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Autonomic personalized handover decisions for mobile services in heterogeneous wireless networks Joon-Myung Kang a,, John Strassner b , Sin-seok Seo a , James Won-Ki Hong b a Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH), San-31, Hyoja-dong, Nam-gu, Pohang, Kyungbuk 790-784, Republic of Korea b Division of IT Convergence and Engineering, Pohang University of Science and Technology (POSTECH), San-31, Hyoja-dong, Nam-gu, Pohang, Kyungbuk 790-784, Republic of Korea article info Article history: Available online 17 February 2011 Keywords: Mobility management Handover decision Personalization Autonomic management Heterogeneous wireless networks abstract In this paper, we present an autonomic management method to provide personalized handover decisions for customized mobility management in heterogeneous wireless networks. A handover decision is a significant problem, especially in a heterogeneous net- work environment. This is exacerbated when the goal is to provide personalized services for mobile users. Personalized handover decisions should not only consider received signal strength, which is a traditional handover decision factor, but also context information, user preferences, user profiles, and other non-functional requirements. We present two metrics for evaluating access points: access point acceptance value and access point satisfaction value. Our algorithm uses a combination of functional and non-functional metrics to select the access point that has the maximum satisfaction value. In our simulation study, we show that our decision algorithm is better than other decision algorithms in terms of end user satisfaction. Ó 2011 Elsevier B.V. All rights reserved. 1. Introduction Growth in ubiquitous and mobile computing systems has led to the early introduction of a wide variety of new access networks and Internet capable devices [1]. More- over, the network trend towards next-generation networks has been moving towards an architecture that supports wireless technologies, mobile users, multiple radio access technologies, heterogeneous networks, and network con- vergence. Wireless networks have been emphasized due to their ability to provide Internet connection regardless of location and mobility. Therefore, mobility management is very important when trying to achieve seamless mobility in a heterogeneous network environment. The vision of seamless mobility is to provide simple, uninterrupted access to any type of information desired at any time, inde- pendent of place, network and device [2]. One of the research issues for mobility management is ‘‘handover’’, which is a well-known term in cell-based net- works. In this paper, we focus on handover decisions which occur when a mobile device needs to choose a different access network to which to connect. Traditionally, hand- over decisions have been based on manual evaluation of the Received Signal Strength Indicator (RSSI) at the mobile device to support Always-Best-Connected (ABC) communi- cation [3]. However, end users want to use mobile services simply, conveniently, and with high performance without considering any technical aspect such as handover. The current handover decision methods based on RSSI or simple pre-defined policies do not provide good solutions because they do not take into account services that satisfy the preferences of a user at a given time, location, and/or application context [4]. Therefore, handover decisions should be based on additional considerations, such as the capacity of each network link, usage charge of each network 1389-1286/$ - see front matter Ó 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.comnet.2011.01.021 Corresponding author. E-mail addresses: [email protected] (J.-M. Kang), johns@postech. ac.kr (J. Strassner), [email protected] (S.-s. Seo), jwkhong@postech. ac.kr (J.W.-K. Hong). Computer Networks 55 (2011) 1520–1532 Contents lists available at ScienceDirect Computer Networks journal homepage: www.elsevier.com/locate/comnet

Autonomic personalized handover decisions for mobile services in heterogeneous wireless networks

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Page 1: Autonomic personalized handover decisions for mobile services in heterogeneous wireless networks

Computer Networks 55 (2011) 1520–1532

Contents lists available at ScienceDirect

Computer Networks

journal homepage: www.elsevier .com/locate /comnet

Autonomic personalized handover decisions for mobile servicesin heterogeneous wireless networks

Joon-Myung Kang a,⇑, John Strassner b, Sin-seok Seo a, James Won-Ki Hong b

a Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH), San-31, Hyoja-dong, Nam-gu, Pohang,Kyungbuk 790-784, Republic of Koreab Division of IT Convergence and Engineering, Pohang University of Science and Technology (POSTECH), San-31, Hyoja-dong, Nam-gu, Pohang, Kyungbuk790-784, Republic of Korea

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

Article history:Available online 17 February 2011

Keywords:Mobility managementHandover decisionPersonalizationAutonomic managementHeterogeneous wireless networks

1389-1286/$ - see front matter � 2011 Elsevier B.Vdoi:10.1016/j.comnet.2011.01.021

⇑ Corresponding author.E-mail addresses: [email protected] (J.-M. Ka

ac.kr (J. Strassner), [email protected] (S.-s. Seoac.kr (J.W.-K. Hong).

In this paper, we present an autonomic management method to provide personalizedhandover decisions for customized mobility management in heterogeneous wirelessnetworks. A handover decision is a significant problem, especially in a heterogeneous net-work environment. This is exacerbated when the goal is to provide personalized servicesfor mobile users. Personalized handover decisions should not only consider received signalstrength, which is a traditional handover decision factor, but also context information, userpreferences, user profiles, and other non-functional requirements. We present two metricsfor evaluating access points: access point acceptance value and access point satisfactionvalue. Our algorithm uses a combination of functional and non-functional metrics to selectthe access point that has the maximum satisfaction value. In our simulation study, weshow that our decision algorithm is better than other decision algorithms in terms ofend user satisfaction.

� 2011 Elsevier B.V. All rights reserved.

1. Introduction

Growth in ubiquitous and mobile computing systemshas led to the early introduction of a wide variety of newaccess networks and Internet capable devices [1]. More-over, the network trend towards next-generation networkshas been moving towards an architecture that supportswireless technologies, mobile users, multiple radio accesstechnologies, heterogeneous networks, and network con-vergence. Wireless networks have been emphasized dueto their ability to provide Internet connection regardlessof location and mobility. Therefore, mobility managementis very important when trying to achieve seamless mobilityin a heterogeneous network environment. The vision ofseamless mobility is to provide simple, uninterrupted

. All rights reserved.

ng), johns@postech.), jwkhong@postech.

access to any type of information desired at any time, inde-pendent of place, network and device [2].

One of the research issues for mobility management is‘‘handover’’, which is a well-known term in cell-based net-works. In this paper, we focus on handover decisions whichoccur when a mobile device needs to choose a differentaccess network to which to connect. Traditionally, hand-over decisions have been based on manual evaluation ofthe Received Signal Strength Indicator (RSSI) at the mobiledevice to support Always-Best-Connected (ABC) communi-cation [3]. However, end users want to use mobile servicessimply, conveniently, and with high performance withoutconsidering any technical aspect such as handover. Thecurrent handover decision methods based on RSSI or simplepre-defined policies do not provide good solutions becausethey do not take into account services that satisfy thepreferences of a user at a given time, location, and/orapplication context [4]. Therefore, handover decisionsshould be based on additional considerations, such as thecapacity of each network link, usage charge of each network

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J.-M. Kang et al. / Computer Networks 55 (2011) 1520–1532 1521

connection, power consumption of each network interface,battery status of the mobile device, and user preferences.We call these and similar data context information fromthe network, the system, and the user.

