A survey of vertical handover decision algorithms in Fourth

Computer Networks 54 (2010) 1848–1863

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Computer Networks

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A survey of vertical handover decision algorithms in Fourth Generationheterogeneous wireless networks

Xiaohuan Yan a, Y. Ahmet S�ekercioglu a,*, Sathya Narayanan b

a Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, Victoria, Australiab School of Information Technology and Communication Design, California State University, Monterey Bay, California, United States

a r t i c l e i n f o a b s t r a c t

Article history:Received 16 September 2009Received in revised form 31 January 2010Accepted 10 February 2010Available online 14 February 2010

Keywords:Wireless networks4GVertical handoverFourth Generation

1389-1286/$ - see front matter � 2010 Elsevier B.Vdoi:10.1016/j.comnet.2010.02.006

* Corresponding author. Tel.: +61 3 99053503; faE-mail address: [email protected] (Y. Ahmet S�ek

Vertical handover decision (VHD) algorithms are essential components of the architectureof the forthcoming Fourth Generation (4G) heterogeneous wireless networks. These algo-rithms need to be designed to provide the required Quality of Service (QoS) to a wide rangeof applications while allowing seamless roaming among a multitude of access networktechnologies. In this paper, we present a comprehensive survey of the VHD algorithmsdesigned to satisfy these requirements. To offer a systematic comparison, we categorizethe algorithms into four groups based on the main handover decision criterion used. Also,to evaluate tradeoffs between their complexity of implementation and efficiency, we dis-cuss three representative VHD algorithms in each group.

� 2010 Elsevier B.V. All rights reserved.

1. Introduction

Growing consumer demand for access to communica-tion services anywhere and anytime is accelerating thetechnological development towards the integration of var-ious wireless access technologies, nowadays called asFourth Generation (4G) wireless systems [2]. 4G wirelesssystems will provide significantly higher data rates, offera variety of services and applications previously not possi-ble due to speed limitations, and allow global roamingamong a diverse range of mobile access networks [12,33,15,52,20,44].

In a typical 4G networking scenario, handsets or mobileterminals with multiple interfaces will be able to choosethe most appropriate access link among the available alter-natives (these include IEEE 802.11 Wireless Local Area Net-work (WLAN) [13], IEEE 802.16 Worldwide Interoperabilityfor Microwave Access (WiMAX) [45]), satellite systems [5]and Bluetooth [38], in addition to the traditional cellulartelephony networks which are almost universally accessi-ble today. For a satisfactory user experience, mobile

. All rights reserved.

x: +61 3 99053454.ercioglu).

terminals must be able to seamlessly transfer to the ‘‘best”access link among all available candidates with no perceiv-able interruption to an ongoing conversation (which couldbe a voice or video session). Such ability to hand over be-tween heterogeneous networks is referred to as seamlessvertical handovers [30]. As an important step towardsachieving this objective, the emerging IEEE 802.21 standardcreates a framework to support protocols for enablingseamless vertical handovers [42]. Since the 802.21 providesonly the overall framework, the actual algorithms to beimplemented are left to the designers. To fill this gap,numerous vertical handover decision (VHD) algorithmshave been proposed in the research literature.

A number of studies published earlier have surveyedVHD algorithms [28,53,41]. In the earliest one [28], a tuto-rial on the design and performance issues of VHD policiesis presented along with analysis and comparison of severalVHD algorithms. However, the focus of this study wasquite narrow and only covered cost function and receivedsignal strength (RSS) based VHD algorithms. In a laterstudy [53], the authors presented a framework to comparethe performance of different vertical handover algorithmson system resource utilization and Quality of Service(QoS) perceived by users, but only included the evaluation

VerticalHandover Decision

Algorithms

Received signal strength

Network connection time

Available bandwidth

Power consumption

Monetary cost

HandoverDecision

X. Yan et al. / Computer Networks 54 (2010) 1848–1863 1849

of two VHD algorithms. A subsequent survey’s focus [41]was on various mathematical models used in verticalhandover decisions. In our paper, we approach this taskby classifying the VHD algorithms into four groups, andoffering a comparative analysis of selected three represen-tative algorithms in each group. We also update these ear-lier studies by incorporating recently published algorithmsinto this survey.

Security

User preferences

Fig. 1. Parameters used for making VHD decisions.

2. Vertical handovers

2.1. Overview of handover processes and vertical handovers

Handover is the process of maintaining a user’s activesessions when a mobile terminal changes its connectionpoint to the access network (called ‘‘point of attachment”),for example, a base station or an access point [1]. Depend-ing on the access network that each point of attachmentbelongs to, the handover can be either horizontal or verti-cal [32]. A horizontal handover takes place between pointsof attachment supporting the same network technology,for example, between two neighboring base stations of acellular network. On the other hand, a vertical handoveroccurs between points of attachment supporting differentnetwork technologies, for example, between an IEEE802.11 access point and a cellular network base station.

A handover process can be split into three stages: hand-over decision, radio link transfer and channel assignment[1]. Handover decision involves the selection of the targetpoint of attachment and the time of the handover. Radiolink transfer is the task of forming links to the new pointof attachment, and channel assignment deals with the allo-cation of channel resources.

VHD algorithms help mobile terminals to choose thebest network to connect to among all the available candi-dates. Here, we only focus on the research efforts and re-cent developments on improving the efficiency of VHDprocess. In contrast to horizontal handover decision algo-rithms which mainly consider RSS as the only decision cri-terion, for VHD algorithms, criteria such as cost of services,power consumption and velocity of the mobile terminalmay need to be taken into consideration to maximize usersatisfaction [32].

2.2. VHD criteria

Several parameters as shown in Fig. 1 have been pro-posed in the research literature for use in the VHD algo-rithms. We briefly explain each of them below.

