JOURNAL OF NETWORKS, VOL. 8, NO. 2, FEBRUARY

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1 JOURNAL OF NETWORKS, VOL 8, NO 2, FEBRUARY A Novel Ranking Algorithm Based Network Selection For Heterogeneous Wireless Access Mohamed Lahby, Leghris Cherkaoui, Abdellah Adib Department of Computer Science, LIM Lab Faculty of Sciences and Technology of Mohammedia BP 146 Mohammedia, Morocco {mlahby, cleghris, adib adbe}@yahoofr Abstract In order to provide ubiquitous access for the users, future generation network integrate a multitude of radio access technologies (RAT S) which can interoperate between them However, the most challenging problem is the selection of an optimal radio access network, in terms of quality of service anywhere at anytime This paper proposes a novel ranking algorithm, which combines multi attribute decision making (MADM) and Mahalanobis distance Firstly, a classification method is applied to build a classes which having the homogeneous criteria Afterwards, the Fuzzy AHP, MADM method is applied to determine weights of inter-classes and intraclasses Finally, Mahalanobis distance is used to rank the alternatives The simulation results show that the proposed algorithm can effectively reduce the ranking abnormality and the number of handoffs Index Terms Heterogeneous Multi-Access, Network Selection, Multi Attribute Decision Making, Fuzzy AHP, Mahalanobis Distance I INTRODUCTION With the growth demand of services which require high bandwidth such as video conferencing, voice over IP and on line game, new technologies representing 4G such as WIMAX and LTE are deployed by service providers However in spite of the radio access technologies, no single wireless network technology is considered to be more favorable than other technologies in terms of QoS In other words, each network access in RAT s seems to be specifically characterized by the bandwidth offered, the coverage ensured by the network as well as the cost to deliver the service Moreover, due to complementarity between the infrastructure based on WIFI, UMTS and 4G, the operators of telecommunication have not yet willing to change their infrastructure based on 2G and 3G For instance WIFI can provide a higher bandwidth with a cover limited, while UMTS ensures a large cover with lower bandwidth However two issues are involved The first one is how to allow the users the possibility to benefit of the all radio access technologies under the principle Always Best connected (ABC) [1] The concept ABC allows the users to be always best connected to different services anywhere at anytime with devices multi-interfaces The second issue is how to ensure the interoperability and the convergence between different technologies which have heterogeneous specifications To cope with the first issue, the operators have designed and developed new terminals which are equipped with multiple interfaces Moreover, the vertical handover [2] is intended to maintain the convergence between heterogeneous networks The handover vertical means that terminal mobile can transfers call from one base station (BS) or point of attachment (AP) which is based on one of RAT s to another base station which based on different RAT s with seamless manner This process can be divided into three steps: 1) Handover initiation: mobile terminal can discover the points of attachment (PoA) to which it can be attached In addition, the terminal collects some informations received signal strength (RSS), link quality, in order to identify the need for handover 2) Handover decision: is called also network selection In this step, the mobile terminal selects the best network access available by using algorithm of decision 3) Handover execution: it consists on establishing the target access network by using mobile IP protocol The present work concentrates on the second step of the handover vertical For that, our work focuses on the optimization of the network selection process for users in order to support many services with best QoS in heterogeneous multi-access environments The network selection problem is considered as a complex problem and mapped in NP-Hard problem [3] Several tasks need to be addressed in order to select the most suitable network in terms of quality of service Generally, it may be considered that three issues dominate the network selection problem The first issue is the identification of the criteria: this issue involves the identification of the appropriate criteria that should be used during the network selection decision The network selection depends on multiple criteria [4], from terminal side: battery, velocity, life user preferences, and from the network side: provider s profile, current QoS parameters In addition, these criteria are static or dynamic that will influence the network selection decision The second issue is the estimation of the criteria weights This issue involves the determination of the appropriate weighting algorithm that allows to weigh each criterion In the literature, there are several methods used to weigh the criteria such as fuzzy logic, random weighting, entropy method and multi attributes decision making (MADM) methods The MAMD includes some methods doi:104304/jnw