This paper proposes a novel autonomic handover deci-sion method for satisfying the end user’s demand for differ-ent types of services in heterogeneous wireless networks byusing fuzzy logic and a utility function as part of thedecision-making process. We call it AUHO, which is anabbreviation of AUtonomic HandOver. A handover decisionuses the collected information as input to evaluate theavailable access networks and to select the network bestcapable of satisfying the user’s request at a particular time.We name such a network an Always-Best-Satisfying(ABS) network. As we mentioned earlier, previous hand-over management has focused on always-best-connected(ABC). However, current and future applications that wishto offer smart handover decisions for personalized servicesshould consider ABC and ABS. The ABS network providesalways-on-connectivity as well as giving the user the bestservice according to his or her preference at any time orplace. Our proposed method supports Context-aware ABS(CABS) to satisfy user preferences with available contextinformation, and uses fuzzy logic to evaluate differenttypes of context information. We introduce two measure-ment metrics for selecting access networks: (a) Access PointAcceptance Value (APAV) and (b) Access Point Satisfaction Va-lue (APSV). We then present an autonomic architecture thatuses a feedback control loop for maintaining handoverdecisions as contexts change dynamically. We evaluateour proposed method by creating our own network simu-lator, which was developed for testing mobile communica-tions in heterogeneous wireless network environments.The results of our evaluation show that our proposedmethod provides a higher degree of user satisfaction thanother handover decision algorithms, including those usingRandom, RSS-based, Cost-based, Quality-based, and Life-time-based decision-making algorithms.

The organization of this paper is as follows. Section 2introduces our solution for autonomic handover decision-making and some useful use cases. Section 3 presents adecision method based on fuzzy logic and utility functions.Evaluation and results are described in Section 4. Section 5covers related work on handover decision management.Finally, Section 6 provides conclusions and future work.

2. Solution approach

We propose a handover decision method for maximiz-ing end user satisfaction using context information. First,we define and categorize context information for handoverdecisions. Second, we develop a decision method by evalu-ating each access network using a weighted combination ofcontext information, user preferences, and service require-ments. We define how to measure and evaluate the qualityof each Access Point (AP) and then calculate the end usersatisfaction for achieving our main hypothesis. We use afuzzy logic-based inference system to process all appropri-ate context information, which has different types of val-ues, to represent access point acceptance. We then select

the ‘‘best satisfying’’ AP for supporting Context-aware Al-ways-Best Satisfying (CABS) based on a utility functionthat maximizes user preferences. Third, we evaluate theperformance of the proposed method using a network sim-ulator that we developed. Finally, we compare our methodwith other decision methods.

2.1. Motivating scenarios

When a user wants to use a high quality VoIP service ata low price, traditional approaches only operate in terms ofone functional parameter, such as quality or power con-sumption. This parameter is optimized, but its interactionwith other parameters is excluded. Our approach optimizesmultiple parameters as a tuple, thereby ensuring that inter-dependencies among them are taken into account. Forexample, we can recommend one AP that has high qualityand low price by determining satisfaction scores. This canbe further refined by using user preferences, which can dif-ferentiate APs by their available context data, such as timeor location. We also present another example related toseamless roaming. Power consumption of mobile devices(battery lifetime) is very important to end users. However,this is very sensitive to context data. For example, let ussuppose a user can use his or her mobile device in a coun-try with low power consumption without considering cost,because that user can use a 3G network at a flat rate. How-ever, when that user roams in another country, the usermay instead have to use a 3G network at a usage-basedrate, which is more expensive. Since the power consump-tion of a 3G network is lower than that of a WiFi network,we can define a policy to represent this choice as ‘‘Lifetimeis important’’. Although the user is roaming in anothercountry, certain calls are very important to the user. Forexample, when the user talks with his or her boss, thequality of service is also important. In that case, we can de-fine another policy as ‘‘If caller is my boss, Quality of serviceis important’’. In the above example, although the quality ofservice is important, we cannot ignore high costs. We cantherefore define a third policy as ‘‘If location is home countryand caller is my boss, Quality of service and Cost are impor-tant’’. We can apply different weights for each user prefer-ence metric with a User Profile (UP). For example, we canassign the weight of Quality to 0.7 and that of Cost to0.3. This means that in this example, the quality is moreimportant than the cost by a 7:3 ratio. However, theweights are not absolute but comparative.

2.2. Context information

We define some context information for handover deci-sions as follows:

– User Contextj User Preferences

� Network values: Bandwidth, Network Type,

Power Consumption, Security, RSS, Power� Network-independent values: Quality, Lifetime,

Cost

j Subscription Information, User Profile, User’s Cur-

rent Status (mobility, location, etc.)

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1522 J.-M. Kang et al. / Computer Networks 55 (2011) 1520–1532

– Application Requirementsj Bandwidth, Packet Error Rate, Delay, Jitter, Packet

Loss Ratio (PLR)– Service Classification

j Conversational, Streaming, Interactive, Background– Network Context

j Network Traffic Load, Network Cost, Coverage, Sup-ported Classes of Service

– Link Context (Network Interface)j Received Signal Strength (RSS), Signal-to-Noise Ratio

(SNR), Signal-to-Interference Ratio (SIR), and BitError Rate (BER)

– Device Capabilitiesj Current Battery Power Level, Power Consumption

Rate

3. Autonomic personalized handover decision

We propose AUHO, which is a method to help select ac-cess networks and APs for different kinds of applicationsbased on a weighted combination of different preferencesfor an end user that is using a mobile device with multiplenetwork interfaces. Our AUHO method can help select aproper AP using available context information and servicerequirements. In this section, we first introduce terminol-ogy for describing our method. Then, we introduce ourpolicy definition and examples for computing a handoverdecision. Finally, we present our decision algorithm andarchitecture.

