[ieee 2006 ieee 17th international symposium on personal, indoor and mobile radio communications -...

The 17th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC'06)

1-4244-0330-8/06/$20.002006 IEEE

AN ACCESS NETWORK SELECTION ALGORITHM DYNAMICALLY ADAPTED TO USER NEEDS AND PREFERENCES

Antonio Iera, Antonella Molinaro, Claudia Campolo, and Marica Amadeo Dept. D.I.M.E.T. - University ``Mediterranea'' of Reggio Calabria

Reggio Calabria, Italy

ABSTRACT In this paper we present a novel multi-criteria network selection algorithm for always best connected service provisioning. It relies on a suitably defined cost function, which at the same time takes into account metrics reflecting both objective, i.e. network related, and subjective, i.e. user preference related, conditions. Point of strength of our proposal is the implementation of the selection algorithm at a middleware layer; this hiding both network cost computation and 4G scenario complexity from user and application layers. The good performance observed is mainly due to the possibility of associating a weight to each cost parameter that is dynamically adapted to user preferences and profile not only on a per-session basis but also within the same session.

I. INTRODUCTION

Heterogeneity of networks involved in typical 4G wireless systems highlights the possibility of offering the user a rich portfolio of services (richer than ever before) with different quality requisites, always tailored to the specific needs. In beyond 3G and 4G systems the very difference with respect to previous systems is undoubtedly given by the evolution from the typical paradigm of 2G and 3G systems, namely “Always Connected”, to the fresh new paradigm expected for future communications, namely “Always Best Connected” (ABC) [1]. What Always Best Connected means to a user is the possibility of always exploiting the requested service through the best access technology and the best terminal available. The point is that the perception of “best connectivity” is highly subjective and depends on the importance the user gives to some aspects of the communications with respect to others; this necessarily makes best connectivity always strictly related to user exigencies and function of his/her profile and personal preferences. As emphasized in [2], the meaning of best connected is related to a great number of aspects: user preferences, his current activity, dimension and property of exploited devices, available networks, etc.. Therefore, to deploy ABC services it is necessary to consider the following issues: Context awareness, I-Centric services, Access Discovery and Terminal Discovery, Access Selection, Authentication, Authorization, and Accounting (AAA), Mobility handling. Access selection is one of the most important aspects of the whole process. The main focus when deploying an effective ABC service platform is the identification of parameters the access selection process has to be based on, and the definition of a selection algorithm that exploits cited parameters to enable the user to always being served by the best connection. Various criteria may drive the selection process. Quite a number of architectural proposals are available in the literature which collect context information and use it to

suitably select the access network [3][4][5]. Perhaps, ANWIRE (Academic Network on Wireless Internet Research in Europe) [6] project is the one which primarily focused on the ABC paradigm. A particularly interesting aspect, in the view of intended research of the present paper, is the definition, within ANWIRE activity framework, of some metrics exploited during the network selection process. A similar metric analysis is performed in [7]. What emerges is that feasible metrics in input to the selection process are: type of service, network conditions, device capability, location, speed, user usual trajectory, implicit/explicit preferences of the user. The necessity of taking into account listed requisites, makes the selection process during handoff very complex and somehow ambiguous. A winning solution is the use of a mathematical function based on a subset of listed cost parameters and on the weight of each parameter with respect to the choice to be made. Such an approach is followed by authors in [8], which compute the cost of each network interface through a given function and select the one with the minimum cost. Problems with the use of the function proposed in [3] have been pointed out in [9], where a different decision model. A similar (modified) approach has also been used in [7]. Our research tries to give contribution to the development of suitable and effective criteria for the best network selection procedure by introducing a higher dynamicity in the computation of both cost function parameters and their weights. This is achieved by developing middleware infrastructures. A suitably defined cost function, is introduced which takes into account more metrics (namely, cost parameters). It has been designed to be compliant with the ABC paradigm and, at the same time, to reflect dynamicity, user personalization, and highly automated behavior of a situation&location-aware middleware.

II. REFERENCE MAS BASED MIDDLEWARE ARCHITECTURE

The selection procedure we propose in the present paper is implemented as a part of a wider middleware platform, namely SALOME’ (Situation And LOcation aware MiddlEware). This is a multi-agent systems (MAS) based platform introduced in [10] whose objective is guaranteeing dynamic service adaptation to either the network profile and the user’s preferences as well as resource allocation according to the ABC paradigm. The reference architecture foresees the presence of four typologies of cooperating agents: • User Agent (UA). UA is client-side agent, which is

assigned to a user equipped with a particular device and running a particular application from any location.

• Network-side User Agent (NUA). NUA is the network-side “image” of the user; it "follows" the user when he/she moves across heterogeneous networks and accesses services offered by different Providers.


