Journal Editorial Board Editor-in-ChiefProf. Thomas Byeongnam YOON, PhD.
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ICACT Transactions on the Advanced Communications Technology (TACT) Editors
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
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Dr. Min-Hong Yun, ETRI, Korea
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Dr. Ho-Jin CHOI, KAIST(Univ), Korea
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Dr. Brownson ObaridoaObele, Hyundai Mobis Multimedia R&D Lab , Korea
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Prof. Chwen-Yea Lin, Tatung Institute of Commerce and Technology, Taiwan
Dr. Ting Peng, Chang'an University, China
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Prof. kirankumar M. joshi, m.s.uni.of baroda, India
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Copyright ⓒ 2014 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 ⓒ 2014 GiRI (Global IT Research Institute)

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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Volume. 3 Issue. 3

1

Automatic Music Genre Classification Using Timbral Texture and Rhythmic Content Features

6J

434

Babu Kaji Baniya, Deepak Ghimire, Joonwhoan Lee
Division of Computer Science and Engineering Chonbuk National University, Jeonju 761-756, South Korea

2

Performance Analysis of Power Allocation and Relay Location in a Cooperative Relay Network

444

Muhammad Hasan Danish Khan, Mohammed S Elmusrati
Communication and System Engineering Group, University of Vaasa, Finland

3

Road Side Unit Assisted Stochastic Multi-hop Broadcast Scheme for Instant Emergency Message Propagation

450

Xing Fan*, Bo Yang*, Ryo Yamamoto**, Yoshiaki Tanaka*,***,* Global Information and Telecommunication Institute, Waseda University Shinjuku-ku, Tokyo, Japan. ** Research Institute for Science andEngineering,

4

Greening Potential Estimation of Data Network Equipment
Yuhwa Suh*, Jongseok Choi*, Changho Seo**, Yongtae Shin** Department of Computer Software, Soongsil University, Seoul, South Korea. ** Department of Computer Science and Engineering, Seoil University,
Seoul, South Korea

458

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

434

Automatic Music Genre Classification Using
Timbral Texture and Rhythmic Content Features
Babu Kaji Baniya*, Deepak Ghimire, Joonwhoan Lee
Division of Computer Science and Engineering
Chonbuk National University, Jeonju 761-756, South Korea
everwith_7,deep,chlee@jbnu.ac.kr

Abstract— Music genre classification is a vital component forthe music information retrieval system. There are two importantcomponents to be considered for better genre classification, which
are audio feature extraction and classifier. This paper
incorporates two different kinds of features for genre classification,
timbral texture and rhythmic content features. Timbral texture
contains the Mel-frequency Cepstral Coefficient (MFCC) withother several spectral features. Before choosing a timbral feature
we explore which feature contributes a less significant role on
genre discrimination. This facilitates the reduction of feature
dimension. For the timbral features up to the 4-th order central
moments and the covariance components of mutual features are
considered to improve the overall classification result. For therhythmic content the features extracted from beat histogram are
selected. In the paper Extreme Learning Machine (ELM) withbagging is used as the classifier for classifying the genres. Based on
the proposed feature sets and classifier, experiments are
performed with two well-known datasets: GTZAN and the
ISMIR2004 databases with ten and six different music genres,
respectively. The proposed method acquires better andcompetitive classification accuracy compared to the existing
approaches for both data sets.
Keyword— Classification, music genres, ELM (Extreme
Learning Machine) with bagging, covariance matrix, timbral
texture, rhythmic contents

I. INTRODUCTIONTUTOMATIC music genre classification is an important for the
information retrieval task since it can be applied forpractical purposes such as efficient organization of datacollections in the digital music industry. There have been
several well-known distinct approaches put forward on this.
Still, efficient and accurate automatic music information
processing remains as the key issue, and it has been consistently
Manuscript received May 9, 2014. This work was partially supported by a
National Research Foundation of Korea grant funded by the Korean
government (2011-0022152) and PK21PLUS project.
Babu Kaji Baniya is with the Computer Engineering Department ofChonbuk
National University,
Jeonju, South
Korea (e-mail:everwith_7@jbnu.ac.kr).
Deepak Ghimire is with the Computer Science and Engineering Departmentof Chonbuk National University, Jeonju, South Korea (e-mail:deep@jbnu.ac.kr).
Corresponding author: Joonwhoan Lee is with the Computer Engineering
Department of Chonbuk National University, Jeonju, South Korea (phone:+82-10-9855-2406; Fax: +82-63-270-2406; e-mail: chlee@jbnu.ac.kr).

attracting the attention of a growing number of researchers,
musicians, and composers. A current challenging topic in
automatic music information retrieval is the problem oforganizing, describing, and categorizing music contents on theinternet [1]. Although music genre classification is done
manually, sometimes it is difficult to precisely define the genreof music content. The reason for such difficulties is due to factthat music is a state of art that evolves, where composers andmusicians have been influenced by the music of other genres.
Despite these difficulties, there are still some possibilities thatremain for genre classification. The audio signals of music
belonging to the same genre mean they share the certain
common characteristics, because they are composed of similartypes of instruments, having similar rhythmic patterns, andsimilar pitch distributions [2]. The extracted features must becomprehensive (representing music very well), compact, andeffective.
The overview of our music genre classification is shown in
Fig.1. It depicts the backbone of genre categorization. There are
two associated problems that need to be addressed in genre
classification, i.e., feature extraction and classification. The firststage is to extract the meaningful and relevant features fromaudio that could sufficiently discriminate the music genre. The
next stage is to classify the genre based on the extracted features.
In our method the extreme learning machine (ELM) combinedwith bagging is used as a classifier. Several bags of the dataset
are constructed and each bag is trained using individual ELMs.
The final decision is made based on the majority voting score.
ELM is an unstable classifier, therefore ELM combined withbagging increases the stability, as well as generalization
performance of the classifier.
For constructing a robust music genre classifier, extracting
features that allows direct access to the relevant genre-specificinformation is crucial. Most musical genre classification
systems utilize the low-level spectral features of the short time
audio signal in the range of 10ms to 100ms, such as pitch
extraction, mel-frequency cepstral coefficients (MFCCs), andother timbral texture features [3]. Then the short-time low-level
spectral features are integrated on long duration. The mostwidely used integrating method is mean and standard deviationof the short time feature [4, 5].
In this paper, we attempt to implement timbral texture
features which represent short-time spectral information, andrhythmic content features like beat histogram which represent
Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

the long-term properties. Timbral texture features include
spectral centroid, flux, rolloff, flatness, energy, zero crossing,and MFCCs, respectively. We divide the timbral texture
features into two groups for convenience; the first group (FG1)does not include MFCCs and the second group (SG2) includes
only MFCCs. After the frame-wise extraction of each timbral
texture feature among FG1 from all genres of music, the nextstage is to calculate the standard deviation for all genres ofmusic. The aim of calculating the standard deviation for eachfeature in whole genres is to find out which feature isinsignificant for genre discrimination. The feature which has asmall value of standard deviation contributes the insignificant
impact on genre discrimination. Based on the standard deviation
value, we considered a limited number of timbral features.
A Similar procedure has been preceded for the SG2 of MFCC
features as well. Out of thirteen, twelve coefficients givemeaningful standard deviation values. This shows that twelve
MFCC coefficients are meaningful for genre classification. For
our experiment, we consider both the first seven and twelve
coefficients separately for genre classification.
Timbral texture features are based on short time low-level
spectral components that are integrated on long duration. The
integration method is mean and standard deviation. Beside this,high order moments such as skewness and kurtosis are also
implemented for integrating the frame-wise features. The aim ofconsidering the high order moment is that even if there are thesame values of mean and standard deviation, the position oflocation (shape of skewness and kurtosis position) could be
different because each feature cannot be modelled by theGaussian distribution.
Ultimately, the high order moment increases theclassification accuracy when it is combined with other low level
spectral features. It generally provides the supplementary
statistical information for the audio signal. Skewness is ameasure of the asymmetry of the data distribution regarding thesample mean, which represents the relative disposition of thetonal and non-tonal components of the audio signal. Kurtosis is
the measure for the degree of peakedness or flatness of a
distribution [6]. Therefore we have considered 4n components
for the n texture features.
In addition we propose to use the covariance components ofmutual timbral texture features. Each of them gives thestatistical property of mutual random variables associatedfeatures. For each song the covariance values of selectedfeatures from FG1 and SG2 are calculated, respectively.
Therefore, additional n(n-1)/2 components are included for n
timbral texture features.
Note that we can have 4n+ n(n-1)/2 for n features, which
increases rapidly as the number of features increases. This is thereason why we remove the relatively less important features bychecking the corresponding variances.
Rhythm is a property of an audio signal that represents achanging pattern of timbral and energy over time. Rhythmic
features characterize the movement of music signals over timeand contain such information as the regularity of the rhythm,
beat, tempo, and time signature. The feature set for representing
the rhythmic structure is based on detecting the most salient
periodicities of the signal and it is usually extracted from the435

Fig.1. Overview of music genre classification

beat histogram. Rhythmic content features contain relative
amplitude of the first and second histogram peaks, period offirst and second peaks, ratio of the amplitude of the second peak
divided by the amplitude of first peak, and overall sum of thehistogram.
There are different types of classifiers which have been
proposed for genre classification. We prefer the distinct
classifier than the previously applied one. Extreme Learning
Machine (ELM) is a recently proposed classifier which has highgeneralizing capability and takes minimum time for training.
The reason for selecting ELM is that it does not require a tuning
parameter, has the smallest training error, and is free from thelocal maxima problem. However, ELM is unstable because theweights connected with hidden units are randomly determined.
Therefore, we combine ELM with bagging in order to increase
the stability. Bagging is almost always more accurate than asingle classifier. Other classifiers like K-Nearest Neighbour(K-NN), Neural Networks (NN) have some drawbacks. In caseof neural network, when learning rate is too small, the algorithm
converges very slowly. It also requires a tuning parameter andprobably faces the local maxima problem. K-Nearest Neighbour
is a simple nonparametric classifier. It is proven that the error of
K-NN’s is twice large than Bayesian error rate.
This paper is organized as follows. A review of related work
is provided in section II. Feature extraction is the critical portionof genre classification; and is describes in section III. Section IV
deals with the classifier, similarly section V explains theexperimental setup and data preparation, and section VI
explains the result and analysis. Finally, section VII describes
the conclusion of the proposed method and future work of thegenre.
II. RELATED WORK
Many different features have been introduced for music
genre classification. The primary aim of feature extraction is toacquire a meaningful representative part of music in the reduced
form. The acoustic features include tonality, pitch, beat, andsymbolic features extracted from the scores, and text-based
features can be obtained from the song lyrics. In this paper, weonly focus on timbral texture and rhythmic content which are
sub-groups of content-based features.
The content-based acoustic features are divided into timbral
texture features, rhythmic content features, and pitch content
features [7]. Timbral features are often calculated for every
short-time frame of sound based on the Short Time Fourier
Transform (STFT) [8]. Timbral texture features contain MFCCs,
spectral centroid, spectral flatness, spectral flux, spectral rolloff,
zero crossing, energy, and Linear Prediction Coefficients (LPCs)[7, 8]. These features are widely used in different applications
based on the requirement of applications. MFCCs have been
Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

extensively used in speech recognition [8]. Later, MFCC
features are used for discriminating the music and speech aswell. Rhythmic content features possess information about
continuity of rhythm, beat and tempo. Tempo and beat tracking
are excessively used in music search and retrieval systems. The
tempo value is a number which represents the speed of music ormusic measured by beats per minute (bmp) [9, 10]. The pitch
content feature deals with frequency information of music.
Bergstra et al. [11] extracts the several timbral texture
features like MFCCs, spectral centroid, spectral flux, spectral
rolloff, zero crossing, energy, and Linear Prediction
Coefficients. These features are almost similar with the features
used in [3, 5]. AdaBoost is used as a classifier.
C.-H. Lee et al. [12] considers the Octave-Based Spectral
Contrast (OSC) and MFCC for feature extraction. There is arange of nine different frequencies in octave-based spectral
contrast. Music genres are classified by using Linear
Discriminant Analysis (LDA). Recently, Seo et al. [13] also
implemented the Octave-Based Spectral Contrast (OSC) forfeature extraction. Beside this, he consider the high ordermoment for improving the performance of classification
accuracy. The genre classification is performed by using
Support Vector Machines (SVM).
Li et al. [1] mention several audio feature extraction
methodologies. Later, he proposed a new approach for feature
extraction, i.e. Daubechies Wavelet Coefficients Histograms(DWCHs). The effectiveness of this new feature is comparedusing various machine learning algorithms, SVMs, Gaussian
Mixture Models (GMMs), K-NNs, and LDAs.
The spectral similarity of the timbral texture feature is
described by Pampalk et al. [14]. The audio signal is chopped
into thousands of very short frames and their order in time isignored. Each frame is described by MFCCs. The large set offrames is summarized by a model obtained by clustering theframes. The distance between two pieces is computed bycomparing their cluster models. Later, GMM is considered forgenre classification.
Tzanetakis and Cook [7] proposed a comprehensive set offeatures for direct modelling of music signals and explore thedifferent applications of those features for musical genre
classification using K-Nearest Neighbor and GMM. Other
researchers like Lambrou et al. [15] use statistical features in thetemporal domain as well as three different wavelet transformdomains to classify music into rock, piano, and jazz.Soltau et al.
[16] propose an approach of representing temporal structures of
input signals. He shows that this new set of abstract features canbe learned via artificial neural networks and can be used formusic genre identification. Deshpande et al. [17] use Gaussian
Mixtures, SVM, and K-Nearest Neighbor to classify the music
into rock, piano, and jazz based on timbral texture features.
III. FEATURE EXTRACTION
Feature extraction encompasses the analysis and extractionof meaningful information from audio in order to obtain acompact and concise description that could be machine readable.
Features are usually selected in the context of a specific task anddomain. The features that are used in our research are divided

436

Fig. 2. Overview of Timbral texture features extraction of audio.

into two categories, the timbral texture feature and rhythmic
content feature.
A. Timbral Texture features
These features are used to differentiate mixture of sounds thatpossibly have similar pitch and rhythm [8]. The features used torepresent timbral texture are based on standard features
proposed for music-speech discrimination [18]. To extract the
timbral features, audio signals are first divided into frames byapplying a windowing function at fixed intervals. The windowfunction of this research is hamming window which helps toremove the edge effects. Timbral texture features in Fig.2 havebeen computed and later we calculated different statisticalvalues like mean, standard deviation, skewness, kurtosis, andcovariance matrix from feature values. The mean (µ) andstandard deviation (σ) for frame-wise feature values (Xn) in a N-frame song are given by
1 N
Mean( µ ) =(1)∑ Xn
N n =11 N
2(2)
Std (σ ) =∑ (Xn − µ)=1nNThe skewness is a measure of asymmetry of the distribution,
which can represent the relative disposition of the tonal andnon-tonal components of each band. If the tonal components
occur frequently in a band, the distribution of its spectrum willbe left-skewed otherwise it will be right-skewed.
Mathematically, the skewness in a song can be defined as
N
3∑ (Xn − µ)Skewness = n=1(3)3( N − 1)σKurtosis is a measure of whether the data are peaked or flat
relative to a normal distribution. That is, data sets with highkurtosis tend to have a distinct peak near the mean. It is difficult
to specify the exact contribution of kurtosis in music genre
classification [13]. However, the kurtosis measure can sketch
the effective dynamic range of the audio spectrum.
Mathematically it can be defined as
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

N
4
∑ ( Xn − µ)Kurtosis = n=1(4)4 −3( N − 1)σCovariance is measured between two random variables orfeatures. The aim of considering the covariance is usually to see
if there is any relationship between the random variables. It isuseful to measure the polarity and the degree of the correlation
between two features. The covariance of two features Xn and Yn
in a song is given as

Cov( X n , Yn ) =1 N∑ ( X −µ )(Y −µ ) ,N n=1 n X n Y(5)where µ X and µY are corresponding means of Xn and Yn ,respectively. For n timbral texture features we acquiredn( n − 1) / 2 mutual covariance values.
We consider two groups of timbral texture features FG1 andSG2 described as1) FG1 features
Spectral flux: It is defined as the variation value of thespectrum between the adjacent two frames in a short-time
analyze window. It measures how quickly the power spectrum
changes and is used to determine the timbral of an audio signal.
N
2
Ft = ∑ ( Nt [n] − Nt −1[ n] )(6)
n =1
where Nt[n] and Nt-1[n-1] are normalized magnitudes of theFourier transform at the present frame t, and previous frame t-1
respectively.
Spectral centroid: The spectral centroid is described as thegravity centre of the spectral energy. It determines the point in
the spectrum where most of the energy is concentrated and iscorrelated with the dominant frequency of the signal. It isclosely related to the brightness of a single tone.
N∑ M t [n] * n

C t = n =1
N
∑ M t [n]

(7)

n =1

where Mt[n] is the magnitude of the Fourier transform at frame t
and frequency bin n.
Short Time Energy: The short time energy measurement of
an audio signal can be used to determine voiced and unvoiced
speech. It can also be used to detect the transition from unvoiced
to voice and vice versa [19]. The energy of voiced speech ismuch greater than the energy of unvoiced speech. Short-timeenergy can be defined asN
2
En = ∑ [ x ( m) w( n − m )]
(8)
m =1

where, x(m) is discrete time audio signal, n is time index ofshort-time energy, and w(m) is window of length N.
Zero Crossing: It is a process of measuring the number oftimes in a given time interval that the amplitude of speech
signals crosses through a value of zero. It is random in nature.
Moreover, the zero crossing rate for unvoiced speech is greaterthan that of voice speech. Moreover, it is often used as a crucial
parameter for voiced/unvoiced classification and end point
detection.