Received signal strength (RSS) is the most widely usedcriterion because it is easy to measure and is directlyrelated to the service quality. There is a close relation-ship between the RSS readings and the distance fromthe mobile terminal to its point of attachment. Majorityof existing horizontal handover algorithms use RSS asthe main decision criterion, and RSS is an important cri-terion for VHD algorithms as well.Network connection time refers to the duration that amobile terminal remains connected to a point of attach-

ment. Determining the network connection time is veryimportant for choosing the right moment to trigger ahandover so that the service quality could be main-tained at a satisfactory level. For example, a handoverdone too early from a WLAN to a cellular networkwould waste network resources while being too latewould result in a handover failure. Determining thenetwork connection time is also important for reducingthe number of superfluous handovers, as handing overto a target network with potentially short connectiontime should be discouraged. The network connectiontime is related to a mobile terminal’s location andvelocity. Both the distance from the mobile terminalto its point of attachment and the velocity of the mobileterminal affect the RSS at the mobile terminal. The var-iation of the RSS then determines the time for which themobile terminal stays connected to a particular net-work. Network connection time is especially importantfor VHD algorithms because heterogeneous networksusually have different sizes of network coverage.Available bandwidth is a measure of available data com-munication resources expressed in bit/s. It is a goodindicator of traffic conditions in the access networkand is especially important for delay-sensitiveapplications.Power consumption becomes a critical issue especially ifa mobile terminal’s battery is low. In such situations, itwould be preferable to handover to a point of attach-ment which would help extending valuable battery life[32].Monetary cost: For different networks, there would bedifferent charging policies, therefore, in some situationsthe cost of a network service should be taken into con-sideration in making handover decisions.Security: For some applications, confidentiality or integ-rity of the transmitted data can be critical. For this rea-son, a network with higher security level may be chosenover another one which would provide lower level ofdata security.User preferences: A user’s personal preference towardsan access network could lead to the selection of onetype of network over the other candidates.

RSS and network connection time based decision crite-ria are widely used in both horizontal and vertical hand-

1850 X. Yan et al. / Computer Networks 54 (2010) 1848–1863

over decisions. Others are mainly seen in VHD schemesonly.

2.3. Classification of VHD algorithms

There are various ways to classify VHD algorithms[21,26]. In this article, we have chosen to divide VHD algo-rithms into four groups based on the handover decisioncriteria used and the methods used to process these.

RSS based algorithms: RSS is used as the main handoverdecision criterion in this group. Various strategies havebeen developed to compare the RSS of the current pointof attachment with that of the candidate point of attach-ment [54,29,48]. In [37] RSS based horizontal handoverdecision strategies are classified into the following sixsubcategories: relative RSS, relative RSS with threshold,relative RSS with hysteresis, relative RSS with hysteresisand threshold, and prediction techniques. For VHD, rel-ative RSS is not applicable, since the RSS from differenttypes of networks can not be compared directly due tothe disparity of the technologies involved. For example,separate thresholds for each network. Furthermore,other network parameters such as bandwidth are usu-ally combined with RSS in the VHD process.Bandwidth based algorithms: Available bandwidth for amobile terminal is the main criterion in this group[22,51,10]. In some algorithms, both bandwidth andRSS information are used in the decision process[54,16]. Depending on whether RSS or bandwidth isthe main criterion considered, an algorithm is classifiedeither as RSS based or bandwidth based.Cost function based algorithms: This class of algorithmscombine metrics such as monetary cost, security, band-width and power consumption in a cost function, andthe handover decision is made by comparing the resultof this function for the candidate networks [56,18,43].Different weights are assigned to different input met-rics depending on the network conditions and userpreferences.Combination algorithms: These VHD algorithms attemptto use a richer set of inputs than the others for makinghandover decisions. When a large number of inputs areused, it is usually very difficult or impossible to developanalytical formulations of handover decision processes.Due to this reason, researchers apply machine learningtechniques to formulate the processes. Our literaturesurvey reveals that fuzzy logic and artificial neural net-works based techniques [55,35] are popular choices.Fuzzy logic systems allow human experts’ qualitativethinking to be encoded as algorithms to improve theoverall efficiency. Examples of applying this approachinto VHD can be found in [47,6,55,19,25]. If there is acomprehensive set of input-desired output patternsavailable, artificial neural networks can be trained tocreate handover decision algorithms [17,31,35]. It isalso possible to create adaptive versions of these algo-rithms. By using continuous and real-time learning pro-cesses, the systems can monitor their performance andmodify their own structure to create highly effectivehandover decision algorithms.

2.4. Performance evaluation metrics for VHD algorithms

VHD algorithms can be quantitatively compared undervarious usage scenarios by measuring the mean and max-imum handover delays, the number of handovers, thenumber of failed handovers due to incorrect decisions,and the overall throughput of a session maintained overa typical mobility pattern. These metrics are further ex-plained below:

Handover delay refers to the duration between the initi-ation and completion of the handover process. Hand-over delay is related to the complexity of the VHDprocess, and reduction of the handover delay is espe-cially important for delay-sensitive voice or multimediasessions.Number of handovers: Reducing the number of hand-overs is usually preferred as frequent handovers wouldcause wastage of network resources. A handover is con-sidered to be superfluous when a handover back to theoriginal point of attachment is needed within a certaintime duration [11,48], and such handovers should beminimized.Handover failure probability: A handover failure occurswhen the handover is initiated but the target networkdoes not have sufficient resources to complete it, orwhen the mobile terminal moves out of the coverageof the target network before the process is finalized.In the former case, the handover failure probability isrelated to the channel availability of the target network[46], while in the latter case it is related to the mobilityof the user [4].Throughput: The throughput refers to the data ratedelivered to the mobile terminals on the network.Handover to a network candidate with higher through-put is usually desirable.

3. Vertical handover decision algorithms

In this section, we discuss a representative set of VHDalgorithms. Their operational fundamentals are summa-rized along with their comparative advantages and disad-vantages. These algorithms are assigned into one of thefour categories described in Section 2.3 (as shown inFig. 2). Some of the algorithms use more than one VHD cri-teria, and in such cases, we consider the main criterionthey use for classification.

3.1. RSS based VHD algorithms

RSS based VHD algorithms compare the RSS of the cur-rent point of attachment against the others to make hand-over decisions. Because of the simplicity of the hardwarerequired for RSS measurements, not surprisingly, a largenumber of studies have been conducted in this area[36,27,54,29,48,7]. We describe three representative RSSbased VHD algorithms in the following sections and pres-ent a comparative summary of them in Table 1.

Cost Function Based

RSS Based

Bandwidth Based

Combination Algorithms

Zahran et al.’s algorithm [53], Lee et al.’s algorithm [22], Yang et al.’s algorithm [51]

Yan et al.’s algorithm [48],

Mohanty and Akyildiz’s

algorithm [29]Chi et al.’s

algorithm [10]

Zhu and McNair’s

algorithm [56],

Tawil et al.’s algorithm [43]

Hasswa et al.’s algorithm

[18]

Nasser et al.’s algorithm [31]

Pahlavan et al.’s algorithm

[35]

Xia et al.’s algorithm [47]

Fig. 2. The four categories of VHD algorithms and twelve selected representative schemes.

Table 1A summary of RSS based VHD algorithms.

