FAHP and Modified GRA Based Network Selection in Heterogeneous Wireless Networks

Transcription

1 2017 2nd Internatonal Semnar on Appled Physcs, Optoelectroncs and Photoncs (APOP 2017) ISBN: FAHP and Modfed GRA Based Network Selecton n Heterogeneous Wreless Networks Xaohan DU, Zhqng HUANG, Zhuyng LIN ABSTRACT Heterogenety s one of the man features of future networks. Vertcal Handover s the essental functon n heterogeneous networks and handover decson s one of the most mportant step n vertcal handover. Most of the network selecton algorthms n handover decson follow the ABC (Always-Best-Connecton) rules. In ths paper, we propose a network selecton method. Such a method follows the ASC (Always-Sutable-Connecton) rules that consder both the status of avalable networks and user preferences. A smulaton study n NS-2.29 s llustrated to show the effectveness of the proposed method. INTRODUCTION Wth the rapd development of technology and the requrements of applcatons. There are many communcaton technologes, but any sngle one can t meet all the needs of users. And now, termnals are able to connect to dfferent networks to get the best servces. The handover between dfferent networks called Vertcal Handover, s the essental functon n these termnals. Vertcal handover process s carred out n three man steps: handover nformaton gatherng, handover decson, and handover executon [1]. Network selecton algorthm perform n the handover decson step. Most of network selecton algorthms follow the ABC [2] (Always-Best-Connecton) rules. But n some condtons, the best network s not the most sutable one. It s obvous that Network A s the better choce because of Network B s overloaded, as shown n Table 1. TABLE 1. THE COMPARASION BETWEEN NETWORKS A AND B. Metrc Weght Value of Metrc: Network A Value of Metrc: Network B Monetary Cost Lnk Qualty Lnk Load Numerous algorthms for network selecton based on ABC rules can be found. An mproved method based on combng weghtng methods for GRA (Grey Relatonal Analyss) s proposed n [3], [4] and [5]. Reference [6] ams to select the most effectve Xao-han DU, Zh-qng HUANG, Zhu-yng LIN Beng Engneerng Research Center for IoT Software and Systems, Faculty of Informaton Technology, Beng Unversty of Technology, Beng, , Chna. Correspondng author: duxaohan1991@126.com 321

2 network by the use of Fuzzy Logc technology. In reference [7], the author proposed a Fuzzy Mult-Crtera Decson (MCD) Framework. But all these algorthms wll select Network B n case of Table 1 because of the weghted metrcs of Network B s greater than Network A. So,Instead of ABC, ASC (Always-Sutable-Connecton) law has been proposed to solve ths problem [8]. And [9] uses Varance Coeffcent Weghtng to calculate weghts and MGRA to decde swtchng cost functon, proposed an algorthm based on ASC. Ref. [10] also proposed an ASC algorthm to mprove the utlzaton of wreless resources. But user preference always be gnored n these algorthms, so we propose a method for network selecton n vertcal handover that based on user preference and ASC. OVERALL STRUCTURE AND METHOD Both user preference and network status wll be taken nto account. In our proposal the man decson factors of network status are throughput, delay, tter, and cost. Frst of all, we use fuzzy Analytc Herarchy Process (FAHP) to calculate the weght of each network parameters, whch can take nto account the applcaton needs and user preferences, to avod the rratonal swtch caused by unreasonable weght settng. Then we use the modfed Grey Relatonal Analyss (MGRA) to calculate Grey Relatonal Coeffcent (GRC) of each avalable network. And the last, we make a network selecton by the weght calculated by FAHP and GRC by MGRA. Network Selecton Method. Calculate weght of each network parameters Accordng to [11], we use FAHP to calculate weghts: Compare the decson elements a1, a2,, an parwse accordng to ther mportance to the target and get the fuzzy matrx. The element of the fuzzy matrx: r range from 0.1 to 0.9 and the descrpton of the value shown n Table2. TABLE 2. THE IMPORTANCE DESCRIPTION OF IMPORTANCE SCALE. Importance scale Importance descrpton 0.5 Equal mportance of parameter and parameter 0.6 Weak mportance of parameter over parameter 0.7 Strong mportance of parameter over parameter 0.8 Demonstrated mportance of parameter over parameter 0.9 Absolute mportance of parameter over parameter 0.1,0.2,0.3,0.4 Inverse comparson The fuzzy matrx can be gven as: r11 r12 r1 p r21 r22 r 2 p P rp1 rp2 rpp It must satsfy the followng condtons at the same tme: (1) r 0.5, 1,2,, p (2) r 1 R,, 1,2,, p (3) r r r + 0.5,,, k 1, 2,, p k k 322