2 264 JOURNAL OF NETWORKS, VOL 8, NO 2, FEBRUARY 2013 such as analytic hierarchy process (AHP), fuzzy analytic hierarchy process (FAHP), analytic network process (ANP) and fuzzy analytic network process (FANP) Moreover, the third issue is the ranking of networks The ranking of the available networks is based on the identification of the most algorithm that exploits these criteria in order to select the optimal network Many existing algorithms have been proposed in the literature to cope with this issue According to [5], we can categorize the network selection algorithms into four kinds such handover based RSS, handover based bandwidth, cost function and combination algorithms The last category includes handover algorithms that use fuzzy logic, neural networks, genetic algorithms and MADM methods MADM algorithms represent a promising solution to choose dynamically the optimal access network, which can satisfying the QoS from the available networks In addition the MADM algorithms can be achieved the trade-off among the network condition, the service cost, the application characteristics, and the user preferences Recently, various vertical handover decision algorithms based on MADM approach have been proposed such as multiplicative exponent weighting (MEW), simple additive weighting (SAW), technique for order preference by similarity to ideal solution (TOPSIS), grey relational analysis (GRA), distance to ideal alternative (DIA) and VIKOR According to [6] and [7] the all handover algorithms based on MADM still present two weaknesses: 1) Ranking abnormality: means that the ranking of candidate networks changes when low ranking alternative is removed from the candidate list, which can make the selection problem inefficient 2) Number of handover: unnecessary handoffs should be minimized as they waste network resources and increase processing overheads The main task of this paper is to deal with these limitations by proposing a novel ranking algorithm Our proposed algorithm combines two methods such as Fuzzy AHP and Mahalanobis Distance Firstly, a classification method is applied to build a classes which having the homogeneous criteria Afterwards, the Fuzzy AHP method is applied to determine weights of inter-classes and intraclasses Finally, Mahalanobis distance which can take into account the correlation between different criteria will be used to rank the alternatives This paper is organized as follows Section II presents review of related work concerning the network selection algorithms based on MADM approach Section III describes some MAMD methods Section IV presents our novel ranking algorithm for the network selection Section V includes the simulations and results Conclusions are drawn and future work are indicated in section VI II RELATED WORK Recently, various network selection algorithms based on MADM approach have been developed exhaustively in the literature In [8], [9], [10] and [11], the network selection algorithm combines two MADM methods AHP and GRA The AHP method is used to determine weight for each criterion and GRA method is applied to rank the alternatives In [12], [13], [14] and [15] the network selection algorithm is based on AHP and TOPSIS The AHP method is used to calculate the weights of the criteria and TOPSIS method is applied to determine the ranking of access network In [16] and [17] the network selection decision is modeled using tow MAMD methods AHP and SAW The AHP method is used to provide a weight for each criterion involved in the network selection SAW algorithm is applied to provide a ranking of all alternatives In [18] four vertical handover decision algorithms namely, SAW, MEW, TOPSIS and GRA are studied and compared for all four traffic classes namely, conversational, streaming, interactive and background Among MADM methods mentioned above, TOPSIS method and GRA method have been used extensively to solve the network selection problem However, these two MADM methods still suffer from ranking abnormality Some proposals were presented to avoid the ranking abnormality problem in TOPSIS and GRA In [19], the authors have proposed