Heuristic Applicable area Feature Advantages Disadvantages

Zahran et al.’sheuristic [53]

Between 3Gand WLANs

The RSS is combined with anestimated lifetime or theavailable bandwidth to decidethe handover time

� Adaptation to applicationrequirements and user mobility

� Improvement on the availablebandwidth

� Long packet delay� Extra lookup table

Mohanty andAkyildiz’sheuristic [29]

Between 3Gand WLANs

A dynamic RSS threshold iscalculated and compared withthe current RSS to determine thehandover time from WLAN to 3G

� Reduction of the false handoverinitiation and handover failureprobabilities

� Increased handover failureprobability from 3G to WLAN

� Wastage of network resources

Yanet al.’sheuristic [48]

Betweencellularnetworks andWLANs

A dynamic time threshold iscalculated and compared withthe predicted traveling timeinside the WLAN to help withhandover decisions

� Minimization of the handoverfailure,unnecessary handover andconnection breakdown probabilities

� Extra handover delay


3.1.1. An adaptive lifetime based handover heuristicZahran and Liang [54] proposed an algorithm for hand-

overs between 3G networks and WLANs by combining theRSS measurements either with an estimated lifetime met-ric (expected duration after which the mobile terminal willnot be able to maintain its connection with the WLAN) orthe available bandwidth of the WLAN candidate. We de-scribe their method through the following scenarios.

In the first scenario, when the mobile terminal movesaway from the coverage area of a WLAN into a 3G cell, ahandover to the 3G network is initiated. The handover istriggered under the conditions that (a) RSS average of theWLAN connection falls below a predefined thresholdðMOTWLANÞ, and (b) the estimated lifetime is less than orequal to the handover delay. The mobile terminal continu-ously calculates the RSS average using the moving averagemethod

RSS½k� ¼ 1Wav

XWav�1

i¼0

RSS½k� i�: ð1Þ

Here RSS½k� is the calculated average of RSS at time instantk, and Wav is the window size, a variable that changes withthe velocity of the mobile terminal. Then, the lifetime met-ric EL½k� is calculated by using RSS½k�, the RSS change rateS½k�, and a parameter called Application Signal StrengthThreshold (ASST) as follows:

EL½k� ¼ RSS½k� � ASSTS½k� : ð2Þ

The RSS change rate S½k� varies with the window size of theslope estimator and the RSS sampling interval. For detailson calculating S½k�, please refer to Eqs. (4)–(6) in [54]. TheASST is an application dependent parameter which repre-sents a composite of the channel bit error rate, applicationerror resilience and application QoS requirements. A look-up table for the optimal ASST values is provided in thepaper.

In the second scenario, when the mobile terminalmoves towards a WLAN cell, the handover to the WLANis triggered if the average RSS measurements of the WLAN


signal is larger than a threshold ðMITWLANÞ and the avail-able bandwidth of the WLAN meets the bandwidthrequirements of the application. The flowchart of Zahranet al.’s heuristic is depicted in Fig. 3.

Benefits of Zahran et al.’s algorithm can be summarizedas follows. First, by introducing the lifetime metric, thealgorithm adapts to the application requirements and theuser mobility, reducing the number of superfluous hand-overs significantly. Second, there is an improvement onthe average throughput for the user because of the mobile

RSS monitoring

Handover to WLAN

YES

RSSWLAN

MOTWLAN&

EL[k] handover delay?

≤

≤

NO

RSS average RSS[k] Calculation

Lifetime EL[k] estimation

On WLAN?

RSS change rate S[k] estimation

RSSWLAN

MITWLAN&

Sufficient bandwidth?

WLAN candidate available

bandwidth estimation

≥

YES YES

NO NO

Start

Handover to 3G

Fig. 3. Zahran et al.’s VHD algorithm [53].

RSS monitoring

RSS < ?

Handover initiation

YESNO

Distance to cellboundary estimation

RSS thresholdestimation

RSS decreasingcontinuously?

YESNO

athS

athS

On WLAN?

WLAN detected?NO

YES

YES NO

Start

BAL

Fig. 4. Mohanty and Akyildiz’s VHD heuristic [29].

terminal’s ability to remain connected to the WLAN cell aslong as possible. However, packet delays grow with an in-crease in the lifetime, due to the deterioration of the chan-nel condition as the mobile terminal approaches the edgeof the WLAN cell. This issue can be critical for delay sensi-tive applications and degrade their performance. To solvethis problem, ASST is tuned according to various systemparameters, including delay thresholds, mobile terminalvelocities, handover signaling costs and packet delaypenalties.

3.1.2. An RSS threshold based dynamic heuristicMohanty and Akyildiz [29] proposed a WLAN to 3G

handover decision method based on comparison of thecurrent RSS and a dynamic RSS threshold ðSdthÞwhen a mo-bile terminal is connected to a WLAN access point. Sdth (indBm) is calculated as

Sdth ¼ RSSmin þ 10b log 10d

d� LBA

� �þ �; ð3Þ

where RSSmin (in dBm) is the minimum level of the RSS re-quired for the mobile terminal to communicate with an ac-cess point, b is the path loss coefficient, d is the side lengthof the WLAN cell (in meters, a WLAN cell is assumed tohave a hexagonal shape in this study), LBA is the shortestdistance between the point at which handover is initiatedand WLAN boundary, and � (in dB) is a zero-mean Gaussianrandom variable with a standard deviation that representsthe statistical variation in RSS caused by shadowing. Thedistance LBA depends on the desired handover failure prob-ability pf , the velocity of the mobile terminal v, the WLANto 3G handover delay s, and is calculated as

LBA ¼ s2v2 þ d2 pf � 2þ 2ffiffiffiffiffiffiffiffiffiffiffiffiffi1� pf

q� �h i12: ð4Þ

The use of a dynamic RSS threshold helps reducing theincidences of false handover initiation and keeping thehandover failures below a limit (the flowchart of the heu-ristic is shown in Fig. 4.). However, in this algorithm, thehandover failure probability from 3G network to a WLANcell is considered to be zero since the 3G network coverageis assumed to be available all the time, and thus accordingto the mechanism, a handover to a WLAN is always desir-able whenever the mobile terminal enters the WLAN cov-erage. Yan et al. [48] (discussed in the next section) pointout in their study that this is not efficient when the mobileterminal’s traveling time inside a WLAN cell is less than thehandover delay, and in such cases a handover may result inwastage of network resources.