• Radio Resource Agent (RRA). It handles the network resource assignment in the roaming region areas.

• Service Agent (SA). SA is in charge of describing services offered by a given ISP and available in a given access area. SA maintains an updated service profile.

A Profile Database (PDB) is also foreseen, which is the location where user profiles are stored. The Multi-Agent Systems (MAS) based middleware has been implemented through JADE, the Java Agent DEvelopment Framework platform developed by TILAB (Telecom Italia Lab, Italy), and the LEAP libraries.

III. SELECTION ALGORITHM AND COST FUNCTION

A. Access network selection algorithm In the remaining part of the paper we assume that mobile user u accesses the heterogeneous wireless system through a device d equipped with manifold network interfaces and able to transfer a session to different network interfaces while maintaining the service continuity. Under the conditions stated above, the access network selection process is needed whenever either user u requests to access the new service i or service i is already being executed and an horizontal/vertical hand-off procedure is triggered. By focusing, for the sake of simplicity and without loosing in generality, on the sample case of access to a new service, we can say that user u, in selecting service i, interacts with his User Agent to specify the current preferences in terms of a set of parameters (better addressed in the following). At the middleware level, UA in the terminal of user u activates the device physical interfaces to “feel” available access networks, gathers a list of candidate networks, and sends it to NUA together with information relevant to requested service i. NUA can thus activate a so called pre-selection process aiming at verifying that candidate networks are actually able to support service i. To this aim, it contacts the SAs associated to each network and sends them the profile of service i. If service i is supported by network z and its requisites are satisfied, then SA sends back to NUA information of both economic cost to access service i and guaranteed security level to i. NUA contacts the entity in charge of the resource handling, i.e. the RRA associated to

the network z, and communicates it MAXuiB , and MIN

uiB , (max and min required bandwidth) the user specifies for service i. Only if it is possible to allocate a bandwidth matching the constraints, then RRA sends NUA: value of allocable bandwidth, coverage level offered to the user, and network unreliability (see below). At this point NUA has all necessary parameter values available to start access network selection.

B. Cost Function for access network selection Chosen metrics take into account context parameters derived from user and network profiles and from the ontology of foreseen agents: monetary cost of the session; power consumption associated to the physical interfaces of the device; network capacity of serving the user location; security level offered by the network for the requested service; unreliability (a fresh new defined index, see below) of the

network; allocated bandwidth requirements for the specific service. These cost parameters contribute to define the overall cost of a given access network. The chosen network will be the one with the minimum resulting overall cost. A Cost Function, ztF _cos , allows to simultaneously account for all parameters associated to the generic access network z. Cost parameters, properly normalized, account for objective conditions, i.e. network/device related, while subjective conditions are taken into account by some weights dynamically derived from profile information expressing the importance given by the user to each cost parameter. The cost function, of a generic network can be written as follows:

( ) ( ) ( )∑∑==

===m

jjj

m

jjjmmt xfpxgxgxgfF

1111cos )(),...,( (1)

jp are normalized weights associated to functions ( )jxf expressing the normalized cost parameters. Parameter and weight normalization is necessary to make heterogeneous values comparable with each other. A normalized cost

parameter jx is expressed as valuereferencej

valuecurrentjj x

xx

__

__= ; this

implying that 10 ≤≤ jx . Cited weights jw assume a value such that 10 ≤≤ jw . The normalized weight value is defined

as ∑=

=m

lljj wwp

1

. Obviously, m is the total number of

considered parameters for the selection. A wise choice of ( )jxf shape is of outmost importance. We start from the

requisites (widely accepted by related literature [3], [9]) that ( )jxf has to satisfy. It has to furnish a value in-between zero

and one and has to be increasing (resp. decreasing) monotone when jx value is directly (resp. inversely) proportional to the overall cost of the candidate network. Furthermore, our

( )jxf has to reflect this condition: the closer we are to the limit condition in which valuereferencejvaluecurrentj xx ____ = the higher is the impact on the cost function deriving from a fixed increase in parameter jx . This makes the selection process preferably choose networks working far from their limit conditions. This aspect is very relevant in highly changing wireless network environments. A mathematical function satisfying listed properties is the power function ( ) n

jj xxf = with 10 ≤≤ jx and n chosen according to the relevance associated to the last condition. The overall cost of network z is, thus, expressed by the weighted sum of power functions ( )∑∑ −+=

p

npp

k

nkkzt xpxpF 1_cos

C. “Monetary cost of the service” parameter As its name suggests, this is just the normalized cost of the session served by a candidate network. We define the monetary cost of the service

OMAXi

zizi C

CCOST _

,, = ,

where ziC , expresses the monetary cost that network z charges to transfer service i and OMAX

iC _ is the maximum


value of monetary cost among those offered by all networks. This parameter is surely directly proportional to the overall

cost of the access network; therefore, ( )n

OMAXi

zizi C

CCOSTf

= _

,,

.