an iterative approach for application of TOPSIS for network selection problem The disadvantage of this method is the computation time to perform the handover For instance, if we have n available networks, we must repeat iterative TOPSIS n-1 until the best interface network is reached In [20] the authors have presented the DIA algorithm which can select the alternative that is the shortest euclidean distance to positive ideal alternative (PIA) One of the main disadvantages of DIA is doesn t take into consideration the type of normalization When the low ranking alternative is removed from the candidate list, the normalized attribute values of all alternatives will be changed and the ranking order of the alternative will be changed as well In addition the euclidean distance used by DIA doesn t take into account the correlation between different criteria, all the components of the vectors will be treated in the same way In [21] the authors have proposed the ranking algorithm based on finding the median of each column in the normalized weighted decision matrix The disadvantage of this method is that, the ranking algorithm consists on summing each value of the row elements By doing so, all the values of the vectors are treated in the same way, and the different criteria are not correlated as well In [22] the authors proposed GRA-based network selection method to avoid the rank reversal phenomenon in GRA method This one is based on absolute min-max values normalization type The network selection is also influenced by the heterogeneity of the multiple parameters considered in this process, when the MADM methods are applied The heterogeneity is that these criteria dont have the same measure unit and there is an absence of correlation between them For that in [23] the authors have proposed a hybrid method based MADM which combines two methods such as AHP and TOPSIS The proposed method can facilitate the network selection of the mobile

3 JOURNAL OF NETWORKS, VOL 8, NO 2, FEBRUARY terminal while avoiding the ranking abnormality problem In this paper we propose a novel ranking algorithm which combines two methods such as Fuzzy AHP and Mahalanobis distance The proposed algorithm can deal with ranking abnormality problem, and also it allows to reduce the number of handoffs III MULTI ATTRIBUTE DECISION MAKING A AHP The AHP is one of the extensive multi-attribute decision making developed by Saaty [24] The AHP approach has been widely used in network selection process to assign weights for different criteria The AHP approach is based on five steps: 1) Construct of the structuring hierarchy: a problem is decomposed into a hierarchy, this one contains three levels: the overall objective is placed at the topmost level of the hierarchy, the subsequent level presents the decision factors and the alternative solution are located at the bottom level 2) Construct of the pairwise comparisons: to establish a decision, AHP builds the pairwise matrix comparison such as: x 11 x 12 x 1n x 21 x 22 x 2n x ii = 1 A = where, x ji = 1 x n1 x n2 x nn (1) Elements x ij are obtained from the table I, it contains the preference scales TABLE I SAATY S SCALE FOR PAIRWISE COMPARISON Saaty s scale The relative importance of the two sub-elements 1 Equally important 3 Moderately important with one over another 5 Strongly important 7 Very strongly important 9 Extremely important 2,4,6,8 Intermediate values x ij 3) Construct the normalized decision matrix: A norm is the normalized matrix of A(1), where A(x ij ) is given by, A norm (a ij ) such: x ij a ij = n x (2) ij a 11 a 12 a 1n a 21 a 22 a 2n A norm = (3) a n1 a n2 a nn 4) Calculating the weights of criterion: the weights of the decision factor i can be calculated by n j=1 W i = a ij n and W i = 1 (4) n j=1 With n is the number of the compared elements 5) Calculating the coherence ratio (CR): to test consistency of a pairwise comparison, a consistency ratio (CR) can be introduced with consistency index (CI) and random index (RI) Let define consistency index CI CI = λ max n n 1 (5) Also, we need to calculate the λ max by the following formula: n λ max = n i j=1 such b i = i a ij n W i (6) We calculate the coherence ratio CR by the following formula: CR = CI (7) RI The values of RI are represented in the following table II If the CR is less than 01, the pairwise comparison is considered acceptable TABLE II VALUE OF RANDOM CONSISTENCY INDEX RI criteria RI B FAHP The FAHP is one of the extensive multi-attribute decision making Fuzzy AHP is an extension of AHP has been developed to solve hierarchical fuzzy problems [25] In the fuzzy AHP procedure, the pairwise comparisons in the