3.1.3. A traveling distance prediction based heuristicTo eliminate unnecessary handovers in the method pre-

sented in Section 3.1.2, Yan et al. [48–50] developed a VHDalgorithm that takes into consideration the time the mo-bile terminal is expected to spend within a WLAN cell.The method relies on the estimation of WLAN travelingtime (i.e. time that the mobile terminal is expected tospend within the WLAN cell) and the calculation of a timethreshold. A handover to a WLAN is triggered if the WLANcoverage is available and the estimated traveling time


inside the WLAN cell is larger than the time threshold. Thetraveling time ðtWLANÞ is estimated as

tWLAN ¼R2 � l2

OS þ v2ðts � tinÞ2

v2 ts � tinð Þ ; ð5Þ

where R is the radius of the WLAN cell, lOS is the distancebetween the access point and where the mobile terminaltakes an RSS sample, v is the velocity of the mobile termi-nal, and ts and tin are the times at which the RSS sample istaken and the mobile terminal enters the WLAN cell cover-age, respectively. lOS is estimated by using the RSS informa-tion and log-distance path loss model.

The time threshold ðTWLANÞ is calculated based on vari-ous network parameters as

TWLAN ¼2Rv sin sin�1 vs

2R

� �� p

2P

� �ð6Þ

where s is the handover delay from cellular network toWLAN and P is the maximum tolerable handover failure,or unnecessary handover probability. A handover to thecellular network is initiated if the WLAN RSS is continu-ously fading and the mobile terminal reaches a handovercommencement boundary area based on the mobile termi-nal’s speed. Fig. 5 shows Yan et al.’s heuristic.

The main advantage of this heuristic is that it minimizeshandover failures, unnecessary handovers and connectionbreakdowns. But the method relies on sampling and aver-aging RSS points, which introduces increased handover de-lay. The performance impact of the handover delay shouldbe further discussed and balanced against the probabilityof unnecessary handovers.

YESNO

On WLAN?YES NO

Traveling timetWLAN

estimation

Time thresholdT

WLAN calculation

tWLAN

> TWLAN

?

WLAN detected &Signal stable?

NO

YES

Handover initiation

WLAN RSS fading continuously?

NO

Handover time estimation

Time to initiatehandover?

YESNO

Start

RSS monitoring

Fig. 5. Yan et al.’s VHD heuristic [49–51].

3.2. Bandwidth based VHD Algorithms

Bandwidth based VHD algorithms consider availablebandwidth for a mobile terminal or traffic demand as themain criterion [34,22,51,10]. In this section, three typicalbandwidth based VHD algorithms are discussed in detail.

3.2.1. A QoS based heuristicLee et al. [22] devised a QoS based VHD algorithm

which takes residual bandwidth and user service require-ments into account in deciding whether to handover froma WLAN to Wireless Wide Area Network (WWAN) and viceversa.

When the mobile terminal is connected to a WLAN, thehandover algorithm is initiated if the measured RSS is con-sistently below a threshold ðRSST1Þ. The algorithm alsotakes the state of the mobile terminal into consideration.If the mobile terminal is in the idle state, a handover tothe preferred access network is performed, otherwise thehandover decision is based upon the user application type.For delay-sensitive applications, a handover occurs only ifthe current serving WLAN is not able to provide enoughbandwidth for the application while the WWAN is ableto provide the necessary bandwidth. For delay-tolerantapplications, a handover takes place if the WWAN provideshigher bandwidth than the WLAN. Approximate value ofthe residual bandwidth of the WLAN is evaluated by thefollowing formula:

residual bandwidth ¼ throughput � ð1� a� channel utilizationÞ� ð1� packet loss rateÞ; ð7Þ

where throughput is the throughput that can be sharedamong mobile terminals in the WLAN, channel_utilizationis the percentage of time the access point senses the med-ium is busy using the carrier sense mechanism, a is a factorthat reflects IEEE 802.11 MAC overhead (it is set to 1.25 in[22]), and packet_loss_rate is the portion of transmittedmedium access control (MAC) protocol data units (MPDUs)that require retransmission, or are discarded as undeliver-able. The values of channel_utilization and packet_loss_rateare obtained from the information in the beacon frame car-rying the QoS basic service set (QBSS) load sent by an ac-cess point, as defined in IEEE 802.11e [14].

When the mobile terminal is connected to a WWAN,a similar process is carried out if consecutive beaconsfrom the WLAN with RSS above a threshold ðRSST2Þ arereceived. The flowchart of Lee et al.’s algorithm is de-picted in Fig. 6.

By considering the available bandwidth as the mainVHD criterion, this heuristic is able to achieve high systemthroughput, and by taking application types into account,lower handover latency for delay-sensitive applications isachieved. However, acquiring the available bandwidthinformation in a cellular network for handover decisionsis difficult [22]. Furthermore, in this method, a handoverto the preferred network is performed when the mobileterminal is in the idle state. However, when the mobile ter-minal is staying in the preferred network for only a short

RSSmonitoring

YES

Handover initiation

YES

NO

System idle?YES

Real-time?

NO

NO

BWLAN

< BNEED

&B

WWAN > B

NEED?

NO

NO

YES

YES

YES

NRSS

> NRSST1

?YES

NO

WLAN preferred? System idle?

Real-time?

NO

YES

NRSS

> NRSST2

?

YES

YES

NO

YES

NO

NO

BWWAN

< BNEED

&B

WWAN < B

WLAN ?

WWAN preferred?

BWLAN

> BWWAN

?

NO

YES

YES

NO

NOYES

RSS < RSST1

? RSS > RSST2

?NO NO

YES

On WLAN?

Start

BWWAN

> BWLAN

?

Fig. 6. Lee et al.’s VHD heuristic [22].

SINR Monitoring

SINRTARGET

> SINRCURRENT

?

Handover initiation

YES

NO

Equivalent SINR conversion

Start

Fig. 7. Yang et al.’s VHD heuristic [48].


period, the movement can result in high blocking rate fornew applications.

3.2.2. A signal to interference and noise ratio (SINR) basedheuristic

Yang et al. [51] presented a bandwidth based VHDmethod between WLANs and a Wideband Code DivisionMultiple Access (WCDMA) network using Signal to Inter-ference and Noise Ratio (SINR). The SINR calculation ofthe WLAN signals is converted to an equivalent SINR tobe compared with the SINR of the WCDMA channel

cAP ¼ CAP 1þ cBS

CBS

� �WBSWAP� 1

24

35 ð8Þ

where cAP and cBS are the SINR at the mobile terminalwhen associated with WLAN and WCDMA, respectively.C is the dB gap between the uncoded Quadrature Ampli-tude Modulation (QAM) and channel capacity, minus thecoding gain, and CAP equals to 3dB for WLAN and CBS

equals to 3dB for WLAN, as stated by the authors. WAP

and WBS are the carrier bandwidth of WLAN and WCDMAlinks. A handover to the network with larger SINR is per-formed, as shown in the flowchart (Fig. 7).