3.1. Terminology

We introduce and define terminology for describing ourmethod as follows:

j User Preferences (UPref) is a set of attributes of asender or receiver that indicates the choice of a set ofparticular information or behaviors instead of othersthat are available. If multiple options are possible, thenit orders them in terms of most desired to least desired.Preferences can also be used to indicate default behav-ior. In this paper, we use ‘‘RSS (R)’’, ‘‘Cost (C)’’, ‘‘Quality(Q)’’, and ‘‘Lifetime (L)’’ as exemplary UPrefs for calcu-lating a handover decision.

j User Profile (UP) describes a specific set of user-programmable attributes that control how the ownerof the profile interacts with the environment using aspecific entity. This enables some or all of the function-ality of the entity to be programmed. One or moreUPrefs can select a specific UP in our approach.

j Policy (P) is typically described as a set of principles orrules to guide decisions and achieve a set of rationaloutcome(s). We define policies to select a specific UPaccording to context and the end user’s UPrefs.

j AP Acceptance Value (APAV) represents the suitabilityof a particular AP for an end user based on a given setof UPrefs (e.g., RSS, Quality, Cost, and Lifetime). The rangeof an APAV is from 0.0 to 1.0. APAVs are not absolutevalues but relative values. For example, if the APAV ofAP1 is greater than that of AP2 in terms of the specificUPref, AP1 is better than AP2 for selecting the optimal AP.

j AP Satisfaction Value (APSV) represents how well aparticular AP satisfies the needs of the end user basedon his or her user profile, as used in this context. ThisAPSV represents an objective value for end user satis-faction. The range of an APSV is from 0.0 to 1.0. APSVsare also relative values. For example, if the APSV ofAP1 is greater than that of AP2 in terms of user’s UP,AP1 is preferred (i.e., has a higher user satisfaction) thanAP2

3.2. Policy for handover decision

This section describes the policy definition for ourAUHO method. Our policy rule is defined as an event-condition-action tuple. In the context of AUHO calculations,each event is related to applications of a mobile device;conditions are defined using context information; actionsare then executed based on whether a set of conditionsfor that policy rule are evaluated as TRUE (or FALSE); theevaluation of the conditions is triggered by a set of eventsin the policy rule. The notation of UP is a set of weights ofuser preferences, such as:

UP ¼ ðWR;WC ;WQ ;WLÞ; where WR þWC þWQ þWL

¼ 1:0: ð1Þ

Examples of our policy for handover decisions are:

IF location ¼ home AND serv ice ¼ VoIP THEN UP

¼ ð0:1;0:4;0:4;0:1Þ; ð2aÞIF location ¼ office AND service ¼ VoIP THEN UP

¼ ð0:7;0:1;0:1;0:1Þ: ð2bÞ

Although we use the same service, user preference can dif-fer according to the current context such as location. Met-rics for evaluating each user preference for an applicationare as follows:

j RSS: measured RSSI from network link layerj Cost: different cost modelj Quality: bandwidth, delay, jitter, BER, throughput, burst

error, and packet loss ratio (PLR)j Lifetime: transmit, receive, and idle power consumption

values of the network interface card

Most ordinary users do not have much knowledgeabout access network technology and parameters for eval-uating each access network. Therefore, we divide user pro-file settings into two modes: ordinary and advanced mode.The former addresses the needs of ordinary users, andconsists of a set of pre-defined weight tables; the latterprovides users with personalized settings. In this paper,we did not consider optimization of each weight for opti-mizing performance; this is part of our future work.

3.3. Decision making algorithm

Fig. 1 is a flowchart of our proposed algorithm. First, auser starts an application on a mobile device, and thenselects a network. After that, the mobile device gets a listof candidate APs from the network interface manager

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Fig. 1. Flowchart of autonomic handover decisions with a feedback control loop.

J.-M. Kang et al. / Computer Networks 55 (2011) 1520–1532 1523

and loads the policy for the current application and thecurrent context. If there is an appropriate policy in the pol-icy repository, it is used. Otherwise, the default policy isloaded. We assume that all policy rules are conflict free be-cause policy conflict checking is beyond the scope of thispaper. After that, a speed filter and a service contract filterare applied to remove APs that do not support the speedrequirements and Service Level Agreements (SLAs) of theuser. We then calculate the APAVs and APSVs for all candi-date APs, and determine the AP that best satisfies the cur-rent application and context requirements. If the newcandidate AP is the same as the old AP, no handover is per-formed. In addition, if the APSV of the new AP is higherthan that of the current AP, we must consider handoveroverhead such as latency. We control handover overheadusing the threshold d, which we set by analyzing handoveroverheads among APs. Otherwise, handover to the new APis performed. This process is continuously repeated with apre-defined timeout that is defined by profiling. This time-out is changed adaptively based on a mobile device’sspeed. We show how to calculate APAVs and APSVs in de-tail in the following subsections. After connecting the bestsatisfying AP, we repeat a maintenance loop by evaluating

the current connected AP. If a connected AP exists, the net-work selection task is stopped. We then calculate theAPAVs of the current AP and calculate the APSV based onthem. If the APSV of the current AP is lower than the pre-defined threshold (b in Fig. 1), the network selection taskis started again and all candidate APs are evaluated forselecting the best satisfying AP.

3.3.1. AP acceptance value (APAV)We calculate an APAV using fuzzy logic. Fuzzy logic pro-

vides the ability to use data values that can have a range ofvalues that are resolved at runtime; as such, it allows flex-ible engineering design but is simple to implement. Due tothese benefits, fuzzy logic is widely used for various appli-cations including air conditioning, digital image process-ing, elevator control, and pattern recognition. Theparameters that are used in our proposed method areambiguous in many cases (e.g., the constraint ‘‘delay ishigh’’ does not specify a concrete delay level), but conveyimportant concepts to validate against, especially fornon-functional parameters. We can represent these ambig-uous parameters easily, and make a decision based on fuz-zy rules by using fuzzy logic. We used Mamdani Fuzzy

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Inference Systems (FIS) [5] in our proposed method be-cause it has great expressive power and interpretability.We also define input metrics, membership functions, andfuzzy rules for calculating APAVs for the given APs. Mam-dani FIS has four steps; Fuzzification, Rule evaluation,Aggregation, and Defuzzification.

The following is a simple example for determining qual-ity of service in this paper. If a delay of voice call traffic,which is 25 ms, is entered as an input, then the member-ship function can evaluate whether this delay is low orhigh. The evaluated results are passed to the Rule evalua-tion step. In the Rule evaluation step, membership valuesthat were passed from the Fuzzification step are evaluatedusing fuzzy rules, which are stored in the Rule base. Forexample, let us assume that we have a rule as follows:

\IF delay IS low AND jitter IS low;

THEN quality of voice call IS high:" ð3Þ

A FIS takes the delay and the jitter measurement,translates these values into fuzzy sets using the member-ship functions in the Fuzzification step, and then decidesthe quality of a voice call based on the rules in the Ruleevaluation step. Every result that was evaluated by fuzzyrules in the Rule evaluation step is aggregated into onefuzzy set for each output variable in the Aggregation step.The output fuzzy sets are converted into appropriate out-put values in the Defuzzification step. Finally, the outputvalues are used by the system that is outside of the FIS. Inour scheme, we used the FIS output value to generateAPAVs.