judgment matrix are fuzzy numbers that are modified according to the designers focus The FAHP is based on four stages: 1) Construct of the structuring hierarchy: is the same of first step of AHP approach 2) Construct of the pairwise comparisons: to establish a decision, FAHP builds the pairwise matrix comparison such as: r 11 r 12 r 1n r 21 r 22 r { 2n rii = 05 A = where, r ij + r ji = 1 r n1 r n2 r nn (8) Elements r ij are obtained from the table III, it contains the preference scales 3) Calculating the weights of criterion: the weights of the decision factor i can be calculated by: Where W i = b i n j=1 b i and n W i = 1 (9) j=1 1 b i = [ n j=1 1 r ij ] n (10) 4) Calculating the coherence ratio (CR): to test consistency of a pairwise comparison, a consistency ratio

4 266 JOURNAL OF NETWORKS, VOL 8, NO 2, FEBRUARY 2013 (CR) can be calculated as equation 6 Where the consistency index (CI) can be calculated by: IC = [ n (AW ) i j=1 nw i ] (11) n 1 If the CR is less than 01, the pairwise comparison is considered acceptable TABLE III SAATY S SCALE FOR FUZZY PAIR-WISE COMPARISON Saaty s scale The relative importance of the two sub-elements 05 Equally important 055(or 05 06) Slighly important 065(or 06 07) Important 075(or 07 08) Strongly important 085(or 08 09) Very strongly important 095(or 09 10) Extremely important C TOPSIS The TOPSIS methode has been developed in 1981 [26] The basic principle of the TOPSIS is that the chosen alternative should have the shortest distance from the positive ideal solution and the farthest distance from the negative ideal solution The procedure can be categorized in six steps 1) Construct of the decision matrix: the decision matrix is expressed as d 11 d 12 d 1m d D = 21 d 22 d 2m d n1 d n2 d nm (12) Where d ij is the rating of the alternative A i with respect to the criterion C j 2) Construct the normalized decision matrix: each element r ij is obtained by the euclidean normalization d ij r ij = m, i = 1,, m, j = 1,, n d ij 2 (13) 3) Construct the weighted normalized decision matrix: The weighted normalized decision matrix v ij is computed as: m v ij = W i r ij where W i = 1 (14) 4) Determination of the ideal solution A and the antiideal solution A : A = [V 1,, V m] and A = [V 1,, V m ], (15) For desirable criteria: V i = max{v ij, j = 1,, n} (16) V i = min{v ij, j = 1,, n} (17) For undesirable criteria: V i = min{v ij, j = 1,, n} (18) V i = max{v ij, j = 1,, n} (19) 5) Calculation of the similarity distance: Sj = m (Vi v ji ) 2, j = 1,, n (20) and 6) Ranking: D GRA S j = m (v ji V i ) 2, j = 1,, n (21) S j Cj = Sj + S j, j = 1,, n (22) A set of alternatives can be ranked according to the decreasing order of C j The GRA algorithm is belonging to the grey system theory [27], which is suitable MADM method to analyze the relational grade for several discrete sequences and select the best sequence The basic principle of the GRA is based on gray rational coefficient (GRC) The GRC is used as the coefficient to describe the similarity between each candidate network and the best reference network The GRA method consists of the following steps: 1) Construct of the decision matrix: the decision matrix is expressed as equation 12 2) Construct the normalized decision matrix: each element r ij is obtained by Max method normalization For benefit attribute, the normalized value of r ij is computed as: r ij = d ij d max j (23) where d max j is the maximum performance rating among alternatives for attribute C j For cost attribute, the normalized value of r ij is computed as: r ij = dmin j (24) d ij where d min j is the minimum performance rating among alternatives for attribute C j 3) Construct the weighted normalized decision matrix: can be calculated as equation 14 4) Determination of the ideal solution R : For benefit attribute: For cost attribute: R = [R 1,, R m] (25) R i = max{v ij, j = 1,, n} (26) R i = min{v ij, j = 1,, n} (27)

5 JOURNAL OF NETWORKS, VOL 8, NO 2, FEBRUARY ) Calculation of gray rational coefficient (GRC): is used as the coefficient to describe the similarity between each candidate network and the ideal solution The GRC is calculated as: 1 GRC j = m j=1 v ij Rj j = 1,, n (28) + 1, 6) Ranking: a set of alternatives can be ranked according to the decreasing order of GRC j E DIA The DIA algorithm is belonging to the MADM category that we developed [20] to select dynamically the best network interface and deals with the ranking abnormality of the TOPSIS method In the following, we summarize the main steps of DIA algorithm: 1) Construct of the decision matrix: can be calculated as equation 12 2) Construct the normalized decision matrix: can be calculated as