SINR based handovers can provide users higher overallthroughput than RSS based handovers since the availablethroughput is directly dependent on the SINR, and thisalgorithm results in a balanced load between the WLANand the WCDMA networks. But such an algorithm may alsointroduce excessive handovers with the variation of theSINR causing the node to hand over back and forth be-tween two networks, commonly referred to as ping-pongeffect [37].

3.2.3. A wrong decision probability (WDP) prediction basedheuristic

In [10], Chi et al. proposed a VHD heuristic based on thewrong decision probability (WDP) prediction. The WDP iscalculated by combining the probability of unnecessaryand missing handovers. Assume that there are two net-works i and j with overlapping coverage, and bi and bj

are their available bandwidth. An unnecessary handoveroccurs when the mobile terminal is in network i and deci-des to handover to j, but bj is less than bi after this decision.A missing handover occurs when the mobile terminal deci-des to stay connected to network i, but bi is less than bj

after this decision.

RSS monitoring

Active services list

Start


A handover from network i to network j is initiated ifPr < q� l0 or bj � bi 6 L, where Pr is the unnecessary hand-over probability, q is the traffic load of networki; l0 ¼ 0:001, and L is a bandwidth threshold. The flowchartof this algorithm is shown in Fig. 8.

The authors show that this algorithm is able to reducethe WDP and balance the traffic load, however, RSS is notconsidered. A handover to a target network with highbandwidth but weak received signal is not desirable as itmay result in connection breakdown.

A summary of the bandwidth based VHD heuristics isshown in Table 2.

3.3. Cost function based VHD algorithms

The cost function based algorithms combine metrics ina cost function. Many studies have been done in this area[3,8,56,9,23,18,43,40,24]. In this section, we evaluate threerepresentative cost function based VHD algorithms.

3.3.1. A multiservice based heuristicZhu and McNair’s [56,57] VHD algorithm relies on a cost

function which calculates the ‘‘cost” of possible target net-works. The algorithm prioritizes all the active applications,and then the cost of each possible target network for theservice with the highest priority is calculated by

Bandwidth Monitoring

Pr < ρ x l0or bj –bi ≥ L ?

Handover initiation

YES

NO

Unnecessary handoverProbability calculation

Start

Fig. 8. Chi et al.’s VHD heuristic [10].

Table 2A summary of bandwidth based VHD algorithms.

Heuristic Applicablearea area

Feature Adv

Lee et al.’sheuristic[22]

BetweenWWANsand WLANs

The bandwidth is combined withthe RSS, system status andapplication type to makehandover decisions

��

Yang et al.’sheuristic[51]

BetweenWCDMAand WLANs

The SINR values are comparedto determine the handoverdecision

��

Chi et al.’sheuristic[10]

Between anytwowirelessnetworks

Available bandwidth, networktraffic and unnecessary handoverprobability are considered in thehandover decision criteria

�

�

Cns ¼

XWn

s;jQns;j; En

s;j – 0; ð9Þ

where Cns is the per-service cost for network n; Q n

s;j is thenormalized QoS provided by network n for parameter jand service s; Wn

s;j is the weight which indicates the impactof the QoS parameter on the user or the network, and En

s;j isthe network elimination factor, indicating whether theminimum requirement of parameter j for service s can bemet by network n. The total cost is the sum of the cost ofeach QoS parameter, including the bandwidth, batterypower and delay. The service is handed over to the net-work with the minimum cost. The flowchart of this algo-rithm is shown in Fig. 9.

The primary benefits brought by the use of cost functionand by independently initiating handovers for differentapplications are the increased percentage of user satisfiedrequests and reduced blocking probability. However, the

antages Disadvantages

High system throughputLow handover latency forreal-time transmission

� Difficulty in acquiring availablebandwidth information

� Increased new application blockingrate

High overall throughputBalance of the network loadbetween WLANs andWCDMA

� Excessive handovers� Ping-pong effect

Reduced unnecessaryhandover probabilityBalance of the traffic load

� Increased connection breakdownprobability without considering theRSS

Unassigned service left?YES

NO

Cost calculation

Service with highestpriority selection

Database update

Current cost > Candidate cost?

NO

YES

Handover initiation

Fig. 9. Zhu and McNair’s VHD heuristic [56,57].


authors did not discuss how the QoS factors are normalizedor how the weights for the QoS factors are assigned.

3.3.2. A cost function based heuristic with normalization andweights distribution

Similar to Zhu and McNair’s method [56,57], Hasswaet al. also proposed a cost function based handover deci-sion algorithm in which the normalization and weightsdistribution methods are provided [18,32]. A networkquality factor is used to evaluate the performance of ahandover target candidate as

Q i ¼ xcCi þxsSi þxpPi þxdDi þxf Fi; ð10Þ

where Q i is the quality factor of network i; Ci; Si; Pi; Di

and Fi stand for cost of service, security, power consump-tion, network conditions and network performance, andxc; xs; xp; xd and xf are the weights for these networkparameters. Since each network parameter has a differentunit, a normalization procedure is used and the normalizedquality factor for network n is calculated as

Q i ¼xcð1=CiÞ

maxðð1=C1Þ; . . . ; ð1=CnÞÞþ xsSi

maxðS1; . . . ; SnÞ

þ xpð1=PiÞmaxðð1=P1Þ; . . . ; ð1=PnÞÞ

þ xdDi

maxðD1; . . . ;DnÞ

þ xf Fi

maxðF1; . . . ; FnÞ: ð11Þ

A handover necessity estimator is also introduced to avoidunnecessary handovers. Fig. 10 depicts the operation ofthis algorithm.

High system throughput and user’s satisfaction can beachieved by introducing Hasswa’s heuristic, however,some of the parameters such as security and interferencelevels are difficult to estimate, and the authors have yet

RSS monitoring

Handover initiation

Weights distribution

Quality factor calculation

Network parameterscollection

Handover necessary?

YES

NO

Current quality < Candidate quality ?

YES

NO

Start

Fig. 10. Hasswa et al.’s VHD heuristic [18,32].

to provide information on how to measure theseparameters.

3.3.3. A weighted function based heuristicTawil et al. [43] presented a weighted function based

VHD algorithm which delegates the VHD calculation tothe visited network instead of the mobile terminal. Theweighted function of a network candidate is defined as

Q i ¼WBBi þWDP

1DPi

þWC1Ci; ð12Þ

where Qi represents the quality of network i; Bi; DPiand Ci

are bandwidth, dropping probability and monetary cost ofservice, and WB; WDP and WC are their respective weightswhere

WB þWDP þWC ¼ 1: ð13Þ

The network candidate with the highest Qi is selected asthe handover target. The process of this algorithm is shownin Fig. 11.