437

N
Zt = 1 ∑ | sgn( x[ n] − sgn( x[ n −1])|(9)
2 n =1
where sgn is a short notation of sign function. The sgn functionis 1 for positive arguments and 0 for negative arguments andx[n] is the time domain for signal for frame t.
Spectral Rolloff: It is a measure of the bandwidth of the audio
signal. It is the fraction of bins in the power spectrum in which
85% of the power is at lower frequencies.

Rt
N∑ Mt [n] = 0.85 ∑ Mt [n]

n=1

(10)

n−1

where Mt[n] is the magnitude of the Fourier transform at frame t
and frequency bin n.
Spectral flatness: It is used to characterize an audio spectrum.
Spectral flatness is typically measured in decibels, and provides
a way to quantify how tone like a sound is, as opposed to being
noise-like.

 1 N −1∑ ln x(m)  N m=0


exp 
F=

(11)

1 N −1∑ x(m)
N m=0

where x(m) represents the magnitude of bin number m.
From the above mentioned features in FG1, the normalized
standard deviation of all the data has been calculated. Since thestandard deviation generally depends on the mean value ingeneral, the standard deviation is divided by corresponding
mean to find out the less important features. Note that a smaller
value of the standard deviation means a smaller change in thevalues of the frame-wise timbral texture feature. This means any
derived central moments from the feature and the covariancewith the feature is not significant for the discrimination of music
genres. Therefore we removed such features to reduce thefeature dimension.
Spectral centroid, flux, short time energy, and zero crossing
possess large normalized standard deviations compared to therolloff and flatness as shown in Table IV. We only consider fourfeatures (Spectral centroid, flux, short time energy, and zerocrossing) and their mean, std, skewness, kurtosis and n(n-1)/2
covariance components, respectively. The feature dimension is
given in Table I.
2) SG2 Features: Mel-Frequency Cepstral Coefficients
TABLE IFEATURE DIMENSION OF FOUR DIFFERENT TIMBRAL TEXTURE FEATURES

Mean
4

Std. dev.
4

Skew.
4

Kurt.
4

Cov.
6

Total features
22

Earlier MFCCs widely used in automatic speech recognition
later on evolved into one of the prominent techniques in every
domain of audio retrieval. They represent most distinctive
information of signal. MFCCs have been successfully
implemented to timbral measurements by H. Terasawa [20].
We took the MFCC feature based on the paper thatmentioned the mel frequency cepstral coefficients for musicmodelling [21]. Fig. 3 shows the process of creating MFCC
features. The first step is to divide the audio signal into frames,by applying a window function at fixed intervals. The aim is to
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

model small (having 10ms) sections of the signal that are
statistically stationary. The window function is hamming
window. We generate the cepstral feature vector for each frame.
The next step is to take the Discrete Fourier Transform (DFT).
The phase information has been discarded because perceptual
studies have shown that the amplitude of the spectrum is much
more important than the phase. The logarithm of the amplitude
spectrum has been taken because the perceived loudness of asignal has been estimated to be approximately logarithmic. The
next stage is to smooth the spectrum and emphasize
perceptually meaningful frequencies. This is achieved bycollecting the spectral components into frequency bins. As weknow, lower frequencies are perceptually more important thanthe higher frequencies. Therefore, the bin spacing follows theso-called ‘Mel’ frequency scale [22]. The components of the
Mel-spectral vectors calculated for each frame are highly
correlated. In order to reduce the number of parameters in the
MFCC, we need to apply a transform to the Mel-spectral vectors
which decorrelates their components. The cepstral features ofeach frame are obtained by using DCT.
by applying a window function at fixed intervals. The aim is
to model small (having 10ms) sections of the signal that are
statistically stationary. The window function is hamming
window. We generate the cepstral feature vector for each frame.
The next step is to take the Discrete Fourier Transform (DFT).
The phase information has been discarded because perceptual
studies have shown that the amplitude of the spectrum is much
more important than the phase. The logarithm of the amplitude
spectrum has been taken because the perceived loudness of asignal has been estimated to be approximately logarithmic. The
next stage is to smooth the spectrum and emphasize
perceptually meaningful frequencies. This is achieved bycollecting the spectral components into frequency bins. As weknow, lower frequencies are perceptually more important thanthe higher frequencies. Therefore, the bin spacing follows theso-called ‘Mel’ frequency scale [22]. The components of the
Mel-spectral vectors calculated for each frame are highly
correlated. In order to reduce the number of parameters in the
MFCC, we need to apply a transform to the Mel-spectral vectors
which decorrelates their components. The cepstral features ofeach frame are obtained by using DCT.
There are thirteen coefficients in the mel-frequency cepstral

438

TABLE IIFEATURE DIMENSION OF MFCC CONSIDERING FIRST SEVEN COEFFICIENTS

Mean
7

Std. dev.
7

Skew.
7

Kurt.
7

Cov.
21

Total features
49

TABLE IIIFEATURE DIMENSION OF MFCC CONSIDERING TWELVE COEFFICIENTS

Mean
12

Std. dev.
12

Skew.
12

Kurt.
12

Cov.
66

Total features
114

B. Rhythmic Content Features
Rhythmic content features characterize the movement ofmusic signals over time and contain such information as theregularity of the rhythm, beat, and tempo. For the rhythmic
feature, beat histogram has been taken. It is a compact global
representation of the rhythmic content of audio music. The beathistogram [5] can be obtained by the wavelet decomposition ofa signal and can be interpreted as successive high-pass andlow-pass filtering of the time domain signal. The decomposition
is defined byyhigh [ k ] = ∑ x[ n] g[2k − n](12)nylow [ k ] = ∑ x[ n]h[2k − n ]

(13)

n

where yhigh [ k ] and ylow [ k ] are the output of high-pass andlow-pass filters respectively, and g[n] and h[n] are the filter
coefficients for the high-pass and low-pass filters associated to
the wavelet function for fourth order Daubechies wavelets(DW) [22]. Wavelet Transform deals with the similarity of thedecomposed signal to the octave filter band. Once the signal isdecomposed, the additional signal processing operation isrequired. The building blocks as shown in Fig. 4 are used for thebeat analysis feature extraction.
1) Full Wave Rectification:y[n] = abs ( x[n])(14)where x[n] is the output of the wavelet decomposition at thatspecific scales.

Fig.4. The block diagram of beat histogram for feature extraction

Fig. 3. Mel frequency cepstral coefficients feature extraction of audio.

coefficient. After analysis of the normalized variance weselected 12 out of 13 coefficients. The last coefficient has a verysmall value of the variance as shown in Table V, so that it could
be removed. We try to implement the early seven and twelve
coefficients separately. The first choice is just for reducing thedimension. The different feature dimension of MFCC whileconsidering seven and twelve coefficients are given in table II
and III.

2) Low-Pass Filtering:a[n] = (1 − α ) y[ n] + α a[n − 1](15)
For one-pole filter with an alpha value of 0.99 which is used tosmooth the envelope.
3) Downsampling:b[ n] = a[kn](16)
Downsampling the signal reduces computation for the
autocorrelation calculation without affecting the performance of
the algorithm. The value of k is 16.
4) Normalization (mean removal)
Mean removal is applied in order to make the signal centeredto zero for the autocorrelation stage.
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

c[n] = b[ n] − E[b[ n]](17)5) Autocorrelation
1
d [ k ] = ∑ c[ n] c[ n − k ](18)N nwhere c[n] is periodic signal with period N.
6) Periodicity detection and beat histogram calculation:
There are six different features extracted from the beathistogram. They are relative amplitude of the first and second
histogram peak, period of the first and second histogram peakmeasure in beat per minute (bpm), ratio of the amplitude of thesecond peak divided by the amplitude of the first peak, andoverall sum of the histogram.
IV. CLASSIFIER
Traditionally, all the parameters of the feed-forward
networks need to be tuned and thus there exists the dependency
between different layers of parameters (weights and biases). In
particular the gradient descent-based methods have been used invarious learning algorithms of feed-forward neural networks[23]. However, the weakness of this kind of learning method isthat it is generally very slow due to diverse learning steps andmay easily converge to local minima. They also require many
iterative learning steps in order to obtain better learning
performance.
ELM [24] resolves the problem associated with thegradient-based algorithm by analytically calculating the optimal
weights of single-hidden layer feed-forward neural networks(SLFNs). Where the weights between input layers and thehidden layer biases are arbitrarily selected and then the optimalvalues for the weights between the hidden layer and output layer
are determined by calculating the linear matrix equations.

ɶ hidden nodes, the activation
For N distinct samples and N
function g(x) of the SLFN neural network is defined asNɶ∑ β i g ( wi . x j + bi ) = o j , j = 1, ....., Ni =1

 g ( w1 . x1 + b1 ) ⋯ g ( w Nɶ . x1 + b Nɶ ) 


⋮
⋯
⋮

H=
 g ( w .x + b ) ⋯ g ( w ɶ .x + b ɶ ) 
1 N
1
N N
N  N × Nɶ

β T 
tT 
 1 
1 
β =  ⋮ 
and T =  ⋮ 
β T 
 tT 
 N  N ×m
 Nɶ  Nɶ × m

T

the ith hidden node and the input nodes, β i = [ β i1 , β i 2 , .., β im ]

T

(22)

(23)

From the above equation (21), the target vector T and theoutput matrix of the hidden layer H can comprise a linear
system. Thus, the learning procedure of the network helps tofind the optimal weight matrix β between the output layer andthe hidden layer β can be determined by using theMoore-Penrose generalized inverse of H.
†(24)βˆ = H T
From the above equation (24) we can draw the following
important properties. The first one is that we can take minimum†
training error, because the solution βˆ = H T is one of theleast-square solutions of the general linear system H β = T . In
addition, the optimal βɶ is also minimum norm among thesesolutions. Thus, ELM has the best generalization performancecompared to the typical back propagation network. In summary
the ELM algorithm can be summarized as follows.
Algorithm
ELM:
For
the
given
trainingsetnmℵ = {( xi , ti ) | xi ∈ R , ti ∈ R , i = 1,..., N }, activation function
g(x), and hidden neuron number Nɶ ,1) Assign random input weight wi and bias bi,
i=1,…, Nɶ .
2) Calculate the hidden layer output matrix H.
3) Calculate the output weight β :(19)where wi = [ wi1 , wi 2 ,.., win ] is the weight vector connecting439†

βˆ = H T†

Where H is the Moore-Penrose generalized inverse of hidden
the layer output matrix H.

is the weight vector connecting the i-th hidden nodes and output
nodes, and bi is the threshold of the i-th hidden node. wi.xj
denotes the inner product of wi and xi.
The standard SLFNs with Nɶ hidden nodes with theactivation function g(x) can approximate these N samples withzero error means thatNɶ

∑ || o − t ||= 0 i.e., there exist βi ,
j =1

j

j

wi, and bi such thatNɶ
∑ β g ( wi .x j + bi ) = t j , j = 1,....., Ni =1 i(20)

where tj is the target vector of the j-th input data. Equation (19)can be written as a matrix equation to form a new equation byusing the output matrix of the hidden layer H.
Hβ = T(21)
where

Fig. 5: Block diagram of music genre classification

A. Bagging Algorithm
Bagging [25] is a well-known ensemble learning algorithm thathas been shown to be very effective in improving generalization
performance compared to the individual base models. Breiman
indicated that bagging is a smoothing operation which turns out

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

to be advantageous when aiming to improve the predicative
performance of regression or classification. It is a “bootstrap”
ensemble method that creates bags for its ensemble by training
each classifier on a random redistribution of the training set.
Each classifier's training set is generated by randomly drawing,with replacement; many of the original samples may be repeatedin the resulting training set while others may be left out. Eachbag classifier in the ensemble is generated with a different
random sample of the training set. The algorithm then applies abase classifier to classify each bag. Bagging is almost always
more accurate than a single classifier. Finally the decision istaken by majority voting of all the base classifier results. Fig. 5
shows the overview of genre classification. Our base classifier is
ELM.
The Bagging Algorithm
Inputs: Training set S, based classifier L, integer T (number ofbootstrap sample)
for i = 1 to T {Si=bootstrap sample from S (i.i.d. sample withreplacement)
Ei=L(Si) }*E ( x) = arg max∑1y∈Y i:Ei ( x)= y(the most often predicated label y)

Output: Compound classifier E *training and testing set instead of five-fold cross validation.
VI. RESULT AND ANALYSIS
At first, in order to reduce the dimensionality of the extracted
feature set, the normalized standard deviation of each timbral
texture feature is calculated in both the GTZAN and
ISMIR2004 dataset. As the number of timbral texture feature
increases the dimensionality of the extracted feature set
increases rapidly, therefore we removed the relatively lessimportant features by checking the corresponding normalized
standard deviations. Table IV and V contain the average
normalized standard deviation of the GTZAN and ISMIR2004
datasets. The data seen in table IV indicates that flatness androlloff are less significant for genre classification than the otherfour in FG1. A similar approach is also applied for SG2 of
MFCC coefficients. There is only one MFCC coefficient which
has a relatively small value of normalized standard deviation.
Aside from that coefficient, the remaining twelve coefficientsare useful for genre classification.
TABLE IVNORMALIZED STANDARD DEVIATION OF TIMBRAL TEXTURE FEATURES(EXCLUDING MFCC)

N o r .S td

E n e r gy

C e n tr o id

F lu xZ e r o c r os sing

F la tn e s s

R o llo f f

1 .0 8 5

0 .6 9 0

0 .8 8 6

0 .6 7 1

0 .2 3 9

0 .2 7 4

TABLE V

V. EXPERIMENTAL SETUP AND DATA PREPARATION
Different datasets widely used for music genre classification
are employed for performance comparison. The first dataset(GTZAN) consists of 1000 songs over ten different genres:
Classical, Blues, Hiphop, Pop, Rock, Gazz, Reggae, Metal,
Disco, and Country. Each class consists of 100 songs having
duration of 30s. The dataset was collected by Geroge
Tzanetakis [26]. Each song in the database was stored as a22050Hz, 16bits, and mono audio file. The second dataset is
ISMIR2004 [27] which were used in the Music GenreClassification Contest 2004. This dataset has an unequal
number of distributions of music tracks in each class. It consistsof six different classes: Classical, Pop and Rock, Metal andPunk, Electronic, World, and Jazz and Blues respectively. Thisdataset consists of 1458 music tracks in which 729 music tracks
are used for training and the other 729 tracks for testing. The
audio files are stored in MP3 format having a sampling
frequency of 44.1 kHz, 128-kbps, 16 bit, and stereo files. For
our research, each stereo MP3 file was first converted into a44.1 kHz, 16 bit, mono audio file before feature extraction. Insummary, the music tracks used for training/testing include
320/320 tracks of Classical, 115/114 tracks of Electronic, 26/26
tracks of Jazz/Blues, 45/45 tracks of Metal/Punk, 101/102
tracks of Rock/Pop, and 122/122 tracks of World music genre.
A five-fold cross validation scheme is used to evaluate theperformance of the proposed system in the GTZEN dataset
whereas in order to compare our proposed method with theresults from the ISMIR2004 Music Genre Classification
Content, our experiment on the ISMIR2004 genre dataset used
the same training and testing set as in the contest. In the contest,
the classification performance is evaluated based on 50:50

440

NORMALIZED STANDARD DEVIATION OF MFCC
MFCC Coefficients

1

2

3

4

5

6

7

Normalized Standard Deviation

2.03

2.31

2.36

2.43

2.49

2.38

2.43

MFCC Coefficients (contd.)

8

9

10

11

12

13

Normalized Standard Deviation

2.24

2.28

2.48

2.29

1.60

0.02

The genre classification result of the GTZAN and
ISMIR2004 datasets is shown in table VI and VII respectively.
Several experiments have been conducted among the different
feature sets. The first experiment was conducted within timbral
texture features in FG1 like spectral centroid, flux, energy, andzero crossing for genre classification (excluding MFCC). The
second and third experiments were only conducted for SG2 withseven and twelve mel-frequency cepstral coefficients (feature
dimension shown in table II and III respectively). The fourthand fifth experiments only considered mean, standard deviation,
skewness, and kurtosis of timbral texture feature including
seven and twelve MFCC coefficients separately with therhythmic content feature. The final experiment was conductedtaking the covariance matrix and all other features. The
experiment was performed into two different steps to find theclassification accuracy in regard to minimum (seven MFCC
coefficients) and maximum (twelve MFCC coefficients) feature
dimensions considering mean, standard deviation, skewness,
and kurtosis.
The combination of different feature sets gives different
classification accuracy. The feature extracted from FG1 ofenergy, centroid, flux, and zero crossing gives 68.33 % of
accuracy. Similarly, SG2 (MFCC feature sets) with seven and

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

twelve coefficients give 64.62% and 66.26% accuracy
respectively. This experimental result shows that seven or
eleven coefficients of MFCC do not make a big difference ingenre classification. We also tried to find out the overall impact
of classification accuracy with covariance components. The
classification accuracy of GTZAN dataset without covariancecomponents comes around 78.26% (7-MFCCs) and 80.21%(12-MFCCs) respectively. Among them, maximum accuracy is
obtained while combining the covariance components withother feature sets. The classification accuracy of the GTZAN
dataset increases from 80.21% to 85.58% while including
covariance components. Similarly, ISMIR2004 classification
accuracy also increases from 81.53% to 86.46%. Hence,covariance components had significant impact in improving the
genre classification.

