EAI Endorsed Transactions on Mobile Communications and Applications

Transcription

1 EAI Endorsed Transactons on Research Artcle Rank Average for handover decson makng n heterogeneous wreless networks Sada Drouache,, Nab Naa, Abdellah Jamal 2 STRS laboratory, INPT, Rabat, Morocco 2 IR2M laboratory, FST, Hassan st Unversty, Settat, Morocco Abstract Vertcal handover mechansm s an mportant ssue n heterogeneous wreless networks. Contnuty of servce durng handover and QoS are relevant ssues to deal wth. Ths paper focuses on vertcal handover decson makng. It proposes a Rank Average method for the best access network selecton. Ths method s an aggregaton of two Mult Attrbutes Decson Makng (MADM) methods: Technque for Order Preference by Smlarty to Ideal Soluton (TOPSIS) and VIse Krterumska Optmzaca kompromsno Resena (VIKOR), together wth Shannon entropy-based weghts. Entropy s an adequate tool to wegh up the handover crtera n a quet easy and straghtforward way. Rank Average outperforms TOPSIS and VIKOR n terms of throughput, end to end delay, packet loss, and sgnfcantly reduces the number of unnecessary handovers related to the png-pong effects. Performance results are obtaned wthn the scope of MIH through NS3 smulator. Receved on 3 May 27; accepted on 2 November 27; publshed on January 28 Keywords: heterogeneous networks, vertcal handover, qualty of servce, contnuty of servce Copyrght 28 Sada Drouache et al., lcensed to EAI. Ths s an open access artcle dstrbuted under the terms of the Creatve Commons Attrbuton lcense ( whch permts unlmted use, dstrbuton and reproducton n any medum so long as the orgnal work s properly cted. do:.48/ea Introducton The evoluton of heterogeneous access technologes, and real tme multmeda applcatons has created a plenty of new wreless connectvty scenaros showng an ncreasng number of devces. So, the ntegraton of Heterogeneous Networks (HetNets) n such a scenaro, s offerng better communcaton channels and rasng the possblty of affordng better Qualty of Servce (QoS). Vertcal Handover (VH) occurs when a user moves among dfferent networks. Ths process s dvded nto three steps: The frst step s the network dscovery when the termnal equpment dentfes all the possble accessble networks. The second step s the decson of handover, when the termnal equpment selects the target network. The thrd step s the handover executon, when termnal equpment swtches to the selected network. Seamlessness n network swtchng s stll consdered as one mportant challenge faced n the handover management process []. In order to be always best connected, the handover process should start at the approprate tme and select Correspondng author. Emal: drouache@npt.ac.ma the most adequate target nterface. The desgn of such an ntellgent VH algorthm s a challenge. As a means of provdng nteroperablty and seamless handover n HetNets, IEEE ntroduced a standard called IEEE 82.2 or MIH [2]. It allows IEEE and non- IEEE technologes to be ntegrated. At the same tme, t ensures both vertcal and horzontal handovers. It defnes an entty called MIH Functon (MIHF) as a generc nterface between the dfferent technologes of the lnk layer and the upper layers. Lower layers coordnate the exchange of nformaton and commands between devces nvolved n the handover decson makng. Each node has a set of MIHF users whch are typcally the protocols of moblty management. They use MIHF to montor and collect nformaton related to the handover. Desgnatng a network that satsfes user demands s another challenge, as some crtera may conflct wth each other. The selecton process becomes a multcrtera decson makng problem [3]. The sgnfcance of ths paper s that t proposes an aggregaton approach, whch mxes two MADM methods: TOPSIS and VIKOR. It uses the rankng results obtaned from TOPSIS [4][] and VIKOR [6][7]. It provdes a rank

2 S. Drouache,N. Naa, A. Jamal average for each alternatve (access network). we also employ the entropy method to obtan the obectve weghts of the crtera. Real tme applcatons such as vdeo streamng and VOIP demand rgorous QoS requrements. So, handover delay, throughput, end to end delay, and packet loss rate are used to measure QoS and network performance. The remanng part of the paper s organzed as follows: secton 2 recaps related work. Secton 3 presents mpled MADM methods. The proposed selecton method s presented n secton 4. Secton presents the calculated results, explans the smulaton scenaro, and compares the evaluaton results of the Rank Average, TOPSIS, and VIKOR. Concluson s provded n secton Related work Varous VH algorthms [8] have been proposed. The usual polcy of VH decson s based on Rado Sgnal Strength (RSS). Several RSS based algorthms have been suggested [9]. These