By assigning the calculation to the visited network, theresource of the mobile terminal can be saved so that thesystem is able to achieve short handover decision delay,low handover blocking rate and high throughput. How-ever, the method requires extra cooperation between themobile terminal and the point of attachment of the visitednetwork, which may cause additional latency and exces-sive load to the network when there is a large number ofmobile terminals.

A summary of the cost function based VHD heuristics isshown in Table 3.

3.4. Combination algorithms

Combination algorithms are based on artificial neuralnetworks or fuzzy logic, and combine various parametersin the handover decision such as the ones used in the costfunction algorithms. Many combination algorithms havebeen proposed [6,55,17,19,25,31,35,47]. In the following

Handover initiation

Qualitycalculation

Network parameterscollection

PoA of the visited network

Mobile terminal

RSS monitoring

Current quality < Candidate quality ?

YES

NO

Start

Fig. 11. Tawil et al.’s VHD heuristic [43].

Table 3A summary of cost function based VHD algorithms.

Heuristic Applicablearea

Feature Advantages Disadvantages

Zhu and McNair’sheuristic [56]

Between any twoheterogeneous wirelessnetworks

� A cost function is intro-duced and users’ activeapplications are individ-ually handed over to tar-get networks with theminimum costs

� Increased user satisfaction� Low blocking probability

� Missing detailed informationsuch as normalizationmethod and weights assign-ment to make the algorithmrealistic

Hasswa et al.’sheuristic [18]


� Normalization andweights distributionmethods are provided

� A handover necessityestimator is proposed

� High throughput� High users’ satisfaction

� Difficulty in estimatingparameters such as securityand interference level

Tawil et al.’sheuristic [43]


� A weighted function isintroduced

� The handover calcula-tion is delegated to thevisited network insteadof the MT

� Short handover delay� Low handover blocking rate� High throughput

� Requirement of cooperationbetween the MT and thepoint of attachment of thevisited network


sections, we analyze and evaluate three typical combina-tion algorithms.

3.4.1. A multilayer feedforward artificial neural networkbased heuristic

Nasser et al. developed a VHD algorithm based on arti-ficial neural networks (ANNs) [31]. As shown in Fig. 12, themobile device collects features of available wireless net-works and sends them to a middleware called verticalhandover manager through the existing links. These net-work features are used to help with handover decisionsand include network usage cost, security, transmissionrange and capacity. The vertical handover manager con-sists of three main components: network handling man-ager, feature collector and ANN training/selector. Amultilayer feedforward ANN is used to determine the besthandover target wireless network available to the mobiledevice, based on the user’s preferences.

The topology of the ANN is shown in Fig. 13. It consistsof an input layer, a hidden layer and an output layer. Theinput layer consists of five nodes representing variousparameters of the handover target candidate networks.The hidden layer consists of variable number of nodes

Available networks

Which network to choose

Available networks reported

(including their features)

Ver

Fig. 12. Architecture of Nass

which are activation functions. The output layer has onenode which generates the network ID of the handover tar-get. All the neurons use sigmoid activation function [39].

The authors have adopted the same cost function as in[18], and for ANN training they have generated a seriesof user preference sets with random weights. Then the sys-tem has been trained to select the best network among allthe candidates.

The authors report that by properly selecting the learn-ing rate and the acceptable error value, the system wasable to find the best available network successfully. How-ever, the algorithm suffers from a long delay during thetraining process.

3.4.2. A method that uses two neural networksPahlavan et al. [35] proposed two neural network based

decision methods for horizontal and vertical handovers.Here, we only discuss the vertical handovers mechanism.

In the method for vertical handovers, an ANN is used forhandovers from the WLAN to the General Packet Radio Ser-vice (GPRS). The ANN, as shown in Fig. 14, consists of an in-put layer, two middle layers and an output layer. Themobile node performs periodical RSS measurements, and

Networkhandling manager

Feature collector

ANNtraining

ANNselector

tical handover manager

er et al.’s system [31].

a5

Input layer Hidden layer Output layer

…

b1

bn

a1

b2

b3

a2

a3

a4

cID of the handover target

Cost of service preference

Security preference

Power consumption preference

Bandwidth preference

Class label of the wireless network type

Fig. 13. Topology of the feedforward ANN used in Nasser et al.’s VHD scheme [31].

a5

Input layer Layer 1 Output layer

…

b1

bn

a1

b2

b3

a2

a3

a4

d ‘1’ or ‘0’

RSS sample

RSS sample

RSS sample

RSS sample

RSS sample

Layer 2

…

c1

cn

c2

c3

Fig. 14. Structure of the ANN used for vertical handover decisions in Pahlavan et al.’s VHD scheme [35]. RSS measurements are done periodically and mostrecent five samples are fed into the ANN.


five most recent RSS samples of the access point are fedinto the ANN. The output is a binary signal: The value ‘1’leads to a handover to the GPRS, and the value ‘0’ meansthat the mobile node should remain connected to the ac-cess point.

The ANN is trained before used in the decision process.Training is done by taking a number of RSS samples fromthe access point and, using a pattern recognition tech-nique, selecting the most suitable network, while minimiz-ing the handover delay and ping-pong effect.

This heuristic can reduce the number of handovers byeliminating the ping-pong effect, but the paper lacks detail

on how the neural network is trained and why the partic-ular parameters are selected. This algorithm also has thedisadvantage of the increased algorithm complexity andthe training process to be performed beforehand.

3.4.3. A fuzzy logic based heuristicBesides artificial neural networks, fuzzy logic [19,25,47]

is also used for creating schemes to deal with a rich set ofinput parameters for making vertical handover decisions.Xia et al.’s method [47] is a good representative exampleof this approach. This scheme is used to handle handoversbetween WLANs and Universal Mobile Telecommunica-


tions Systems (UMTS). A pre-decision unit is used in thisscheme.