As we mentioned earlier, we use four user preferencesfor handover decisions. We define four APAVs, one eachfor each of the following user preferences: APAVR, APAVC,APAVQ, and APAVL. APAVR is calculated using received sig-nal strength. RSS is measured by a network interface man-ager. We normalize this RSS value from 0 to 1 directlybecause the values of APAVR and RSS are proportional.The cost of each access network is defined by each access

Fig. 2. Input fuzzy membership fun

network provider, and can be represented as $/min, $/bytesor a flat-rate. We can normalize it from 0 to 1 for repre-senting APAVC. APAVC and its associated cost are inverselyproportional. That is, as the cost rate becomes more expen-sive, a larger quantity of end users will not accept it for thebest access point.

For calculating APAVQ, fuzzy rules should be defined tosatisfy the service requirement. We defined seven inputmetrics and seven input membership functions for theFuzzification step (shown in Fig. 2) and defined differentfuzzy rule sets for each application type for the Rule eval-uation step. We also defined one output membership func-tion for generating APAV (shown in Fig. 3). The outputvalues are SA (Strong Accept), WA (Weak Accept), NU(Neutral), WR (Weak Reject), and SR (Strong Reject). Fromthe various available Defuzzification methods, we used theCenter of Gravity (CoG) method, which finds the pointwhere a vertical line would slice the aggregate set intotwo equal sections.

For this paper, we considered three applications: voicecall, streaming multimedia, and ftp. We defined differentfuzzy rules for them. When we measure the quality of avoice call, delay and jitter are important factors. We de-fined nine fuzzy rules for voice calls using these two factors(Fig. 4). In the case of streaming applications, bandwidth,jitter, and throughput are important factors and delay isa very important factor. We defined eighty-one fuzzy rulesfor streaming applications using these factors. For an ftpapplication, BER, burst error, and PLR are important factors.We defined twenty-seven fuzzy rules for ftp applications.Using these fuzzy rules, we can calculate APAV for eachapplication with their requirements.

For calculating APAVL, fuzzy rules should also be de-fined to satisfy the service requirement. We calculate thepower consumption as follows:

Power Consumption ðApplicationÞ¼WTx � PowerðTxÞ þWRx � PowerðRxÞ þWIdle

� PowerðIdleÞ: ð4Þ

ctions for calculating APAVQ.

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Fig. 3. Output fuzzy membership function for calculating APAV.

Fig. 4. Sample fuzzy rules for calculating APAVQ of a voice call application.

J.-M. Kang et al. / Computer Networks 55 (2011) 1520–1532 1525

We defined three input metrics (Transmit (Tx), Receive(Rx), and Idle power consumption rates) and three inputmembership functions for the Fuzzification step (Fig. 5),and defined different fuzzy rule sets for each applicationtype for the Rule evaluation step. We also used the sameoutput membership function as shown in Fig. 3. The powerconsumption of each application is different because theirconsumption patterns are different. For example, when weuse a voice call application, the power consumption ishighly dependent on the Tx and Rx power consumptionrates. We defined twenty-seven fuzzy rules for voice callsusing these two factors (Fig. 6). In the case of streamingapplications, the power consumption is dependent on theRx power consumption. We defined twenty-seven fuzzyrules for streaming applications using them. For ftp appli-cations, the power consumption is dependent on the Idlepower consumption rate. We defined twenty-seven fuzzy

Fig. 5. Fuzzy membership functi

rules for ftp applications. Using these fuzzy rules, we cancalculate the APAV for each application with theirrequirements.

3.3.2. AP satisfaction value (APSV)In the previous section, we calculated APAVs for all can-

didate APs. We represent the APAV vector of each AP asfollows:

APAVðAPi;jÞ�������!

¼ APAVRðAPi;jÞ APAVcðAPi;jÞ�

APAVQ ðAPi;jÞ APAVLðAPi;jÞ�; ð5Þ

where APij denotes the jth access point of the ith accessnetwork.

By using this vector, we can select the best AP (i.e., theone that has the maximum value of RSS, Cost, Quality, and/or Lifetime). However, we assert that the AP selected by

ons for calculating APAVL.

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Fig. 6. Sample fuzzy rules for calculating APAVL of a voice call application.

1526 J.-M. Kang et al. / Computer Networks 55 (2011) 1520–1532

the maximum APAV is not always the AP that best satisfiesthe needs of the user for a specific context. For example, ifthe user wants to use an application with high quality, theAP that has the maximum APAVQ is the best one. However,if the user wants to use an application with high qualityand low cost, the AP that has the maximum APAVQ maynot be the best one, since its APAVC may be unacceptablylow. This also applies to the AP that has the maximumAPAVc (i.e., the AP that has the least cost) because it mayhave an APAVQ that is unacceptably low. Thus, APAVs bythemselves are not enough to decide on the best AP touse if the user has multiple user preferences. Therefore,we define APSVs for solving these problems. An APSV rep-resents the degree of satisfying the user with this AP whenthe given user profile (selected according to the end user’spreferences) is used within a specific context. We define autility function to calculate APSVs of all candidate APs byapplying a weighted user preference (UP). This is an addi-tive aggregate utility function.

APSVðAPi;jÞ ¼ UP�!�APAVðAPi;jÞT��������!

¼ ðWR WC WQ WLÞ �

APAVRðAPi;jÞAPAVCðAPi;jÞAPAVQ ðAPi;jÞAPAVLðAPi;jÞ

0BBB@

1CCCA

¼WR � APAVðAPi;jÞR þWC � APAVðAPi;jÞCþWQ � APAVðAPi;jÞQ þWL � APAVðAPi;jÞL; ð6Þ

where APij denotes the jth AP of the ith access network. Weselect the best AP, which has the maximum APSV, after cal-culating all APSVs.

4. Evaluation

In this section, our proposed AUHO method is evaluatedusing several simulations. Each simulation is performedusing the HMNToolSuite [6], which we developed for test-ing mobility management in heterogeneous wireless net-works as an open source project. We used it becausethere is no simulation tool for handover decisions in heter-ogeneous wireless networks. This tool includes packettrace files generated from the traditional network simula-tor tools, such as an NS-2 or OpNet. As such, this tool isnot completely independent from traditional simulators.We will show a validation of our proposed decision algo-rithm using a case study with our tool.

4.1. Experimental setup

In our experiment, the subject uses a mobile devicethat includes the following handover decision algorithms:

Random, RSS-based, Cost-based, Quality-based, and Life-time-based. We compare each of these algorithms withour proposed AUHO algorithm. The measurement metricis end user satisfaction, which we measure by using an APSVfor the selected AP. First, we construct a heterogeneouswireless network environment, as illustrated in Fig. 7. Then,we create a mobile device that supports multiple networkinterfaces and applications. We then assign a moving pathfor the mobile device; this is shown in Fig. 7 as the largehorizontal arrow. We then apply three different types ofapplication traffic, which we generated from an NS-2network simulator. Finally, we measure the APSV of eachhandover decision algorithm and compare them.

