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Intelligent network selection using fuzzy logic for 4 g wireless networks
 

Intelligent network selection using fuzzy logic for 4 g wireless networks

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    Intelligent network selection using fuzzy logic for 4 g wireless networks Intelligent network selection using fuzzy logic for 4 g wireless networks Document Transcript

    • International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME451INTELLIGENT NETWORK SELECTION USING FUZZY LOGIC FOR4G WIRELESS NETWORKSC.Amali [1],Bibin Mathew[2], B.Ramachandran[3][1][2][3]Department of Electronics and Communication EngineeringS.R.M University, Chennai, India-603203ABSTRACTDue to the heterogeneity and the diversity of access networks, various userapplications with different Quality of Service (QoS) requirements pose new challenges onmulti-interface Mobile Terminal (MT) in designing optimal network selection algorithm forguaranteeing seamless QoS support to the users. Thus, service adaptive QoS metrics ofmobile users can be improved by sharing the resources of different Radio Access Networks(RAN) efficiently. A new intelligent Vertical Handoff (VHO) scheme is presented thatutilizes Fuzzy Logic based Linguistic Variables to estimate the necessity of handoff and todetermine a new point of attachment in order to fulfill the end users requirements. As eachtraffic has a different set of QoS requirements, separate Fuzzy Logic Controllers (FLC) areused for each traffic to improve the overall performance of proposed system. To maintaincontinuous services while moving in heterogeneous environments, a Fuzzy Multi AttributeDecision Making (MADM) access network selection function is used to select a suitablenetwork. In order to achieve the service continuity, a vertical handoff decision scheme isproposed to enhance the service mobility by selecting the suitable network based on QoSrequirements of applications and network characteristics.Keywords— Fuzzy logic, Handoff Decision, Heterogeneous Networks, NEF, QoS1. INTRODUCTIONThe next generation wireless communication systems are expected to integratemultiple Radio Access Technologies (RAT) in terms of services and applicationrequirements. Thus, a user will get access to a range of services having different bandwidthsand QoS requirements with the multimode terminal in "Always Best Connected" (ABC) [1]manner.INTERNATIONAL JOURNAL OF ELECTRONICS ANDCOMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)ISSN 0976 – 6464(Print)ISSN 0976 – 6472(Online)Volume 4, Issue 2, March – April, 2013, pp. 451-461© IAEME: www.iaeme.com/ijecet.aspJournal Impact Factor (2013): 5.8896 (Calculated by GISI)www.jifactor.comIJECET© I A E M E
    • International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME452The strengths of third generation (3G) cellular networks such as UMTS and CDMA2000 consist of their global coverage where as their weakness lie in bandwidth capacity andoperation costs. With the fast evolution of wireless technologies, a number of wireless accessnetworks are now emerging thereby creating a heterogeneous network environment for bothmobile users and internet service providers (ISPs). However, any single type of these wirelessnetworks cannot provide all types of services, e.g., wide coverage and high bandwidth. Onthe other hand wireless LAN (WLAN) offers higher bandwidth with low service costs, but itprovides small coverage area [2]. IEEE802.16x or WiMAX systems are optimized to providereal time high data rate services in WMAN environments. There is no single system that isgood enough to replace all the other technologies.The strength of 4G system relays on integrating the existing and newly developedwireless systems instead of putting efforts into developing new radio interfaces andtechnologies to provide seamless mobility and better service quality for mobile users. In thispaper, the coexistence of UMTS, WLAN and WiMAX access networks are considered as aheterogeneous wireless network. When connections have to switch between heterogeneousnetworks for performance and high availability reasons, seamless vertical handoff isnecessary to provide uninterrupted services to the mobile users.VHO is the seamless transfer of an ongoing user session between differentheterogeneous radio access technologies. The vertical handover decision process determineswhen and where to hand over in a heterogeneous environment when the user is on the move.Decision criteria include MT speed, user preferences, network conditions and applicationrequirements. For each network, there is a Received Signal Strength (RSS) threshold valuebelow which connection break with active base station. Therefore, the