In this algorithm, if the mobile terminal is connected tothe WLAN, and the velocity of the mobile terminal ðvÞ ishigher than a velocity threshold ðvTÞ, a handover to theUMTS is directly initiated to prevent a connection break-down. Otherwise, the pre-decision unit checks whetherthe predicted RSS satisfies its requirements. If the pre-dicted RSS from the WLAN ðPRW Þ is larger than its thresh-old ðPrWÞ, or the predicted RSS from the UMTS ðPRUÞ issmaller than its threshold ðPrUÞ, no handover is triggered.After the pre-decision, the fuzzy logic based normalizedquantitative decision (FNQD) is applied. The FNQD hasthree procedures: fuzzification, normalization and quanti-tative decision. The three inputs, current RSS, predictedRSS and bandwidth, are fuzzified and normalized to gener-ate performance evaluation values (PEV), and the VHD ismade by comparing PEVs of the network candidates.

If the mobile terminal is connected to the UMTS and theWLAN connectivity is available, the pre-decision unit isused to eliminate unnecessary handovers when the veloc-ity of the mobile terminal is larger than the threshold ðvTÞ.A similar process is executed as the one described in thehandover from WLANs to UMTSs. The process of this algo-rithm is illustrated in Fig. 15.

The heuristic in this study is able to achieve improvedperformance by reducing the number of unnecessaryhandovers and avoiding the ping-pong effect. However,when the PEVs are calculated, fixed weights are assignedto the three inputs. This is not practical because the net-work condition and user requirements vary in different sit-uations. In addition, more performance evaluation criteriasuch as handover delay and system load need to beaddressed.

YES

NO

v > vT

?

Predicted RSS estimation

Velocity estimation

RSS monitoring

PRW

< PrW

&PR

U> P

rU?

FNQD

Handover initiation

PEVUMTS

> PEVWLAN

?

YES

YES

NO

NO

On WLAN?

YES

YESNO

v > vT

?

Predicted RSS estimation

PRW

> PrW

FNQD

PEVWLAN

> PEVUMTS

?

YES

NO

NO

NO

YES

Start

Fig. 15. Xia et al.’s VHD heuristic [47].

A summary of the cost function based VHD heuristics isshown in Table 4.

4. Comparison of the approaches

So far we have discussed twelve VHD algorithms andclassified them into four groups based on the criteria theyuse for making handover decisions. To provide an overallcomparison of the four groups, we summarize their fea-tures on five aspects: networking technologies that theycan be applicable, input parameters, handover target selec-tion criteria, complexity and reliability in Table 5.

The applicable network technologies for RSS based VHDalgorithms are usually between macrocellular and micro-cellular networks, e.g. 3G and WLANs. The algorithms tendto make full usage of WLANs because of their high band-width and low cost. The other three types of VHD algo-rithms can be applied for handovers between a variety ofwireless networks.

As for the input parameters, RSS is used as the main in-put in RSS based VHD algorithms, while the RSS combinedwith the bandwidth information is usually adopted inbandwidth based VHD algorithms. Various networkparameters are used in cost function based or combinationalgorithms, such as monetary cost, bandwidth, securityand power consumption.

For handover target selection criteria, the candidatenetwork with the most stable RSS and highest bandwidthis selected as the handover target in RSS and bandwidthbased VHD algorithms, respectively. On the other hand,combination or cost function based algorithms attempt tochoose the target network with the highest overall perfor-mance. The overall performance is calculated based on thevarious network parameters.

In terms of complexity, RSS based algorithms are usu-ally the simplest, followed by the bandwidth based algo-rithms. Cost function based VHD algorithms tend to bemore complex as they need to collect and normalize vari-ous network parameters, and combination algorithms arethe most challenging ones because of their pre-trainingrequirements.

Finally, reliability varies among the algorithms. Fluctu-ations of RSS decreases the reliability of RSS based VHDalgorithms, and the difficulty in measuring available band-width reduces the reliability of bandwidth based VHDalgorithms. In cost function based algorithms, someparameters such as security level are hard to measure,and they degrade their reliability. As for combination algo-rithms, since the systems are trained beforehand, they canbe considered as the most reliable among the four groups.

For a better understanding of the performance of differ-ent VHD algorithms, we provide a quantitative comparisonbased on the performance metrics mentioned in Sec-tion 2.4. Since the authors of each algorithm provide differ-ent performance parameters in their studies, directcomparisons are impossible. In Table 6, we provide a sum-mary quantitative comparison based on four performanceparameters: delay, number of handovers, handover failureprobability and throughput, based on the information pro-vided in the papers. As can be seen, relatively high delays

Table 4A summary of combination algorithms.

Heuristic Applicablearea

Feature Advantages Disadvantages

Nasser et al.’sheuristic [31]

Between any twoheterogeneouswireless networks

A cost function is adoptedand the system is trainedbefore being used in thehandover decision

� High success rate infinding the best net-work candidate

� Training delay� Increased system complexity

Pahlavan et al.’sheuristic [35]

From WLANs to GPRS The RSS samples arecollected as the inputs ofthe neural network and thesystem is trained beforebeing used in the handoverdecision

� Reduced number ofhandovers

� Elimination of theping-pong effect

� Lack of detail on trainingprocess and parameters selection

� Training delay� Increased system complexity

Xia et al.’sheuristic [47]

Between WLANsand cellular networks

Current RSS, predicted RSSand bandwidth arefuzzificated and normalizedto be used as the handoverdecision criteria

� Reduced number ofhandovers

� Elimination of theping-pong effect

� Fixed weights which fail to meetthe need of continuously changingwireless environment

Table 5A comparative summary of the four groups.

Group Applicable networkingtechnologies

Input parameters Handover targetselection criteria

Complexity Reliability

RSS based VHDalgorithms

Usually betweenmacrocellular andmicrocellular networks

RSS as the main input The network candidatewith the most stable RSS

Simple Reduced reliability because ofthe fluctuation of RSS

Bandwidthbased VHDalgorithms

Between any twoheterogeneous networks

Bandwidth combinedwith other parameterssuch as RSS

The network candidatewith the highestbandwidth

Simple Reduced reliability because ofthe changing availablebandwidth

Cost functionbased VHDalgorithms


Various parameters suchas cost, bandwidth andsecurity

The network candidatewith the highest overallperformance

Complex Reduced reliability because ofthe difficulty in measuringsome parameters

Combinationalgorithms


Different inputparameters dependingon different methods

The network candidatewith the highest overallperformance

Verycomplex

High reliability because of thetraining of the system


occur by using RSS based algorithms proposed in [48,54],while the authors in [22,43] argue that their bandwidthand cost function based algorithms are able to maintainshorter handover delays. Combination algorithms sufferfrom the longest delay among the four groups because ofthe system complexity. For the case of number of hand-overs, the use of algorithms in [48,54] lead to reducednumber of handovers, the algorithm in [51] introducesexcessive handovers because of the variation of SINR, thealgorithm in [10] is able to keep the unnecessary handoverprobability at a low level, and algorithms in [35,47] reducethe number of handovers by eliminating the ping-pong ef-fect. Handover failure probability can always be kept underthe desirable value for algorithms in [29,48], while highhandover failure probability is observed for the algorithmin [10] without inclusion of RSS. The algorithm in [43]can achieve low failure rate due to its distribution of thedecision calculation. As for the throughput, bandwidthand cost function based algorithms are able to achievehigher throughput than RSS based algorithms. Unfortu-nately the throughput of combination algorithms are notprovided by the authors.