441

Chang-Hsing Lee’s method [26] and better than all otherapproaches including the ISMIR Music Genre Classification
Contest (classification accuracy 84.07%) shown in table IX.

TABLE VI
CLASSIFICATION ACCURACY (CA) OF GTZAN DATASET IN DIFFERENT FEATURE
SETS

Feature set

ELM(CA)

Energy+Centroid+Flux+Zerocrossing

68.33%

MFCC (7 coff)

64.62%

MFCC (12 coff)

66.26%[ECFZ+MFCC, 7 coff.] [without covariance]+beat histogram

78.26%[ECFZ+MFCC, 12 coff. ] [without covariance]+beat histogram

80.21%[ECFZ+MFCC , 7 coff.] [with covariance]+beat histogram

84.52%[ECFZ+MFCC, 12 coff.] [with covariance]+beat histogram

85.15%

Fig. 6: Number of bags Vs classification accuracy of ZIGEN dataset.

TABLE VII
CLASSIFICATION ACCURACY (CA) OF ISMIR2004 DATASET IN DIFFERENTFEATURE SETS

Feature set

ELM(CA)

Energy+Centroid+Flux+Zerocrossing (ECFZ)

73.62%

MFCC (7 coff)

66.58%

MFCC (12 coff)

68.78%(ECFZ+MFCC (7 coff.)) (without covariance)+beat histogram

79.65%(ECFZ+MFCC (12 coff.)) (without covariance)+beat histogram

81.53%TABLE VIIICOMPARISON OF CLASSIFICATION ACCURACY WITH OTHER APPROACH OFGTZAN DATASETS (OUR APPROACH BASED ON FIVE-FOLD CROSS(ECFZ+MFCC (7 coff.)) (with covariance)+beat histogram

85.15%VALIDATION(ECFZ+MFCC (12 coff.)) (with covariance)+beat histogram

86.46%

In our approach, the extreme learning machine combinedwith bagging algorithm is used for the classification of the music
genre. Before we applied the ELM with bagging as a classifier,
we attempted to find out how many bags were needed to obtainmaximum classification accuracy. There were twenty three bags
combined in case of the GTZAN dataset to get maximum
classification as shown in Fig.6. In the ISMIR2004 dataset
maximum classification accuracy was achieved when twenty
five bags were used as shown in Fig.7.
Table VIII compares our proposed method with otherapproaches in terms of average classification accuracy in the
GTZAN dataset. It is clear that our proposed method achieves
the classification accuracy of 85.15% which is better than otherapproaches. Similarly, Table IX shows the comparison resultswith previous different approaches as well as the ISMIR2004
Music Genre Classification Contest. The classification accuracy
is 86.46%. It is also comparatively competitive with

Fig.7: Number of bags Vs classification accuracy of ISMIR2004 dataset.

Reference
Our approach
Jin S. Seo [11]Bergstra et al [9]Li et al.[1]Tzanetakis [5]CA
85.15%
84.09%
82.50%
78.50%
61.00%TABLE IXCOMPARISON OF CLASSIFICATION ACCURACY WITH OTHER APPROACH OF
ISMIR2004 DATASETS

Reference
Our approach (ECFZ+MFCC+beat histogram)
Chang-Hsing Lee[10]Jin S. Seo [11]Pampalk et al. [12]Bergstra et al [9]Our approach (ECFZ+MFCC+beat histogram)CA
86.46%
86.83%
84.90%
84.07%
82.34%
86.46%

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

To get a better picture of the classification accuracy of anindividual music genre, the confusion matrices are given. The
confusion matrix is n x n matrix, at which each column of thematrix represents the instances in a predicted class, while eachrow represents the instances in an actual class. The diagonal
entries of the confusion matrix are the rates of music genre
classification that are correctly classified, while the off-diagonal
entries correspond to misclassification rates.
Table VIII shows the confusion matrix of the GTZAN
dataset. The genres are arranged in the order of Classical (Cl),Blues (Bl), Hiphop (Hi), Pop (Po), Rock (Ro), Jazz (Ja), Reggae(Re), Metal (Me), Disco (Di), and Country (Co) respectively.
Similarly, Table IX shows the confusion matrix of genre
classification of the ISMIR2004 dataset. The genres arearranged in the order of Classical (Cl), Pop and Rock (P&R),
Metal and Punk (M&P), Electronic (Ele), World (Wo), and Jazzand Blues (J&B) respectively.
Form the confusion matrix of the GTZAN dataset; we can seethat some music genres are classified with significant accuracy
like Classical, Pop, Metal, and Reggae. Except for Rock, othermusic genre classification rates are also competitive. Rockmusic has a minimum classification rate. It is confused with
Metal. Beside this, it is diverse in nature as compared to othergenre and also overlaps its characteristic with other genres.
Music genre Disco and Country are also confused with Reggae
and Metal respectively.
Similarly, Table XI shows the confusion matrix of the
ISMIR2004. The classification rate of Classical, Pop and Rock,and Electronic are significant. Genres like Jazz and Blues, andWorld are also relatively better than Metal and Punk. The
World music is diverse in nature, so it is confused with Classicaland Pop and Rock. Genre like Jazz and Blues are also confusedwith Classical. Among the six genres, Electronic has minimum
classification rate as compared to others.
TABLE XCONFUSION MATRIX OF GTZAN DATASETS CLASSIFICATION ACCURACY WITH

442

VII. CONCLUSIONSIn this paper, first we analysed the validity of timbral texture
features. The validity criterion is determined by the normalized
standard deviation of each feature. In the second stage, theframe-wise features have been integrated by using central
moments including mean, standard deviation, skewness andkurtosis. Also, we propose the covariance components between
timbral texture frame-wise features to be included for improving
the classification performance. By considering these feature
values, several experiments have been performed separately toanalyse classification accuracy among the different feature sets.
The ELMs combined with bagging is used to build the classifier.
The ELM is an unstable classifier; therefore ELMs with bagging
improved the classification accuracy as well as thegeneralization performance.
The classification accuracy of both datasets (GTZAN and
ISMIR2004) is shown in table IV and V, respectively.
According to our proposed method, the classification accuracyof 85.15% is achieved in GTZAN datasets. The experimental
results on the ISMIR2004 genre datasets have also shown thatour proposed approach achieves higher classification accuracy(86.46%) than the ISMIR Music Genre Classification Contest
with classification accuracy (84.07%) competitive with
Chang-Hsing Lee (86.83%).
Experimental results show that there are no significant
differences seen while considering seven and twelve MFCC
coefficients for genre classification. It concludes that theminimum feature dimension is also sufficient for music genre
discrimination. In addition, our experiment shows thatcovariance components have a significant impact in improving
the genre classification. By adding the components we could
improve approximately 5% of the overall accuracy. We expectthat a more accurate classifier can be constructed with morefeatures added such as segment-based ones after partitioning
audio data into pieces, even though it increases the complexityof the classifier.

TIMBRAL TEXTURE AND RHYTHMIC CONTENT FEATURES

Cl
Bl
Hi
Po
Ro
Ja
Re
Me
Di
Co

Cl
95.0
0.0
0.0
0.0
2.18
5.21
0.0
0.0
0.0
0.0

Bl
3.67
85.25
3.24
0.0
1.15
4.0
0.0
0.0
0.0
6.12

Hi
0.0
5.25
81.26
0.0
0.0
0.0
0.0
0.0
2.35
0.0

Po
0.0
0.0
2.06
96.82
1.39
0.0
8.64
3.89
0.0
4.19

Ro
0.0
0.0
7.39
1.14
74.92
2.52
2.23
0.0
4.95
0.0

Ja
0.0
0.0
0.0
0.0
0.0
83.12
3.68
5.13
0.0
0.0

Re
0.0
2.48
0.0
0.0
3.55
0.0
85.45
0.0
6.25
0.0

Me
0.0
4.52
0.0
0.0
7.09
0.0
0.0
89.57
2.35
9.18

Di
0.0
2.50
8.03
2.04
5.03
0.0
0.0
2.28
83.90
0.0

Co
1.33
0.0
0.0
0.0
2.69
5.15
0.0
1.15
0.0
80.51

Acknowledgement
This work was partially supported by the National Research
Foundation of Korea grant funded by the Korean government
2011-0022152 and BK21PLUS project.
REFERENCES[1]

TABLE XICONFUSION MATRIX OF ISMIR2004 DATASET CLASSIFICATION ACCURACY OFTIMBRAL TEXTURE AND RHYTHMIC CONTENT FEATURES

Cl

P&R

M&P

Ele

Wo

J&B

Cl

95.75

0.0

0.0

3.14

1.13

0.0

P&R

1.45

90.62

0.0

4.58

3.35

0.0

M&P

7.18

10.19

78.63

0.0

2.52

1.48

Ele

0.0

3.28

4.46

87.02

3.85

1.39

Wo

6.47

7.82

0.0

2.86

80.85

0.0

J&B

8.14

2.62

0.0

3.32

0.0

85.92

[2]

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Xu, N. C. Maddage, and X. Shao, “Automatic music classification andsummarization,” IEEE Trans.Speech Aud. Processing, vol. 13,no. 3, pp.
441–450, May 2005.
A. Meng, P. Ahrendt, J. Larsen, L. Hansen, Temporal feature integration
for music genre classification, IEEE Transactions on Audio, Speech, andLanguage Processing 15 (2007).
Babu Kaji Baniya, Deepak Ghimire, and Joonwhoan Lee, “Evaluation ofdifferent audio features for musical genre classification” in proc. IEEE
workshop on Signal Processing Systems, Taipei, Taiwan, 2013
Babu Kaji Baniya, Deepak Ghimire, and Joonwhoan Lee, “A Novel
Approach of Automatic Music Genre Classification Based on Timbral
Texture and Rhythmic Content Features”, Int. conference on Advance
Communications Technology (ICACT), pp.96-102, 2014

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

R. Groeneveld, G. Meeden, Measuring skewness and kurtosis, The
Statistician 33 (1984) 391-399[7] Tzanetakis and P. Cook, “Musical genre classification of audio signals,”IEEE Trans Speech Audio Process., vol. 10, no.3, pp. 293-302, Jul. 2002
[8] L. Rabiner and B. Juang. Fundamentals of Speech Recognition.
Prentice-Hall, NJ, 1993.
[9] B. Logan. Mel frequency cepstral coefficients for music modeling. In
Proc. Int. Symposium on Music Information Retrieval ISMIR, 2000.
[10] Gouyon, A. Klapuri, S. Dixon, M. Alonso, G. Tzanetakis, C. Uhle , P.
Cano, An Experimental Comparison of Audio Tempo Induction
Algorithms, IEEE Transactions on Speech and Audio Processing, vol.14,
page 1832-44, 2006.
[11] Bergstra, J., Casagrande, N., Erhan, D., Eck, D. and Kegl B. “Aggregatefeatures and AdaBoost for music classification”, Machine Learning, Vol.
65, No. 2-3, pp. 473-484, 2006.
[12] Chang-Hsing Lee, Jau-Ling Shih, Kun-Ming Yu, and Hwai-San Lin,
“Automatic Music Genre Classification Based on Modulation Spectral
Analysis of Spectral and Cepstral Features”, IEEE Trans. of Multimedia,
Vol.11, no. 4, pp. 670-82, June 2009.
[13] Jin S. Seo, Seungjae Lee, Higher-order moments for musical genre
classification, Signal Processing, vol. 91, Issue 8, pp. 2154-57, 2011[14] Pampalk, E., Flexer, A. and Widmer, G. “Improvements of audio-based
music similarity and genre classification”, Proceedings of the Sixth
International Symposium on Music Information Retrieval, London, UK,2005.
[15] T. Lambrou, P. Kudumakis, R. Speller, M. Sandler, and A. Linney.
Classification of audio signals using statistical features on time andwavelet transform domains. In Proc. Int. Conf. Acoustic, Speech, andSignal Processing (ICASSP-98), volume 6, pages 3621–3624, 1998.
[16] H. Soltau, T. Schultz, and M. Westphal. Recognition of music types. In
Proceedings of the 1998 IEEE International Conference on Acoustics,
Speech and Signal Processing, 1998.
[17] H. Deshpande, R. Singh, and U. Nam. Classification of music signals inthe visual domain. In Proceedings of the COST-G6 Conference on
Digital Audio Effects, 2001.
[18] E. Scheirer and M. Slaney, “Construction and evaluation of a robust
multifeature speech/music discriminator,” in Proc. Int. Conf. Acoustics,
Speech, Signal Processing (ICASSP), 1997, pp.1331-1334.
[19] T.Zhang and C.J.Kuo, “Audio Content Analysis for Online Audiovisual
Data Segmentation and Classification,” IEEE Trans. Speech and Audio
Processing, Vo1.9, No.4, pp. 441-457, May 2000.
[20] H. Terasawa, M. Slaney, and J. Berger. “Perceptual distance in timbrals
space”. In Proceedings of Eleventh Meeting of the International
Conference on Auditory Display, pages 61-68 Limerick, Ireland, July
2005.
[21] Beth Logan, Mel Frequency Cepstral Coefficients for Music Modeling,
Int. Symposium on Music Information Retrieval (2000)[22] I. Daubechies, “Orthonormal bases of compactly supported wavelets,”
Commun. Pure Appl. Math, vol. 41, pp. 909-996, 1988
[23] K. Hornik, “Approximation capabilities of multilayer feedforward
networks,” Neural Networks, vol. 4, pp. 251—257, 1991.
[24] Huang, G.B.; Zhu, Q.Y.; Siew, C.K. Extreme Learning Machine: Theory
and Applications. Neurocomputing 2006, 70, 489-501.
[25] Beriman, L. Bagging Predictors. Machine Learning 1996, 24, 123-140.
[26] Marasys, “Data sets” http://marsysas.info/download/data[27] [Online]. Available: http://ismir2004.ismir.net/ISMIR_Contest.html

443

[6]

BIOGRAPHIES
Babu Kaji Baniya received the B.E. degree in
Computer Engineering from Pokhara University, Nepal
in 2005 and M.E. degree in Electronic Engineering from
Chonbuk National University, Rep. of Korea in 2010.
Currently he is pursuing his Ph.D. degree in Computer
Science and Engineering at Chonbuk National
University, Rep. of Korea from 2011. His main researchinterest includes audio signal processing, music information retrieval, source
separation, pattern recognition etc.

Deepak Ghimire received the B.E. degree in
Computer Engineering from Pokhara University, Nepal
in 2007 and M.S. degree in Computer Science andEngineering from Chonbuk National University, Rep.
of Korea in 2011. Currently he is pursuing his Ph.D.
degree in Computer Science and Engineering at
Chonbuk National University, Rep. of Korea from2011. His main research interest includes image
processing, computer vision, pattern recognition, facial
emotion analysis etc.

Joonwhoan Lee received his BS degree in Electronic
Engineering from the University of Hanyang, Rep. of
Korea in 1980. He received his MS degree in Electricaland Electronics Engineering from KAIST University,
Rep. of Korea in 1982 and the Ph.D. degree in Electricaland Computer Engineering from University of Missouri,
USA, in 1990. He is currently a Professor in Departmentof Computer Engineering, Chonbuk National University, Rep. of Korea. His
research interests include image processing, computer vision, emotion engineeringetc.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014444

Performance Analysis of Power Allocation andRelay Location in a Cooperative Relay Network
Muhammad H.D. Khan, Mohammed S. Elmusrati
Communication and System Engineering Group, University of Vaasa, Finland
hazandanish@yahoo.com, moel@uwasa.fi

Abstract— Transmission using cooperative relays is a newparadigm in wireless communication. The cooperating relays
facilitate the process of communication by performing theoperations like data transmission and data processing in a
distributed manner. In this wireless system every node is anactive element and can act as a relay node. So when a group ofthese cooperating nodes are involved in a communication stream,a virtual multiple input multiple output (MIMO) system isformed which provides the networks with additional benefits ofspectral efficiency and error reduction. Since this system is basedon the traditional wireless sensor network (WSN) in which eachnode has a limited power and computational resource. Therefore,
energy efficiency achieved by employing cooperating relays is not
sufficient enough. Some extra measures need to be taken to
decrease the power consumption of the network. This paper is aneffort in this direction, as a power efficient allocation algorithm
has been proposed which allocates transmission power optimallyto the source node and the involved relay nodes. In the first partof the paper mathematical expressions have been derived forvarious phases in a cooperative relay transmission. The
performance efficiency of the system has been presented using
average bit error rate (ABER) as a performance criteria. In thesecond part, a power allocation algorithm has been derived andemployed in a multi-hop cooperative relay network having
4-nodes, with amplify and forward (AF) protocol as its relaying
technique. The efficiency of the power allocation algorithm(OPA) has been further investigated with respect to relay
location in a network. Simulation results validate theperformance efficiency of OPA in different transmission
scenarios.
Keyword— Power Allocation, Cooperative Relay Network,
Multi-hop Transmission, Relaying Technique, Multiple Input
Multiple Output and Diversity Combining.