smple mplementatons provde low handover latency but a low to medum throughput. The basc dea of a cost functon based algorthm [][22] s to defne a cost based on a combnaton of network parameters. Consequently, the canddate systems performance can be weghed up, n order to select the best network. Generally, cost functon algorthms do not yeld a hgher throughput than smple approaches. Besdes, delays are due to the complexty durng nformaton collecton and cost functon calculaton. Fuzzy logc and artfcal neural networks [][2], are wdely used n the lterature to make handover decsons. The applcaton of these complcated algorthms s necesstated by the complexty of handover decsons and wreless networks dynamc condtons. The Context-Aware [9] handovers are based on nformatons related to the Moble Termnal (MT), network, and other contextual factors. These nformatons can nclude the network capacty, locaton, subscrber preferences, type of servce, etc. MADM methods are extensvely appled for real world problems [3].They have been adopted [2] n order to select the sutable access network for a handover. MADM technques commonly used are: Smple Addtve Weghtng (SAW), TOPSIS [2], Analytc Herarchy Process (AHP), GRA (Grey Relatonal Analyss), Weghted Sum Model (WSM), Weghted Product Method (WPM) or Multplcatve Exponent Weghtng (MEW), ELmnaton Et Chox Tradusant la REalte (ELECTRE), VIKOR, and Lnear Assgnment. They combne nformatons n a problem decson matrx to ascertan a fnal rankng or selecton from among the alternatves. Some MADM methods have been proposed [4][] to make VH decsons. In [4], the author compares two MADM methods: TOPSIS and SAW, n terms of handover delay. For many consdered crtera, TOPSIS performance s good enough compared to SAW. Two classes of algorthms are consdered []: Sngle Performance Metrc Optmzaton (SPMO) and Multple Performance Metrc Optmzaton (MPMO). The MPMO technques consder dfferent performance attrbutes smultaneously durng the selecton process, and try to fnd a compromse between the used attrbutes. Among the MPMO approaches, TOPSIS algorthm shows satsfactory performance for all consdered metrcs. MADM algorthms provde a hgher throughput. Unfortunately, the attrbutes evaluaton complexty ncreases the handover delay. Ths s also true for more complex approaches such as AI and context-aware methods. Hstory of VH decson makng exhbts helpful utlzaton of MADM tools []. To facltate and assure a relable access network selecton, the experts evaluate and select target network by several metrcs rather than sngle crteron. TOPSIS and VIKOR, are capable of attendng n wde range of access network selecton problems. Comparatve study of VIKOR and TOPSIS has been performed n [2][23]. VIKOR, TOPSIS, PROMETHEE (Preference Rankng Organzaton METHod for Enrchment of Evaluatons) and AHP [4] are appled to seek the most sutable target network [7][4]. Authors n [] found out that the fnal rankng of alternatves vary across methods, especally n problems wth many alternatves. TOPSIS and VIKOR use dstnct normalzaton technques [7] and ntroduce some aggregatng functons for rankng. Authors n [6] compared the extended VIKOR method wth TOPSIS. In [7] presented a comparson of SAW, TOPSIS and VIKOR. They observed that TOPSIS and SAW had dentcal rankngs, whle VIKOR produced dfferent rankngs. They concluded that both TOPSIS and VIKOR are sutable for weghng analogous problems and provde results close to realty. The selecton of best MADM method for a specfc problem s a dffcult task. There are many factors that should be consdered before selectng a MADM method or a combnaton of MADM methods. Ther bg defect s that n a sngle problem, they present dfferent results. To deal wth ths, some methods have been suggested [3] known as aggregaton methods, where a problem wth several MADM methods s ranked, and then, the fnal selecton may be made on the bass of an aggregaton of those methods results. Based on MADM lterature, the reason that researchers explore new aggregaton methods for decson makng s to augment selecton confdence of exstng rankng methods. n addton to known MADM methods, aggregaton methods perform the rankng of alternatves effcently and accurately. 2