In summary, RSS and bandwidth based VHD algorithmsare usually simple, but they only consider one or two

handover criteria as the inputs and other importantparameters such as monetary cost or power consumptionlevel of the networks are ignored. Furthermore, they areusually targeted to only two specific types of networktechnologies. Cost function based and combination algo-rithms are more complex, and they take into account widerrange of network parameters as compared to others. How-ever, they are mostly in the theoretical analysis stage orstill too complex for implementation.

5. Concluding remarks and future research directions

Unfortunately currently proposed VHD algorithmseither lack a comprehensive consideration of various net-work parameters or the studies reporting these algorithmslack enough detail for implementation. Research into verti-cal handover decision algorithms in heterogeneous net-works is still a challenging area. The main difficulty isdevising an algorithm which is truly useful in a wide rang-ing conditions and user preferences. One possible solutionwould be, given that computational power of handsets im-prove phenomenally every year, to implement several VHDalgorithms in a handset and adopt adaptive methods that

Table 6A comparative summary of the 12 VHD algorithms presented in this survey.

Groups/heuristics

Delay Number of handovers Handover failureprobability

Throughput

RSS based Zahran et al.’salgorithm [53]

Relatively high packet delayprobability (up to 1%) but can bereduced by adjusting ASST

Reduces up to 85% comparingwith traditional hysteresis VHD

Not provided Decreases as the velocity increases; Can provideoverall higher throughput (up to 33%) thantraditional hysteresis VHD

Mohanty andAkyildiz’salgorithm [29]

Not provided Not provided Can be always kept under thedesirable value (2%) as thevelocity increases

Not provided

Yan et al.’salgorithm [48]

Extra RSS sampling delay(up to 2 s)

Decreases as the velocityincreases; The unnecessaryhandover probabilitycan be always kept under thedesirable value (0.04)

Can be always kept under thedesirable value (0.02) as thevelocity increases

Not provided

Bandwidthbased

Lee et al.’salgorithm [22]

Short handover delay (average455ms) achieved by consideringapplication types

Not provided Not provided Higher throughput (up to 400%) than thetraditional method in the handover period

Yang et al.’salgorithm [51]

Not provided Excessive handovers can beintroduced because thevariation of SINR

Not provided Higher overall throughput (up to 40%) than RSSbased handover algorithms

Chi et al.’salgorithm [10]

Not provided Small unnecessary handoverprobability (up to 1.5%)

High handover failureprobability withoutconsidering RSS

High throughput achieved by balancing the trafficload

Cost functionbased

Zhu andMcNair’salgorithm [56]

Not provided Not provided Not provided High overall throughput achieved by spreadingusers’ services over several networks

Hasswa et al.’salgorithm [18]

Not provided Not provided Not provided Increases by up to 57.9% in different backgroundtraffic

Tawil et al.’salgorithm [43]

Around 50% shorter handover delaycompared to centralized VHD

Not provided Low handover failurerate due to the distributionof the decision calculation

Around 17% higher throughput compared tocentralized VHD

Combinationalgorithms

Nasser et al.’salgorithm [31]

Long handover delay because of thetraining needed

Not provided Not provided Not provided

Pahlavan et al.’salgorithm [35]

Long delay because of the increasedcomplexity and the training

Reduced number of handoversby eliminating the ping-pongeffect

Not provided Not provided

Xia et al.’salgorithm [47]

Not provided Reduced number of handoversby eliminating the ping-pongeffect

Not provided Not provided

X.Yan

etal./Com

puterN

etworks

54(2010)

1848–1863

1861


choose an algorithm intelligently based on conditions anduser preferences.

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Xiaohuan Yan (Iris) is a Ph.D. student at theDepartment of Electrical and Computer Sys-tems Engineering at Monash University, Mel-bourne, Australia. She has received her B.Sc.double degree in Network Engineering andFinance from the University of Electronic Sci-ence and Technology of China, Chengdu,China in 2006. Her current research area isvertical handover algorithms in next-genera-tion heterogeneous networks.

Ahmet S�ekercioglu is a member of the aca-demic staff at the Department of Electricaland Computer Systems Engineering of Mon-ash University, Melbourne, Australia. He wasthe leader of the Applications Program ofAustralian Telecommunications CRC until thecompletion of the centre’s research activities(December 2007). He has completed his Ph.D.degree at Swinburne University of Technol-ogy, and B.Sc., M.Sc. (all in Electrical andElectronics Engineering) degrees at MiddleEast Technical University, Ankara, Turkey. He

has lectured at Swinburne University of Technology, Melbourne, Australiafor 8 years. Prior to his academic career, he has held numerous positionsas a research engineer in private industry. He has published 13 journal

ks 54 (2010) 1848–1863 1863

articles, 2 book chapters, 56 conference papers and has 2 patents.His recent research interests are distributed algorithms for self-organi-zation in wireless networks. He is also interested in application of intel-ligent control techniques for multiservice networks as complex,distributed systems.His e-mail address is [email protected] and his Web site can be found athttp://titania.ctie.monash.edu.au.

Sathya Narayanan is an assistant professorand computer science program director atCalifornia State University, Monterey Bay. Hereceived his M.C.A from College of Engineer-ing, Guindy, India, in 1994, M.S. in ComputerScience from Temple University, Philadelphia,in 1998 and Ph.D. in Computer Science fromNYU-Polytechnic Institute, Brooklyn, NY, in2006. He has over ten years of industrialexperience in both research and developmentof networking technologies. Between 1999and 2007, he was employed with Panasonic

Princeton Laboratory at Princeton, NJ, where he held a senior scientistposition.His research interests include 802.11 MAC, co-operative communications,
IPv6 mobility, Voice over IP and peer-to-peer networking. He was activein IETF working groups related to IPv6 mobility, SIP (Session InitiationProtocol) and has co-authored multiple internet-drafts. He was a con-tributing engineer to CableLabs and wrote acceptance test plans forDOCSIS1.1 standard.
http://titania.ctie.monash.edu.au

Documents

A survey of vertical handover decision algorithms in Fourth