signal strength must begreater than threshold point to maintain the connection with serving network. The signalbecomes weak as mobile moves far away from serving base station and gets stronger signaltowards new base station as it moves closer. There is a need for Handoff if RSS of activebase station decreases below threshold level to maintain the connection.The aim of this work is to design and simulate an application specific VHO amongWiMAX, UMTS and WLAN using fuzzy tool. The applications taken into the considerationare conversational and real time video streaming and each of these applications require adifferent QoS. All the three technologies do not support these applications with equal QoS.Each wireless technology has a limit on mobility support and cost of service offered. Userswill always look for lower cost, keeping QoS intact; hence a VHO algorithm must try toselect the most cost effective network as target network. WLAN has the least cost of servicefollowed by WiMAX and UMTS. In this work, speed of the vehicle (mobility) is consideredas an important factor to evaluate the networks before ranking the networks based on theapplication requirements.The remainder of this paper is organized as follows: Related and existing works arediscussed in section 2. Overview of proposed algorithm is presented in section 3. Section 4comprises various modules of proposed algorithm. Performance assessment is carried out insection 5 and finally conclusion is given in section 6.2. RELATED WORKTo improve the performance of the handoff scheme, a network discovery algorithmbased on the fuzzy logic multiple objective decision making system is presented in [3]. In [4]& [5], an adaptive fuzzy based handoff decision system is developed which considerparameters such as data rate, cost and RSSI to obtain training elements. With the training
    • International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME453element of ANFIS from fuzzy based system, the rules and the membership function can beproperly tuned to optimize the handoff performance. In [6], a Modified Weight Functionbased Network Selection Algorithm (MWF-NSA) that considers user preference andapplication profile is proposed in deciding the weight functions of the networks. In [7] fuzzymultiple attribute decision is used for vertical handoff decision. The application requirementsfor both conversational and streaming are given in [8].Although, there have been various vertical handoff algorithms available in theliterature, our proposed work aims to incorporate the user speed, characteristics of networks,user preference, cost and QoS requirements of different traffic in fuzzy logic based networkselection algorithm to select the optimal network to provide efficient vertical handoff forheterogeneous wireless networks. In the proposed algorithm, the network related, terminalrelated, user related and service related attributes are included, which improves the accuracyof the VHO.3. OVERVIEW OF PROPOSED ALGORITHMDue to the heterogeneity and the diversity of access networks, various userapplications with different QoS requirements pose new challenges in designing optimalnetwork selection algorithm for guaranteeing seamless QoS support to the users. Thus, VHOis necessary to provide uninterrupted services to mobile users anywhere and anytime in 4GNetworks. The various techniques used for executing handoff can be classified into Mobile-Controlled Handoff (MCHO), Network-Controlled Handoff (NCHO) and Mobile-AssistedHandoff (MAHO). In MCHO, the mobile node continuously monitors the signal strength ofaccess points and initiates the handoff procedure when certain handoff decision is triggered.Fuzzy logic based VHO algorithms are best suitable for MCHO in integrated networks. Thefuzzy logic controllers are designed for the different modules of proposed algorithm usingfuzzy rules base. The fuzzification comprises the process of transforming the crisp inputs intothe fuzzy sets via the membership functions. The fuzzy rules base consists of a collection offuzzy if-then rules to represent the human knowledge about the problem.In fuzzification process, if inputs X={x1, x2, …, xm} are memberships of fuzzy setY={ A~, B~,…, M~}, respectively, then the degree of membership of X={ x1, x2, …, xm} isgiven by[0,1])X(Ywhere),,( ∈µµµµ XHXMXLThe reason for using Fuzzy MADM is thati) multiple parameters can be processed simultaneously and provides the best solutionfor VHO decision when the input exhibits uncertainty.ii) the traditional fuzzy based Vertical Handoff Decision Algorithm (VHDA) needsdefuzzification, which may increase the handoff delay. But the proposed VHDA selects thebest suitable network based on the values of network evaluation function.