3 (4) the dfference value of any two rows s a constant Assume that the weghts of r satsfy: r a( w w ),, 1,2,, p where a > 0, and a can be dfferent value wth the dfference of parameters and the number of parameters. We can get several weghts by dfferent values of a and then select the most sutable one. The subectve weghts can be expressed as: S S S S w w1 w2 w n Calculate the obectve weghts by standard devaton method [12]. The obectve weghts can be expressed as: w w1 w2 w n The comprehensve weghts can be calculated as: S 0 w w w n ( S 0 ) 1/2 w 1 w Calculate Grey Relatonal Grade (GRC) By Modfed GRA The grey mult-attrbute decson s a mult-obectve and mult-attrbute decson makng usng GRA. The tradtonal grey mult-attrbute decson frstly defnes a set of deal values of each network parameters n the deal network, and then, calculate the GRC of each network. The GRCs of each avalable network. The GRCs shown that how smlar each avalable network s to the deal. The selected network A can be expressed as: * GRA A * GRA arg max Γ where Γ 0 s the network th GRC, M s the collecton of all avalable networks. However, the standard GRA method can make an ABC selecton, t can t make an ASC selecton under the case of Table 1 we dscussed before. In order to make an ASC selecton, we should consder not only the deal values of the parameters nut also the worst parameters. And we should calculate the GRCs wth the deal values and the worst values. So, the network could not be selected f some QoS parameters are better than deal value but at the same tme some are much worse than the lowest value. The modfed GRA (MGRA) s mplemented as follows: (1) Classfy the network parameters. Assume that there are n possble networks ( S1, S2, S n ) are compared, each network has m parameters, classfy the m parameters nto two groups: larger-the-better (throughput), smaller-the-better (cost). (2) Defne upper bound ( U ) and lower bound ( L ) of parameter elements for 1, 2,, n. ( U ) max{ S ( ), S ( ),, S ( )} M ( L ) mn{ S ( ), S ( ),, S ( )} (3) Normalze the parameter. For the smaller-the-better parameters, the normalzed value of s parameter can be derved as: s U S ( ) U L n n 323

4 For the larger-the-better parameters, the normalzed value of s parameter can be derved as: s S ( ) L U L (4) Defne the deal and the worst parameters. The deal parameters are maxmum values of these parameters, defned as S { r1, r2,, r n } ; the worst parameters are basc requrement values of these parameters, defned as S { r1, r2,, r n }. (5) Calculate Grey Relatonal Coeffcent(GRC) of the parameters wth S + whch can be derved as: + + max mn r r + max max r r + r + + r r + max max r r And GRC of the parameters wth S can be derved as: max mn r r + max max r r + r r r + max max r r Network Selecton based on FAHP and MGRA Accordng to the weght of each parameters calculated by FAHP and GRC of each parameter calculated by MGRA, the Global Grey Relatonal Grade of the parameters wth S + can be derved as: + m + w m γ r r, 1, 2,, m w γ, 1, 2,, m And then, we can calculate the value E r m m E of each canddate networks by: + ( r ) + ( r ) + 2 2, 1, 2,, n The most sutable network s the one wth the largest SIMULATION STUDY Smulaton Descrpton. To evaluate the network selecton method we proposed, we mplemented our method usng the NS The smulaton scenaro s shown n Fgure 1. E. 324