equation 13 3) Construct the weighted normalized decision matrix: can be calculated as equation 14 4) Determination of the ideal solution A and the antiideal solution A (see equations 15, 16, 17,18,19) 5) Calculate the Manhattan distance to the positive and negative attribute: and D j = D j m = m V i V ji, j = 1,, n (29) V ji V i, j = 1,, n (30) 6) Determine the positive ideal alternative (PIA) which has minimum D, and maximum D P IA = {min(dj ), max(d j )}, j = 1,, n (31) 7) Calculate the distance of an alternative to the PIA: R j = (Dj min(d j ))2 + (D j max(d j ))2 (32) A set of alternatives can now be ranked according to the increasing order of R j IV A NOVEL RANKING ALGORITHM BASED NETWORK SELECTION PROCESS A Framing the solution We consider a set A of a finite number of alternatives which are the possible interfaces network for the mobile terminal A = {A i where i = 1, 2,, N} (33) Then we consider a set P of attributes in which the alternatives have to be judged P = {P j where j = 1, 2,, M} (34) We construct K classes homogeneous, named C k from the set P such as: k P = C i and C i C j = if i j (35) with C i = {P k, P k P and k = 1M} The homogeneity is assured through two characteristics: The same measure unit, The correlation between different criteria B scheme for the network selection process The suggested scheme for the network selection is shown in figure 1 The scheme has three components listed below: First, intra-classes weighting system; Second, Inter-classes weighting system; Third, Mahalanobis distance algorithm Figure 1 scheme for the network selection process 1) Intra-classes weighting system: For each set C i, the Fuzzy AHP system is applied to get the weights In other words, every Fuzzy AHP i system produces W C i vector that contains the degree of importance of each alternative A i W C i = [C i A 1, C i A 2,, C i A n ], i = 1,, k (36) 2) Inter-classes weighting system: The inter-classes weighting system provides the weight of every single class, we note that: W = [W 1, W 2,, W k ] where k W i = 1 (37) The combination of each vector concerning each class, allows to construct the decision matrix NW This matrix is defined as: W 1 C 1 A 1 W 2 C 2 A 1 W k C k A 1 W NW = 1 C 1 A 2 W 2 C 2 A 2 W k C k A 2 W 1 C 1 A n W 2 C 2 A n W k C k A n (38) with k is the number of the used classes Moreover both the intra-classes and inter-classes

6 268 JOURNAL OF NETWORKS, VOL 8, NO 2, FEBRUARY 2013 weighting systems are based on Fuzzy AHP The FAHP approach is introduced to deal with the uncertainty by expressing the pairwise comparisons of the decision factors 3) Mahalanobis distance algorithm: is a metric introduced by PC Mahalanobis in 1936 [28] It has played a fundamental and important role in statistic, and data analysis, with multiple measurements In addition mahalanobis distance takes into account the correlation between attributes values The mahalanobis distance between an individual x and a population s multivariate mean u is computed by: D M (x) = (x u) T S 1 (x u) (39) Where S 1 is the inverse covariance matrix In order to select the optimal network while avoiding abnormality of MADM methods, we propose novel ranking algorithm based on mahalanobis distance and Fuzzy AHP The mahalnobis distance allows to measure the distance between each alternative A i and the weighted normalized decision matrix The best alternative is the smallest mahalanobis distance In the following, we summarize the main steps of our novel ranking algorithm: 1) Construct of the decision matrix: can be calculated as equation 10; 2) Construct the normalized decision matrix: can be calculated as equation 11; 3) Construct the weighted normalized decision matrix: can be calculated as equation 12; 4) Calculate the mahalanobis distance of each A i : can be calculated as equation 39, the result as expressed by: D M (A i ) = [D i1,, D im ] (40) 5) Calculate the mean of the attributes vector obtained as equation 40 m j=1 C i = D ij (41) m A set of alternative can now ranked according the increasing order of C i V SIMULATION AND RESULTS A The simulation scenario In this simulation, we consider a heterogeneous environment, which entails six candidate networks, and each network with six parameters The scenario consists of two 3G cellular networks: UMTS1 and UMTS2, two WLANS: WLAN1 and WLAN2, and two WMANS: WIMAX1 and WIMAX2 We consider also six attributes associated in this heterogeneous environment The attributes are: Cost per Byte (CB), Available Bandwidth (AB), Security (S), Packet Delay (D), Packet Jitter (J) and Packet Loss (L) Cost per Byte(CB): This attribute is a measure of the operator s transport cost for a