4.2. Simulation environment

In this experiment, we used CDMA, Mobile WiMax, andIEEE 802.11 based WLAN access networks, as illustrated inTable 1. The area of the simulation network is 1000 m by1000 m. Three CDMA Base Stations (BSs), one MobileWiMax Radio Access Station (RAS), and three WLAN APscover the area. In this paper, we assume that BSs, RASs,and APs each function as APs. These access nodes are con-nected to the Router via 100 Mbps trunks with differenttraffic parameters. The coverage of each access point isrepresented by the illustrated circle. The MIPv6 protocolhas been chosen as the IP mobility management protocolfor the mobile nodes. One mobile node, MN1, has been ad-dressed in our simulation environment. This mobile nodemoves from (147,316) to (864,504) with a speed of40 m/s. The MN1 has three different types of networkinterfaces: CDMA, Mobile WiMax, and WLAN, which en-ables it to communicate with each access network for thespecific application. The context server gathers the contextinformation from each access network. We control eachnetwork parameter of each network device. The applica-tion server provides three different types of applicationtraffic: VoIP, Streaming Multimedia, and FTP. We createdtraffic for each application using an NS-2 network simula-tor. In the experiment, we configured network parametersfor our case studies as shown in Table 1. Each of the sixlocations represents different control points to calculate ahandover decision. The characteristics of each location,and the different semantics that they provide, are asfollows:

j Location 1: This location is a starting point. Only oneaccess network, CDMA (BS1), is available. All decisionalgorithms select it.

j Location 2: The delay and jitter of BS1 are higher thanthose of BS2, and the speed of the MN1 is changed to10 km/h.

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Fig. 7. Simulation environment for handover decisions in CDMA, WLAN, and Mobile WiMax access networks.

Table 1Network device parameter settings at each location.

Access network (access point) CDMA (BS1) CDMA (BS2) CDMA (BS3) WLAN (AP1) WLAN (AP2) WLAN (AP3) Mobile WiMax (RAS1)

Coverage (meter) 1000 1000 1000 400 400 400 800Bandwidth (kbyte) 1000 1000 1000 11,000 11,000 11,000 2000Delay (ms) 25 19 22 8 25 45 25Jitter (ms) 7 6 7 4 8 10 8Bit error ratio (dB) 0.001 0.001 0.001 0.00001 0.00001 0.00001 0.0001Throughput (Mbyte/s) 1.3 1.7 1.7 25 25 25 15Burst error 0.6 0.5 0.5 0.2 0.2 0.2 0.1Packet loss ratio 0.08 0.07 0.07 0.04 0.04 0.04 0.02Cost rate ($/min) 0.9 0.9 0.9 0.2 0.2 0.2 0.5Power Tx (W) 1.4 1.4 1.4 2.8 2.8 2.8 2.0Power Rx (W) 0.925 0.925 0.925 0.495 0.495 0.495 0.7Power idle (W) 0.045 0.045 0.045 0.082 0.082 0.082 0.06Minimum speed (km/h) 0 0 0 0 0 0 0Maximum speed (km/h) 300 300 300 12 12 12 80

J.-M. Kang et al. / Computer Networks 55 (2011) 1520–1532 1527

j Location 3: The power consumption rate of CDMA islower than that of WLAN.

j Location 4: The quality of WLAN is lower than that ofCDMA. However, the cost of WLAN is lower than thatof CDMA.

j Location 5: The speed of MN1 is changed to 40 km/h.WLAN is filtered by the speed filter. The quality of BS2is higher than that of BS3.

j Location 6: The cost of Mobile WiMax is lower than thatof CDMA.

We will show that our proposed algorithm selects thebest AP at all locations in terms of end user satisfaction,and hence performs better than the other algorithms.

4.3. Experimental results

To evaluate our proposed AUHO method, we com-pared its performance with the following five handoverdecision methods: (1) Random decision (RD), (2) RSS-based decision (RSSD), (3) Cost-based decision (CD), (4)Quality-based decision (QD), and (5) Lifetime-baseddecision (LD). First, we compare available access net-works, then select candidate access networks by speedfiltering, then reduce the number of candidate accessnetworks by SLA filtering. We then compute all APAVsand APSVs of all candidate APs, and calculate the APselected by all handover decision algorithms at all loca-tions in Fig. 7. Second, we compare the APSVs of the

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1528 J.-M. Kang et al. / Computer Networks 55 (2011) 1520–1532

selected AP by all handover decision algorithms to proveour hypothesis.

As a case study, we use a voice call application withthree different user profiles as the ordinary mode: Cost &Quality (CQ), Quality & Lifetime (QL), and Cost, Quality &Lifetime (CQL). The duration of our simulation is 651 s.Table 2 shows the experimental results of a voice call withthe user profile, CQ.

At location 1, all decision algorithms select BS1 as thebest AP. At location 2, RD, RSSD, CD, and LD select CDMA(BS1) as the best AP, whereas QD and AUHO select CDMA(BS2) as the best AP. Although the RSS of BS1 is strongerthan the RSS of BS2, the quality of voice call traffic of BS2is better than that of BS1 (because the delay and jitter ofBS1 are higher than those of the BS2). That is, the APAVQ

and APSV of BS2 are higher than the APAVQ and APSV ofBS1. In this experiment, BS2 is the best AP because theUP is CQ. Our AUHO provide a better solution than RD,RSSD, CD, and LD at location 2. At location 3, RD, RSSD,CD, QD, and AUHO select WLAN (AP1) as the best AP,whereas LD selects the CDMA (BS1) because the powerconsumption rate of CDMA is lower than that of WLAN.In this location, AP1 is the best AP because the UP is CQ.Our AUHO method provides a better solution than LD. At

Table 2Experiment results at each location (Application = Voice call, UP = Cost & Quality)

Location 1 2 3 4

Simulation time(sec)

25 44 157 553

Available accessnetworks(AP)

CDMA(BS1)

CDMA(BS1,BS2)

CDMA (BS1,BS2),WLAN (AP1)

CDMA ((AP2), M(RAS1)

Speed filtering(AP)

CDMA(BS1)

CDMA(BS1,BS2)

CDMA (BS1,BS2),WLAN (AP1)

CDMA ((AP2), M(RAS1)

SLA filtering(AP)

CDMA(BS1)

CDMA(BS1,BS2)

CDMA (BS1,BS2),WLAN (AP1)

CDMA ((AP2), M(RAS1)

AP (APAVR) BS1(0.816) BS1(0.779),BS2(0.019)

BS1(0.530),BS2(0.335),AP1(0.703)

