Case Study on Handoff Strategies for Wireless Overlay Networks

Computer Standards & Interfaces 35 (2013) 170–178

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Computer Standards & Interfaces

j ourna l homepage: www.e lsev ie r .com/ locate /cs i

Case study on handoff strategies for wireless overlay networks

Ali Çalhan a,⁎, Celal Çeken b

a Duzce University, Technology Faculty, Computer Engineering Department, Turkeyb Kocaeli University, Networked Control Systems Laboratory, Turkey

⁎ Corresponding author.E-mail addresses: [email protected] (A. Çalhan

(C. Çeken).

0920-5489/$ – see front matter © 2012 Elsevier B.V. Alldoi:10.1016/j.csi.2012.06.002

a b s t r a c t
a r t i c l e i n f o
Article history:Received 14 December 2009Received in revised form 6 June 2012Accepted 28 June 2012Available online 6 July 2012

Keywords:Vertical HandoffHeterogeneous networksFuzzy logicMulti‐objective decision makingSAW

One of the most challenging topics for next‐generation wireless networks is the process of vertical handoffsince many of wireless technologies overlap each other and build a heterogeneous topology. Several param-eters, pertaining to user/application requirements and network conditions, such as data rate, service cost,network latency, speed of mobile, and etc. must be considered in the handoff process of heterogeneous net-works along with RSSI information. In this paper, adaptive fuzzy logic‐based vertical handoff decision‐makingalgorithms are presented for wireless overlay networks which consist of GSM/GPRS/Wi-Fi/UMTS/WiMAXtechnologies. The parameters data rate, monetary cost, speed of mobile and RSSI information are processedas inputs of the proposed fuzzy‐based systems. According to these parameters, an output value, which variesbetween 1 and 10, is produced. This output value is utilized to determine whether a handoff process is nec-essary or not and to select the best candidate access point in the vicinity. The results show that, compared tothe traditional RSSI‐based algorithm significantly enhanced outcomes can be achieved for both user and net-work as a consequence of the proposed fuzzy‐based handoff systems. The simulation results are also com-pared with those of classical MADM (Multiple Attribute Decision Making) method, i.e. SAW (SimpleAdditive Weighting). According to the results obtained, the proposed vertical handoff decision algorithmsare able to determine whether a handoff is necessary or not, properly, and select the best candidate accessnetwork considering the aforementioned parameters. Moreover, fuzzy‐based algorithm noticeably reducesthe number of handoffs compared to SAW‐based algorithm.

© 2012 Elsevier B.V. All rights reserved.

1. Introduction

In recent years, the exponential growth of wireless communica-tion technologies has forced the emerging diverse technologies, e.g.;wireless cellular networks, WLANs (Wireless Local Area Networks),WWANs (Wireless Wide Area Networks) etc. to coexist. This newtrend, also referred as the next‐generation networks, aims that allthe wireless technologies work together in order to provide QoS‐supported and cost‐efficient services for mobile users at anywhereand anytime. Wireless terminals deployed in such a heterogeneousnetwork structure might be aware of all the networks around. Con-sidering this requirement, it is obvious that, the recent wireless tech-nology called cognitive radio (CR) that is capable of connectingdifferent wireless technologies with perception and adaptation attri-butes will be remedied.

Although mobile users expect to obtain both real-time services(voice transfer, video conferencing etc.) and non-real time services(video transfer, simple message service, etc.) with minimum costand optimum quality, a single wireless access point cannot satisfythe requirements of all current and upcoming services. Therefore,

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the coexistence of heterogeneous wireless networks to accomplishapplication requirements and user expectations is an imperioustrend. The next‐generation wireless networks should be integratedto form a heterogeneous network which ensures user mobility andservice continuity by seamless switching between different technolo-gies at anywhere and anytime. In order to provide seamless mobilitybetween various wireless technologies overlapped, vertical handoffalgorithms need to be developed for next‐generation wirelessstructures.

Handoff is described as a process of transferring an ongoing call ordata session from one access point to another in homogeneous or het-erogeneous wireless networks. Traditional handoff process, referredalso as horizontal handoff, is taken place between two adjacent cellswhich have the same wireless technologies. Horizontal handoff pro-cess, commonly, considers the link quality condition parameterssuch as RSSI (received signal strength indicator) or SNR (signal tonoise ratio) to manage handoff procedure. If these parametersdrop below a specified threshold then handoff is decided. Sincethe next‐generation networks have a heterogeneous structure, itis evident that the traditional handoff mechanisms will not be suf-ficient when triggering handoff and choosing the new servingstation.