particular access network It can be measured in (USD/byte) Available Bandwidth (AB): This attribute is a measure of the bandwidth available in the access network It can be measured in (Mbps) Security (S): This attribute is a measure of the security level of the link layer It can be measured from the rang 0 to 10 Packet Delay (D): This attribute is a measure of the average delay variations within the access system It can be measured in (milliseconds) Packet Jitter (J): This attribute is a measure of the average delay variations within the access system It can be measured in (milliseconds) Packet Loss (L): This attribute is a measure of the average packet loss rate within the access system over a considerable duration of time It can be measured in (packet losses per million packets) TABLE IV ATTRIBUTE VALUES FOR THE CANDIDATE NETWORKS criteria network CB (%) S (%) AB (mbps) D (ms) J (ms) L (per10 6 ) UMTS UMTS WLAN WLAN WIMAX WIMAX During the simulation, the measures of every criterion for candidate networks are randomly varied according to the ranges shown in table IV B Criteria classification During the construction classes the homogeneity of the criteria should be considered We suggest three classes presented as follows: Class1: Available Bandwidth (AB), Packet Delay (D), Packet Jitter (J) and Packet Loss (L) Class2: Security (S) Class3: Cost per Byte (CB) C The results of the simulation In order to validate the use of our network selection algorithm based on Fuzzy AHP and Mahalanobis Distance (FADM), we present performance comparison between four algorithms namely: TOPSIS, GRA, and FAMD We perform three simulations, for four traffic classes [29] namely background, interactive, conversational and streaming In each simulation the four algorithms were run in 100 vertical handoff decision points by using MATLAB simulator In each simulation we provided the average values for two performance evaluation of ranking abnormality and number of handoffs In the first simulation the AHP method is used to find the relative importance for each criterion In the second simulation we apply the scheme for the network selection displayed in figure 1 but instead of using the FAHP method we use AHP method Finally, in the third simulation we use our scheme based on intra-classes and inter-classes weighting to weigh different criteria

7 JOURNAL OF NETWORKS, VOL 8, NO 2, FEBRUARY D The simulation 1 In this simulation, the all traffic classes are analyzed in order to provide the performance comparison between four vertical handoff decision algorithms such as TOPSIS, GRA, DIA and FADM The importance weights of the criteria for these algorithms are calculated by using the classical AHP The all vector weights of each traffic class are displayed in figure 2 interactive traffic the FADM provides a value of 60% On the other hand, the DIA method reduces the risk with a value of 60% and 80% for conversational and interactive respectively Moreover GRA method reduces this problem with a value of 80%, 60%, 60% and 70% for background, conversational, interactive and streaming respectively Finally TOPSIS method reduces the risk with a value of 70%, 80%, 70% and 60% for background, conversational, interactive and streaming respectively For all traffic classes, FADM provides better performances concerning the number of handoffs than the all algorithms Figure 2 The importance weights by using classical AHP 1) Ranking abnormality: Figure 3 shows that the FADM method reduces the risk to have an abnormality problem with a value of 18%, 15%, 20% and 27% for background, conversational, interactive and streaming respectively While the GRA method reduces the risk with a value of 20%, 18%, 25% and 30% for background, conversational, interactive and streaming respectively Moreover DIA method reduces the risk with a value of 30%, 27%, 33% and 30% for background, conversational, interactive and streaming respectively Finally TOPSIS method provides a higher value than the all algorithms The all values provides by FADM are lower than the other values which represent the other methods We deduce that FADM based on AHP reduces the ranking problem better than GRA, DIA and TOPSIS for all traffic classes Figure 4 Average of number of handoffs by using classical AHP E The simulation 2 In this simulation, we apply the scheme based on intra-classes and inter-classes weighting to weigh different criteria Both the intra-classes and inter-classes weighting systems are based on AHP method We present the performance comparison between four vertical handoff decision algorithms such as TOPSIS, GRA, DIA and FADM for all traffic classes The inter-classes weighting