F

I. INTRODUCTION
or the past few years there has been a substantial increasein wireless communication services and applications. This———————————————————————
Manuscript received May 10, 2014. This work was supported in part by theCommunication and System Engineering Group at University of Vaasa, 65200
Vaasa, Finland. Part of the results in this paper was presented at theInternational Conference on Advanced Communications Technology,
Pyeongchang, South Korea, 2014.
Muhammad H. D. Khan is with the University of Vaasa, 65200 Vaasa,
Finland. (phone: +92-346-5280626; e-mail: hazandanish@yahoo.com).
Mohammed S. Elmusrati is with the University of Vaasa, 65200 Vaasa,
Finland. (phone: +358-504-003763; e-mail: moel@uwasa.fi).

advancement has brought us to the critical issue of efficiently
utilizing the network resources. Communication throughcooperating relay nodes is one such technology that uses the
network resources dynamically rather than the traditional fixedaccess approach. These cooperating relays share their network
resources to enhance the network productivity by providing
distributed transmission [1]. This new wireless network seems
quite promising, as it incorporates extra capabilities to a
traditional WSN and at the same time increases spectrum
efficiency in a significant way [2]. In a nutshell, a cooperative
relay network (CRN) can provide its end users with highbandwidth efficiency, extended service coverage andubiquitous connectivity.
In this technique spatial diversity gain is achieved bysharing the antenna resource of cooperating relay nodes toform a virtual MIMO system, as proposed in [3][4]. The
source node and the involved relay nodes simultaneouslytransmit data over independent fading paths. At the receiver
node these multiple streams of same correlated data is used toachieve a spatial diversity gain. The gain achieved due todiversity offers advantages like reduction in both error rate
and the required transmission power. The relaying protocols
being run on the nodes can be categorized into different types[5]. Among these types, amplify and forward (AF) protocol isa widely adopted one. This protocol simply amplifies thereceived signal and then forwards it to the next node without
any complex processing, making it an efficient and lesscomplex technique [6]. So, the system analysis has been
carried out using AF relaying protocol.
In order to practically integrate this promising technique inthe future wireless networks there are few issues, which need
to be addressed. Among them transmission power
management is a crucial one. Although extensive research has
been carried out for relay networks but very few are focusing
the issue of power efficiency. Optimal power allocation(OPA), is one of the techniques that allocates the transmission
power optimally between the source and the involved relay
nodes. Therefore, a substantial amount of node power is saved
while maintaining the link quality [7]. The power allocation ofa two-hop AF cooperation network was examined in [8] and[9] for Rayleigh fading, but they only considered a single relay
network. In [10], the power allocation has been discussed for a
multi-node network but decode-and-forward (DF) relaying
protocol has been employed over Rician fading channel.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

445

Moreover in [11] [12] and [13], OPA has been derived for AF
dual-hop network with arbitrary number of relays overRayleigh fading. In [14], an OPA technique has been proposed
for a multi-hop cooperative relay network but the network
scenarios based on the location of relay in the network havebeen discussed very briefly.

                                YR1  [n]  =     𝑃!         𝐺!"! .  X[n]  +  nSR1[n](1)                                Y’R2  [n]  =     𝑃!         𝐺!"! .  X[n]  +  nSR2[n](2)  The remainder of this paper is organized as follows.
Section II, presents the network model and parameters. In
Sections III, the mathematical expressions of the received
signals have been derived for different phases of transmission.
ABER has been taken as criteria to show the performanceefficiency of power allocation algorithm. In Sections IV, theexpressions for the required transmission power of the source
and the relay nodes have been derived for the corresponding
4-node network configuration. The performance efficiency
gained by employing the proposed technique is presented inform of performance curves, in Section V. Section VI,
concludes the paper.

After Phase-II, the relay2 node has received two signals   Y’R2
and YR2, first one was in phase-I (broadcasting) and the second
one was forwarded by relay1 with its amplification factor βR1
in phase-II. GR1R2 is the channel gain for relay1-relay2 link andnR1R2 is the noise at the relay node2 in phase-II. But since thelink between source-relay2 is weak as compared to the link
between relay1 and relay2 so the node relay2 discards thesignal Y’R2 and the received signal YR2 is defined as:                            YR2  [n]  =  βR1  .  YR1[n]  .    𝐺!!!!   +  nR1R2[n]Relay gains βR1 and βR2 are used to properly fine-tune thepowers at the corresponding relays to reduce variations in thesource-relay and relay–destination links. The relaying node’s
amplifier can provide a maximum gain defined by thefollowing expressions:

βR1  =  II. MULTI-HOP COOPERATIVE NETWORK
A. Network Model
For this scenario the network model is shown below in Fig
1. This network has four communication nodes; a source nodeS, two relay nodes namely R1 and R2 and lastly a destination
node D. The channel gains for the source to destination,
source to relay1, relay1 to relay2 and relay2 to destination are
represented by terms GSD, GSR1, GR1R2 and GR2D respectively.
The network model is designed based on few assumptions.
The channel is assumed to be a Rayleigh fading channel and isnormalized so that the fading coefficient matrix is complex
Gaussian with zero-mean and variance σ2. Everycommunicating node in the network will obey rules of half
duplex transmission that means it will either transmit or listen
at any given instant. The multiple signals received by thereceiver node will be combined using Maximal Ratio
Combining (MRC) technique. Finally, the power constraints
are applied for the whole communication link instead ofintermediate hops and AF protocol has been employed as arelaying strategy.

(3)  βR2  =  !!!
  !! |!!!!   |!   !  !!   

                                                                  (4)!!!
  !!! |!!!!!   |!   !  !!   

                                                            (5)  Since, amplify-and-forward protocol may induce some noise
amplification but the MRC detector employed is quite
competitive therefore with the help of its weights it cancompensate effect of induced noise [15]. The output of the
MRC detector at the destination node will be:

YMRC  [n]  =  α0(   𝑃!     .𝐺!" .  X[n]+  nSD[n])  +                                                α1(βR2  .  YR2[n].  𝐺!!! +  nR2D[n])                                    (6)  Substituting equation (3) into (6):YMRC  [n]  =  α0(   𝑃!     .𝐺!" .  X[n]+  nSD[n])  +  α1(βR2  .  (βR1  .  YR1[n]  .  𝐺!!!! +  nR1R2[n])  .                               𝐺!!! +  nR2D[n])                                                                    (7)  Substituting equation (1) into (7):YMRC  [n]  =  α0(   𝑃!     .𝐺!" .  X[n]+  nSD[n])  +  
R
1

R
2

α1(βR2  .  (βR1  .  (   𝑃!     𝐺!"! .  X[n]  +  nSR1[n])  .  𝐺!!!! +                                                    nR1R2[n]).  𝐺!!! +  nR2D[n])                                                    (8)  SDFigure 1. Cooperative Relay Network (2-Relays)

In the first phase the source node S broadcasts the data X with
its transmission power PS to both relays and destination node,
as shown in the next figure. The channel gains for thesource-relay1 and from source- relay2 links are GSR1 and GSR2
respectively. The AWGN present at the relay 1 and relay 2
nodes will be nSR1 and nSR2 respectively. The equation ofreceived signals in the phase-I at the relay 1 and relay 2 will
be expressed as YR1 and Y’R2 respectively

In the above equation (6-8), α0 and α1 are the weights of the
maximal-ratio-combiner.
These
combiningweightscompensate for the effects of likely incorrect decisions and are
therefore chosen accordingly. Here α1, is the weight of thesignals which is being forwarded by the relays placed in a
linear topology.

α0  =  α1=  !!   !!" ∗!!

!!     .    !!! .    !!!   .  !!"! ∗   .    !!!!! ∗   .    !!!! ∗!!

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

III. OPTIMAL POWER ALLOCATION
Now, the probability of bit error for the 4-node configuration
will be calculated using Moment Generating Function (MGF)
approach. With the help of moment generating function
approach the error probability for the QPSK modulation
scheme will be expressed as an exponential function of γ,
defined below.
!!!
!
!

!

𝑃! =       !
!

!
!!! 𝑁!!

!
𝑃
! !!

𝑁!! =
!

𝑁!!

!

𝑑𝜃

(9)

𝛾 𝑒 𝑑𝛾

= 1+

!"#ө!

!"#! ө

!!"#$!"#! ө

(10)

𝛾!

!!

!!!
!!
!!!

⁼
⁼

!!!!

  

!!! .  ! ! !"    .  ! ! !!!!   
  !

(!!. !!    .  ! ! !"    .  ! ! !!!!    )!

!!!!

!!! .  ! ! !"    .  ! ! !!!   

!
!!

(12)

!"#! ө

  (!!. !!    .  ! ! !"    .  ! ! !!!    )!

!!

≅    𝑁!!

!"#ө!

𝑃! =   !

!

𝛾! . 𝛾! !!

!

(13)

3𝑁! !
1
1
      𝑃! =   
+   
  
  
8   𝑃! .   𝜎 !!"    .   𝜎 !!"!    𝑃! . 𝑃! .   𝜎 !!"    .   𝜎 ! !!!!   
1+                                                                       (14)  !
𝑃! . 𝑃! .   𝜎 !"    .   𝜎 ! !!!   

As the outage probability has been formulated for this 4-nodeconfiguration, now this system will be expressed as aconstrained optimization problem with transmission power asa constraint entity.
!

!

!
!!! 𝑁!!

Minimize 𝑃! =       !!
!

!
!"#! ө𝑑𝜃  !
! 𝑃! ≤ 𝑃!

Subject to 𝑃! +Now as we have established the constrained optimization
problem, a closed form expression of this optimization
problem will be found using Lagrange Method. The Lagrange
cost function of this problem is defined as:

!!! !

!

!

  !!   !   ! ! !"    ! ! !"!   

                                    

!

  

+

  

!! !!   ! ! !"    ! ! !!!   

(19)

(20)

𝐴 − 4𝐵 +𝐴2 + 8𝐴𝐵4(𝐴 − 𝐵)
2
𝑃𝑇
3

𝑃! =

𝑃𝑇

𝜎!!
(𝑃 − 𝑃! )𝜎!! + 𝜎!!!! !

𝑃!   = 𝑃! − 𝑃! − 𝑃!
Where, in the above equation A=(𝜎!!! + 𝜎!!!! )! . 𝜎 ! !!!! . 𝜎 !!"!
and B= 𝜎 ! !!! . 𝜎 ! !!!!
IV. SIMULATION RESULTS
Fig. 2, compares the performance of OPA with that of EPA interms of ABER for a cooperative network having 2 relays. The
results show that OPA outperforms EPA when employed to acooperative relay network having similar configuration. The
performance gap remains same for the increasing values of
SNR. It was also an observation that the performance of asystem employing EPA can be increased only if the relays
were placed symmetrically but OPA performs better for both
symmetric and asymmetric configurations.

!
! 𝑃! 	  −	
  𝑃! )	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  (15)	
  J  = 𝑃! +   𝜆  (𝑃! 	
  +	
                            J⁼

+𝜆=0After some algebraic manipulations the above equations areused to solve the value of source node power PS, relay1 node
power P1 and relays2 node power P2 .

𝑃!   =

!"#ө!

(18)

⁼𝑃! +    𝑃! +    𝑃! − 𝑃! = 0

Since (𝛾! >> 1) equation (12) becomes:

𝑁!!

+𝜆=0

  

(11)

!!

!!

                                

!!

!"

!"

= 1+

!"#ө!

𝑁!!

!!"#$

                                

446

!

  

!! !!   ! ! !"    ! ! !!!!   

++   𝜆(𝑃! +𝑃! + 𝑃!    − 𝑃! )      (16)  Now taking the partial derivatives of the Lagrange costfunction J (P, 𝜆), with respect to Ps, P1, P2 and 𝜆. Afterwards
these derivatives will be equated to zero, like defined below  
  

  

!!
!!!

⁼

!!!!
!

  

  

!!!! .  ! ! !"    .  ! ! !"!   
  (!!   ! .  ! ! !"    .  ! ! !"!    )! −   !! .  ! ! !"    .  ! ! !!!!   
  (!! .!! .  ! ! !"    .  ! ! !!!!    )!

−                                                                            !! .  ! ! !"    .  ! ! !!!   
  (!! .!! .  ! ! !"    .  ! ! !!!    )!

+𝜆=0

(17)

Figure 2. Equal power allocation and Optimal power allocation

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

Fig. 3, compares the performance of case 1: ( 𝜎 !!" =1;  𝜎 !!"! = 1; 𝜎 ! !!!! = 1;  𝜎 ! !!! = 10 ) with case 2:( 𝜎 !!" = 1;  𝜎 !!"! = 10; 𝜎 ! !!!! = 1;  𝜎 ! !!! = 10 ) in termsof ABER for a cooperative network having 2 relays. Here,channel link quality is denoted by “10” if the two
communicating nodes are closer to each other while if the two
nodes are far from each other channel link quality is “1”. For
this network simulation, case 2:( 𝜎 !!" = 1;  𝜎 !!"! =10; 𝜎 ! !!!! = 1;  𝜎 ! !!! = 10 ) shows the best performance
because in this case relays2 is closer to destination node andsource node is closer to relay1 node. Case 1: ( 𝜎 !!" =1;  𝜎 !!"! = 1; 𝜎 ! !!!! = 1;  𝜎 ! !!! = 10) shows slightly worse
performance because only relay2 node is closer to thedestination node.

447

because in this case the channel link quality for everytransmisson hop is strong. Case 3: ( 𝜎 !!" = 10;  𝜎 !!"! =10; 𝜎 ! !!!! = 1;  𝜎 ! !!! = 1 )showsslightly
worse
performance because only relay1 node is closer to source
node.

Figure 5. Cooperative Transmission Scenarios (Case5 & Case6)

Figure 3. Cooperative Transmission Scenarios (Case1 & Case2)

Fig. 4, compares the performance of case 3: ( 𝜎 !!" =10;  𝜎 !!"! = 10; 𝜎 ! !!!! = 1;  𝜎 ! !!! = 1 ) with case 4:(𝜎 !!" = 1;  𝜎 !!"! = 10; 𝜎 ! !!!! = 10;  𝜎 ! !!! = 10) in termsof ABER for a cooperative network having 2 relays.

Fig. 5, compares the performance of case 5: ( 𝜎 !!" =1;  𝜎 !!"! = 1; 𝜎 ! !!!! = 1;  𝜎 ! !!! = 1 ) with case 6:( 𝜎 !!" = 10;  𝜎 !!"! = 1; 𝜎 ! !!!! = 1;  𝜎 ! !!! = 10 ) in termsof ABER for a cooperative network having 2 relays. Out of all
simulations, case 5: ( 𝜎 !!" = 1;  𝜎 !!"! = 1; 𝜎 ! !!!! =1;  𝜎 ! !!! = 1) is the worst one as every tranmsission link is
weak . On the contrary, Case 6: ( 𝜎 !!" = 10;  𝜎 !!"! =1; 𝜎 ! !!!! = 1;  𝜎 ! !!! = 10 ) is the best possible scenario
because not only relay2 node is closer to destination node but
also the direct link between source and destination is alsostrong.

Figure 6. Allocated Power Levels (Case1 & Case2)
Figure 4. Cooperative Transmission Scenarios (Case3 & Case4)

For this network simulation, case 4:( 𝜎 !!" = 1;  𝜎 !!"! =10; 𝜎 ! !!!! = 10;  𝜎 ! !!! = 10) shows the best performanceFig. 6, compares the transmission power values for case 1:(𝜎 !!" = 1;  𝜎 !!"! = 1; 𝜎 ! !!!! = 1;  𝜎 ! !!! = 10) represented
by blue bar and case 2: (𝜎 !!" = 1;  𝜎 !!"! = 10; 𝜎 ! !!!! =1;  𝜎 ! !!! = 10) represented by red bar.
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

For case 1: (𝜎 !!" = 1;  𝜎 !!"! = 1; 𝜎 ! !!!! = 1;  𝜎 ! !!! = 10)
the only strong link is between relay2 and destination node. So
it is visible from the above graph that the value oftransmission power (0.0986) allocated to relay2 node is thelowest when compare to the power values of source node
(0.6236) and relay1 node (0.2778). For case 2: ( 𝜎 !!" =1;  𝜎 !!"! = 10; 𝜎 ! !!!! = 1;  𝜎 ! !!! = 10 )
the
power
allocation algorithm assigns a transmission power of (0.529)
to the source node, a power of (0.348) to the relay1 node and atranmission power of (0.123) to the relay2 node, satisfying thetotal power constraint of “1”, as defined in the derived
mathematical expressions.

448

1; 𝜎 ! !!!! = 1;  𝜎 ! !!! = 10) the power allocation algorithm
assigns a transmission power of (0.6236) to the source node, apower of (0.2778) to the relay1 node and a tranmission powerof (0.0986) to the relay2 node, satisfying the total power
constraint of “1”.