Contrast to horizontal handoff, vertical handoff decision requires a tra-deoff among many handoff metrics including application requirements,

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user requirements and network conditions. When dealing with the verti-cal handoff process, these requirements involve the evaluation of suchpa-rameters as QoS, monetary cost, RSSI, SNR, and etc. which may havedifferent levels of importance.

As stated earlier, networks in heterogeneous structure have to acttogether with various working conditions for dynamically adapting tothe environment. In order to manage seamless transmissions be-tween existence diverse networks, cognitive radio concept whichcan be described as a self-aware communication system that effi-ciently uses available spectrum in an intelligent way [1] would be as-sistance for next‐generation networks.

In this study, a Smart Mobile Terminal (SMT) is proposed thatscans the environment for available Radio Access Technologies(RATs).1 It evaluates the working conditions of RATs using its fuzzylogic‐based algorithm, triggers handoff process if necessary, andchooses the best Access Point (AP) to camp on. The SMT has an adap-tive multi-criteria handoff decision system which has the ability toadapt its structure according to the user/application requirementsand network conditions. Together with the other functions, the pro-posed SMT is capable of sensing the environment for available APsand changing its working parameters such as frequency band, band-width, modulation scheme, MAC protocol, etc. to be able to camp onthe more appropriate AP.

The proposed SMT has been modeled and simulated using OPNETModeler Software for more realistic performance evaluation. Thefuzzy logic‐based handoff algorithms incorporated in SMT havebeen implemented in MATLAB Software as well. In the study, the re-sults of fuzzy‐based handoff algorithm are compared with those ofSAW algorithm.

The remainder of the paper is organized as follows. The nextsection presents related works about the vertical handoff in theliterature. In Section 3, a brief introduction on handoff procedureis given. Overall properties and design stages of the proposedsmart terminal together with related algorithms are describedcomprehensively in Section 4. Section 5 includes an example het-erogeneous network scenario that has overlapping RATs with dif-ferent working parameters as well as proposed SMT, followed byperformance evaluation. The last section gives the summaryabout the proposed scheme and simulation results with finalremarks.

2. Related works

A number of proposals have been found for vertical handoff de-cision algorithms in the literature. A fuzzy logic‐based algorithmthat adopts the received signal strength (RSS) threshold, band-width and cost values as input parameters was proposed in Xia etal. [2]. And the weight of each QoS metric is adjusted along withthe networks changing to trace the network condition. In Ling etal. [3], a fuzzy‐based vertical handoff decision algorithm is aimedbetween GSM, GPRS and WLAN networks with bandwidth, cover-age area, power consumption and sojourn time parameters. Anoth-er fuzzy‐based vertical handoff decision algorithm with Elmanneural network is proposed between WLAN and UMTS [4]. Band-width, mobile speed and number of user values are taken as inputparameters in the study. Stoyanova and Mahonen [5] provide amulti-criteria decision‐making algorithm based on fuzzy logicand artificial neural networks. It considers SIR, BER and transmis-sion rate for handoff possibility. In Nkansah‐Gyekye and Agbinya[6], fuzzy logic and genetic algorithm‐based vertical handoff deci-sion algorithm was explored with RSS and cost parameters onUMTS-WiMAX networks. The IEEE 802.21 standard aims atsupporting algorithms which enable seamless handoff between

1 In this case study, RATs supported by the SMT are Wi-Fi, GSM, UMTS, and WiMAX.

heterogeneous network types called Media independent handover(MIH) or vertical handoff [7]. The standardization study is still on-going and our proposal is an alternative to the IEEE 802.21standard.

Because the proposed algorithms have adaptive architecture fornetworks including Wi-Fi, GSM, UMTS, and WiMAX, they differ fromthose found in literature.