To compute)(,, ksessionuiCOSTw we use M

ksessionuiC )(,, , i.e. the

maximum cost the user was willing to tolerate for the same service and in the same situation during the previous sessions, and MAX

ksessionuiC )(,, , i.e. the maximum cost the user u is willing to pay to obtain service i during session k. Initially, the user specifies a value for this parameter (it strictly depends on his monetary resource and on the context [11]) and, subsequently, the profile learning machine will estimate it through EWMA.

Thus, )(,, ksessionuiCOSTw is simply M

ksessionui

MAXksessionui

COST CC

wksessionui

)(,,

)(,,1)(,,

−= ,

and gives indications on the importance the user gives to the monetary cost of current session compared to the past.

D. “Power Consumption” parameter Each physical interface of device d has a specific power consumption level associated, whose value is mainly related to the distance between mobile terminal and BTS/AP. Let’s define: dzP ,

, the power consumption of the physical interface

used by device d to access z and MAXdP the maximum value

among the power consumption values associated to all physical interfaces of d. As a consequence, the Power Consumption associated to the physical interface card of the device can be defined

MAXd

dzdz P

PPOWER ,

, = . Being the power

consumption directly proportional to the overall cost of the

candidate network, it will be ( )n

MAXd

dzdz P

PPOWERf

= ,

,

A wise choice is to relate )(,,, ksessionduiPOWERw value to the

residual functioning time interval of the device d before it needs to be recharged. The UA monitors the battery status and updates the value

dLIFET , that is the residual battery life of d. It is straightforward that: if the battery charge level is high, then Power Consumption has to be a negligible parameter for the computation of the overall cost; else Power Consumption has to play a relevant role in that computation. At the same time, )(,,, ksessionduiPOWERw is related to service i duration: the

longer is the time required to execute a service, the higher is the estimated power consumption. Therefore,

)(,,, ksessionduiPOWERw has to increase with the estimated duration

of the requested service. Let )1(,, −ksessionuiT be the average duration, referred to the previous sessions in which user u accessed service i in the same situation. NUA derives the following weight of the cost parameter “power consumption”:

dksessiondui

LIFE

ksessionuiPOWER T

Tw )(,,

)(,,,= .

E. “Network ability in covering user next-locations” We assume that our system is equipped with suitable user location prediction algorithms. Our assumption urges us to take into account also a cost parameter expressing the network ability in covering user u next-locations

zuLOC ,during the computation of the network overall cost.

Let us assume that the user is in the location identified by (x,y) and there are q possible next-hops, each one with an estimated probability jP . The ability of network z to cover location of user u is named zuL , and is equal to the sum of probabilities associated to the l (with ql ≤ ) next-locations the network is able to serve with its coverage. In general, it holds: ∑

≤

=

=ql

Jjzu PL

1,

. As a consequence, we define zuLOC , as the

rate between zuL , and the maximum coverage ability, i.e. the network ability of covering all q possible next-locations:

zuzu

m

jj

zuzu L

L

P

LLOC ,

,

1

,, 1

===

∑=

Last point, zuLOC , is inversely proportional to the overall cost,

then ( ) ( )nzuzu LLOCf ,, 1−= . The weight uLOCw , associated to

zuLOC , , has to be such that the more the prediction successes, the higher the weight of the cost parameter network ability in covering next-locations. This condition is simply represented by exploiting a prediction success counter, namely “c_success”, increased each time the algorithm is able to individuate the right next-location of the user. c_success values have to be evaluated with respect to the total number of predictions, “c_predictions”, performed by the algorithm since the user first switched on the mobile device. Therefore,

spredictioncsuccessescw

uLOC __= . Research activity on effective location

estimation algorithms does not fall within the scopes of the paper; therefore, during performance evaluation we will not consider such a feature in our test-bed.