    • International Journal of Electronics and0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, MarchFig.1: Functional Block Diagram of Proposed MechanismFuzzy logic based proposed algorithm is divided into four modules.block diagram of proposed mechanism is shown in fig.whether handover is necessary when the mobile terminal is moving across heterogeneouswireless networks. The next modhandoff threshold value. In the second module, User Satisfaction Dbased on the speed, network load and cost of service in order to select the netwosupports. In the third module,streaming traffic are considered to determineuninterrupted services to mobile users. In fourth module, the perfoevaluated by calculating Network Evaluation Fand QoS factor of ongoing service.common to both urban and suburban environments whereThe simulation is based on locations where all the three networks are available.coverage area is larger than UMTS and WLANFig.2: Simulation Scenario for Urban and Suburban Environments4. FORMULATION OF PROPOSED ALGORITHMThe proposed FUZZY based VHOin fig.3. In this algorithm, MT speed, service cost, application profile and netwcharacteristics are considered for the evaluation of available networks. The detailedexplanation of the modules is described in the following sub sections.International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME454: Functional Block Diagram of Proposed Mechanismbased proposed algorithm is divided into four modules. The functionalsed mechanism is shown in fig.1. The first module determineswhether handover is necessary when the mobile terminal is moving across heterogeneousess networks. The next module is executed only if handoff factor is high. In the second module, User Satisfaction Degree (USD)network load and cost of service in order to select the netwosupports. In the third module, QoS requirements of conversational and real time videostreaming traffic are considered to determine QoS factor for the available networks to provideuninterrupted services to mobile users. In fourth module, the performance of networevaluated by calculating Network Evaluation Function (NEF) based on the vaongoing service. The simulation scenario is shown in fig.2. The scenario iscommon to both urban and suburban environments where all the networks will be present.locations where all the three networks are available.ea is larger than UMTS and WLAN for simulation in the proposed scheme: Simulation Scenario for Urban and Suburban EnvironmentsFORMULATION OF PROPOSED ALGORITHMThe proposed FUZZY based VHO algorithm is formulated using 4 modules as shownIn this algorithm, MT speed, service cost, application profile and netwcharacteristics are considered for the evaluation of available networks. The detailedexplanation of the modules is described in the following sub sections.Communication Engineering & Technology (IJECET), ISSNApril (2013), © IAEMEThe functionalThe first module determineswhether handover is necessary when the mobile terminal is moving across heterogeneousfactor is higher than the(USD) is evaluatednetwork load and cost of service in order to select the network thatrequirements of conversational and real time videofactor for the available networks to providermance of networks isunction (NEF) based on the values of USD2. The scenario isall the networks will be present.locations where all the three networks are available. WiMAXfor simulation in the proposed scheme.: Simulation Scenario for Urban and Suburban Environmentsmodules as shownIn this algorithm, MT speed, service cost, application profile and networkcharacteristics are considered for the evaluation of available networks. The detailed
    • International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME455Fig.3: Flow Diagram of Proposed Algorithm4.1 HANDOFF FACTOR ESTIMATIONThis module examines the conditions of serving network and estimates the handofffactor. By estimating the handoff factor to initiate the handoff at the right time, end usersatisfaction can be maximized which in turn improves the overall QoS in heterogeneouswireless networks. RSS of serving network is estimated using suitable path loss models basedon the environments in which mobile terminal is moving. If RSS of serving network keep ondecreasing, there is a need for handoff to another network to provide uninterrupted services tomobile users.For this case, FLC is deployed using fuzzy logic toolbox in MATLAB. The Fuzzy InferenceSystem (FIS) accepts the fuzzified values and interprets the fuzzified values to the outputsbased on the user defined rules. Whenever the RSS drops below the threshold value, theprobability of handoff becomes high. Thus, handoff factor is estimated to select the bestsuitable network. The next stage of network evaluation process is evaluated only whenhandoff factor is high. The corresponding FIS model for handoff Factor estimation is shownin fig.4. Mamdani system is used here because of its wide spread acceptance and it is wellsuited to human input.Fig.4: RSS Handoff Decision controller4.2 USER SATISFACTION DEGREE (USD) CALCULATIONOur proposed scheme chooses only a few parameters that are critical to maximize theend users satisfaction while performing VHO in heterogeneous environment. In this stage, theperformance of networks are evaluated based on the parameters like MT speed, service costand network load. The QoS requirements of ongoing service can be provided easily bymaximizing end user satisfaction based on their preference, location and applicationrequirements. Thus available networks are evaluated to select the network capable ofsatisfying the user request during VHO operation. For example, networks with less coveragearea cannot support the users with high speed. It requires large number of handoff tocomplete the ongoing service in MT.