3 Rank Average for handoverdecsonmakngn heterogeneouswreless networks 3. MADM tools Handover decson makng can be treated as a problem where there are n canddate networks, and m crtera. Rows and columns of the decson matrx present the alternatves A... A n and crtera C... C m, respectvely. a defnes the quantty of alternatve A aganst crteron C. Weghts w...w m have to be postve and desgnated to all crtera. They defne the crteron mportance to the decson makng. 3.. TOPSIS TOPSIS as one of the wdely adapted classcal MADM tools used for rankng problems was developed n order to reach non nferor solutons. It has been satsfactorly mplemented n dfferent applcaton felds [24]. It s based on the followng dea: the best alternatve s supposed to have the shortest dstance from the postve deal soluton (made up of the best value of each crteron dsregarded the alternatves), and the longest dstance from negatve deal soluton (made up of the worst value of each crteron regardless of alternatves). TOPSIS s a relable method for rsk avodance because the desgners may desre a decson that not only maxmses proft but also avods rsk. TOPSIS ncludes many steps: step : decson matrx normalzaton a p = ( n= ) () a 2 step 2: weghts are multpled to the normalzed matrx as follows v = w p (2) step 3: postve deal soluton s A + = (v +,..., v+,..., v+ m), where v + s the best value of the th attrbute over all the avalable alternatves. Negatve deal soluton s A = (v,..., v,..., v m), where v s the worst value of the th attrbute over all the avalable alternatves. They are computed as follows: A + = {(max v J), (mn v J ) =, 2,..., n} A = {(mn v J), (max v J ) =, 2,..., n} (3) J{, 2,..., m} and J {, 2,..., m} are the sets of crtera whch need to be maxmzed and mnmzed, respectvely. step 4: the normalzed eucldean dstance between alternatves and deal solutons s appled m m d + = (v v + )2 and d = (v v )2 = = (4) step : the relatve closeness C to the deal soluton s computed d C = d + d + () The best ranked alternatve s the one wth maxmum value of C VIKOR VIKOR was set up for the mult crtera optmsaton of complex systems. It s a helpful tool, especally when the preference s unknown for a decson maker at the begnnng of the system desgn. Each alternatve s measured based on an aggregate functon. So, the compromse rankng of alternatves s acheved by comparng the measure of closeness to the deal soluton. Any excluson or ncluson of an alternatve can nfluence VIKOR rankng results. Ths algorthm prepares a mnmum of ndvdual regret and maxmum group utlty for opponent and maorty, respectvely [2]. ν s the strategy weght assgned to the maorty of attrbutes. One of VIKOR characterstcs s that aggregate functon always s closest to the best solutons. For TOPSIS aggregate functon s not always very close to the deal solutons. Ths makes VIKOR sutable for obtanng maxmum proft. The VIKOR procedure s descrbed below: step : determnaton of aspred (f + ) and tolerable (f ) levels of beneft and cost crtera, respectvely where =, 2,..., m f + = max a, f + f = mn a, f = mn = max a a (6) step 2: calculaton of utlty S and regret R usng the followng where =, 2,...m S = m = f + w f + f f R = max w f + f + f f (7) step 3: The ndex Q s calculated. S mn and R mn are the mnmum values of S and R, respectvely. S max and R max are ther maxmum values, respectvely. Q = ν S S mn + ( ν) R R max (8) S max S mn R mn R max Q, S, and R, are three rankng lsts. The alternatves are arranged n a descendng order n accordance wth Q values. They are also arranged n accordance wth S and R values separately. The best ranked alternatve A s the one wth mnmum value of Q. A s the compromse soluton f: Condton : Q(A 2 ) Q(A ) (/(n )), where A 2 s the second best alternatve ranked by Q. Condton 2: A must be also best ranked alternatve by 3

4 S. Drouache,N. Naa, A. Jamal S and/or R. If one of the condtons s not fulflled, a group of compromse solutons s proposed: A and A 2 f only condton 2 s not satsfed. A, A 2,..., A m f condton s not satsfed. A m s defned by the relaton Q(A m ) Q(A ) (/(n )). 4. Rank Average Evdently, dstnct decson makng methods provde dfferent results accordng to ther hypotheses and approaches. Snce seamless VH decson makng s very mportant, t s better to use more than one method. In order to overcome ths problem, we ntroduce an aggregate method called Rank Average. As t mplcates other methods (TOPSIS and VIKOR) results and partculars, ths method s able to be perfect for target access network selecton. Rank Average ranks alternatves based on the average of calculated rankngs of the mpled MADM methods. The rankng R mxed () of the th canddate network s acqured as follow, where k s the number of mpled methods: R mxed () = R k () Ths average rankng leads to satsfactory performance. It s accepted as nvaluable because t s able to add the respectve powers of every approach. In our scenaro, TOPSIS and VIKOR calculate the rankng of alternatves. Then Rank Average calculates the fnal results for all alternatves. We choose TOPSIS and VIKOR for three reasons: () Each of them s advantageous and effcent for handover decson makng. (2) They employ dfferent aggregaton and normalzaton functons. So, they gve dstant results for the same decson problem. For example, a selected