system is applied to weigh class1, class2 and class3 For all traffic classes, the weight vector of class1, class2 and class3 are given in table V In addition the intra-classes weighting system is used to weigh each criterion of each class The weight vector of class1 is shown in table VI The weight vector of class2 and class3 are 1 Finally a set of importance weights of the criteria are displayed in figure 5 TABLE V THE INTER-CLASSES WEIGHTS FOR CLASS 1 BY USING AHP Traffic class Class1 Class2 Class3 Background Conversational Interactive Streaming Figure 3 Average of ranking abnormality by using classical AHP 2) Number of handoffs: Figure 4 shows that FADM and DIA diminish the number of handoffs with a same value of 33% and 4167% and 2857 for background and streaming respectively While for conversational traffic, FADM reduces the risk with a value of 50%, and for 1) Ranking abnormality: Figure 6 shows that the FADM method reduces the risk to have an abnormality problem with a value of 15%, 12%, 18% and 25% for background, conversational, interactive and streaming respectively While the GRA method reduces the risk with a value of 20%, 15%, 20% and 35% for background, conversational, interactive and streaming respectively

8 270 JOURNAL OF NETWORKS, VOL 8, NO 2, FEBRUARY 2013 TABLE VI THE INTER-CLASSES WEIGHTS FOR CLASS 1 BY USING AHP Traffic class AB D J L Background Conversational Interactive Streaming that the FADM method provides better performances concerning the number of handoffs than all algorithms Figure 7 Average of number of handoffs based AHP system Figure 5 The importance weights by using weighting AHP system Moreover DIA method reduces the risk with a value of 23%, 20%, 30% and 28% for background, conversational, interactive and streaming respectively Finally TOPSIS method provides a higher value than the all algorithms The all values provides by FADM are lower than the other values which represent the other methods We deduce that FADM based on AHP reduces the ranking problem better than GRA, DIA and TOPSIS for all traffic classes F The simulation 3 In this simulation, the all traffic classes are analyzed We present the performance comparison between four vertical handoff decision algorithms such as TOPSIS, GRA, DIA and FADM we apply our scheme based on intra-classes and inter-classes weighting to weigh different criteria The inter-classes weighting system is applied to weigh class1, class2 and class3 by using FAHP For all traffic classes, the weight vector of class1, class2 and class3 are given in table VII In addition the intra-classes weighting system is used to weigh each criterion by using FAHP The assignment of weights for different criteria of class1 are shown in table VIII The assignment of weights of class2 and class3 are 1 for all traffic classes Finally a set of importance weights of the criteria which are determined by our system are displayed in figure 8 TABLE VII THE INTER-CLASSES WEIGHTS FOR CLASS 1 BY USING FAHP Traffic class Class1 Class2 Class3 Background Conversational Interactive Streaming Figure 6 Average of ranking abnormality based AHP system 2) Number of handoffs: Figure 7 shows that FADM diminishes the number of handoffs with a value of of 50%, 45%, 60% and 50% for background, conversational, interactive and streaming respectively On the other hand, the DIA method reduces the risk with a value of of 55%, 50%, 70% and 52% for background, conversational, interactive and streaming respectively Moreover GRA method reduces this problem with a value of 70%, 55%, 60% and 65% for background, conversational, interactive and streaming respectively Finally TOPSIS method provides a higher value of the number of handoffs than the all algorithms The all values provides by FADM are lower than the other values which represent the other methods We deduce TABLE VIII THE INTRA-CLASSES WEIGHTS FOR CLASS 1 BY USING FAHP Traffic class AB D J L Background Conversational Interactive Streaming ) Ranking abnormality: Figure 9 shows that the FADM method reduces the risk to have an abnormality problem with a value of 10%, 9%, 17% and 20% for background, conversational, interactive and streaming respectively While the GRA method reduces the risk with a value of 1250%, 12%, 25% and 18% for background, conversational, interactive and streaming respectively