3. Handoff process

Handoff is a process of switching a user call or data session be-tween cells each has the same or a different type of network technol-ogies seamlessly provided that the connection would never bedropped. One of the handoff processes, horizontal handoff, occurs inhomogenous cellular systems when the user moves out from the cov-erage area of serving cell to a new cell [8]. Unlike homogenous cellu-lar systems, in wireless overlay networks, mobiles need to switchtheir connections both horizontally and vertically [9]. The verticalhandoff takes place between the wireless systems each has diversenetwork technology. In Fig. 1, vertical and horizontal handoff process-es are illustrated.

In homogenous networks, a handoff process is mainly triggeredbased on the received signal strength (RSS) or other link quality con-dition parameters. Contrast to homogenous structures, in wirelessoverlay networks, just RSS parameter individually is not sufficientfor handoff process since the diverse networks overlap each other.Other metrics related to network conditions, application require-ments, and user preferences should be considered for vertical handoffdecision. When the aforementioned requirements are regarded, onecan easily deduce that in order to implement an efficient verticalhandoff decision architecture more intelligent approaches are need-ed. Therefore, in this study, we propose two fuzzy logic‐based handoffalgorithms which considers the parameters; data rate, monetary cost,(and speed of mobile), and RSSI as inputs in order to handle anyhandoff process.

4. Smart mobile terminal architecture

The smart terminal proposed in this study is in complete charge ofmanaging the handoff process as well as its other functions. SmartMobile Terminal (SMT) senses the wireless medium by scanning thefrequency spectrum periodically in order to get the available APs’working parameters. These parameters extracted from the handoffbroadcast packets are used in the proposed fuzzy logic‐based handoffalgorithms which have the capability of deciding handoff process andchoosing the best candidate AP in the vicinity.

The following subsections include the proposed SMT terminal pro-cess model implemented using OPNET MODELER simulation tool andadaptive fuzzy logic‐based handoff decision algorithms which are in-corporated into the SMT and developed using MATLAB software.

4.1. Smart Terminal Process Model

The proposed SMT process model is developed using OPNET Mod-eler software and its cross layer design is outlined in Fig. 2. It includesphysical, MAC, and some upper‐layer functions. It is composed of aCSMA/CA MAC module for the Wi-Fi capability (abbreviated as C inFig. 2.), a GSM module in order to handle GSM operations (B), aUMTS module for 3 G functionality (D), a WiMAX module (E), and afuzzy logic‐based smart handoff unit (A) which is in charge of manag-ing all of the handoff operations.

Module A of the process model performs spectrum scanning oper-ations as well as managing handoff procedure by means of the pro-posed fuzzy logic‐based algorithms that are able to find out newcandidate APs in the vicinity. It scans the spectrum periodically forpotential APs considering the aforementioned input parameters. Just

Fig. 1. An illustration of vertical handoff and horizontal handoff.

Fig. 2. The SMT cross‐layer process model.

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Fig. 3. Block diagram of the proposed handoff decision algorithm.


before the scanning process starts, the candidacy value of the currentAP is determined. After that, the module scans the entire spectrum forpossible APs. When any AP is detected, its working parameters arestored in a table, e.g.; HDT (Handoff Decision Table), for further con-sideration. As soon as the scan operation completed the SMT com-pares the APCV of current AP with the ones added to the table,respectively. If the difference between these values (i.e.; APCV of po-tential AP—APCV of serving APCV) is greater than Handoff Resolution(HR2) value, then the handoff process is performed. Otherwise, thescan process is terminated for a specified time span in order to reducethe latency.

During the spectrum sensing phase, SMT listens to the wirelessmedium for any AP handoff broadcast packet. Each of APs aroundperiodically sends its own handoff broadcast packets which in-clude required attributes for further handoff operations. Duringthe listening period, the SMT changes its working parameterssuch as frequency, modulation, data rate, and bandwidth in orderto adapt itself to any possible AP and then receives handoff broad-cast packets. SMT then extracts the parameters from the handoffbroadcast packet for the evaluation process. The parameters fromhandoff broadcast packets are invoked in fuzzy‐based handoff de-cision algorithms which accept these parameters as inputs, andthen an output named Access Point Candidacy Value (APCV) is pro-duced. APCV here is utilized to quantify the candidacy level of APsand has a value varying from 1 to 10. Next, all the aforementionednetwork parameters with APCV are stored in the Handoff DecisionTable (HDT) for further usage.