    • International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME456Before evaluating the networks based on given application, it is necessary to evaluatethe networks based on speed, user preference (cost) and network conditions (load) to reducethe outage probability and also to avoid unnecessary handovers in heterogeneous wirelessnetworks. Thus, the end users can specify their needs and preferences by assigningmembership functions to each system parameter. FLC is designed by assigning membershipfunctions to speed, cost and network load to evaluate the networks according to theircharacteristics. FIS accepts the input fuzzy sets and maps with outputs based on the fuzzyrules. For example, if the MT speed is low and user preference is low cost, then WLAN willbe selected. The corresponding membership functions are given in fig.5Fig.5: Membership Functions of Speed, Load and Cost4.3 QoS FACTOR EVALUATIONAfter estimating the user satisfaction degree, the characteristics of different trafficmust be taken into account in designing optimal network selection algorithm for guaranteeingseamless QoS support to the users. In this module, two fuzzy logic controllers are deployedfor conversational and video streaming traffic. Service sensitivity parameters such as RSS,data rate and connection delay are considered to achieve guaranteed QoS to mobile users.The membership functions are assigned as low, medium and high to the input variables andfuzzy rules are made as per the requirements of 3GPP QoS classes.For conversational traffic, RSS is an important factor to maintain a good link betweenMT and BS with low delay and minimum bandwidth requirements. But, real time videostreaming application requires large bandwidth and RSS with tolerable delay. The applicationrequirements considered for the simulation is shown in Table1.Table 1: Conversational and Streaming Application RequirementsApplication Symmetry Data rate Delay RSSConversational Two Way 4-64 kbit/s <150 ms<400 ms(max limit)HighStreaming One Way 16-384kbit/s< 10 s High
    • International Journal of Electronics and0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March4.4 NETWORK EVALUATION FUNCTIONAfter filtering the available networks usingNEF is calculated. NEF is determined to provide optimization between user satisfactiondegree and QoS factor. The outputs of the previous modules are combined in parallelinclude the mobility, QoS requirements of applications and network characteristics in order toprovide best solution to the network selection problem.By selecting the network with maximumnumber of handoffs required forguaranteed QoS. Thus, the MT is alwayscontinuity and also to guarantee QoS5. PERFORMANCE ASSESSMENTThe performance of the VHDAscenario as shown in fig.2. The NEF is calculated using SIMULINK model of MATLAB.The user satisfaction degree is plotted with respect to velocity for different percentage oftraffic load and cost of service. The corresponshows at low percentage of cost and traffic load,Km/hr thereafter WiMAX USDWiMAX are having same valueit supports higher velocity. For medium cost and lobetter which is shown in fig.6.3. The cost of servictraffic load, that’s why UMTS USD is higher in fig.Fig.6: User Satisfaction Degree of WLAN, UMTS and WiMAX10% Traffic Load.(6.2) for 10% Cost and 50% Traffic Load. (6.3) for 50% Cost and10% Traffic Load. (6.4) for 70% Cost and 70% Traffic LoadInternational Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME457NETWORK EVALUATION FUNCTION ESTIMATIONAfter filtering the available networks using user satisfaction degree and QoSNEF is determined to provide optimization between user satisfactionfactor. The outputs of the previous modules are combined in parallelinclude the mobility, QoS requirements of applications and network characteristics in order toprovide best solution to the network selection problem.By selecting the network with maximum USD and QoS factor as target nequired for MT to complete its connection can be reduced with, the MT is always connected to the best suitable network for serviceQoS requirements of ongoing service.PERFORMANCE ASSESSMENTVHDA is tested within the framework of chosen simulationThe NEF is calculated using SIMULINK model of MATLAB.The user satisfaction degree is plotted with respect to velocity for different percentage of. The corresponding figures are shown in fig.6ntage of cost and traffic load, WLAN is the preferred network up to 20value is increasing. In fig.6.2 after 20km/hr both UMTSup to 50km/hr. After that WiMAX USD is increasing since. For medium cost and low traffic load WiMAX USD6.3. The cost of service is high in UMTS and it supports highUSD is higher in fig.6.4.n Degree of WLAN, UMTS and WiMAX (6.1) for 10% Cost andfor 10% Cost and 50% Traffic Load. (6.3) for 50% Cost and10% Traffic Load. (6.4) for 70% Cost and 