alternatve as the best by TOPSIS may be consdered as the worse by VIKOR. (3) Rank Average can take advantage from ther complementary powers regardless of ther dfferences, and make effcent handover decsons. Entropy [8][9] has been used to calculate the adequate weght of every crteron. Compared to other obectve weghtng methods such as b-crtera programmng, entropy has the advantages of computatonal smplcty and effcency. Decson makers are also more lkely to capture the analyss results more easly. Greater entropy value engender smaller weght and less mportance of the crteron n the decson makng process. When dfferences of alternatves n ths partcular crteron are small, there s less nformaton provded. In other words, a crteron has less mportance f all canddate materals have smlar performance ratngs for that attrbute. Ths makes the results more correct and logcal. Entropy determnes the weghts through the followng steps: k k (9) step : normalzaton of the decson matrx usng equaton (), n order to elmnate the crtera unts. step 2: calculaton of the entropy value for each crteron, where k s the Boltzmann s constant E = k n = p ln p where k = ln n step 3: extracton of obectve crtera weghts w = E m= ( E ). Performance Evaluaton and Results () () In ths secton, we evaluate Rank Average performance. To ths end, we added MIH module to NS3 under whch we have run smulatons. We have consdered a HetNet of WF, LTE, and WMAX access networks. MTs are outftted wth three network devces of every access technology, and an MIH nterface. MIH s needed to construct a lst of local nterfaces, obtan states and control ther behavour. Once the MT s swtched on, the VH procedure s actvated. In the smulaton scenaro, we montor four MTs: MT runs a VoIP applcaton whle movng wth a constant speed equal to m/s. The VoIP applcaton uses a G.729 codec, wth 8,Kbps data rate and 6B packet sze. MT2 runs a vdeo streamng applcaton whle movng wth a constant speed equal to m/s. The vdeo streamng applcaton sends MPEG4 stream usng H.263 codec, wth 6Kbps bt rate. MT3 runs web browsng applcaton. For ths, we used an HTTP traffc generator lbrary. The generated traffc represents real world http traffc. Ths MT s movng across networks wth a speed of.3m/s. MT4 downloads e-mals as a background applcaton wth 8.Kbps data rate and B as packet sze. Smulaton results for Rank Average, TOPSIS, and VIKOR wth MIH across WF, WMAX and LTE are presented n ths secton. The obectve s to evaluate and compare these methods through some crtcal performance metrcs: throughput, end to end delay, packet loss rate, and handover decson delay [2]. The measurements are taken every s. Table. shows the smulaton parameters... Throughput Throughput fgures among mportant QoS statstcs. In our context, t s the number of bts receved successfully by the MT dvded by the dfference between last packet recepton tme and the frst packet transmsson tme. The results n fgure shows that Rank average s able to mprove transmsson throughput for real tme servces lke VoIP and non-real 4

5 Rank Average for handoverdecsonmakngn heterogeneouswreless networks Throughput(Kbps), 4,8 4,6 4,4 4,2 Rank Average TOPSIS VIKOR 4, (a) VoIP Throughput(Kbps) 38, 28, 8, 8, 98, 88, (b) Vdeo streamng,3, Throughput(Kbps),2,2,,, Throughput(Kbps) 9, 9, 8, 8, 7,, 7, (c) Web browsng (d) E-mals download Fgure. MTs throughput Table. Smulatonparameters Smulatonparameters Values IEEE82. frequencybandwdth GHz IEEE82. transmson radus m IEEE82. data rate 2Mbps IEEE82.6 frequencybandwdth GHz IEEE82.6 transmson radus 6m IEEE82.6 channelbandwdth MHz Propagatonmodel COST23_PROPAGATION IEEE82.6 modulatonand codng OFDMQAM6_2 MAC/IEEE82.6UCDnterval s MAC/IEEE82.6DCDnterval s LTE uplnkbandwdth 2 resourceblocks LTE downlnkbandwdth 2 resourceblocks LTE lnk data rate Gbps LTE channelbandwdth MHz Maxmumtransmson Power 3.dBm LTE path loss model Frs propagaton LTE transmson radus 2m Mobltymodel constant-poston tme lke downloadng e-mals. Throughput offered by Rank Average s a bt hgher than that of TOPSIS and VIKOR..2. End to end delay End to end delay s calculated for every receved packet. Fgure 2 shows that Rank Average has a better end to end delay performance than TOPSIS and VIKOR. Snce real tme flows are very senstve to delay, we can say that decreased delay s a potental beneft of Rank Average. Hence, t guarantees a better QoS n terms of end to end delay..3. Packet loss rate In order to acheve seamless VH, t s necessary to guarantee servce contnuty and QoS, whch means low latency and packet loss durng handover. Fgure 3 shows that the three evaluated approaches assure low packet loss rate. Furthermore, Rank Average guarantees null packet loss. Ths mproves the QoS for real tme servces.