4.2. Handoff decision algorithm

In this subsection, the handoff decision‐making algorithm [10] to-gether with the simulation model is presented. For vertical handoff,different handoff decision algorithms have been proposed in the liter-ature as mentioned earlier. Artificial intelligence‐based systems suchas Fuzzy Logic and Artificial Neural Networks are good candidates forpattern classifiers due to their non-linearity and generalization capa-bility [11].

As discussed before, vertical handoff decision algorithms mustconsider available network interfaces (link capacity, power consump-tion, link cost, etc.), system information (remaining battery), anduser/application requirements (cost, QoS parameters, etc.) when trig-gering handoff process. The block diagram of our first vertical handoffalgorithm incorporated into the proposed smart terminal is given inFig. 3.

Fuzzy‐based handoff algorithm is preferred in the SMT proposed asfuzzy logic is capable of dealing with imprecise data and modelingnonlinear functions. During the handoff initiation process, we consid-er three widely used parameters; received signal strength (RSS), datarate (DR) andmonetary cost (C). Fig. 4 illustrates the essential compo-nents of the fuzzy‐based vertical handoff decision system.

As can be shown in Fig. 4 the first step of the handoff system is tofeed the considered context parameters into a fuzzifier. The role ofthe fuzzifier here is transforming the real-time measurements intofuzzy sets. For example, if RSS is considered in crisp set, in thecorresponding fuzzy set, the signal can be represented as quiteweak, medium or strong. The membership values, i.e. μ, are obtainedby mapping the values obtained for a particular parameter into amembership function. After that, we need to perform fuzzy conver-sions by using a reverse engine commonly named defuzzifier to gen-erate APCVs. As a last step, the calculated APCV is utilized for decidinghandoff initialization and choosing the best candidate AP.

2 Handoff Resolution is a value determined by user. It introduces a hysteresis to theproposed vertical handoff algorithm.

Membership functions of the fuzzy system inputs are given inFigs. 5, 6, and 7, respectively. Trim and trapezoid are chosen as thefuzzy membership functions due to their capability of achievingbetter performance for especially real time applications [10,12].

The Data Rate (DR) input can dynamically change its structureaccording to the application requirements. For instance, if the datarate requirement of an application is 9.6 Kbps (GSM data transfer)then the membership function is similar to the one given in Fig. 5a.On the other hand, when the application needs more bandwidth,e.g., 25 Kbps (GPRS Class 6 traffic), then it dynamically changes itsstructure to adapt the new working condition as seen from Fig. 5b.

The cost parameter from APs is obtained and sent to the SMT to beconsidered in the handoff decision process. In our simulation scenar-io, we assume that networks have specific unit price informationwhich is known by AP and sent to the SMT in the handoff broadcastpacket. Its membership function can be seen in Fig. 6.

The RSSI input of the fuzzy system has also the ability to change itsstructure according to the network requirements as data rate. TheRSSI membership function for GSM, UMTS and WiMAX is shown inFig. 7a, and for Wi-Fi network, it is shown in Fig. 7b, respectively.

The output of the fuzzifier is aggregated into a single fuzzy vari-able, using inference engine that has a knowledge base determinedby the experiences, and passed to the defuzzifier to be convertedinto a precise quantity referred as APCV.3 Eventually, handoff processis decided considering the output of the fuzzy inference system, i.e.,APCV.

The analytic model of the fuzzy inference system is as follows[3,9]. Three‐dimensional pattern vectors (input of the fuzzifier) forcandidate access points is:

PVc ¼ DRc;Cc;RSc½ � ð1Þ

where DR is data rate, C is monetary cost, and RS is the RSSI value ofavailable AP. Three‐dimensional fuzzy pattern vectors (output of

3 APCV describes the candidacy level of APs and has a value varying from 0 to 10.

Fig. 4. Block diagram of the proposed fuzzy logic‐based handoff decision system.


fuzzifier and input of inference engine) for candidate access pointsis:

PVF ¼ PF1;PF2;PF3½ � ð2Þ

Since the product inference rule is utilized in the fuzzy inferenceengine, then, for a new pattern vector, the contribution of each rulein the fuzzy rule base is computed by:

Cr ¼ ∏3

i¼1μFi

Pið Þ ð3Þ

Since we have 27 rules and a center average defuzzifier is utilized,the output of the defuzzifier (APCV) is:

Ma ¼

P27l¼1

yl ∏3

i¼1μFi

Pið Þ� �

P27l¼1

∏3

i¼1μFi

Pið Þ� � ð4Þ

where, yl is the output of the rule l.