BS3(0.25RAS1(0.1

AP (APAVC) BS1(0.100) BS1(0.100),BS2(0.100)

BS1(0.100),BS2(0.100),AP1(0.800)

BS3(0.10RAS1(0.5

AP (APAVQ) BS1(0.500) BS1(0.500),BS2(0.900)

BS1(0.500),BS2(0.900),AP1(0.900)

BS3(0.61RAS1(0.5

AP (APAVL) BS1(0.500) BS1(0.500),BS2(0.500)

BS1(0.500),BS2(0.500),AP1(0.500)

BS3(0.50RAS1(0.2

AP (APSV) BS1(0.372) BS1(0.368),BS2(0.452)

BS1(0.343),BS2(0.483),AP1(0.800)

BS3(0.36RAS1(0.4

Random(best AP)

CDMA(BS1)

CDMA (BS2) WLAN (AP1) WLAN (

RSS (best AP) CDMA(BS1)

CDMA (BS1) WLAN (AP1) CDMA (

Cost (best AP) CDMA(BS1)

CDMA (BS1) WLAN (AP1) WLAN (

Quality(best AP)

CDMA(BS1)

CDMA (BS2) WLAN (AP1) CDMA (

Lifetime(best AP)

CDMA(BS1)

CDMA (BS1) CDMA (BS1) CDMA (

AUHO (best AP) CDMA(BS1)

CDMA (BS2) WLAN (AP1) WLAN (

location 4, RD, CD, and AUHO select WLAN (AP2) as thebest AP, whereas RSSD, QD, and LD select CDMA (BS2) asthe best AP. The quality of a voice call application of BS2is higher than that of AP2, but the cost of BS2 is higher thanthat of AP2. In terms of Quality, BS2 is the best AP. How-ever, AP2 is the best AP in terms of Cost. This situationdemonstrates the strength of our proposed AUHO methodparticularly well. In a complex situation such as this, wemeasure the satisfaction value, APSV, of each AP, basedon the user profile. In the APAV calculation phase, theAPAVQ of BS2 is 0.9, whereas the APAVC is 0.1. The APAVQ

of AP2 is 0.5, whereas the APAVC is 0.8. If we consider onlyone metric, Quality or Cost, we would simply select BS2 orAP2. In this experiment, if we consider two metrics, Costand Quality, as the user profile, we cannot select the bestsatisfying AP with only APAVs. We need to calculate theAPSVs of all APs to solve this problem. Considering enduser satisfaction, the APSV of BS2 is 0.503, whereas thatof AP2 is 0.584, which is higher than that of BS2. At loca-tion 4, our AUHO method provides a better solution thanQD.

At location 5, the speed of MN1 changes to 40 km/h. Thespeed filter removes the WLAN (AP2) from the candidateaccess network list because the supporting maximum

.

5 6

585 651

BS2,BS3), WLANobile WiMax

CDMA (BS2,BS3), WLAN(AP3), Mobile WiMax(RAS1)

CDMA (BS3),Mobile WiMax(RAS1)

BS2,BS3), WLANobile WiMax

CDMA (BS2,BS3), MobileWiMax (RAS1)

CDMA (BS3),Mobile WiMax(RAS1)

BS2,BS3), WLANobile WiMax

CDMA (BS2,BS3), MobileWiMax (RAS1)

CDMA (BS3),Mobile WiMax(RAS1)

6), BS2(0.527),14), AP2(0.141)

BS3(0.598), BS2(0.203),RAS1(0.501)

BS3(0.652),RAS1(0.385)

0), BS2(0.100),00), AP2(0.800)

BS3(0.100), BS2(0.100),RAS1(0.500)

BS3(0.100),RAS1(0.500)

4), BS2(0.900),00) AP2(0.500)

BS3(0.614), BS2(0.900),RAS1(0.500)

BS3(0.614),RAS1(0.500)

0), BS2(0.500),03), AP2(0.500)

BS3(0.500), BS2(0.500),RAS1(0.203)

BS3(0.500),RAS1(0.203)

1), BS2(0.503),32), AP2(0.584)

BS3(0.395), BS2(0.470),RAS1(0.470)

BS3(0.401),RAS1(0.459)

AP2) CDMA (BS3) CDMA (BS3)

BS2) CDMA (BS3) CDMA (BS3)

AP2) Mobile WiMax (RAS1) Mobile WiMax(RAS1)

BS2) CDMA (BS2) CDMA (BS3)

BS2) CDMA (BS3) CDMA (BS3)

AP2) CDMA (BS2) Mobile WiMax(RAS1)

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J.-M. Kang et al. / Computer Networks 55 (2011) 1520–1532 1529

speed of the WLAN is 12 km/h. RD, RSSD, and LD selectCDMA (BS3), CD selects Mobile WiMax (RAS1), and QDand AUHO select CDMA (BS2). CD selects RAS1 becausethe cost rate of Mobile WiMax is lower than that of CDMA.In terms of Cost, RAS1 is the best. However, the quality ofRAS1 is lower than that of CDMA. In this case, APSVs of BS2and RAS1 are equal. When they are equal, our proposedalgorithm selects the AP that has the stronger RSS (sincethe UP is CQ). At location 6, RD, RSSD, QD, and LD selectCDMA (BS3), whereas CD and AUHO select Mobile WiMax(RAS1). The quality of BS3 is higher than that of RAS1,whereas the cost of CDMA is higher than that of MobileWiMax. The APSV of BS3 is 0.401, whereas that of RAS1is 0.459. Our proposed AUHO selects RAS1 as the best AP.

Fig. 8 shows the comparison of our proposed AUHOalgorithm with the different decision algorithms over thetotal duration. We compared the APSVs of the selectedAP. Significantly, our proposed AUHO algorithm alwaysprovides ABS mobility compared to the other decisionalgorithms. In addition, we performed the experimentswith two other user profiles, Quality and Lifetime (QL)and Cost, Quality, and Lifetime (CQL), and the resultsshowed that our AUHO algorithm provides a better solu-tion than the other decision algorithms. Due to space lim-itations, we cannot include the detailed experimentalresults of these two additional experiments but they canbe found in [15]. However, we compared the mean of theAPSVs of the AP selected by all handover decision algo-rithms in Fig. 9. In this graph, we show that our proposedAUHO algorithm provides a better APSV than other

Fig. 8. Comparison of AUHO with different decision algorithms over time (User p(d) Quality-based, and (e) Lifetime-based vs. AUHO).

decision algorithms on average. That is, it outperformsother decision algorithms in terms of Always-Best-Satisfy-ing (ABS) mobility.

