and so on. Every I/O phases we denoted as an I/O
operation point.
For the serial program Q running on one compute node in
Figure 1(a), let NS be the amount of I/O operation points. Isintis the average time interval among I/O operation points, andon each I/O operation point the average I/O service time isIsser, then all the execution time of I/O-free phases is Tsnon, so
the time of the program Q runs on a node serially is:
S
(1)
TSto
= T snon + N s  I sser = T snon + (T snon I sint ) I sser
For the parallel program Q running on P compute nodes in
Figure 1(b), in regard to the i-th (0≤ i＜P) process in the P
processes, we denote that NiP is the amount of I/O operation
points, Iipint is the average time interval among I/O operation
points, Iipser is the average I/O service time on each I/O
operation point, and Tipnon is the execution time of I/O-freephases, so the average values of the parameters related to allprocesses can be given as follows.
The average value of I/O operation points:
Np =1 P −1 p
 Ni
P i =0

The average value of average time intervals among I/O
operation points:
I pint =1 P −1 pint Ii
P i =0

The average value of average I/O service time on every
I/O operation point:
I pser =1 P −1 pser
 Ii
P i =0

The average value of average I/O-free execution time:

T1
S
P

Time

T
Time

817

Fig. 1. Execution state of a program on a P node system. (a)Single process.
(b)P processes in parallel.

T pnon =1 P −1 pnon
 Ti
P i =0

Then the real execution time of program Q on P processors
is approximated as follows:
P
(2)
TSto
≈ T pnon + N p  I pser = T pnon + (T pnon I pint ) I pser
So we give the definition of storage speedup model.
Definition 1 (Storage Speedup). When program Q runs
on a parallel system, the storage speedup achieved is defined

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

as follows:
S

P
Sto

T
=
T

(3)

According to (1) & (2), suppose that SP is the speedup of
the I/O-free part in the program Q, while N = I pser I pint
and M = I sser I sint , the storage speedup can be converted
below:
P
S Sto
=

=

(
)
(
)
I )
1+ M
1
=S 
=S 
−M
N
1+ N
I )
1+

S
T snon + T snon I sint  I sser
TSto
≈
P
TSto
T pnon + T pnon I pint  I pser(
(1 + IT snon 1 + I sser
T pnon

pser

sint

pint

P

Let us define
O(P) =

(4)

P

1+ M

N −M
1+ M

(5)

which reflects the variation of I/O execution with the parallel
application scaling and is called the storage workload factor.
As a result, our storage speedup formula is refined to
1
P
S Sto
= SP 
1 + O( P)(6)which indicates the speedup affected by I/O performancevariation with the processors scales.
In formula (6), if SP is instantiated by Amdahl’s speedup
and Gustafson’s speedup, we obtain different storage
speedup formula as follows.
Amdahl Storage Speedup
1
P
1
P
S Amdahl=
− Sto = S Amdahl 
1 + O ( P) 1 + f ( P − 1) 1 + O ( P)(7)

Gustafson Storage Speedup
11P
SGustafson= ( f + P (1 − f ) )− Sto = SGustafson 
1 + O( P)
1 + O( P)(8)

Due to the competition of I/O resources, I/O service time isprolonged in parallel computing, resulting in O(P) ≠ 0.
Ideally, if O(P)=0, our two storage speedup models are
simplified into the traditional Amdahl’s and Gustafson’s
speedup formulas.
P
(9)
S Sto
= SP
B. System Classification
First we make some symbols conventions. We denotef ( x )  g ( x ) if lim x →∞ f ( x ) g ( x ) is ∞ and f ( x )  g ( x ) if
lim x →∞ f ( x ) g ( x ) is a positive constant or ∞ . Adversely,
P

sSto

10000
9000

SP

8000
7000

O(P)=K

6000
5000
4000

O(P)=KlgP

3000
2000

O(P)=KPlgP

1000

indicates

01000 2000 3000 4000 5000 6000 7000 8000 9000 10000 P

Fig. 2. Three examples about storage speedup of different kinds of systems
based on O(P).