Fig. 5. Fuzzy membership fun

5. Computer simulation

5.1. Assumptions

In our simulation scenario that is modeled using OPNET Modelerand illustrated in Fig. 8, there are four wireless networks; i.e., GSM,Wi-Fi, UMTS andWiMAX networks, each has specific working param-eters. The proposed SMT explained in former sections is also modeledin OPNET. The implementation of our fuzzy handoff strategies whichare incorporated into the SMT is completed with MATLAB FIS editor.

5.2. Simulation results and discussion

SMT is capable of generating time sensitive voice traffic (13 Kb/s),GSM data traffic (9.6 Kb/s), Class 6 GPRS data traffic (25 Kb/s), andimage traffics (50 Kb/s and 100 Kb/s). It moves along with the trajec-tory shown in Fig. 8 with a pedestrian speed during the simulationrun time, senses the environment periodically for possible AP, hasthe aforementioned adaptive fuzzy logic‐based handoff decision sys-tems, and has the capability of performing GSM, GPRS, Wi-Fi, UMTSand WiMAX functionalities. The GSM base station, in our scenario,supports 9.6 Kb/s data transfer and voice transfer application, where-as UMTS has more bandwidth, i.e. adequate for multimedia applica-tions, and can achieve 25 Kb/s data transfer with GPRS serving.WiMAX, on the other hand, supports faster mobile users, provides

ctions for data rate (DR).

Fig. 6. Fuzzy membership functions for monetary cost (C).


much more bandwidth with, has a higher monetary cost anddeployed commonly in rural areas. The simulation parameters aretabulated in Table 1.

When the simulation begins, the SMT camps on the Wi-Fi hotspotsince it has a higher RSSI, sufficient bandwidth and extremely lowcost. After 300 s, data rate of Wi-Fi decreases dramatically. Fuzzy‐based handoff decision algorithm running on the SMT, in turn, de-cides to change the AP considering the application requirements al-though the Wi-Fi has appropriate RSSI and cost parameters. As canbe seen in Fig. 8 there are three alternative APs other than the servingWi-Fi.

For data transfer with 9.6 Kb/s and voice transfer with 13 Kb/s,this dramatic decrease in bandwidth consequently reduces theAPCV of the Wi-Fi as shown in Fig. 9, since the data rate is one ofthree inputs for the fuzzy‐based algorithm developed. The GSM basestation, similar to the others except Wi-Fi, is able to provide appropri-ate bandwidth for the applications referred. When compared with theother ones, the GSM base station has lower cost and higher RSS pa-rameters. Therefore, it has a better APCV, which implies it is thebest candidate base station, as illustrated in Figs. 9 and 10respectively.

For the same scenario except introduced traffic, i.e. 25 Kb/s datatransfer, the GSM base station is not appropriate in spite of its lowcost and high data rate. So, there exist two alternatives; UMTS andWiMAX. Since the monetary cost of the UMTS is comparativelylower (at least it has chosen lower than WiMAX in our scenario),then it is chosen as the new serving base station as can be seenfrom Fig. 11.

As in the data transfer application with 25 Kb/s, the GSM again isnot appropriate in terms of bandwidth for the image transfer applica-tion with 50 Kb/s. Moreover, the UMTS base station is able to satisfy

Fig. 7. Fuzzy membership f

the QoS requirements of the application traffic with low cost. So,the UMTS base station has a better APCV (Fig. 12) and this makes itthe new hotspot.

As in the image transfer application with 50 Kb/s, the GSM again isnot appropriate in terms of bandwidth for the image transfer applica-tion with 100 Kb/s. Moreover, the UMTS base station is even not ca-pable to satisfy the QoS requirements of the application traffic dueto its higher bandwidth requirement. The WiMAX base station, ascan be seen from the Fig. 13, has a better APCV and consequentlythe SMT chooses it as the new serving base station.