70% Traffic LoadCommunication Engineering & Technology (IJECET), ISSNApril (2013), © IAEMEuser satisfaction degree and QoS factor,NEF is determined to provide optimization between user satisfactionfactor. The outputs of the previous modules are combined in parallel toinclude the mobility, QoS requirements of applications and network characteristics in order toand QoS factor as target network, theits connection can be reduced withconnected to the best suitable network for servicechosen simulationThe NEF is calculated using SIMULINK model of MATLAB.The user satisfaction degree is plotted with respect to velocity for different percentage of6. The fig.6.1preferred network up to 20after 20km/hr both UMTS andincreasing sincew traffic load WiMAX USD is performinge is high in UMTS and it supports high10% Cost andfor 10% Cost and 50% Traffic Load. (6.3) for 50% Cost and
    • International Journal of Electronics and0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, MarchThe SIMULINK model of the propoaccording to the functional block diagram given inFig.7: SIMULINK model of the proposed mechanismInternational Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME458The SIMULINK model of the proposed algorithm is shown in fig.7. The model worksl block diagram given in Fig.1.: SIMULINK model of the proposed mechanismCommunication Engineering & Technology (IJECET), ISSNApril (2013), © IAEME7. The model works
    • International Journal of Electronics and0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, MarchThe NEF table is shown in table 2. As an exampleeach USD case, four different QoS cases(a) 15 % speed and 10% cost of service and traffic load. (b) 30% speed, 50% traffic load and10% cost of service. (c) 50% speed and cost of service and 10% traffic load. (d) 70% ofspeed, cost of service and traffic load. For all the above examplesparameters are considered. QoSby high, medium and low in table 2. ThisNEF value calculated from USD and QoS values. Delay is considered to be an importantfactor in conversational application whereas data rate is more important inwhich means bandwidth requirement is moreWiMAX is having higher bandwidth when compared to UMTS. UMTS and WiMAX ishaving higher coverage area and supports more load than WLAN.diagrams of the examples shown in tablethat for low value of speed, traffic load and cost, WLAN isconversational and video streaming applicata corresponding change in the final NEF. For medium values ofis the preferred network for conversational and WiMAX forthe cost of service increases, UMTSWiMAX. If QoS parameters like RSS and data rate have athen all the networks have a low NEF for all the cases of USD.selected for incoming traffic.Table 2: Example of NEF Values for ConversatiInternational Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME459The NEF table is shown in table 2. As an example, four cases of USD are taken and infour different QoS cases are considered. The examples considered here a(a) 15 % speed and 10% cost of service and traffic load. (b) 30% speed, 50% traffic load and10% cost of service. (c) 50% speed and cost of service and 10% traffic load. (d) 70% ofspeed, cost of service and traffic load. For all the above examples, four cases of QoSconsidered. QoS parameters are RSS, data rate and delay and are, medium and low in table 2. This table gives the final selection of network based onNEF value calculated from USD and QoS values. Delay is considered to be an importantfactor in conversational application whereas data rate is more important in video suirement is more in video streaming applications. WLAN andWiMAX is having higher bandwidth when compared to UMTS. UMTS and WiMAX ishaving higher coverage area and supports more load than WLAN. The corresponding bardiagrams of the examples shown in table 2 are given in fig.8. From the fig.8, it is understoodtraffic load and cost, WLAN is the preferred network for bothstreaming applications. As the USD & QoS values changes, there isa corresponding change in the final NEF. For medium values of speed, load and cost, UMTSthe preferred network for conversational and WiMAX for video streaming applications, UMTS is selected because of its higher cost than WLAN andrs like RSS and data rate have a low value and when delay is highlow NEF for all the cases of USD. In this case, no: Example of NEF Values for Conversational and Streaming ApplicatioCommunication Engineering & Technology (IJECET), ISSNApril (2013), © IAEMEfour cases of USD are taken and inconsidered. The examples considered here are(a) 15 % speed and 10% cost of service and traffic load. (b) 30% speed, 50% traffic load and10% cost of service. (c) 50% speed and cost of service and 10% traffic load. (d) 70% ofur cases of QoS, data rate and delay and are representedtable gives the final selection of network based onNEF value calculated from USD and QoS values. Delay is considered to be an importantvideo streaming,streaming applications. WLAN andWiMAX is having higher bandwidth when compared to UMTS. UMTS and WiMAX isThe corresponding barit is understoodthe preferred network for bothchanges, there iseed, load and cost, UMTSstreaming applications. Asthan WLAN andwhen delay is high,no networks areonal and Streaming Applications