lim x → ∞ f ( x ) g ( x )Θ(x) is a set consisting of all functions of x, f(x) ∈ Θ(x) andg(x) ∈ Θ(x). If lim x →∞ f ( x ) g ( x ) is a positive constant, it
denotes f ( x ) =Θ ( g ( x ) ) or g ( x ) =Θ ( f ( x ) ) .
From the definition of storage speedup formula (6), it iseasy to observe that O(P) is the key factor in storage speedup
variations. According to the characteristic of O(P) related todifferent parallel systems, considering these systems to beclassified as follows.
Definition 2 (Constant and Incremental Systems).
Suppose that a program Q satisfying lim P →∞ S P = ∞ runs on a
W system. If O ( P )  Θ(1) , the W system is considered to be aconstant system. And if O ( P )  Θ(1) , the W system isconsidered to be an incremental system.
As shown in Figure 2, there are three examples about theclassification based on O(P). If O ( P ) = K  Θ(1) , where K is apositive constant, the W system is a constant system. We findthe storage speedup increases linearly with the number ofnodes which indicates that the scalability of the application is
not bound by the I/O performance obviously.
If O ( P ) = KP lg P  Θ(1) , the W system is an incremental
system. This is different from above; the scalability of theapplication is bound by the I/O performance.
C. General Storage Speedup
In this subsection we generalize the storage speedup by
considering not only the time cost introduced in the last
subsection but also costup related to investment. Due to a
powerful support provided by supercomputer, scientificexploration develops rapidly. Since more and more data need
to be analyzed or calculated, I/O-intensive applications are
becoming an important part of the scientific applications on
the supercomputer. In order to meet the requirements of
I/O-intensive applications, storage system of supercomputerneed more large capacity and high performance equipment,
and also more flexible and efficient software stack. All ofthese lead to more cost. The expansion of supercomputer
system not only meets the requirements of applications, butalso considers the cost-effectiveness of all the investment of
manpower and money. So we want to find a metric tomeasure the cost-effectiveness of investment forsupercomputer under the storage constraint.
We generalize a new speedup called general storage
speedup to try to solve the problem. This generalization isincreasingly important for modern petascale especially future
exascale supercomputing systems as the storage systems
employed can be more costly.
Based on the costup presented by D. A. Wood and Mark
Hill [9], we introduced costups to analyze the storage
scalability which is more cost-effective related to various I/Oarchitecture.
Definition 3 (Storage Costup). When program Q runs on
a parallel system from one processor to P processors, thestorage costup is defined as follows:cos tupS =0

is 0 andf ( x)  g ( x)indicates lim x →∞ f ( x ) g ( x ) is a non-negative constant. Supposef ( x)  g ( x)

S
Sto
P
Sto

818

cost PS
cost1S(10)The cost could include not only the compute system cost
but also the storage system cost. And the cost also include the

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

819

Fig. 5. Distributed and Parallel I/O Architecture.

Fig. 3. Centralized I/O Architecture.
P

sSto 2800

P

10000

sSto

2400

8000

2000

6000

1600
1200

4000

800

O(P)=K

O(P)=KPlgP

400

2000

0
0

1000

2000

3000

4000

5000

P

Fig. 4. Centralized I/O Architecture.

hardware and software cost. We assume that the computesystem cost grows proportionally with the application scaleP.
cos tupS =cost Pcomp + cost Psto cost1comp  P + cost Psto=
cost1comp + cost1sto
cost1comp + cost1sto(11)

Let us write C as cost1sto cost1comp , M as cost Psto cost1sto . So
we have
P + MC
1 + ( M P )C=P
cos tupS =
1+ C
1+ C

(12)

Obviously, M represents storage investment trends which
are closely related to system architecture, market variation,
application requirements and so on.
Definition 4 (General Storage Speedup). When
program Q runs on a parallel system, the general storage
speedup achieved is defined as follows:
GP
S Sto
=

P
S Sto
cos tupS

(13)

0
0

2000

4000

6000

10000 P

8000

Fig. 6. A case about storage speedup which is not affected by the I/O
performance related to distributed and parallel I/O architecture.

abbreviated as CDP I/O architecture). The analyses for each
I/O architecture related to storage speedup and general
storage speedup are as follows.
A. The Analysis of Storage Speedup
Centralized I/O Architecture
Figure 3 shows a centralized I/O architecture which isoften used in small-scale supercomputers, such as NAS. The
supercomputer storage system with centralized I/Oarchitecture is easy to be configured and managed, but has apoor scalability. It can only scale up instead of scaling out,
since the storage system cannot scale horizontally to multiple
servers but only to enhance the performance of a single
server vertically. Therefore the performance of storage
system is severely limited by the I/O architecture. As the
CN0

CN1

CN2

...

CNP

Storage
Server

（a）

...

CNP

CN3

According to (12), we have
GP
=
S Sto

1+ C
P
S Sto
P (1 + ( M P )C )Interconnection Network(14)

Since C is a constant, the general storage speedup isdetermined by P, M and storage speedup.
M denotes the growth rate of the storage system cost withthe compute system scaling. The general storage speedup
allows us to study both the performance effect and cost effectof storage system to the whole parallel system.
IV. I/O ARCHITECTURE ANALYSIS
According to the interconnection relationship between
compute and storage in supercomputing systems, thearchitecture of supercomputing storage systems can bedivided into three categories: centralized architecture,
distributed and parallel architecture, centralized, distributedand parallel architecture (The following in the paper is

Storage
Server

CN0

Storage
Server

CN1

CN2

...
CN3

Interconnection Network
ION1

ION2

ION3

...