Figure 8. Allocated Power Levels (Case5 & Case6)

V. CONCLUSION

Figure 7. Allocated Power Levels (Case3 & Case4)

Fig. 7, compares the transmission power values for case 3:(
𝜎 !!" = 1;  𝜎 !!"! = 10; 𝜎 ! !!!! = 10;  𝜎 ! !!! = 10)
represented by blue bar and case 4: (𝜎 !!" = 10;  𝜎 !!"! =10; 𝜎 ! !!!! = 1;  𝜎 ! !!! = 1) represented by red bar. For case
3: ( 𝜎 !!" = 1;  𝜎 !!"! = 10; 𝜎 ! !!!! = 10;  𝜎 ! !!! = 10 ) thechannel link quality for every transmisson hop between thesource and destination is strong so the value of tranmission
power allocated to the the relay1 and relay2 node will be equal(0.2113) and source node will be allocated the maximum
value of (0.5774). For case 4: ( 𝜎 !!" = 10;  𝜎 !!"! =10; 𝜎 ! !!!! = 1;  𝜎 ! !!! = 1) the power allocation algorithm
assigns a transmission power of (0.5144) to the source node, apower of (0.2428) to the relay1 node and a tranmission powerof (0.2428) to the relay2 node, satisfying the total power
constraint of “1”.
Fig. 8, compares the transmission power values for case 5:( 𝜎 !!" = 1;  𝜎 !!"! = 1; 𝜎 ! !!!! = 1;  𝜎 ! !!! = 1 ) represented
by blue bar and case 6: (𝜎 !!" = 10;  𝜎 !!"! = 1; 𝜎 ! !!!! =1;  𝜎 ! !!! = 10 ) represented by red bar.
For case 5:(𝜎 !!" = 1;  𝜎 !!"! = 1; 𝜎 ! !!!! = 1;  𝜎 ! !!! = 1) the channel
link quality for every transmisson hop between the source anddestination is weak so the value of tranmission power
allocated to the the relay1 and relay2 node will be equal(0.2113) and source node will be allocated the maximum
value of (0.5774). For case 6: ( 𝜎 !!" = 10;  𝜎 !!"! =In this paper, we investigated the effect of optimal power
allocation and the effect of relay location for a transmission
using cooperative relays. The results show that instead ofusing the traditional EPA if OPA is employed in a cooperative
relay network then the system performance can be enhanced
substantially. Moreover, OPA is vital for theseinfrastructure-lessnetworks
havingun-balanced
communication links. With help of the above results it has
been found out that the communication links between thesource node (S) and the first-relay (R1) and the link between
the last-relay (RN) and the destination node (D) have the mostsignificant effect on the system performance. Other
communication links have slightly less effect on the system
performance. Finally as a future direction, optimum relays
should be determined first based on their location. Then theserelays should be used for transmission, by doing so the system
performance can be further enhanced.
ACKNOWLEDGMENT
The authors would like to thank Vaasa University Foundation
for the financial support provided by them.
REFERENCES
[1]

[2]

A. Scaglione and Y.-W. Hong, “Opportunistic large arrays: cooperativetransmission in wireless multi-hop ad hoc networks for the reach backchannel,” IEEE Trans. Signal Processing, vol. 51, no. 8, pp. 2082–2092,
Aug. 2003.
Hong, Yao-Win; Wan-Jen Huang; Fu-Hsuan Chiu; Kuo, C.-C.J.,"Cooperative Communications in Resource-Constrained Wireless
Networks," Signal Processing Magazine, IEEE , vol.24, no.3, pp.47,57,
May 2007

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

[3]

J. N. Laneman, G. W. Wornell. “Distributed space-time coded protocols
for exploiting cooperative diversity in wireless networks” [J]. IEEE
Trans. on Information Theory, 2003, 49(10): 2415-2425.
[4] J. N. Laneman, David N. C. Tse, G. W. Wornell. “Cooperative
diversityin wireless network: efficient protocols and outage behavior”[J]. IEEE Trans. on Information Theory, 2004, 50(12): 3062-3080.
[5] Donglin Hu; Shiwen Mao, "Cooperative Relay in Cognitive Radio
Networks: Decode-and-Forward or Amplify-and-Forward?," Global
Telecommunications Conference (GLOBECOM 2010), 2010 IEEE , vol.,no., pp.1,5, 6-10 Dec. 2010[6] Borade, S.; Lizhong Zheng; Gallager, R., "Amplify-and-Forward in
Wireless Relay Networks: Rate, Diversity, and Network Size,"
Information Theory, IEEE Transactions on , vol.53, no.10,
pp.3302,3318, Oct. 2007
[7] Fangzhen Li; Fei Yang; Xuefen Zhang, "An optimal power allocation
algorithm in Distributed Sensor networks," Information Managementand Engineering (ICIME), 2010 The 2nd IEEE International Conference
on , vol., no., pp.414,418, 16-18 April 2010[8] Weifeng Su, Ahmed K.Sadek and K.J.Ray Liu. “Cooperative
communication protocols in wireless networks: performance analysis
and optimum power allocation” [J]. Wireless Personal Commun., 2008,
44(2): 181-217.
[9] Yan Wang, Fei Lin. “SEP performance analysis and power allocation for
amplify-and-forward relay networks” [C].The 3rd IEEE International
Conference on Wireless Communications, Networking and Mobile
Computing, September 21-23, 2007, Shanghai China (Wicom2007):1274-1277.
[10] Mulugeta K. Fikadu, Mohammed Elmusrati, and Reino Virrankoski,“Power Allocation in Multi-node Cooperative Network in Rician Fading
Channels”. IEEE 8th International Conference on Wireless and Mobile
Computing, Networking and Communications (WiMob) 2012: 496-501[11] Sarmad Sohaib and Daniel K. C. So, “Power Allocation for Multi-Relay
Amplify-and-Forward Cooperative Networks”. IEEE Conference on
Communications (ICC), 2011: 1-5[12] Nan Zhang, Jian Hua Ge and Feng Kui Gong , “SEP analysis andoptimal power allocation of multinode amplify-and-forward cooperation
systems”. 2008, 1 – 5[13] Jia Liu, Ness B. Shroff, and Hanif D. Sherali. “Optimal Power
Allocation in Multi-Relay MIMO Cooperative Networks: Theory andAlgorithms.” IEEE Journal on Selected Areas in Communications. 2012,30(2): 331-340[14] Khan, M.H.D.; Elmusrati, M.S.; Virrankoski, R., "Optimal power
allocation in multi-hop cooperative network using non-regenerative
relaying protocol," Advanced Communication Technology (ICACT),2014 16th International Conference on , vol., no., pp.1188,1193, 16-19
Feb. 2014[15] Vien, N.H.; Nguyen, H.H., "Performance Analysis of Fixed-Gain
Amplify-and-Forward Relaying With MRC," Vehicular Technology,
IEEE Transactions on , vol.59, no.3, pp.1544,1552, March 2010

communication
management.

449

Muhammad H. D. Khan (S’13) was born in
Rawalpindi, Pakistan, in 1986. He received the B.E.
degree in electrical engineering from the COMSATSInstitute of Information Technology, Islamabad,
Pakistan, in 2010, and the M.Sc. degree in technology
from University of Vaasa, Vaasa, Finland, in 2013.In
2010, he joined the Department of Avionics
Engineering, Air University, Pakistan, as a Research
Assistant, and in 2011 became a Research Associate.
His current research interests include wirelesssystems, sensor networks and resource allocation

Mohammed Elmusrati (S’00-M’04-SM’12) received the
B.Sc. (with honors) and M.Sc. (with high honors)
degrees in telecommunication engineering from theElectrical and Electronic Engineering Department,
Benghazi University, Libya, in 1991 and 1995,
respectively, and the Licentiate of Science intechnology (with distinction) and the Doctor of Sciencein Technology degrees in control engineering from
Aalto University - Finland, in 2002 and 2004,
respectively. Currently, Elmusrati is full professor and head ofcommunications and systems engineering group at University of Vaasa Finland. His main research interests include Radio resource management inwireless communication, wireless networked control, game theory, and smart
grids.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

450

Road Side Unit Assisted
Stochastic Multi-hop Broadcast Scheme
for Instant Emergency Message Propagation
Xing Fan*, Bo Yang*, Ryo Yamamoto**, Yoshiaki Tanaka*,**** Global Information and Telecommunication Institute, Waseda University
1-3-10 Nishi-Waseda, Shinjuku-ku, Tokyo, 169-0051 Japan** Graduate School of Information Systems, The University of Electro-Communications
1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan*** Research Institute for Science and Engineering, Waseda University
17 Kikuicho, Shinjuku-ku, Tokyo, 162-0044 Japan
fan.xing@asagi.waseda.jp, yangbo_youhaku@ruri.waseda.jp, ryo_yamamoto@is.uec.ac.jp, ytanaka@waseda.jp

Abstract— VANET (Vehicular ad hoc Network) is a special
kind of ad hoc wireless network where every single node is avehicle moving in a relatively high velocity, which leads to
exclusive challenges like rapid changing topologies, safety andprivacy concerns. In this specific network, broadcasts tend to becarrying important messages such as car accident notification,
disaster alert or extreme traffic condition. Thus the propagationof broadcasted emergency messages could be critical to savehuman lives and property. Many researchers have proposedrouting or broadcast protocols to solve this problem in VANET.
With consideration of the other common issues. The objective of
this paper is to propose a broadcast scheme in VANET that is not
likely to cause broadcast storm problem with a reasonable delay
and high delivery rate. Since VANET is an attack-prone network
and any kind of malicious behavior in VANET might cause
serious loss or even death in reality, we should also refrain fromusing beacons to exchange privacy-sensitive information in V2V(Vehicle to Vehicle). In this paper, a multi-hop broadcast schemethat makes use of RSU and V2I (Vehicle to Infrastructure)communication is proposed. The simulation result shows that the
proposed scheme outperforms static stochastic broadcast schemein terms of delivery rate. Comparing to flooding, we offer abetter delay and less network usage.
Keyword—Wireless

VANET,

Broadcast,

Percolation,

Stochastic,———————————————————————
Manuscript received April 21, 2014.
X. FAN is with the Global Information and Telecommunication Institute,
Waseda University, Tokyo, 169-0051 Japan. phone: 080-9442-3698; e-mail:fan.xing@asagi.waseda.jp.
B. YANG is with the Global Information and Telecommunication Institute,
Waseda University, Tokyo, 169-0051 Japan. phone: 090-0495-246104; fax:
090-0495-246104; e-mail: yangbo_youhaku@ruri.waseda.jp.
R. YAMAMOTO is with the Graduate School of Information Systems,
The University of Electro-Communications, Tokyo, 182-8585, Japan. e-mail:ryo_yamamoto@is.uec.ac.jp.
Y. TANAKA is with the Global Information and Telecommunication
Institute, Waseda University, Tokyo, 169-0051 Japan. He is also with the
Research Institute for Science and Engineering, Waseda University, Tokyo,
162-0044 Japan. e-mail: ytanaka@waseda.jp.

I. INTRODUCTIONW

IRELESS ad hoc network is a network that does notrely on existing infrastructure to relay packets and uses
all nodes both as clients and as routers. In this way, it does
not require any type of access points or routers to bepre-installed in the network. It’s decentralized, dynamic andeasy-to-deploy characteristics make this kind of network avery active and challenging research area.
One of the most promising and practical topics is vehicular
ad hoc network (VANET) [1]. Research in this area issignificant because the increasing car accident and safety
related problems [2] are threatening human lives and property,
and intelligent transportation system (ITS) is designed to help
solving these problems. In developing countries, motorizationis happening in urban area with high population and cars are
moving at high rather speed in these regions, which isconsiderably dangerous. Vehicle manufactures are developing
various ITS systems to assist and protect driver’s life and theirproperty. VANET is one of the most basic systems to be
implemented. Due to its wireless ad hoc nature, it could
sustain extremely low connectivity and being establishedquickly, easily even in extreme condition.
VANET is simply wireless ad hoc network formed by
vehicles, which allows nodes to perform vehicle to vehicle(V2V) and vehicle to infrastructure (V2I) communication to
obtain traffic condition, alert messages and benefit fromnavigation system. Despite of the simple concept of VANET,
it introduces a lot of exclusive problems, mainly because cars
are nothing like mobile devices in MANET (Mobile ad hoc
network) or stationary monitors in WSN (Wireless sensor
network). Its specialty also gives the following uniquecharacteristic to VANET:• Nodes move at rather high velocity• Nodes move in an organized, predictable way• Power consumption is usually negligible for vehicles

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

Nodes are assumed to have modern on-board-unit (OBU)
installed, including GPS, WIFI connectivity, etc.
• A special kind of stationary nodes could exist, which iscalled road-side-unit (RSU), as is shown in Figure 1•

Fig. 1. RSU and V2I in VANET

The previous listed characteristics makes every applicable
protocol and scheme for VANET requires much more detailed
design rather than directly import from currently existing ad
hoc network technique such as mobile ad hoc
network(MANET) [3]. In addition to other ad hoc network,
researchers need to take the following risks into consideration
in VANET:• The usage of privacy-sensitive information• Easily fragmented network topology• Security• Scalability
Also, the usage of beacons and geography information
services should be limited. Beacons should not carry privacy
sensitive information such as geo-location, vehicle’s directionor speed, because beacons are prone to interception. We also
shouldn’t make assumption of location services because
emergency broadcast should be working independently fromany infrastructure-dependently network, to make sure its
availability even in extreme condition.
Scalability is another major concern due to theunpredictable size of ad hoc network. In modern VANET, thesize of the network could expand across cities, or evencounties connected by highways or railways. Among theconnections, the network condition is also not unified. For
example cars might be using different OBUs and the
distribution or even existence of RSUs might vary according
to culture and corresponding policies. If a scheme can only
work with strict dependencies it is said not scalable.
Since most of concerns in VANET are safety of the vehicles
and their drivers, it is not difficult to infer that emergency
broadcast is one of the most critical areas in VANET
communication. Every car accident, natural disaster and otheralerts need to be propagated in the network very fast withreliability. Optimization of emergency message propagating
includes many areas such as reliability research, broadcast
schemes in VANET and privacy protection. In these areas,
broadcast schemes are the most direct contributors. For this451

reason, this paper mainly focused on emergency broadcast
schemes in VANET.
As a common methodology inpropagating emergency messages, broadcast is widely used inmany areas. Pure flooding is one of the most simple andeffective methods, namely every node must retransmit thepacket when it receives the one. Although this scheme is goodenough for simple situation, it is not robust to be applied forpractical usage. This is because flooding packets without
constraints could potentially cost too many resources in thehigh density area and increase the duplicate messages while
risking congest the network, which could finally lead tocollisions or even unresponsiveness (broadcast storm problem).
As we stated actual VANET can have a quite large scale, it
might span across cities or even countries, in this caseemergency messages are no longer interested by distantaudience but the cost of propagating those messages can growexponentially. Thus, further design and constraints must
apply.
In order to solve this problem, many approaches were
proposed [4]. In counter-based approaches, number ofrebroadcasting is limited and nodes would not retransmit apacket after the packet reaches a certain counter.
Inlocation-based approaches, only nodes in a specified location
retransmit a packet. In distance-based approaches, only nodes
with furthest distance to a source node can retransmit a packet.
In cluster-based approaches, packet retransmitting relies oncertain nodes within a specified structure.
In
probability-based approaches, every node has a possibility toretransmit packet when they receive one.
Each solution proposed above has its pros and cons.
Counter-based approaches can somehow control thetransmission range in terms of hop count but are prone tomalicious modification since the counters are usually stored in
the message. Also the hop count and actual distance are notdirectly associated, it needs to refer the node density andsometimes network topology. Location-based approaches
specified location area in packets so that it could limit thetransmission area. Thus, it is also prone to attacks sinceattackers can easily obtain information of broadcasting area.
And a stable access to location service is required for eachnode, which is rather hard to guarantee in VANET and might
have negative impact on performance.
Distance-based
approaches are the state of the art broadcast scheme because it
tends to select the furthest nodes as relays. Thus, it maximizes
the dissemination ranges. On the other hand, it does not haveany control scheme once a packet is broadcasted to networks.
Thus, priority or counter based mechanism must be applied tolimit the transmission. Probability-based approaches are
flexible schemes since all their behaviours rely on how todecide the probability, depending on different scenarios
researchers use different approaches to adjust the rebroadcast
probability accordingly. Most of the pragmatic schemes are
hybrids of the four basic schemes or with modifications.
The effectiveness of broadcast also heavily depends on atechnique of packet transmission. Dedicated short-range
communication (DSRC) [5] allows high-speed connection inboth V2V and V2I. It uses 5.9 GHz band and transmission
range could reach 1 km. This allows us to transmit packet
over 3 times further than conventional IEEE 802.11 [6] andalso mitigates a fragmentation of network in low node density
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014452

area. The power consumption of this module is also negligible
since cars are generating power on the run.
As is mentioned previously, there are special class of nodes
called RSU (road side unit) exist exclusively in VANET.
These nodes are usually installed in an intersect area on ahighway, gas station and the other area according to the nodedensity. Usually to form an infrastructure network or directly
connected to one. They can also boost the connectivity intheir own area. However, these RSUs rarely contribute toconventional VANET broadcast.
One of the potential
optimization for broadcast scheme is to use RSUs as aresources.
This paper is organized as follows: introduction of someexisting broadcast schemes are in Section II, the proposed
network architecture and usage of RSU are explained in
Section III, detailed design of broadcast scheme is written in
Section IV, simulation and result are recorded in Section V,
Section VI conclude this paper.

First, every node determines a probability by itself, whichmeans transmission is not used except broadcasting and therelay selection process is considerably safer without bringing
in impact to the performance. This eliminates privacy issues
and provides scalability. Second, it uses a percolation study toestimate a theoretically maximum connectivity and calculate
the optimal node density to form a connected graph. However,this paper does not mention about dynamical adjustment of theprobability according to a network condition changes. In thispaper, the proposed scheme utilizes RSUs to deal with therapid fragmented network.
The objective of this paper is to propose a broadcast that
could propagate emergency messages with high deliver rate,
reasonable delay and does not overwhelm a network withbroadcast storm. Privacy is also taken into consideration bylimit a usage of beacons.