5. Related work

This section summarizes some important exemplary re-lated work on handover decision strategies used by mobiledevices for access network selection. Wang et al. [7] pro-posed a policy-based handover scheme, where the authorsdesigned a cost function to decide the ‘‘best’’ moment andinterface to execute a vertical handover. However, the costfunction presented in the paper is very preliminary andcannot handle sophisticated configurations. The logarith-mic function used in the cost function also has difficultyin representing the cost value when the value of the con-straint factor is zero (i.e., the connection is free of charge).Angermann and Kammann [8] proposed another scheme tomodel the handover with HTTP traffic, but it has problemswith other types of traffic, such as video and audio stream-ing, where the bandwidth demand is much higher thanHTTP traffic. Chen et al. [9] proposed a smart decision mod-el to perform vertical handover to the ‘‘best’’ networkinterface at the ‘‘best’’ moment; this was tested on theUniversal Seamless Handover Architecture (USHA). Thesmart decision model is based on the properties ofavailable network interfaces (e.g., link capacity, power con-sumption and link cost), system information (e.g., remain-ing battery) and user preferences. Although the model

rofile = Cost & Quality) ((a) Random-based, (b) RSS-based, (c) Cost-based,

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Fig. 9. Comparison of mean of APSVs.

1530 J.-M. Kang et al. / Computer Networks 55 (2011) 1520–1532

presented a detailed example on the USHA test-bed, it didnot describe in enough detail how to calculate the proper-ties and the meaning of cost value. Calvagna and Di Modica[10] aimed to understand how to define a metric in orderto devise a solution that balances the overall cost of thevertical handover with the actual benefits they bring tothe user’s networking needs. This way, each mobile usercould autonomously apply the handover decision policy,which is more appropriate to the user’s specific needs.However, this approach did not present a feedback controlloop for adaptive decision and change policies autono-mously by context changes. Hasswa et al. [11] proposeda vertical handover decision function to allow the user tostrategically prioritize the different network characteris-tics such as network performance, user preference, andmonetary cost. This function is simple and can be easilyapplied to any vertical handover approach. The authorspresented some characteristics for creating a decisionfunction such as the cost of service, security, power re-quirements, proactive handover, quality of service andvelocity of the mobile device. However, this study did notdescribe in enough detail how to define each characteristicproperty. In addition, it did not provide a detailed example

Table 3Comparison between handover decision strategies with our proposed algorithm (FL/NN: Fuzzy Logic/Neural Networks; CA: context-aware, AUHO: autonomic handorequirement).

Handover decision strategy RSS-based DF UC

Multi-criteria No Yes Yes

User consideration No Low HighEfficiency Low Medium MediumFlexibility Low High HighImplementation complexity Low Low LowService type

supportedNon-real-time Non-real-time

and real-timeNon-real-tim

Personalization No No YesFeedback control loop No No NoObjective FR FR NFR

to validate the decision function. Finally, it did not considerthe cost function in terms of the service or the application.

There has been less work based on an autonomic ap-proach. [12] introduces a novel design approach based onautonomic components and cross-layer monitoring andcontrol for a seamless and efficient vertical handover byhandling mobility at the application layer. Anothercontext-aware handover decision management techniquewas described in our previous work [13]. We proposed amanagement solution located in a management plane thatcontrols and monitors the data and control layers. Themanagement plane contains an autonomic handover man-ager that cooperates with other entities such as a systemmonitor, a user profile, a network interface, service, orsession manager. The handover manager decides on anappropriate policy by using context information obtainedthrough monitoring, analyzing (evaluating context matrix),planning, and executing (publishing the policy to the ser-vice manager) the results in a closed control loop. Our pre-vious work provided a unique solution approach for thehandover decision but did not provide concrete evaluationresults. This paper not only enhances our previous workbut also provides good evaluation results.

DF: decision function; UC: user-centric; MAD: multiple attribute decision;ver proposed in this paper, FR: Functional requirement, NFR: Non-functional

MAD FL/NN CA AUHO(Proposed)

Yes Yes (FL) Yes YesNo (NN)

Medium Medium High HighHigh High High HighHigh Medium High HighMedium High Medium High

e Non-real-timeand real-time

Non-real-timeand real-time

Non-real-timeand real-time

Multiple typesof services

No No No YesNo No Yes YesFR FR FR FR & NFR

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J.-M. Kang et al. / Computer Networks 55 (2011) 1520–1532 1531

Kassar et al. [14] summarized vertical handover deci-sion strategies in heterogeneous wireless networks. Theydefined five categories: decision function-based, user-centric, Fuzzy Logic (FL) and Neural Network-based, mul-ti-attribute decision-based (MAD), and context-aware(CA) strategies. They presented their comparison resultsas shown in Table 3. CA strategies seem to have the bestperformance, as they best satisfy the majority of the givencharacteristics. This category is followed by MAD and FLstrategies. Thus, it is obvious that FL and CA strategiescan be enhanced in combination with MAD methods. Onthe one hand, the FL concept provides a robust mathemat-ical framework and the CA concept manages contextinformation and evaluates context changes to select appro-priate adaptation methods such as the vertical handover.However, to the best of the authors’ knowledge, none ofthe context-aware handover decision methods considershow to measure end user satisfaction using a feedbackcontrol loop. Previous approaches have focused on func-tional or non-functional requirements for objectives ofhandover decisions. However, we focused on both. Thus,we have added our approach (AUHO) to the previous ap-proaches as shown in Table 3. We have also added person-alization, a feedback control loop and an objective as acomparison metric. Our approach provides context-awarehandover decisions with FL and utility function for enduser satisfaction.

6. Concluding remarks

Seamless mobility and roaming in heterogeneouswireless networks is an important issue. In particular,vertical and horizontal handover should support not onlyAlways-Best-Connected (ABC), but also Always-Best-Satisfying (ABS) for providing personalized mobile ser-vices. In this paper, we proposed a novel handover decisionmethod for supporting ABS based on the end user’s prefer-ences and context information. Our method provides a per-sonalized handover decision method for finding the APthat can best satisfy the requirements of the end user fora particular context. For supporting autonomic handover,we defined a feedback control loop for maintaining hand-over decisions as contexts change dynamically. We thenpresented our decision algorithm by introducing two mea-surement metrics: APAVs and APSVs. The former repre-sents the suitability of a particular AP for an end userbased on a given set of user preferences (e.g., RSS, Cost,Quality and Lifetime), and we used fuzzy logic for calculat-ing them. In contrast, the latter represents how well a par-ticular AP satisfies the needs of the end user based on his orher user profile in the specific context; we used a utilityfunction for calculating this metric. In our approach, APSVrepresents end user satisfaction. By selecting the AP thathas the maximum APSV, our decision algorithm supportsABS. By evaluation with two case studies, we showed thatour method supports better ABS in the given simulationenvironment than other decision algorithms.