Simple additive weighting method (SAW) is simple and widelyused multi-attribute decision method that is based on the weightedaverage. The weights determine the importance of parameters fromAPs. The coefficients, i.e. weights of parameters, are selected to em-phasize the effect of inputs on choosing candidate AP. The simple ad-ditive weighting method evaluates each alternative, APCVi, by thefollowing formula:

APCVi ¼ ∑wj:xij ð5Þ

where xij is the score of the i‐th alternative with respect to the j‐thattribute wj that is the normalized weight. The parameters are nor-malized and multiplied with the particular weight. For example, RSSand data rate are relatively less important parameters for 9.6 Kb/sdata transfer application. For this scenario, shown in Fig. 14, the pa-rameters and weights are given with the weighted vector, W=[RSS,DR, C]=[ 0.1 0.1 0.4].

When the simulation begins, as theWi-Fi is convenient in terms ofmonetary cost, data rate, and RSSI it is chosen as the serving hot spotby SMT. In 300th second, dramatic decrease in bandwidth conse-quently reduces the APCV of the Wi-Fi access point. The GSM basestation, when compared with the other ones, has lower cost andhigher RSS parameters. Therefore, it has a better APCV as illustratedin Fig. 14 and is chosen as the new AP.

For 100‐Kb/s image transfer application, shown in Fig. 15, the pa-rameters and weights are given with the weighted vector, W=[RSS,DR, C]=[ 0.3 0.5 0.1]. When Fig. 13 for fuzzy‐based algorithm andFig. 15 for SAW‐based algorithm are compared with each other,WiMAX comes into prominence for 100 Kb/s transfer application.However, in Fig. 15, Wi-Fi is chosen as the serving AP despite itslow bandwidth provision for the mentioned application traffic. Formobile user with high bound, Wi-Fi must not be a strong candidate.So, one can easily deduce that SAW is not an appropriate algorithmfor this scenario. On the other hand, in Fig. 13., the fuzzy‐based algo-rithm chooses the WiMAX from the beginning to the end of the sim-ulation run time as expected.

Number of handoff is an important parameter when comparingthe handoff algorithm designs. It is desired to have a minimum num-ber of handoff while the requirements of user, application, and net-work are fulfilled. In this study, the proposed fuzzy‐based vertical

unctions for RSSI (RS).

Fig. 8. Example of Vertical Handoff scenario.


handoff system is even compared with SAW‐based algorithm in termsof number of handoff for the application scenario with 9.6‐Kb/s datatransfer. The results show that fuzzy‐based algorithm reduces the num-ber of handoffs. As illustrated in Fig. 16, the fuzzy‐based algorithm de-cides handoff only 7 times while SAW‐based system triggers 11 timesas shown in Fig. 17 for the complex scenario developed.

In our second developed vertical handoff decision algorithm [13],speed of mobile (S) parameter is considered with aforementioned pa-rameters (DR, C and RSSI). The purpose of this algorithm is to observethe impact of mobile speed to handoff process. The mobile speedinput of the fuzzy system has been obtained from trajectory attributeof mobile terminals in OPNET. The membership functions of mobilespeed are shown in Fig. 18. In Rayleigh communication channel,

Table 1Simulation parameters.

Parameter Value

Message size 13⁎Kb/s, 25⁎Kb/s, 9.6⁎Kb/s,50⁎Kb/s, 100⁎Kb/s

Data rate Wi-Fi=1 Mb/s, GSM=270,833 b/s,UMTS=3,840,000 b/s,WiMAX=4,104,000 b/s

Frequency band Wi-Fi=2400 MHz, GSM=890–935 MHz,UMTS=2110–1920 MHz, WiMAX=3300–3400 MHz

Handoff resolution 2Transmitter power Wi-Fi=100 mW, GSM=1.5 W, UMTS=20 mW,

WiMAX=100 mWSpeed mobile 2.5 km/hArea size 4 km×4 kmScan period 100 msMonetary cost Wi-Fi=0.1 GSM=1.8 UMTS=2.0 WiMAX=3.5Channel model Rayleigh fading channel

⁎ General using Exponential Distribution Function Exp(Mean).

GSM and UMTS base stations correspond to velocities of about max60 km/h and WiMAX technology supports faster mobile terminalswith sufficient bandwidth and data rate. Wi-Fi hotspots only supportmobile terminals with pedestrian speed.