    • International Journal of Electronics and0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, MarchFig.8: Graph showing NEF values (conversational and streaming applicationsservice and traffic load. (8.2)service. (8.3)NEF for 50% speed, 10%70% speed and 70% traffic load and 70% cost of serviceInternational Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME460: Graph showing NEF values (from Table 2) of different networks forconversational and streaming applications. (8.1) NEF for 15% speed and 10% of cost.2)NEF for 30% speed, 50% traffic load and10% cost.3)NEF for 50% speed, 10% traffic load and 50% cost of service.70% speed and 70% traffic load and 70% cost of serviceCommunication Engineering & Technology (IJECET), ISSNApril (2013), © IAEMETable 2) of different networks for.1) NEF for 15% speed and 10% of cost ofload and10% cost of. (8.4)NEF for
    • International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME4616. CONCLUSIONThis paper proposes an intelligent fuzzy logic based network selection for 4G wirelessnetworks. The developed algorithm considers the USD as well as QoS parameters for both theconversational and video streaming applications.The USD, QoS and NEF are evaluated usingseparate fuzzy logic controllers and tested using SIMULINK .Whenever there is a need forhandoff, the NEF is calculated using USD and QoS of parameters from the available list ofnetworks present at that time. The network with high NEF is selected as the preferred networkand is going to serve the user. In the proposed algorithm, the network related, terminal related,user related and service related attributes are included to minimize the number of VHO. Thus, thebest suitable network is selected for the ongoing traffic by providing optimization between thecomplexity and improved QoS. The proposed scheme can benefits both users and networks byhandling the uncertainity and time varying information using fuzzy logic rules.For further research, fuzzy logic technique can be integrated with other MADM methodsto provide more efficient network selection and also to analyze the effect of complexity in theproposed scheme.REFERENCES[1] E.Gustaffson and A.Jonsson, “Always Best Connected”, IEEE wireless Communication ,vol.10, no.1, Feb.2003, pp.49-55.[2] Q. Song and A. Jamilipour , “Network Selection in an Integrated Wireless LAN and UMTSEnvironment Using Mathematical Modeling and Computing Techniques”, IEEE WirelessCommunications, June 2005; DOI: 10.1109/MWC.2005.1452853.[3] M. Lahby, C. Leghris. and A. Adib. “A Hybrid Approach for Network Selection inHeterogeneous Multi-Access Environments”,In the Proceedings of the 4th IFIP InternationalConference on New Technologies, Mobility and Security (NTMS), pp.1-5, Paris France, Feb2011.[4] Xu Haibo, Tian Hui, Zhang Ping, “A novel terminal-controlled handover scheme inheterogeneous wireless networks”, Computers and Electrical Engineering 36 (2010) pp 269–279.[5] Ali Çalhan & Celal Çeken, “An Adaptive Neuro-Fuzzy Based Vertical Handoff DecisionAlgorithm for Wireless Heterogeneous Networks” proceedings of 21st Annual IEEE Internationalsymposium on Personal, Indoor and Mobile Radio Communications, 2010, pp 2271-2276.[6] C.Amali and B.Ramachandran, “Modified Weight Function Based Network SelectionAlgorithm for 4G Wireless Networks”, ACM International Conference on Advances inComputing, Communication and Informatics(ICACCI 2012) Chennai, India, pp. 292-299, 3rd–5thAugust 2012.[7] L. Xia, LG. Jiang, and C. He, "A Novel Fuzzy Logic Vertical Handoff Algorithm with Aid ofDifferential Prediction and Pre-Decision Method", Proc. of IEEE ICC06, 2006, pp. 5665-5670.[8] Carlos Ramirez-Perez and Victor M. Ramos R. “A QoS hierarchical decision scheme forvertical handoff”, 2012 8th International Caribbean Conference on Devices, Circuts and Systems(ICCDCS).[9] Prof. J. R. Pathan, Prof. A. R. Teke, Prof. M. A. Parjane and Prof. P.S. Togrikar, “DroppingBased Contention Resolution for Service Differentiation to Provide Qos in Wdm Obs Networks”,International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 1, 2013,pp. 218 - 228, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.[10] Jayashree Agrakhed, G. S. Biradar and V. D. Mytri, “Optimal Qos Routing With PrioritizedRegion Scheduling Over Wmsn”, International Journal of Computer Engineering & Technology(IJCET), Volume 3, Issue 1, 2013, pp. 289 - 304, ISSN Print: 0976 – 6367, ISSN Online: 0976 –6375.