9 JOURNAL OF NETWORKS, VOL 8, NO 2, FEBRUARY Figure 10 Average of number of handoffs based FAHP system Figure 8 The importance weights by using weighting FAHP system Moreover DIA method reduces the risk with a value of 20%, 18%, 28% and 25% for background, conversational, interactive and streaming respectively Finally TOPSIS method can diminish the risk with a value of 1750%, 25%, 36% and 45% for background, conversational, interactive and streaming respectively TOPSIS method provides a higher value than the all algorithms The all values provides by FADM are lower than the other values which represent the other methods We deduce that FADM based on AHP reduces the ranking problem better than GRA, DIA and TOPSIS for all traffic classes VI CONCLUSION In this work, we have proposed a novel ranking algorithm, based on Fuzzy AHP and Mahalanobis distance This algorithm takes into account the correlation and the heterogeneity between different criteria The weighting system based on fuzzy logic allows to assign a suitable weights of different criteria better than AHP method and weighting system based on AHP method The Mahalanobis distance allows to rank the alternatives better than TOPSIS, GRA and DIA algorithms The simulation results show that, our FADM method can reduce the ranking abnormality and the number of handoffs better than TOPSIS, GRA and DIA to all four traffic classes In addition, for all traffic classes the FADM method based on our weighting system provides better performances concerning ranking abnormality and the number of handoffs than FADM method based on classical AHP and FADM method based on weighting system which based on AHP Finally, we conclude that the number of handoffs, is higher because the mobile terminal always switch to the best network in terms of QoS For future work, we investigate to reduce the number of handoffs Figure 9 Average of ranking abnormality based FAHP system 2) Number of handoffs: Figure 10 shows that FADM diminishes the number of handoffs with a value of of 45%, 40%, 55% and 47% for background, conversational, interactive and streaming respectively On the other hand, the DIA method reduces the risk with a value of of 50%, 46%, 66% and 50% for background, conversational, interactive and streaming respectively Moreover GRA method reduces this problem with a value of 60%, 42%, 55% and 60% for background, conversational, interactive and streaming respectively Finally TOPSIS method provides a higher value of the number of handoffs than the all algorithms The all values provides by FADM are lower than the other values which represent the other methods We deduce that the FADM method provides better performances concerning the number of handoffs than all algorithms REFERENCES [1] E Gustafsson and A Jonsson, Always best connected, IEEE Wireless Communications Magazine, vol10, no1,pp49-55, Feb 2003 [2] H Wang, R Katz, J Giese Policy-enabled handoffs across heterogeneous wireless networks, Second IEEE Worshop on Mobile Computing systems and Applications, WMCSA pp 51-60, February 1999 [3] Gazis, V; Houssos, N; Alonistioti, N; Merakos, L, On the complexity of Always Best Connected in 4G mobile networks, Vehicular Technology Conference, VTC IEEE 58th, pp Vol4, Oct 2003 [4] T Ahmed and al, A context-aware vertical handover decision algorithm for multimode mobile terminals and its performance, EATIS 06, Santa Marta, Colombia, pp 1928, February 2006 [5] X Yan et al A survey of vertical handover decision algorithms in Fourth Generation heterogeneous wireless networks, In Comput Networks, vol 54, no 11, pp , 2010 [6] M Kassar, B Kervella, and G Pujolle, An overview of vertical handover decisionstrategies in heterogeneous wireless networks, In Computer Communications, vol 31, no 10, pp , 2008

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the degree in Applied Computer Science from the Cadi Ayyad University of Marrakech in 2003 He is currently working as Assistant Professor of Higher Education at FST of Mohammedia, Morocco He is responsible of various modules in communication networks domain He conducts research on networking under 4ANY project His main research interests the management of mobility in networks across IPv6 networks, multi-access, Mobile Learning and wireless sensor networks Abdellah Adib was born in 1966 in Rabat, Morocco He received the Doctorat de 3rd Cycle and the Doctorat d Etat-es-Sciences degrees in Statistical Signal Processing from the Mohammed V-Agdal University, Rabat, Morocco, in 1996 and 2004, respectively From 1986 until 2008, he was with the Institut Scientifique, Mohammed V-Agdal University, Rabat, Morocco, as an associate professor Since September 2008 he has been with the Department of Informatics, Hassan II University, Mohammdia, Morocco, as professor His teaching includes informatics, statistical and digital signal processing His research interests are in digital communications and statistical signal processing, with emphasis on (blind) array processing