IONi

Interconnection Network
Storage
ServerStorage
Server...

Storage
Server（b）

Fig. 7. Centralized, Distributed and Parallel Architecture.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

computing scalability is not constrained by the performanceof I/O system, as the Figure 6 shows. With the node number
increasing, the performance of storage system expands
proportionally.

P

sSto 2800
2400
2000
1600
1200
800

O(P)=KPlgP

400
0
0

1000

2000

3000

4000

5000

P

Fig. 8. A case about storage speedup which is constrained by the I/O
perfermance related to centralized, distributed and parallel architecture.

storage system with centralized I/O architecture cannot scaleout to more storage nodes, we obtain O ( P )  Θ ( P ) . According
to definition 2, the supercomputer with centralized I/Oarchitecture is an incremental system whose parallel
computing scalability is constrained by the performance of
I/O system. Figure 4 shows us a case about supercomputerwith centralized I/O architecture whose storage speedup isbounded by the I/O performance.
Distributed and Parallel I/O Architecture
Figure 5 shows a distributed and parallel I/O architecture.
A supercomputer storage system with distributed and parallel
I/O architecture usually refers to the architecture in which
each compute node with built-in storage or directed attachedstorage server. Each compute node has file system which canprovide I/O service to itself and other compute nodes. Due tothis special nature, the implementations of the parallel filesystem are complicated due to I/O scheduling and data global
consistency. And the storage servers have different positions
to the compute nodes themselves and other compute nodes;
therefore the system load balance also becomes a problem.
So, distributed and parallel I/O architecture is usuallyadopted by supercomputers with small or medium scale.
For distributed and parallel I/O architecture, the storage
nodes and compute nodes scaling equally. So we
obtain O ( P ) = Θ (1) . The supercomputer with distributed andparallel I/O architecture is a constant system and the parallel
5000

Centralized, Distributed and Parallel Architecture
Figure 7 shows a CDP I/O architecture which is dominant
in the supercomputers of TOP500 [1]. For the Medium-sized
supercomputers (the amount of compute nodes reaches 103),
compute nodes and storage servers are connected directly
through interconnection network as shown in the Figure 7(a).
Parallel storage system consists of storage servers which areusually installed with parallel file system, such as Lustre,
PVFS and so on, while compute nodes access the storage
system by the client of parallel file system. Typical systems
are Titan, Tianhe-1A and so on. With the supercomputer’sdevelopment, I/O nodes (ION) are inserted between computenodes and parallel storage systems to provide the function of
I/O forwarding and management, as the Figure 7(b) shows.
On the one hand, the amount of compute nodes can increase
continually without the limitation of the client amount ofparallel file systems. On the other hand, I/O performance canbe improved by I/O scheduling and caching. All of these are
attributed to the introduction of I/O nodes in the systems. The
typical systems are IBM Bluegene series and Tianhe-2.
Although the system has been improved by the I/O nodes, theessence of I/O architecture doesn’t change. Thus, to simplify
the model, I/O node layer is omitted in the subsequent
analysis.
In the CDP I/O architecture, the storage system can not
only scale up, but also scale out. But they still have
limitations, first, for a specific supercomputer the scale ofstorage system is fixed within a certain time period; second,
parallel file system usually has up limits on the number ofstorage nodes, the aggregate performance and the storage
capacity. So we can also obtain O ( P )  Θ ( P ) . According todefinition 2, the supercomputer with CDP I/O architecture isan incremental system whose parallel computing scalability
is constrained by the performance of I/O system as shown by
Figure 8.
The performance of storage system with CDP I/O
architecture is much higher than the system with centralized
I/O architecture, so the speedup affected by the I/O
4.0

I/O Architecture
centralized
distributed and parallel
CDP

M=X

3000

f

M

M=dX
2000

M=aX/(bXlgX+c)

1000

I/O Architecture
centralized
distributed and parallel
CDP

3.5

General Storage Speedup

4000

820

3.0
2.5
2.0
1.5
1.0
0.5

0
0

1000

2000

3000

4000

5000

P
Fig. 9. Examples of changing law of parameter M under different I/Oarchitectures when the storage system scaling with compute system. a, b, c, dand f are different constants.