II. RELATED WORKSVANET could be simplified into two scenarios: city withhigh node density, highway with sparse density and easily
fragmented. Oversimplify the VANET could lead to assume
the network is connected, which ignores the fact that VANET
is easily fragmented due to its nature [9]. In this paper,
instead of making such assumptions, we use RSUs to mitigate
network fragmentation and use percolation to help us study
fragmentation.
Network fragmentation is a common problem when
network connection between several intermediate nodes are
lost, these nodes often hold connection to a cluster, and as aresult, the connection to another whole cluster of nodes are
lost. In wireless network, fragmentation could appear whennode density is extremely low or a sparse distribution. The
consequences are equally severe: it causes delivery rate todrop significantly.
To deal with fragmentation problems in VANET, this paper
proposes a usage of RSUs to assist a broadcast. This paper
assumes that every node equips an OBU with DSRC and GPS
enabled. Moreover, limited number of RSUs was installed
along the road. Although how and where to install RSUs isout the scope this paper, we assume a generic situation. Thatis, RSUs are deployed in a scattered manner that cannot form
a connected network and every RSU works independently.
In a multi-hop broadcast, when a source node initializes abroadcast, it simply broadcasts a message and utilizes somemechanism to let other nodes relay it. In conventional
broadcast schemes, there are no roles for RSUs to participate
in this process. Theoretically, putting RSUs into broadcast
process by letting them rebroadcast with magnified signal andfurther transmitting range is beneficial. Here, we proposed amechanism to be installed on RSUs that could still work
independently to participate and contribute to the broadcast
process even if they are not connected.
The proposed scheme for RSU in VANET broadcast could
be divided into following two parts. For the first part, RSU
should use a maximum transmission range to rebroadcast a
message when it receives the one. For the second part, tomitigate the broadcast storm, it should suppress a further
rebroadcast in the inner region of its broadcast range by using

Most of the practical broadcast schemes are either variationor hybrid of the 4 basic broadcasts: Counter, Location,
Distance and Probability [4].
Smart broadcast [7] is a variation of distance-based
broadcast, the area around a source node is divided into
different sections as shown in Figure 2, which allows every
node to have their own contention window. In this way, theouter most nodes in S2 have the highest priority to access thechannel. When broadcast starts, other nodes around the source
node will start trying to gain the access to the broadcast
channel but only the node with

Fig. 2. Sections in smart broadcast

higher priority (further distance to the source node) will haveit.
This scheme provides a way to select relay forrebroadcasting using distance-based broadcasting scheme andit is practical and it guarantees maximum dissemination range.
Another interesting broadcasting is called stochastic
broadcast for VANET [8], which is a combination of
distance-based broadcast and stochastic broadcast. In thepaper, every node must decide their own rebroadcast
probability (the probability of being a relay). The paper
proposed two ways to determine a retransmit probability: one
is fixed; another dynamically determined according to a
one-hop distance. That research has following 2 advantages.

III. NETWORK ARCHITECTURE

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

certain approach as shown in Figure 3. The detailed broadcast
scheme for RSU side is described as follows:(1) When RSU receives a broadcast message, check if it isduplicate message.
(2) If it is not duplicated, RSU immediately preform a
rebroadcast burst with a message that contains theoriginal message and also two addition scalar values:
broadcast suppression range Rs and minimum
broadcast suppression Pm.
Note that Rs and Pm are used to suppress the rebroadcast ofnodes in the transmitting range of RSU.

Fig. 3. RSU broadcast suppression

IV. PROPOSED BROADCAST SCHEME
The proposed broadcast scheme was developed on top of
the basic probability-based broadcast, which is explained asfollows:(1) Every node maintains a value Pc, which indicates thecurrent rebroadcast probability.
(2) While receiving a non-duplicate broadcast message, anode enters a probability check process that would
cause the node has the probability Pc to rebroadcast
the message after a default contention window.
(3) After a probability check, no matter the node
retransmits the message or not, Pc is reset to a default
value Pd.
Note that Pc was provided depending on different broadcast
schemes, which will be discarded every probability check. Pd
is the default broadcast probability, which enables every node
to have Pd even if Pc was not set.
To align the behaviour of OBUs to the proposed scheme,
this paper proposes the following scheme for OBU to deal
with broadcasts from RSU:
(1) When an OBU receives a broadcast message from
RSU, it checks the message whether it is a duplicate
message or not.
(2) If it is not duplicated, OBU uses either signal strength
or location services to get its distance to thecorresponding RSU, which is denoted as Rro.
(3) If Rro < broadcast suppression range Rs, set the current
rebroadcast probability to suppression minimum Pm.
(4) Proceed to probability check.

453

Considering the broadcast burst from RSU is likely to cover
more area with stronger signal strength, the proposed scheme
utilizes the scheme mentioned above to ensure that broadcasts
from RSU suppress other broadcast within the radius of Rs to
suppression minimum value Pm.
Although this paper focused on using V2I to maximum theeffectiveness of the broadcast, it is imperative to keep in mind
that V2V is still doing a heavy lifting in the broadcast
propagating process.
Therefore, this paper proposes a
dynamic scheme for V2V communication, which is backed bypercolation study.
The scheme we proposed for V2V communication could
work independently, even without the support of RSUs, this isbecause in large scale network it is not appropriate to assume
there is a reasonable amount of RSUs installed in each area,
thus we developed this self-organized scheme for V2V for
better scalability.
Wireless ad hoc network could be simplified into a modelthat consists of infinite nodes generated by a Poisson processusing certain density. For example, in flooding every node isconnected to the other node only if it’s within the transmission
range, this is because in broadcasting the only connection is totransmit the broadcast message, if the receivers successfullyreceived a message and rebroadcast is then it’s called aconnection. So for a constant transmission range and constantarea, only node density could affect the chance of forming aconnected graph. With the density increased, the chance of
all the nodes forming a connected graph is also increased.
Continuum percolation is a study that dedicates to analyse thismodel [10].
On the other hand, if a node does not send any message
during a broadcast (does not connect to any other node), it canbe treated as if it was not there. In Figure 4 we demonstrate
two scenarios when node S broadcast the message to node D
through relay node I, the scenario 1) is equivalent to scenario2) because nodes that did not rebroadcast the message will not
add new connection. In percolation study we only consider
nodes with connections so those nodes are negligible. Thus
setting a rebroadcast probability to all nodes basically reduce
the active node density, which makes the network an analogueof continuum percolation.

Fig. 4. Nodes did not rebroadcast messages is negligible in wireless broadcast

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

If nodes are distributed in a space by density ρ, we could
denote an expected neighbourhood of a certain node as λ iscalculated using following Eq.(1) [11]:λ = ρπ r 2(1)
where r denotes a node transmission range. With higher λ, itis more likely that infinite nodes are in one connected cluster
as is shown in Figure 5. The author of [8] used λc=4.508 to
4.515 and verified this value to have 99.99% probability toform one connected cluster, which means no minors such as
shown in Figure 5. In this paper, we try to align the proposed
scheme to match this value in order to maximize the broadcast
effectiveness.

⎧⎪0( N = 0)⎪
Pc = ⎨1(0 < N < 5)⎪ 4.5⎪( N ≥ 5)
⎩ N

454

(3)

(1) Reset the neighbour list.
(2) Proceed to probability check.
(3) Reset Pc to Pd
In this way, Pc could be aligned to a reasonable value using
N. Since N is a dynamic value and constantly changing, Pc istheoretically always an optimal value.

V. PERFORMANCE EVALUATION

Fig. 5. Percolation

As is explained previously, if a node does not participate
rebroadcasting in the broadcast process, the node is considered
to be not in this network [8]. In stochastic broadcast, we haveEq.(2):

λs = P ρπ r 2(2)where P denotes the rebroadcast probability. For example, if
λ=10 and the rebroadcast probability for every node is 50%,λs=5 is good enough to ensure the network to be connected,that is, the broadcast can be propagated. As explain incontinuum percolation, please note that the ideal situation is
λs=4.5, which means rebroadcast probability should beroughly 45%.
In order to maintain λs, it is necessary to adjust P
dynamically while the network topology changes. Thus, every
node needs to maintain a neighbour node list using either
beacons or location services. In this paper, we assume every
node maintains a neighbour list using a simple one-hop beaconand total neighbourhood count to be N.
The V2V communication scheme is explained as follows:• Once the node receives a non-duplicate broadcast
message from another vehicle, set Pc using thefollowing Eq.(3):To evaluate the performance of the proposed scheme, asimulation was conducted in two different scenarios using
different sets of parameters.
In this simulation, static
stochastic broadcast scheme, pure flooding scheme and the
proposed is evaluated in both city and highway environment.
General parameter used in this simulation is listed in Table I.
Please note that is this simulation we intended to thefollowings:• Suppress the further broadcast near the RSUs within 80%
of its transmission range.
• Suppressed rebroadcast probability is not set to 0 because
if the node density is high, there might still be
rebroadcast to cover the delivery failure caused bycollisions.
• We set the RSUs with stronger presence by extending thecommunication range to simulate further transmission
range.
• Replace actual nodes as RSUs for propose scheme. This
is to avoid the performance boost brought by addition
nodes (RSUs).
In the simulation, the performance of a broadcast scheme
was evaluated using three metrics: message delivery rate,
network delay and number of broadcasted packets.
Message delivery rate is the basic performance evaluation,
which indicates if the message was successfully delivered to
all nodes. Network delay basically shows how long it takes
for the message to propagate to all possible nodes and it
evaluates the effectiveness of each rebroadcast. As message
travels along many hops, the delay increases. Number ofbroadcasted packets indicates how many broadcast packets areused in the broadcast process, it evaluates the network usage
and potential broadcast storm problems.
Since pure flooding rebroadcast every message it receives,
it can achieve the theoretically highest deliver rate andgenerate the most packets and create the infamous broadcast
storm problem, we only simulation this scheme to compare itwith the other broadcast schemes so please note that it is notfor practical usage.
As shown in Table II, the parameters used in the firstsimulation aim to recreate a broadcast on highway scenario
with the aid of RSUs installed along the road. The result was
shown in Figures 5-7.
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

455

SIMULATION 2 PARAMETERSTABLE I

Parameter

GENERAL STOCHASTIC BROADCAST SIMULATION PARAMETERS

Parameter

Value

Rs

0.8×RSU transmission range

Pm

0.05

Pd

0.2

Communication
range V2V

1 km

RSU burst

3 times without contention window

RSU burst range

1.5 kmBeaconcontention
Rebroadcast
contention

≤1ms
≤2ms
TABLE IISIMULATION 1 PARAMETERS

Parameter
Area
Number of
RSU
RSU
distribution

Value
20km × 0.1km

Number of
RSU
RSU
distribution

9km × 9km
5
RandomMobility

Random waypoint

Speed

10km/h to 30 km/h

Node count

15 to 45 (10 to 40 for RSU-Assisted
broadcast)Since RSUs are distributed randomly in city scenario,
message deliver rate dropped a little comparing to the highway
scenario from Figure 9 and Figure 6. In general, the proposed
broadcast scheme outperforms the static stochastic broadcast
and went toward the upper bound, without bring heavy traffic
to the network and cause broadcast storm problem (comparing
to flooding). Also, there’s no need to acquire the network
condition in order to calculate the broadcast probability before
the broadcast (comparing to static stochastic broadcast).

5
Uniform, installed along the road

Mobility

Random waypoint

Speed

50km/h to 80 km/h

Number of
nodes

Area

Value

15 to 45 (10 to 40 for RSU-Assisted
broadcast)

From Figures 6 and 7, the proposed broadcast scheme
achieved better deliver rate and lower delay at the same timein high node density due to higher RSU’s coverage. Sincedeliver rate has been increased, number of broadcasted packets
will increase accordingly in order to cover more nodes. In
Figure 8, we can see that number of broadcasted packets isincreasing but please note there is an overhead cause byproposed method.
As shown in Table III, the parameters we used in the next
simulation are about recreating a broadcast in city scenario
where RSUs are randomly installed in the city and cars
moving to random way point.
In city scenario the proposed broadcast scheme got a
performance boost in the sense of delay and packet usage dueto the sparse distributed RSUs comparing to highway scenario.
From Figures 9 and 10 the proposed broadcast scheme
achieved higher deliver rate with almost the same delay
comparing with static stochastic broadcast. If comparing
Figure 11 with Figure 8, it is obvious that in city scenario
packet usage also decreased. Achieving high deliver rate withlower delay and lower packet usage means every broadcast
covers more nodes, which is our intention to mitigate
broadcast storm problem and boost the effectiveness of thebroadcast scheme.

Fig. 6. Message deliver rate in highway simulation scenario

Fig. 7. Delay in highway simulation scenario

TABLE III

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

Fig. 8. Packet usage in highway simulation scenario

456

Fig. 11. Packet usage comparison in city simulation scenario

VI. CONCLUSION

Fig. 9. Message deliver rate comparison in city simulation scenario

Fig. 10. Delay comparison in city simulation scenario

This paper introduced the challenges in VANET emergency
message broadcast and its contribution of protecting human
lives and properties. It also summarized our objectives tocontribute to emergency message broadcast in VANET
research. This paper also categorized different broadcast
schemes, analysed their strength and weakness. On top of that,
this paper introduced two broadcast schemes that related to thestudy.
In the discussion of exclusive characteristics of VANET,
this paper analysed some major concerns in VANET such asprivacy, safety and scalability. In order to boost performanceand mitigate network fragmentation, we proposed the usage of
RSUs to assist to broadcasting process, explained the scheme
for RSUs to work actively and individually in the broadcast.
By simplify VANET model using percolation theory, thispaper developed another scheme to be used in V2V
broadcasting.
In this discussion, this paper explained
percolation study and how to apply it to stochastic broadcast
instead of flooding broadcast along with how to achieve thetheoretically highest performance using stochastic model andapply the result in the proposed schemes.
In order to study the performance of the proposed scheme,
this paper conducted two simulations. From the result, theproposed scheme achieves our goals by increasing deliver rateand does not bring huge impact on delay and network usage,
especially in city scenario.
The proposed scheme take privacy and scalability into
consideration by not using beacons to send personal data, notusing location services and every part of the scheme works
independently (V2V scheme will work without RSUs, RSUs
do not form a connected graph and they are not connected to
an infrastructure-depended network), which guarantees
scalability even being deployed in large scale network.
The amount and position of installed RSUs may have
impact on the performance. In the simulation we replace thenodes with RSUs when RSUs are needed, so that they will notbenefit from higher actual node density. Moreover, we avoid
assuming connections to location services but we are stillusing beacons, this is not absolutely the ideal solution. We
still have some future works such as evaluating the
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

performance impact of RSUs and finding alternative, safer
way to calculation node density without using beacons.

REFERENCES[1]

M. L. Sichitiu; M. Kihl, “Inter-vehicle communication systems: asurvey,” IEEE Commun. Surveys & Tutorials, vol. 10, no. 2, pp. 88-105,
Second Quarter 2008.
[2] Bureau of Transportation Statistics, “National transportation statistics,”
US Department of Transportation [Online]. Available:
http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/nation
al_transportation_statistics/index.html, Accessed Oct. 2013.
[3] B. Williams and T. Camp. “Comparison of broadcasting techniques formobile ad hoc networks.” 3rd ACM Int. Symp. Mobile Ad Hoc Netw. &Comput., pp. 194-205, June 2002.
[4] S.-Y. Ni, Y.-C. Tseng, Y.-S. Chen, and J.-P. Sheu, “The broadcast storm
problem in a mobile ad hoc network,” in 5th annual ACM/IEEE Int.
Conf. Mobile Comput. and Netw. pp. 151-162. Aug. 1999.
[5] D. Jiang, V. Taliwal, A. Meier, W. Holfelder, and R. Herrtwich, “Designof 5.9 GHz DSRC-based vehicular safety communication”, IEEE Trans.
Wireless Communication, vol. 13, no. 5, pp. 36-43, Oct. 2006.
[6] X. Ma and X. Chen, “Performance analysis of IEEE 802.11 broadcast
scheme in ad hoc wireless LANs,” IEEE Trans. Vehicluar Technology,
vol. 57, no. 6, pp. 3757-3767, Nov. 2008.
[7] E. Fasolo; A. Zanella, and M. Zorzi, “An effective broadcast scheme foralert message propagation in vehicular ad hoc networks,” in 2006 IEEE
International Conference on Communications, vol.9, pp. 3960-3965.
June 2006.
[8] M. Slavik and I. Mahgoub, “Stochastic broadcast for VANET,” in 7thIEEE Consumer Commun. and Netw. Conf., pp. 1-5. Jan. 2010.
[9] N. Wisitpongphan, F. Bai, P. Mudalige, V. Sadekar, and O. K. Tonguz,“Routing in sparse vehicular ad hoc wireless networks,” IEEE J.
Sel.Areas Commun., vol. 25, no. 8, pp. 1538-1556, Oct. 2007.
[10] B. Bollobas and O. Riordan, Percolation, Cambridge Univ. Press, 2006.
[11] P. Balister, B. Bollobas, and M. Walters, “Continuum percolation withsteps in the square or the disc,” Random Struct. Alg., , vol. 26, no. 4, pp.
392-403, April 2005.