In the same simulation scenario, illustrated in Fig. 8, at the begin-ning of the simulation, the SMT firstly camps on the Wi-Fi hotspotsince it has a high RSSI, sufficient bandwidth and extremely lowcost with pedestrian speed with 5 km/h. After 300 s elapsed aroundthe Wi-Fi access point, data rate of Wi-Fi decreases dramatically. Fordata transfer with 9.6 Kb/s and voice transfer with 13 Kb/s, this dra-matic decrease in bandwidth and 50‐km/h speed value consequentlyreduce the APCV of the Wi-Fi after 300 s from the beginning of thesimulation as shown in Fig. 19. The GSM base station and the otherbase stations are able to provide appropriate bandwidth for the appli-cations referred. When compared with the other ones, the GSM basestation has a lower cost and a higher RSS value. Therefore, it has a bet-ter APCV, which implies that it is the best candidate base station, asillustrated in Fig. 19. Until 1400th seconds with 50‐km/h speed,

Fig. 9. APCV output of the proposed handoff decision algorithm for data transferapplication.

Fig. 10. APCV output of the proposed handoff decision algorithm for voice transferapplication.

Fig. 11. APCV output of the proposed handoff decision algorithm for data transferapplication.

Fig. 13. APCV output of the proposed handoff decision algorithm for image transferapplication.

Fig. 14. APCV output of the SAW MADM for 9.6‐Kb/s transfer application.


SMT stays connected to GSM base station, and then SMT acceleratesto speed of 90‐km/h. Since WiMAX is more appropriate for thisspeed, SMT decides to make handoff to WiMAX base station.

In Fig. 19, the impact of mobile speed to handoff decision can beseen clearly with the developed algorithm [13]. By using our first de-veloped algorithm for this scenario with variable speed; until 1400thseconds with 50‐km/h speed, SMT stays connected to GSM base sta-tion, and then SMT accelerates to speed of 90 km/h. Since WiMAX ismore appropriate for this speed, SMT cannot decide to make handoffto WiMAX base station because of first algorithm does not considerthe speed of mobile. SMT stays connected to GSM base station(Figs. 9 and 10). Consequently, QoS supported service is providedfor mobile user with considering the speed of mobile. The detailed in-formation about speed sensitive handoff algorithm can be found inRef. [13].

Fig. 12. APCV output of the proposed handoff decision algorithm for image transferapplication.

6. Conclusions

Decision making is one of the major challenges in Vertical Handoffissue since there are plenty of parameters that must be considered. Inthis work, we proposed an adaptive fuzzy‐based handoff decision sys-tem that is capable of switching between Wi-Fi, GSM, UMTS, andWiMAX technologies. The developed system is able to combine pa-rameters such as data rate, cost, speed of mobile and RSSI in orderto initiate handover process and select the best candidate AP. Thesimulation results of the proposed system are also compared withthose of classical MADM method, i.e. SAW. According to the resultsobtained, the proposed vertical handoff decision algorithm is able todetermine whether a handoff is necessary or not, properly, and se-lects the best candidate access network considering the aforemen-tioned parameters. It is also observed that, SAW method could notprovide sensitive results desired. Furthermore, fuzzy‐based algorithmnoticeably reduces the number of handoffs compared to SAW‐basedalgorithm.

Fig. 15. APCV output of the SAW MADM for 100‐Kb/s transfer application.

Fig. 16. APCV output of the Fuzzy‐based system.

Fig. 17. APCV output of the SAW-based system.

Fig. 18. Fuzzy membership functions for speed of mobile (S).

Fig. 19. APCV output of the developed handoff decision algorithm for data transfer ap-plication (9.6 and 13 Kbps).


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Vertical Handoff Decision Algorithm, In: The 18st IEEE International Conferenceon Systems, Signals and Image Processing (IWSSIP), 2011, pp. 1–4.

Ali Çalhan received the M.Sc. and Ph.D. degrees from Uni-versity of Kocaeli, Turkey in 2006 and 2011, respectively.His research interests are cognitive radio, wireless hetero-geneous networks, wireless communications and artificialintelligence.

Celal Çeken received the M.Sc. and Ph.D. degrees fromUniversity of Kocaeli, Turkey in 2001 and 2004, respective-ly. His active research interests include wireless communi-cations, broadband networks, ATM networks, high‐speedcommunication protocols, Wireless Sensor Networks andcognitive radio.

Case Study on Handoff Strategies for Wireless Overlay Networks

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