6 S. Drouache,N. Naa, A. Jamal End to end delay (s),2,,, Rank Average TOPSIS VIKOR, End to end delay (s),7,6,,4,3,2, (a) VoIP (b) Vdeo streamng,7,4 End to end delay (s),6,,4,3,2, 9 End to end delay (s),4,3,3,2,2, (c) Web browsng (d) E-mals download Fgure 2. Packet end to end delay betweenmt and ts correspondentnode.4. Handoverdelay Handover delay s the tme taken by the MT to make a decson and select the best access network to move to. Every tme we apply Rank Average, TOPSIS, or VIKOR, we montor the MT for s to get the number of handover occurrences, and measure decson delay. Fgure 4 shows the obtaned results. Number of handover events executed by VIKOR s hgher compared to TOPSIS and Rank Average. For VoIP at s, web browsng at 2s, and e-mals download at s, the three evaluated methods execute a handover, but Rank Average has a greater delay than TOPSIS and VIKOR. Ths s due to the fact that Rank Average wats for the rankng results of TOPSIS and VIKOR, then calculates the average rankng for each avalable access network. Although t requres more delay to decde a handover, Rank Average can acheve better performance than conventonal TOPSIS and VIKOR, wth respect to end to end delay, packet loss, and throughput. Png-pong effect s defned as the unnecessary handover to the neghbourng Base Staton (BS) or Access Pont (AP) that returns to orgnal BS or AP after a very short nterval of tme. Ths unnecessary back and forth handover leads to heavy processng and loads. Rank Average compared to VIKOR reduces the number of unnecessary handovers. Thus, resources are saved, dropped calls and png-pong effect are reduced. 6. Concluson In ths paper, we employed Rank Average as an aggregaton of TOPSIS and VIKOR. Rank Average method has the best performance accordng to smulaton results, except for decson delay. It can reduce the number of unnecessary handovers, pngpong effects, end to end delay and packet loss rate, and mprove throughput. That confrms the ablty of Rank Average to add the powers of appled methods, and fnd a compromse between ther proposed solutons despte ther dfferences. These results lead us to the necessty of reducng handover delays. Lookng ahead, greater ntegraton of artfcal neural networks and fuzzy logc nto a handover management scheme seem to be more promsng reduced decson delays. 6

7 Rank Average for handoverdecsonmakngn heterogeneouswreless networks Packet loss rate (%) Rank Average TOPSIS VIKOR Packet loss rate (%) (a) VoIP (b) Vdeo streamng Packet loss rate (%) (c) Web browsng Packet loss rate (%) Fgure 3. Packet loss rate (d) E-mals download References [] Sagar EL, Bhadla M. A survey of handover mechansm wth moblty management n femtocell & macrocell for lte. cell 2;4(). [2] Al T, Saqub M. Analyss of an nstantaneous packet loss based VH algorthm for heterogeneous wreless networks. IEEE Transactons on Moble Computng 24;3(): [3] Sasrekha V, Chandrasekar C, Ilangkumaran M. Heterogeneous wreless network vertcal handoff decson usng hybrd mult-crtera decson-makng technque. Internatonal Journal of Computatonal Scence and Engneerng 2;(3): [4] Lahby M, Cherkaou L, Adb A. An enhanced-topss based network selecton technque for next generaton wreless networks. In: Telecommuncatons (ICT), 23 2th Internatonal Conference on. IEEE; 23, p. -. [] Bso I, Delucch S, Lavagetto F, Marchese M. Performance comparson of network selecton algorthms n the framework of the 82.2 standard. Journal of Networks 2;():-9. [6] Gul M, Celk E, Aydn N, Gumus AT, Guner AF. A state of the art lterature revew of vkor and ts fuzzy extensons on applcatons. Appled Soft Computng 26;46:6-89. [7] Baghla S, Bansal S. Effect of normalzaton technques n vkor method for network selecton n HetNets. In: Computatonal Intellgence and Computng Research (ICCIC), 24 IEEE Internatonal Conference on. IEEE; 24, p.