5 Fgure 1. Smulaton scenaro. The whole smulated area s covered by LTE, there are two WIFI access pont wthn LTE range and the two WIFI areas are partly overlappng. We generate two dfferent types of applcaton n NS2, namely Conversatonal (voce) and Streamng (streamng vdeo). The smulaton traffc parameters are shown as Table 3. TABLE 3. THE SIMULATION TRAFFIC PARAMETERS. LTE WLAN1 WLAN2 Stream Conversatonal 1 throughput 5 Mbs 1 40 Mbs 1 50 Mbs >5 >1 delay 50 ms 100 ms 120 ms <400 <250 tter 20 ms 50 ms 60 ms <55 <35 1 cost 1/ kb 1 0.1/ kb 1 0.2/ kb <0.5 <0.7 User preference/ Conversatonal User preference/ Stream > >0.2 We collect the network parameters through NS2 when the user moves from start to end so that our method can make a decson among the avalable networks. We assume that the occurrence of both conversatonal and stream traffc follows a Posson dstrbuton and the duraton tme of the traffc follows an exponental dstrbuton. Smulaton Results. We frstly focus on network utlzaton under AHP, GRA, and the method we proposed namely FAHP-MGRA here. Smulaton results are shown n Fgure 2 and Fgure

6 Fgure 2. Performance of network utlzaton n conversatonal traffc. From Fgure 2 and Fgure 3 we can observe that wth the ncrease of smulaton MTs, both the AHP and the GRA method select the WLAN network even though the WLAN network s overloadng. Because the AHP and GRA method can only select the ABC network by the parameters gven n advance. The method we proposed can make an ASC network selecton accordng to the network utlzaton, ths can ensure a good network load balance and user experence. Fgure 3. Performance of network utlzaton n stream traffc. Fgure 4. Performance of handover tmes n conversatonal traffc. 326

7 Secondly, we focus on the handover tmes wth the ncrease of smulaton tme. We also compare our method wth AHP and GRA. The smulaton results are show n Fgure 4 and Fgure 5. We can see that whether n conversatonal traffc or stream traffc, the handover tmes by FAHP-MGRA are obvously lower than AHP and GRA. So our method can avod unnecessary handovers effcently. CONCLUSION We proposed a network selecton algorthm based on FAHP and MGRA. Ths algorthm can make a ASC decson consderng both the status of the networks and the user preferences. Smulaton results show that our algorthm s effcent to avod unnecessary handovers and ensure a good network load balance. REFERENCES Fgure 5. Performance of handover tmes n stream traffc. 1. Stevens-Navarro Enrque, Vncent WS Wong, Comparson between Vertcal Handoff Decson Algorthms for Heterogeneous Wreless Networks, Vehcular Technology Conference, IEEE, 2006, pp Gustafsson, Eva, Annka Jonsson, Always best connected, IEEE Wreless Communcatons, 2003, 10(1), pp Song Xn, Lu Wenmn, Zhang Mngle, Lu Feng, A network selecton algorthm based on FAHP/GRA n heterogeneous wreless networks, IEEE Internatonal Conference on Computer and Communcatons, IEEE, 2017, pp Lahby, Mohamed, and Abdellah Adb, Network selecton mechansm by usng M-AHP/GRA for heterogeneous networks, Wreless and Moble Networkng Conference, IEEE, 2013, pp Verma, Rav, and Nra Pratap Sngh, GRA Based Network Selecton n Heterogeneous Wreless Networks, Wreless Personal Communcatons, 2013, 72(2), pp Ros, Vnícus de Mranda, Paulo Poberto de Lra Gondm, Claudo de Castro Montero, Use of fuzzy logc for networks selecton n heterogeneous wreless envronment, Internatonal Conference on Advanced Communcaton Technology, IEEE, 2012, pp Drss, Maroua, Mohammed Oumss, Drss Aboutadne, A Mult-Crtera Decson Framework for network selecton over LTE and WLAN, Engneerng Applcatons of Artfcal Intellgence, , pp Zhang, Pe, Zhou Wenan, Xe Bng, Song Jnde, A novel network selecton mechansm n an ntegrated WLAN and UMTS envronment usng AHP and modfed GRA, IEEE Internatonal Conference on Network Infrastructure and Dgtal Content, 2010, pp Jang, Wewe, Cu Hongyan, Yan Qangun, Wang Xaouan, Chen Janya, A novel applcaton-orented dynamc network selecton n an ntegrated UMTS and WMAX envronment, Internatonal Conference on Communcatons and Networkng n Chna, 2008, pp

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