457

Yoshiaki Tanaka received the B.E., M.E., andD.E. degrees in electrical engineering from the
University of Tokyo, Tokyo, Japan, in 1974, 1976, and1979, respectively. He became a staff at Department ofElectrical Engineering, the University of Tokyo, in
1979, and has been engaged in teaching and researchingin the fields of telecommunication networks, switchingsystems, and network security. He was a guest
professor at Department of Communication Systems,
Lund Institute of Technology, Sweden, from 1986 to 1987. He was also avisiting researcher at Institute for Posts and Telecommunications Policy, from1988 to 1991, and at Institute for Monetary and Economic Studies, Bank ofJapan, from 1994 to 1996. He is presently a professor at Department ofCommunications and Computer Engineering, Waseda University, and avisiting professor at National Institute of Informatics. He received the IEEE
Outstanding Student Award in 1977, the Niwa Memorial Prize in 1980, theIEICE Achievement Award in 1980, the Okawa Publication Prize in 1994, theTAF Telecom System Technology Award in 1995 and in 2006, the IEICE
Information Network Research Award in 1996, in 2001, in 2004, and in 2006,
the IEICE Communications Society Activity Testimonial in 1997 and in 1998,
the IEICE Switching System Research Award in 2001, the IEICE Best Paper
Award in 2005, the IEICE Network System Research Award in 2006, in 2008,
and in 2011, the IEICE Communications Society Activity Award in 2008, theCommendation by Minister for Internal Affairs and Communications in 2009,
the APNOMS Best Paper Award in 2009 and in 2012, and the IEICEDistinguished Achievement and Contributions Award in 2013. He is a Fellowof IEICE.

Xing Fan was born in Shanxi, China on June 30th.
Xing Fan earned bachelor of engineering in Beijing
University of Technology in July 2012. Xing Fan is amaster student majored in ad hoc network at Waseda
University, Japan while presenting this paper.

Bo Yang received his B. E. degree in computer scienceand technology from Xi Dian University, Xi’an, China,
in 2009. He received his second M.E. degree inInformation Communication from Waseda University,
Tokyo, Japan, in 2012. Currently, he is working toward
the Ph. D degree in the Global Information andTelecommunication Studies, Waseda University, Tokyo,
Japan. He won the ICACT best paper award in Feb.
2012.
Ryo Yamamoto received his B.E. and M.E. degree inelectronic information systems from Shibaura Instituteof Technology, Tokyo, Japan, in 2007 and 2009. Hereceived D.S. in global telecommunication studies fromWaseda University, Tokyo, Japan, in 2013. He was a
research associate at Graduate School of Global
Information and Telecommunication Studies, Waseda
University, from 2010 to 2014, and has been engaged inresearching in wireless communication networks. He ispresently an assistant professor at Graduate School ofInformation Systems, The University of Electro-Communications. He
received the IEICE young researcher's award in 2010, the IEICE Network
System Research Award in 2014. His current research interests are mobile ad
hoc networks and cross-layered protocols.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

458

Greening Potential Estimation of
Data Network Equipment
Yuhwa Suh*, Kiyoung Kim**, Jongseok Choi*, Yongtae Shin**Department of Computer Science and Engineering, Soongsil Universiety, Seoul, South Korea** Department of Computer Software, Seoil University, Seoul, South Korea
zzarara@ssu.ac.kr, ganet89@seoil.ac.kr, jschoi@ssu.ac.kr, shin@ssu.ac.kr
Abstract—Green Internet is becoming a major concern recently.
The main concept of it is to improve energy efficiency in theInternet for reduction of unnecessary energy consumption.
Generally, energy consumption of data network equipment in theInternet is unknown although they use a substantial amount ofenergy. In this area, there is a lack of deeper related studies with a
special focus on wired networking and it still remains to be many
challenges. This paper aims at exploring an impact of data
network equipment for greening the Internet. We first introduce
backgrounds and motivations of green networking. Secondly, we
estimate energy consumption, costs, and energy savings potentialof data network equipment in detail. Thirdly, we assess impacts of
it based on IP traffic type and propose new viewpoint on energy
efficiency focused on the quality of service (QoS). Lastly, we
propose the future works for green networking from theperspective of QoS.
Index Terms—Energy efficiency, green networking, QoS, data
network equipment

I. INTRODUCTIONT

ODAY, a huge numbers of electronics are used and most ofthem have network connectivity. As setting up ubiquitous
computing environments ubiquitous various devices tend toembed computing and networking functionalities. Thus theInternet has been expended quickly with growing greenhousegas (GHG) emissions and electrical requirements from it.
However, these GHG emissions and energy consumption are
becoming key factors of limiting expansion of the Internet.
Manuscript received May 15, 2014. This research was supported by the
MSIP (Ministry of Science, ICT & Future Planning), South Korea, under the
ITRC (Information Technology Research Center) support program(NIPA-2014-H0301-14-1010) supervised by the NIPA (National IT Industry
Promotion Agency).
Yuhwa Suh is with the Department of Computer Science and Engineering,
Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul 156-743, South
Korea (phone: +82-2-826-0690; e-mail: zzarara@ssu.ac.kr).
Kiyoung Kim is with the Department of Computer Software, Seoil
University, 28 Yongmasan-ro 90-gil, Jungnarg-gu, Seoul 131-702, South Korea(e-mail: ganet89@seoil.ac.kr).
Jongseok Choi is with the Department of Computer Science andEngineering, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul
156-743, South Korea (e-mail: jschoi@ssu.ac.kr).
Yongtae Shin is with the Department of Computer Science and Engineering,
Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul 156-743, South
Korea (e-mail: shin@ssu.ac.kr).

To overcome these problems, greening of the Internet has
begun to be studied with the goal of improving energy
efficiency of the current Internet. There are various approaches
found on energy-saving mechanisms and power management
criteria for greening the Internet, but they have mainly focusedon edge devices such as end-user computers and data-centers.
However, the energy consumption associated with network
functionality is a big issue that we need to focus on, due to
growing IP traffic from excess network-connectivity. Thisissue usually referred to as green networking [1], which energy
managing mechanisms exploiting network-specific features
like ad-hoc network have to be added to the wired data network
for boosting the network energy efficiency.
The use of the Internet is a part of daily life, but a volume ofenergy used for providing network connectivity is largely
unknown. In particular, there are few reliable and practicalfigures of greening potential of wired data network equipment
for greening the Internet.
The Internet will eventually be constrained by energy density
limitations rather than by the bandwidth of the physicalcomponents because of its high dependency on electronics. Soenergy efficiency improvement of the current Internet is a
critical issue. But, a subtle trade-off between energy saving andperformance exists because energy efficiency must be treated
together with increasingly diversified demands of services andsophisticated the quality-of-service (QoS) support.
This paper aims at estimating energy consumption andsavings potential in the today’s wired data network equipment,
understanding impacts of them and providing comprehensive
view on green networking. In particular, we are devoted toenterprise network equipment. The paper is organized asfollows. Section II states detailed background of the concerns
and reasonability of study on greening networking. Section III
includes an estimate of energy consumption, costs, and savings
potential for wired data network equipment that primarily
switches and routes IP packet from a source to a destination.
Section IV further gives estimates of impacts of network
equipment depending on traffic type in an aspect of energy
efficiency considering for QoS and presents directions for
future works related to our estimations. Finally, the conclusions
are drawn in section V.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014459

II. PROCEDURE FOR PAPER SUBMISSION
Today, the reduction of energy consumption is the world’s
major concern. Residential and commercial buildings accountfor approximately 32% of global energy use and almost 10% of
total direct energy-related CO2 emissions. Energy demands
from the buildings sector will be more than double by 2050 [2].
Much of this growth is fuelled by the rising number ofresidential and commercial buildings in response to the
expanding global population.
In the building sectors, the huge potential of improvements
in energy efficiency remains untapped, and it will have thelargest impact on energy savings and CO2 emission mitigation
through energy-efficiency technologies. By the International
Energy Agency (IEA) 2DS (2°C scenario) [2], which assumed
policy action consistent with limiting the long-term global
temperature increase to 2°C, buildings sectors can contribute to
2DS objectives about 18% of CO2 reduction shares by 2020.
The Information and Communication (ICT) devices occupy
over 50% [3] of the electronics used in buildings, and theamount of greenhouse gas (GHG) emissions from their energy
use is significant as in Fig. 1. In 2005, CO2 emissions producedin the ICT sector amount to 98.3MtCO2e (carbon dioxide
equivalent) or 1.9% share of the total EU-25 CO2 emissions andthese are projected to double by 2020 as each 187.7MtCO2e or
4.5% in BAU (Business As Usual) scenario assuming no
significant efforts to reduce emissions. However, in
ECO-scenario adopting energy-saving solutions, CO2
emissions are expected to decline to 132.1MtCO2e or about 3%,
this is the amount decreased by about 30% of BAU-scenario.
Fig. 2 shows GHG emissions footprint of networks from2002 to 2020. In 2011, this amounted to 22 % of the total ICT
footprint and represents 5% compound annual growth rate
(CAGR) by 2020. Overall, wired networks’ emissions are
estimated to grow at a 4% CAGR from 2011 to 2020 to reach
0.14 GtCO2e. The GHG emission footprint from wired network
equipment is to amount to approximately 11 % of the total ICT
footprint and approximately 0.25% of total GHG emissions [5].
This is a non-neglected level, so the current wired network
infrastructure needs to challenge for energy efficiency.
IPCC 2007 climate change report [6] presents top-downstudies that indicate substantial economic potential for the
mitigation of global GHG emissions over the coming decades.
As shown in TABLE I, in 2030, global economic mitigation

Fig. 2. Global mobile and wired networks emissions [5]potential estimated and assessed the economy-wide potential of
mitigation options. TABLE I assumed two scenarios, SRESA1B and SRES B2 (SRES: IPCC Special Report on Emissions
Scenarios). They assumed that GHG emissions in SRES A1B
are about 68 GtCO2e and them in SRES B2 is about 49 GtCO2e
in 2030. They are measured economic potential to US$ bydifferent GHG mitigation rates. In TABLE I, on assumptionthat GHG emission is 68GtCO2e/year and GHG mitigation rate
is 13~27% (9~18 GtCO2e/year) in 2030, it is possible to save
20 US$/tCO2e.
With reference to [5][6], we estimated economic potential of
GHG emission mitigation of network-connected equipment to
US$ from 2020 to 2030. Assuming 2% CAGR in BAU, GHG
emissions of global network-connected equipment are expected
to emit about 1.55 GtCO2e in 2030. According to [5], if
network-connected equipment is introduced to other sectors,
global GHG abatement potential enabled by these technologies
is between five and seven times larger than network-connected
equipment’s own footprint. Therefore, global GHG emissions
is likely to reduce annually about 7.8~10.9 GtCO2e, using
TABLE I, we can estimate economic savings potential atapproximately 156~218 billion US$. The energy consumptionof network-connected equipment in the USA reflects theworld’s trend because the USA accounts for the largest amountof the world’s (40% of world, in 2008 [7]).
Appliances take a great share of building electricity use, andare also of growing importance in the service sector in the form
of office equipment. Globally, network connectivity is being
added to appliances which would not previously have had suchfunctionality, and demand for the availability of traditionally
network-connected equipment is increasing.
In the USA which occupies the largest proportion of global
electricity consumption (27%, 2008 [3]), as shown in Fig. 3,
buildings accounts for over 70% of the total electricity
consumption in 2006, and electronics account for about 11% of
TABLE I GLOBAL ECONOMIC MITIGATION POTENTIAL
IN 2030 ESTIMATED FROM TOP-DOWN STUDIES [6]Reduction
Reduction
Economic
relative torelative to
Carbon pricepotential
SRES A1B
SRES B2(US$/tCO2e)(GtCO2e/yr)(68GtCO2/yr)%(49GtCO2e/yr)%
20
9-18
13-27
18-37
50
14-23
21-34
29-47
100
17-26
25-38
35-53

Fig. 1. GHG emission and savings potential (EU-25) of ICT : source [4]Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014460

Fig. 3. Electricity consumption of the USA (2006), source : [3]Fig. 5. The USA energy costs for network-connected equipmentbuilding electricity use. Electricity consumption of
network-connected equipment forms about 52% of electronics
and about 0.6 ~ 0.7% of the total buildings’.
Reference [7] presents global energy consumption of
network-connected equipment in buildings (e.g., PC, Printer,Phones, TV, game console, audio receiver, media player, STB,
router, gateway etc.) and savings potential of them. This isestimated by the degree of market penetration of energy
efficiency technologies (e.g. 20.08 ~ 65%) as shown in Fig. 4.
It is estimated that in 2008, total energy consumption by
network-connected equipment was 423.85TWh and it accounts
for 2.3% of the global electricity consumption (18,603TWh).
Based on market trends, this is projected to increase to 646TWh
in 2015 and 849TWh in 2020-double the 2008 level. The green
parts in Fig. 4 implies that how much energy saving is possible
by energy efficient technologies. The lower- end estimate ofwasted energy is around 20%, as a result of excess connectivity
and/or the use of sub-optimal technologies instead of
cost-effective improved technology. This 20% of energy could
be saved by means of implementation of power management
and power-level reduction policies. The maximum estimate(technical potential) is around 65% of energy, assuming all
network-connected products and components are 1W powerstate lower, and implementation of effective power
management policies are on them. The amount of energy
wasted by excessive connectivity is estimated between 85TWh
and 275TWh in 2008, rising to between 130TWh and 420TWhin 2015, and between 170TWh and 551TWh in 2020, anamount that is superior to the entire electricity consumption of
all network-connected equipment in the USA in 2008 (about
169TWh).
We made an effort to look at a scale of expenditure on energy
use of network-connectivity equipment in BAU and ECO
scenarios. With reference to [7][22], we presented roughly an
estimate of the costs of network-connected equipment in the
USA between 2008 and 2020 (Fig. 5). We supposed that theenergy consumption of network-connected equipment grows
annually at 7.3% from 2008 to 2015 and at 6.3% from 2016 to2020 the same as [7] and ECO scenario has 20% energy savings
potential (as the lower limit) over BAU. As a result, it ispossible to save 3~9 billion US$ every year from andaccumulated saving is about 71 billion US$.
This energy consumption is caused by the absence ofeffective power management of network-connected equipment.
Greater quantities of new network-connected products areentering the market and products are spending more time inhigher power modes due to network-related requirements and alack of effective power management strategies. Powerconsumption in lower power modes is also increasing because
network interfaces require more power in standby modes inorder to maintain a network link and networks are tending
towards faster speeds and higher bandwidth, which increases
power in the absence of effective power management. So, it isimportant to consider energy efficiency related to networking
functionality in electronics. Moreover, it is useful to distinguishbetween edge devices and network equipment (where the mainfunction is to maintain network links) because their functionand energy-saving potential are quite different. So far, edge
devices have become an object of attention in terms of energy
efficiency and many related researches have carried out, but
network equipment has not. In this paper, we therefore discuss
relatively unknown energy consumption and savings potential
by wired data network equipment.

III. ENERGY CONSUMPTION AND SAVINGS POTENTIAL FOR
DATA NETWORK EQUIPMENT

Fig. 4. Projected energy consumption and wasted by network-connected
equipment worldwide, source : [7]In this paper, we specifically deal with the energy
consumption and the implication of wired data network
equipment whose primary purpose is to transport, route, switch,or process network traffic, excluding edge devices such as PCs,servers, other sources and sinks of IP traffic.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

The reasons that we should note to energy conservation of
the wired network equipment are the followings. Firstly, there
is inefficiency of the current networking system. Networking
infrastructure involves high-performance and high-availability
machines. They therefore relay on powerful devices, which areorganized in an over-provisioned and redundant architecture[1]. Traditionally, networking system are designed to endure
peak load and degraded conditions, they are dimensioned withextra capacity to allow for unexpected events. As a result,during low traffic periods, over-provisioned networks are also
over-energy-consuming. For resiliency and fault-tolerance,
networks are also designed in a redundant manner. Devices areadded to the infrastructure with the sole purpose of taking overthe duty when another device fails, which further add to theoverall energy consumption however these objectives areunder-utilized in normal operation [1]. Moreover, network
equipment expends a great deal of energy even when it’s at idle
(they are powered on 24/7). Unlike monitors or othercomputingequipmentthatsatisfy
EnergyStar
recommendations by going into various energy saving states
when it’s at idle, network equipment typically does not (there
are no Energy Star recommendations for them). This is because
maximizing network throughput and minimizing latency are
the primary driving factors in network design [8], so network
areas leave a large room for energy savings.
Secondly, to support new generation network infrastructuresand related services for a rapidly growing customer population,telcos and ISPs need an ever larger number of devices, withsophisticated architectures able to perform increasingly
complex operations in a scalable way. According to [8],high-end IP routers, which provide more and more network
functionalities, continue to increase their capacities, with anincrease factor of 2.5 every 18 months. At the same time,silicon technologies The sole introduction of novel low
consumption silicon technologies cannot clearly cope withsuch trends, and be enough to draw current network equipment
towards a greener future Internet. Thus, there is much likely as
in other areas where energy efficiency is a concern, it isrequired more approaches to networking area for greening theInternet. Today’s network relies very strongly on electronics,
despite the great progresses of optics are in transmission andswitching. Therefore, how energy consumption of the network
equipment is a key factor of growing importance and it will
become more and more significant to consider energy
efficiency in terms of networking.
Thirdly, in many parts of the world, electricity is a scarce
resource and poses one of the barriers to widespread Internet
deployment. In addition, frequent power outages reduce theuptime of the deployed Internet. If energy consumption of theInternet devices is reduced, we can deploy more devices for thesame energy cost and, given the same UPS capacity, have moreof them up and running during periods of power outage thus
improving overall network reliability [9].
Currently, the major solution of the paradigms for green
networking is proportional computing. Many green network
approaches follow this solution. It depicts different energy

461

Fig. 6. Energy consumption as a function of the utilization [10]consumption profiles by its utilization level as shown in Fig 6.
Energy-agnostic devices, whose energy consumption isconstant, independently of their utilization, represent the worst
case: such devices are either on and consume the maximum
amount of energy, or off and inoperative. In contrast, fullyenergy-aware devices exhibit energy consumption figures
proportional to their utilization level. Between these two
extreme situations, there exist an infinite number of possible
intermediate profiles. Today’s many green networking
techniques have been committed to make energy consumption
figures proportional against their utilization level. This strategy
can be introduced into individual devices or components, a
networked system and protocols.
According to [8], the typical access, metro and core device
density and energy requirements in today’s typical networks.
The power consumption of transport and core network
represents about 30% of the overall network requirement, andaccess devices weigh for 70%. Even if power consumption ofan access device is approximately one-sixth of a core device,
the number of access devices is much more than core devices.
Thus power consumption of the today’s overall network isdriven by access networks.
To look at a scale of local and global energy consumption ofaccess network equipment, we estimated it by (1). ). Eg_ne is theglobal energy consumption of network equipment in BAU(assuming that continuity is maintained considering the current
situation and trends) and ECO scenarios (assuming that there isa push for ICT-based energy efficient solutions) and it is sum of
Enei which is each country’s total energy consumption of
network equipment Enei can be calculated with (2). Ei is acountry’s total consumption of electricity [11], Bi is the shareof buildings in the total electricity of a country [12], N is theshare of network equipment in the buildings electricity, whichis assumed 0.7% based on the USA [3]. n in (1) is the numberof countries included and its value is 136.
n

E g _ ne 

E
i 1

nei

E nei  E i  B i  N  wi

(1)
(2)

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

Fig. 7. The energy consumption and costs by access network equipmentwi  I sbi 1(3)

I sbuswi is weight, which the value is used for taking into accountlevels of IT development varied by country. This weight isdetermined by the IDI access sub-index in 2010. IDI (The ICT
Development Index) is one benchmark measure that serves tomonitor and compare developments in information andcommunication technology (ICT) across countries. It wasdeveloped by the International Telecommunication Union(ITU) and aims to capture the evolution of the informationsociety as it goes through its different stages of development,
taking into consideration technology convergence and theemergence of new technologies [13]. Isb access sub-index of
the IDI captures ICT readiness, and includes infrastructure andaccess indicators. We use Isbi the IDI access sub-index of acountry adjusted by Isbus , the IDI access sub-index of the USA,5.75 in 2010, which the USA accounts for the largest amount of
the world’s electricity use.