-6. [8] Bhute HA, Karde P, Thakare V. AVH decson approaches n next generaton wreless networks: A survey. Internatonal Journal of Moble Network Communcatons & Telematcs (IJMNCT) 24;4. [9] Bhute HA, Karde P, Thakare V. Vertcal handover decson strateges n heterogeneous wreless networks. In: nt. Conf. on Recent Trends n Informaton, Telecommuncaton and Computng, ITC. Cteseer; 24,. [] Madaan J, Kashyap I. An overvew of vertcal handoff decson algorthm. Internatonal Journal of Computer Applcatons 2;(3). [] Geetka KB. Handover management n HetNets. Internatonal Journal of Scentfc & Engneerng Research 23;4(4). [2] Munoz P, Laselva D, Barco R, Mogensen P. Dynamc traffc steerng based on fuzzy q-learnng approach n a mult-rat mult-layer wreless network. Computer Networks 24;7:-6. [3] Rao RV. Multple attrbute decson makng n the manufacturng envronment. In: Decson Makng n 7

8 S. Drouache,N. Naa, A. Jamal Handover delay (µs) Rank Average TOPSIS VIKOR (a) VoIP Handover delay (µs) 3 2, 2,, (b) Vdeo streamng 6 Handover delay (µs) Handover delay (µs),8,6,4, (c) Web browsng (d) E-mals download Fgure 4. VHdecsondelay Manufacturng Envronment Usng Graph Theory and Fuzzy Multple Attrbute Decson Makng Methods. Sprnger; 23, p.-. [4] Preeth G, Chandrasekar C. A network selecton algorthm based on ahp-ow a methods. In: Wreless and Moble Networkng Conference (WMNC), 23 6th Jont IFIP. IEEE; 23, p.-4. [] Agrawal A, Jeyakumar A, Pareek N. Comparson between vertcal handoff algorthms for heterogeneous wreless networks. In: Communcaton and Sgnal Processng (ICCSP), 26 Internatonal Conference on. IEEE; 26, p [6] Zhang N, We G. Extenson of vkor method for decson makng problem based on hestant fuzzy set. Appled Mathematcal Modellng 23;37(7): [7] Agular-Gonzalez R, Cardenas-Juarez M, Pneda-Rco U, Arce A, Latva-aho M, Stevens-Navarro E. Reducng spectrum handoffs and energy swtchng consumpton of madm-based decsons n cogntve rado networks. Moble Informaton Systems 26;26. [8] Jang W, Shen P, Lu F, Fang X. An nteractve group decson makng approach based on satsfacton degree. In: LISS 24. Sprnger; 2, p [9] Zamr N, Abdullah L. A new lngustc varable n nterval type-2 fuzzy entropy weght of a decson makng method. Proceda Computer Scence 23;24:42-3. [2] Dou Y, Zhang P, Jang J, Yang K, Chen Y. Mcdm based on recprocal udgment matrx: a comparatve study of e-vkor and e-topss algorthmc methods wth nterval numbers. Appl Math 24;8(3):4-. [2] Wu J, Cheng B, Yuen C, Shang Y, Chen J. Dstortonaware concurrent multpath transfer for moble vdeo streamng n heterogeneous wreless networks. IEEE Transactons on Moble Computng 2;4(4): [22] Budyanto S, Asval M, Gunawan D. Performance Analyss of Genetc Zone Routng Protocol Combned Wth Vertcal Handover Algorthm for 3G-WF Offload. Journal of ICT Research and Applcatons. 24 May ;8(): [23] Cha J, Lu JN, Nga EW. Applcaton of decson-makng technques n suppler selecton: A systematc revew of lterature. Expert Systems wth Applcatons. 23 Aug 3;4(): [24] Jngmeng B, Zeng B, Zhang J, Dunnan L, Mng Z. Benefts comprehensve evaluaton of demand response n SDN based on dstant Grey Relatonal Analyss-Technque for Order Preference by Smlarty to Ideal Soluton. InPower and Energy Engneerng Conference (APPEEC), 26 IEEE PES Asa-Pacfc 26 Oct 2 (pp.843-8). IEEE. 8