0.0
0

1000

2000

3000

4000

5000

P
Fig. 10. Examples of changing law of general storage speedup under different
I/O architectures when the storage system scaling with compute system.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

performance is not obvious until the parallel application
increases to a huge scale.
B. The Analysis of General Storage Speedup
Storage speedup is used to evaluate practical speedup ofparallel application under the constraint of I/O system, while
general storage speedup can be used to show how
cost-effective when the storage system of supercomputerscaling. Since the market variation and the private cost detailsof system construction, there is no specific system to be
described. Instead in this subsection we illustrate someexamples of general storage speedup related to typical I/Oarchitectures.
Figure 9 shows the examples of M function which is a key
factor to reveal the variation ratio of storage cost with thescale of parallel application increasing. Due to a wide rangeof storage devices and complex price variability, these cases
are only used to reveal the possible laws of M function under
different I/O architectures. Different scaling methods needdistinct types and quantities of hardware and softwareequipment, and this lead to different variation trends of M. M
can be measured according to specific system at a specifictime.
Figure 10 shows the examples of general storage speedupof different I/O architectures with the scale of parallel
application increasing. Since the different extension methodsof supercomputer storage system under different I/Oarchitecture, general storage speedup reflects all kinds ofcost-effective characteristics of storage systems. According
to the definition, the larger value of general storage speedup
represents a higher cost-effectiveness.
The examples in the figure 10 shows that the general
storage speedup decreases slowly with the compute systemand storage system scaling for the centralized I/Oarchitecture and CDP I/O architecture, and the storage
system with CDP architecture is more cost-effective thancentralized I/O architecture. Since storage system scales withcompute system proportionally under distributed and parallel
I/O architecture, the general storage speedup isapproximately to be a stable value. So, when the storage
system expands to a larger scale, the system with distributedand parallel I/O architecture has better and more stablecost-effectiveness.
V. CONCLUSIONSThis paper introduces a new speedup metric called storage
speedup taking into account the I/O performance constraint.
The new metric unifies the compute and I/O performance,and evaluates practical speedup of parallel application under
the limitation of I/O system. The existing parallel systems areclassified and analyzed according to the storage speedup, andthe suggestions are acquired on system design andapplication optimization. Based on the storage speedup, ageneral storage speedup is generalized to evaluate thecost-effectiveness of the storage system by considering not
only speedup but also costup. Through the case studies, theeffectiveness of the two new speedup metrics is validated.
In the future, our efforts will mainly focus on thefollowing aspects. For the storage speedup, we will refine thetheory and explore more factors. At the same time, we will
make further research on characteristics of massively parallel

821

applications and supercomputer storage systems to improve
the scalability of system.
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D. E. Culler, A. Gupta and J. P. Singh, Parallel Computer
Architecture: A Hardware/Software Approach, Morgan Kaufmann
Publishers Inc., San Francisco, CA, USA, 1997.
G. M. Amdahl, “Validity of the single processor approach toachieving large scale computing capabilities,” in Proc. AFIPS '67,
1967, pp.483-485.
J. L. Gustafson, “Reevaluating amdahl’s law”, Communications of theACM, vol. 31, no. 5, pp. 532–533, 1988.
X. Sun and L. Ni, “Scalable problems and memory-bounded
speedup,” Journal of Parallel and Distributed Computing, vol. 19, no.
1, pp. 27–37, 1993.
Z. Wang, “Reliability speedup: an effective metric for parallel
application with checkpointing”, in Porc. 2009 International
Conference on Parallel and Distributed Computing, Applications andTechnologies. 2009, pp. 247–254.
X. Yang, J. Du, and Z. Wang, “An effective speedup metric formeasuring productivity in large-scale parallel computer systems”, The
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Guilin Cai is a doctor student in the College ofComputer at National University of Defense
Technology in China. She received the B.S. and M.S.
degrees in computer science from National Universityof Defense Technology in 2005 and 2010, respectively.
Her main research interests include high performancecomputing, cyber security and proactive defense.
Wei Hu received the B.S. degree from PLA Universityof Science and Technology, China, in 2004, and the
M.S. degree from National University of Defense
Technology, China, in 2010. He currently pursues the
Ph.D. degree in the College of Computer, National
University of Defense Technology, Changsha, China.
His research interests include high performancecomputing and machine learning.

Guangming Liu received the B.S. and M.S. degrees
from National University of Defense Technology,
China, in 1980 and 1986 respectively. He is now aprofessor in the College of Computer, National
University of Defense Technology. His researchinterests include high performance computing, massive
storage and cloud computing.

Qiong Li received the B.S., M.S. and Ph.D. degrees
from National University of Defense Technology,
China, in 1993, 1996 and 2010, respectively. She is
now a professor in the College of Computer, National
University of Defense Technology. Her researchinterests include high performance computing andmassive storage.
Xiaofeng Wang has been working as an assistantprofessor in the College of Computer at National
University of Defense Technology in China. He
received the B.S., M.S. and Ph.D. degrees in computerscience from National University of Defense
Technology in 2004, 2006 and 2009 respectively. His
research interests include trustworthy networks andsystems, applied cryptography, network security.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 2, March 2016

822

Wenrui Dong received the B.S. and M.S. degrees
from National University of Defense Technology,
China, in 2009 and 2011, respectively. He currently
pursues the Ph.D. degree in the College of Computer,
National University of Defense Technology. His
research interests include high performancecomputing and massive storage.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)