Therefore the energy consumption of network equipment is :
1 
 E i  B i  N  I sbi E g _ ne I sbus i 1 n

(4)

The results are shown in Fig. 7. According to that, the global
energy consumption by access network equipment is around
57TWh in 2010. Approximately 9TWh costs in the order of 0.9
billion dollars per year (at 9.8 cents per kWh from all sector
average in 2010 [23]) and requires one nuclear reactor unit. A
typical average nuclear power plant have two reactors, each ofwhich generates an average of 9TWh electricity per year [9] (anaverage life of a nuclear reactor is 40~60 years [24]). As shownin Fig. 7, in 2010, the USA access network equipment’s energy
consumption per year is an enormous amount that required
more than two nuclear reactors. Moreover, to look at a scale ofsavings by energy efficient technologies just at a glance, we462

estimated local and global energy consumption of it in BAU
and ECO scenarios between 2010 and 2020. In this work, as thegrowth of network equipment is correlated with that of the
network-connected equipment, similar growth rates canreasonably be applied to network equipment in general. So, theyearly growth rate of the energy consumption of it is assumed
to be the same those in Fig. 4. In here, we supposed that theenergy consumption of access network equipment grows
annually at 7.3% from 2008 to 2015 and at 6.3% from 2016 to2020 and ECO scenarios are assumed to has 20% and 65%
energy savings potential (as the lower and upper limit) overBAU.
As shown in Fig. 7, the 2010 energy consumption of theworld’s access network equipment accounted for 0.3%
(56.6TWh) of the total world’s electricity consumption(17,839TWh [11]) and it is forecasted to show growing trend
continually. In 2020, it is forecasted to increase by about
109.7TWh and this doubles in 2010. If the energy consumptionof the world’s access network equipment was possible to saveby 20% over BAU, it could save 11~22TWh per year from2010 to 2020. In the USA, it is possible to save by 4~7TWh per
year, then this is an amount needed almost one nuclear reactor.
Access network equipment mainly consists of office network
switches and residential equipment. They account ofapproximately 0.7% (2006) [3] and 1% (2008) [14] of power
use in buildings in the USA. In particular, the enterprisenetwork equipment is largely responsible for about 60% (42%at switches, 6% at routers, 10% at security appliances and 2% at
WLAN [14]) of energy use in the global access network. As atypical case, According to the energy use distribution in
Berkeley campus LAN reported by LBNL [14], switches androuters are account for 87% of total energy use of campus LAN.
So, switches would account for most of energy use in generalenterprise network. This clearly implies why switches aretargeted leadingly for energy efficiency.

IV. AN IMPACT OF DATA NETWORK EQUIPMENT BASED ON
TRAFFIC TYPES
We devoted to analyze energy consuming factors in theswitching equipment which is the major cause (over 80%) ofenergy consumption in a general enterprise network.
According to [15], typically, 60 % of power consumption in
networking equipment is associated with packet-processing
part and packet-processing support part such as memories

Fig. 8. Typical switch power allocation [15]Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

(dynamic random access memory (DRAM) and ternary content
access memory (TCAM)). Fig. 8 presents the maximum switch
power consumption happens at the packet processing
subsystem. This subsystem processes vast traffic withfunctionality such as buffering, lookup, forwarding, switching
fabric and I/O. To increase power savings, the best place to startis to reduce the power consumption associated with packet
processing.
The traditional network equipment does not consume energy
proportional to traffic loads, because they consume constantenergy even during idle period. Total energy consumption of
the current typical switches (non-green network switches)
depends on the number of active ports, capacity of each port,
but does not on port utilization. Current Ethernet switches
require both transmitters and receivers to operate continuously
on a link, thus consume energy all the time, regardless of theamount of data exchanged. That is to say, they consume large
energy even at low loads, which means very low energy
efficiency. As solution to this problem, green technologies are
devoted to make its energy consumption nearly proportional to
its traffic load by approaches such as resource consolidationand selective connectedness. Representatively, energy
efficiency Ethernet (EEE) [25] of adaptive link rate (ALR)
solutions is already well advanced and addressed by IEEE Std.
802.3az and has shown important energy savings at low loads.
Network equipment in today’s Internet is being rapidly
replaced to high-end ones and EEE shows better energy
efficiency over high speed link. With such the trend, energy
consumption of switches would become proportional to its
traffic load. However, green approaches of resource
consolidation and selective connectedness are required moresophisticated mechanisms in terms of the quality-of-service(QoS). The current green networking techniques have hardly
taken QoS into account.
In this context, that switches comprehend a volume andcharacteristic of traffic on them and manipulate appropriately
could improve energy efficiency. Moreover, at the line
consuming second-largest energy, it is important that switches
consider a volume and characteristic of traffic to decidesensitive factors like states (sleeping or idle), timers in green
techniques such as ALR. Further, the green networking
techniques need to focus on the quality of the user experience.
The current green networking techniques have hardly taken
into account QoS but should consider energy efficiency withview of QoS in the future. Processing traffic by green levels
classified on the basis of characteristic of traffic could further
improve energy efficiency. For this, switches need tounderstand and manage information about traffic characteristic
in an aspect of energy efficiency. This enables to consider
further QoS, and thereby could provide more sophisticatedenergy efficiency. Also this information can be utilized forsmart decisions and exact predictions in the resource
consolidation and dynamic adaptation for green networking,either.
Fig. 9 shows global IP traffic growth from 2011 to 2016. In
2016, about 93% of the global IP traffic will be comprised of463

Fig. 9 Global IP traffic, 2011-2016, source : [16]the consumer traffic which includes IP traffic generated byhouseholds, university population, and Internet cafes and theconsumer wired Internet traffic of them will account for the
majority (over 70%) of the IP traffic. Thus switchingequipment will require tremendous energy for processing thesehuge amounts of IP traffic. Internet video, file sharing, andweb/email data account for 99% of total. Especially, Internet
video streaming and downloads of them are beginning to take alarger share of bandwidth and will grow to over 54% of all
consumer Internet traffic in 2016 [16].
Depending on the traffic types, volumes of traffic have very
big difference. Their characteristics and needs are different interms of energy efficiency as well. Thus, we put effort to figureout impacts on network equipment by traffic types in an aspectof greening network. We focused on consumer the wired
Internet traffic which is the majority of the Internet traffic. We
classified based on traffic types and estimated their impacts
from the viewpoint of greening.
In this work, we limited network equipment to enterpriseswitching equipment which account for the majority of energy
consumption of access networks and used the forecast in 2016
in [16]. Also, with reference to a power benchmarking for
network devices in [15][17][18], we supposed that a switch has
a line card with 48 full-duplex 1Gbps ports and it consumes
average 3.3mWatts/1Mbps when it transmits at fully load withthe aggregate bandwidth of 48000Mbps.
In (5), Een is the total enterprise switching equipment’s
energy consumption and Se is the share of it in the total accessFig. 10. Ideal and realistic power characteristic of network equipment

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014464

network equipment, therefore 1 - Se - r is a share of theresidential customer premises equipment (r is a share of routers,
security appliances, WLAN and etc. of the enterprise network
devices and it is about 15% in 2008 [14]). We assumed that Se
is assumed about 40% by section III. T is the amount of thetraffic generated per hour and EMB is the energy consumption
per megabyte. p is the energy proportionality formed by Fig.
10.
E en  T  E MB  S e 

1
p

(5)
Fig. 12. Energy Impacts of Traffic Types on Switching Equipment

 MB per Month tan =  0.003Wh  0.4 tan  30 days  24 hours G en  E en  g kwh(6)

Fig. 10 shows the power consumed by network equipment
against the load on it with a maximum load of G bps. Ideally,
the power consumed should be proportional to the load, withthe maximum power, M watts, being as low as possible. In
practice however, the behaviour of network equipment follow
the line marked realistic, with the equipment consuming I watts
even under idle (no load) conditions. Thus, p is the difference
between the ideal and measured lines in Fig. 10 and we definedtan 
as p 
. In here,  and  are angles at the origin fortan 
the ideal and measured power. p = 1 implies that the equipment
has perfect energy proportionality, and p = 0 implies that theenergy consumed by the equipment is completely agnostic tooffered load.
In (6), Gen is the amount of CO2 emission for processing thetraffic and gkWh is the CO2 emission per kWh and 0.524lb [19].
We estimated the global energy consumption on switches
against the energy proportionality (p) by (5), as shown in Fig.
11. The more p approaches to 1, the more the energy
consumption decreases and it drops sharply by p = 0.2.
Although energy efficiency improves lightly, it can achievemuch energy saving. Based on traffic types, we also estimatedimpacts on the total switches in enterprise network by (5) and(6) (Fig. 12 and Fig. 13). As shown Fig.12 and Fig 13, theInternet video traffic is critical for greening network. When theFig. 11. Energy Consumption against Energy Proportionality on Switching
Equipment

Fig. 13. GHG Impacts of Traffic Types on Switching Equipment

Fig. 14. The greening impacts based on traffic typeenergy proportionality drops below 0.2, energy consumption
and GHG emission of it have strong impacts on network.
We normalized the greening impacts in Fig 14 on a basis ofFig. 12 and Fig 13. In Fig. 14, we set the average level is 0 at
average traffic volume and each figures are the relative valuesof it. Internet traffic is possible to be more specificallydistinguished to application-level classification by IP address,
port number, connection duration, packet size, packet arrivaltime, and others, and they can be classified by aspects of energy
efficiency as Fig. 12, Fig. 13, and Fig 14. This view is possible
to classify with more sophisticated green QoS levels based on
traffic type.
As we have said above, the traditional Internet has large
energy savings potential and this field remains to be challenged
a lot. A major shift is truly needed in research and development
to introduce energy-awareness in the network design, withoutcompromising either the QoS or the network reliability. The
ultimate goal of networking is to provide services to end-users,

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 3, May 2014

so the quality of the user experience is topics that span all
branches of the green networking. So, the energy gain fromenergy efficiency techniques must not come at the price of a
network performance loss.
However this delicate tradeoff arises from opposite
principles: networked systems have traditionally been designed
and dimensioned according to principles such as
over-provisioning and redundancy, green networking
approaches praise opposite practices such as resource
consolidation and selective connectedness. The challenge lays
in this case in applying the latter principles in a way that is astransparent as possible to the user. Most of the previous green
studies focused more on the achievable energy gain, and QoS
has at least partly been taken into account. Furthermore they
have been studied in isolation and that could constitute serious
threats to the QoS. Also, the studies and the developments
related to green networking have been concentrated oncontrolling interface on Layer 2.
The techniques based on L2 such as EEE need more delicate
synchronization mechanism for link termination and they haveto take the trade-off between QoS and energy savings into
consideration. Additionally, diverse transitional techniques
will have been required until the current network equipment iscompletely replaced with the green network equipment.
In terms of these, the estimated data in Fig. 12, Fig. 13, andFig 14 can be used as knowledge for building energy efficientvirtual network. As an example, they can be available to build
VLANs, which provide logical segmentation based onbroadcast domains so reduce unnecessary traffic, improve
network performance and offer independency of network.
VLAN supports 8 levels as 802.1Q [26] tag user priority levelfor QoS. These priority levels are possible to be changed bymanagement policy, so they could be classified under the green
priority levels including information such as energy
consumption, CO2 emissions and costs of network equipment
based on traffic types. Traffic could be processed by the levels
and the network resource could be allocated by them. If
VLANs are mapped by sorts of applications, using mapping
based on protocol types, it is possible to make policies by green
levels such as Fig 12, Fig 13 and Fig 14, and dynamically build
and manage networks by green factors.
Moreover, the above traffic classification based on energy
efficiency can help the current green techniques with the
dynamic adaptation approaches involving only local decisions
make precisional decisions and predictions, as aware of
information about patterns and loads of traffic and it can
support sophisticated energy management by using knowledge
in an aspect of QoS. This approach is possible to introduce tobuilding virtual networks at global level because of Ethernet
which has extended to backbone network. We remain todevelop specifically advanced these in the long term.

V. CONCLUSION
In this paper, we analyzed impacts of data network

465

equipment for the green Internet and presented future works forthe green networking. For this work, we introduced motivation
and background of the concern about greening the Internet inview points of energy resource and also presented importanceand reasonability of study on energy efficiency of data network
equipment. Moreover, we explored detailed energy
consumption and savings potential of data network equipment
by top-down approach. Then, we stated the current research
trends for the green networking and presented limitations in
aspects of the quality-of-service (QoS) and a lack of the global
view point in them. In this context, we further estimates
impacts of data network equipment depending on traffic types
and proposed directions for future works related to ourestimates. So far, the studies of energy efficiency in the green
networking field have focused on dimensioning discreetly
components in hardware or controlling network interface by
local decision. Moreover few works exist from the global
viewpoint which have based on network topology and trafficcharacteristic. Our traffic classification based on energy
efficiency is possible to treat more sophisticatedly trade-off
between energy efficiency and QoS. Also these could help
precise decisions and predictions of existing green techniques
as using the knowledge in an aspect of QoS. Further, thisclassification can be used in building L2/L3 virtual networks
considering for both energy efficiency and QoS. For this work,
we need to undertake more precise measurement and modeling,
and they remain as our future works.
Greening the Internet is inevitable due to with the energy
depletion and environmental threats. Therefore, the green
networking solution should be transparent to users and be
undertaken to do more elaborate works for QoS support. It
should also be developed with a comprehensive view at both
local and global - which opens a number of interestingquestions that are so far all unexplored.
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466

Jongseok Choi is a doctor student in the Internet
Convergences and Networking Laboratory at Soongsil
University in South Korea. He received M.S. degree incomputer science and engineering from Soongsil
University in 2012 and B.S. degree in Computer Sciencefrom Bucheon University in South Korea in 2010. His
research areas include Greening the Internet, WLAN,
Mobile Network.
Yongtae Shin has been working as a professor at the
School of Computer Science and Engineering ofSoongsil University in South Korea, since 1995. He is
also the founder of DigiCAP Inc., which is a leader of the
DRM and CAS solution and service providers in South
Korea. He received M.S. and Ph.D. degrees in computerscience from the University of Iowa. His research areas
include DRM, BCN, Wireless Networks, QoS, Network
Security.

Yuhwa Suh is a doctor student in the Internet
Convergences and Networking Laboratory at Soongsil
University in South Korea. She received B.S. and M.S.
degrees in computer science and engineering fromSoongsil University in 2003 and 2005, respectively. She
worked as a researcher at National IT Industry Promotion
Agency in South Korea. Her research interests include
Greening the Internet, VLAN, Ad-hoc/Sensor Networks
and Multicast.
Ki-Young Kim has been working as a professor at the
Department of Computer Software of Seoil University inSouth Korea, since 2004. He received his M.S. and Ph.
D. degrees in computer science and engineering fromSoongsil University in 1999 and 2003, respectively. He
worked as a researcher at Trigem Information &Communication Co. in South Korea from 1995 to 1997.
His research interests include Mobile Computing,Multicast, ITS, Network Security.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)