For future work, we will optimize APAV calculationusing a fuzzy logic optimization. We will also apply realnetwork traffic (instead of relying on simulated traffic) for

each application to our HMNToolSuite to better evaluateour AUHO decision algorithm. We will examine differentutility functions for optimizing our decision algorithms.We will also apply ontology and semantic reasoning forinferring new data and facts to fine-tune our decisionalgorithm; this will provide a more complete autonomicdecision architecture. Finally, we will perform more testsin terms of low level handover overhead and networkperformance.

Acknowledgments

This research was supported by the WCU (World ClassUniversity) program through the National Research Foun-dation of Korea funded by the Ministry of Education, Sci-ence and Technology (No. R31-2010-000-10100-0), the ITR&D program of MKE/KEIT [KI003594, Novel Study onHighly Manageable Network and Service Architecture forNew Generation], and the MKE (The Ministry of KnowledgeEconomy), Korea, under the ITRC (Information TechnologyResearch Center) support program supervised by NIPA(National IT Industry Promotion Agency) NIPA-2010-C1090-1031-0009.

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[3] E. Gustafsson, A. Jonsson, E. Res, S. Stockholm, Always bestconnected, IEEE Wireless Communications 10 (1) (2003) 49–55.

[4] X. Yan, Y.A. Sekerciogl̆u, S. Narayanan, A survey of vertical handoverdecision algorithms in fourth generation heterogeneous wirelessnetworks, Computer Networks 54 (11) (2010) 1848–1863.

[5] E.H. Mamdani et al., Application of fuzzy algorithms for control ofsimple dynamic plant, Proceedings of IEE 122 (12) (1974) 1585–1588.

[6] Heterogeneous Mobile Network Tool Suite (HMNToolSuite).Available from: <http://code.google.com/p/hmntoolsuite/>.

[7] H.J. Wang, R.H. Katz, J. Giese, Policy-enabled handoffs acrossheterogeneous wireless networks, in: Proc. of ACM WMCSA, 1999,p. 51.

[8] M. Angermann, J. Kammann, Cost metrics for decision problem inwireless ad hoc networking, in: Proc. of IEEE CAS Workshop onWireless Communications and Networking, September 2002.

[9] L.–J. Chen, T. Sun, B. Chen, V. Rajendran, M. Gerla, A smart decisionmodel for vertical hanoff, in: Proc. of 4th ANWIRE InternationalWorkshop on Wireless Internet and Research, Athens, Greece, 2004.

[10] A. Calvagna, G. Di Modica, A user-centric analysis of verticalhandovers, in: ACM Wireless Mobile Applications And Services OnWLAN Hotspots 2004, Philadelphia, PA, USA, 2004, pp. 137–146.

[11] A. Hasswa, N. Nasser, H.S. Hassanein, Generic vertical handoffdecision function for heterogeneous wireless networks, in: IFIPConference on Wireless and Optical Communications, March 2005,pp. 239–243.

[12] G.A. Di Caro, S. Giordano, M. Kulig, D. Lenzarini, A. Puiatti, F.Schwitter, A cross-layering and autonomic approach to optimizedseamless handover, in: Proc. of the 3rd Annual Conference onWireless On Demand Network Systems and Services, January2006.

[13] J.M. Kang, H.T. Ju, J.W.K. Hong, Towards autonomic handoverdecision management in 4G networks, IFIP/IEEE InternationalConference on Management of Multimedia and Mobile Networksand Services, LNCS, vol. 4267, Springer, 2006, pp. 145–157.

[14] M. Kassar, B. Kervella, G. Pujolle, An overview of vertical handoverdecision strategies in heterogeneous wireless networks, ComputerCommunications 31 (10) (2008) 2607–2620.

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[15] Joon-Myung Kang, Autonomic Management for PersonalizedHandover Decisions in Heterogeneous Wireless Networks, Ph.D.Thesis, POSTECH, December 2010.

Joon-Myung Kang is a Postdoctoral Fellow inthe Department of Computer Science andEngineering, POSTECH. He received his B.Sc.and Ph.D. degrees in Computer Science andEngineering from POSTECH in 2005 and 2011,respectively. From 2000 to 2004, he worked atAlticast Corporation as a software engineer. Hisresearch interests include autonomic networkmanagement and mobile device management.

John Strassner is a Professor in the Division ofIT Convergence Engineering in POSTECH, andleads its Autonomic Computing group. Previ-ously, he was a Visiting Professor at WaterfordInstitute of Technology in Ireland, where heworked on various FP7 and Irish research pro-grams. Before that, he was a Motorola Fellowand Vice President of Autonomic Research atMotorola Labs, where he was responsible fordirecting Motorola’s efforts in autonomic com-puting and networking, policy management,and knowledge engineering. Previously, John

was the Chief Strategy Officer for Intelliden and a former Cisco Fellow.John is the Chairman of the Autonomic Communications Forum, and thepast chair of the TMF’s NGOSS SID, metamodel and policy working groups,

along with the past chair of several IETF and WWRF groups. He hasauthored two books (Directory Enabled Networks and Policy Based Net-work Management), written chapters for 5 other books, and has been co-editor of 5 journals dedicated to network and service management andautonomics. John is the recipient of the IEEE Daniel A. Stokesburymemorial award for excellence in network management, the Albert Ein-

stein award for innovation in high technology, is a member of theIndustry Advisory Board for both University of California Davis andDePaul University, a TMF Fellow, and has authored over 235 refereedjournal papers and publications. He has 47 patents. He holds BSEE, BSCS,MSCS, and Ph.D. degrees.

Sin-seok Seo received his B.Sc. degree inComputer Science and Engineering from InhaUniversity in 2008. Currently, he is a PhD can-didate in the Department of Computer Scienceand Engineering, POSTECH, Pohang, Republic ofKorea. His research interests include autonomicnetwork management and mobile devicemanagement.

James Won-Ki Hong is Professor and Head ofDivision of IT Convergence Engineering andDean of Graduate School for Information Tech-nology, POSTECH, Pohang, Korea. He received aPh.D. degree from the University of Waterloo,Canada in 1991. His research interests includenetwork management, network monitoring andanalysis, convergence engineering, ubiquitouscomputing, and smartphonomics. He hasserved as Chair (2005-2009) for IEEE ComsocCommittee on Network Operations and Man-agement (CNOM). He is serving as Director of

Online Content for the IEEE Comsoc. He is a NOMS/IM Steering CommitteeMember and a Steering Committee Member of APNOMS. He was GeneralChair of APNOMS 2006 and APNOMS 2008. He was a General Co-Chair of

2010 IEEE/IFIP Network Operations and Management Symposium (NOMS2010). He is an Associate Editor of IJNM and editorial board member ofIEEE TNSM, JNSM, JCN, and JTM.