F. “Cost of the bandwidth degradation” parameter The access network selection and the QoS re-negotiation procedures has to be performed also when the session is on-going, if either the user decides to modify the negotiated QoS parameters, or NUA becomes aware of a variation in available resources which does not allow to respect the agreed QoS contract (QoS degradation in the current network, handover due to user roaming). Following one of the listed events, NUA activates the network selection procedure to decide which network will sustain the service session. The cost parameter bandwidth degradation can be defined :

MAXui

zizui B

BDEGR

,

,,, = and ( )

n

MAXui

zizui B

BDEGRf

−=

,

,,, 1

WhereziB , is the current bandwidth that the RRA of network

z is proposing to allocate to service i and MAXuiB , is the

maximum value of the bandwidth requested by the user u for


service i. )(,, ksessionuiDEGRw has to give an estimation of the

user tolerance to the possible degradation of the allocated bandwidth (i.e. the importance a user gives to a bandwidth degradation). We recall that at the end of each selection process NUA notifies the UA the QoS contract it has been able to establish. The user can accept/reject the proposal. More details can be found in [12]. Thus, cost of the bandwidth degradation can be evaluated by assessing the times the user accepts a QoS contract proposed by the NUA in which a bandwidth (resp. QoS) degradation was foreseen. To establish how much the user is tolerant to the bandwidth degradation, we have to consider only the implicit degradation, i.e. when the bandwidth reduction is not asked by the user but, anyway, accepted by him. Different are cases when the user explicitly requests a modification of the QoS parameters previously declared. We simply define

)(,,1)(,, ksessionuiDEGR BDIw

ksessionui−= . )(,, ksessionuiBDI is the so

called index of user tolerance to bandwidth degradation up to session k and relevant to service i. It is computed as a weighted average of the values of so called Bandwidth Degradation Tolerance (BDT) computed in previous sessions under the same situation, user, and service. This latter index, expressed as T connectionuiimpluiksessionui TBDT

,,_,)(,, = , represents the

ratio of the user perceived (and implicitly accepted) degradation period (Ti,u_impl ) over the connection duration (Ti,u,connection ). A similar approach is followed to compute the importance the user gives to each media flow components.

G. “Level of security offered by the network” parameter As an example, the level of security that user u wants to associate to service i during session k can be declared through indexes )(,, ksessionuiI reported in Table 1.

Table 1: Indexes of security level used by the user Requested security level for user u Index Ii,u

none 0 low 0.25

average 0.5 high 0.75

very high 1

When NUA, during the selection process, contacts SA, this latter sends back to it the value of overall security level furnished by network z, normalized with respect to the maximum security level it is possible to offer to service i. We name this value ziS , . The cost parameter level of security offered by the network for the requested service will simply be zizi SSIC ,, = . It is inversely proportional to overall cost;

thus ( ) ( )nzizi SSICf ,, 1 −= . As for

)(,, ksessionuiSICw , NUA simply

assumes that it is equal to )(,, ksessionuiI expressing the security

level selected by user u for the k_th session of service i.

H. “Unreliability of a given access network” parameter Network unreliability [10] is a novel concept related to statistics on the past behavior of each network and on its capacity of respecting the SLA agreed with the user.

A user, besides his/her interest in achieving the maximum possible QoS for most of the session time (although allowing temporary degradations), could show a keen interest in receiving the service with a given “stability”, i.e. without too frequent bandwidth (and, consequently, QoS) degradations. This is why, in our network profile, both degradation and stability behaviors of a given network contribute to the definition of its “unreliability”. For a given network z and for a given user u, the unreliability function is the following:

−⋅

=∑

= BBBTR MAX

jui

allocated

jui

MAX

juih

jpermanence

zui

r

juiuZ T

,,

,,,,

1 ,,

deg

,,,

Where Ballocated

jui ,, is the bandwidth allocated to user u during the

j-th degradation interval, permanencezuiT ,, indicates the session

duration within the network z, and T r

jui

deg

,,is the j-th degradation

interval experienced by the user during the permanence of his session in z. Obviously, the time intervals when the user explicitly requests a degradation do not belong to this category. Last, h is the number of intervals in which the allocated bandwidth results to be degraded, i.e. lower than declared BMAX

jui ,,. In the equation is quite clear that BB allocated

jui

MAX

jui ,,,,−

indicates the degradation, while T r

jui

deg

,, is the time interval

during which the degradation is perceived by the user u. Simply, the overall network unreliability function stored in RRA historic profile is computed as N

N

iiZZ RR ∑

=

=1

,, where N

is the number of users. If a group of users has received a low performance level by a network, then it is wise to penalize the network by associating a higher “cost” to it. Therefore, let us define zREL as the parameter “cost associated to network unreliability” for network z. Being zR the value of

unreliability registered by RRA for network z and MAXzR the

maximum value of unreliability registered by RRA for

network z, then: MAXz

zz R

RREL = ( )

n

MAXz

zz R

RRELf

=

The weight )(,, ksessionuiRELw is clearly related to the tolerance

of the user to accept degradation in a unreliable network. Therefore, its meaning is analogous to the one of

)(,, ksessionuiDEGRw , defined for a previous cost parameter. This

is why we set: )(,, ksessionuiRELw = )(,, ksessionuiDEGRw

IV. PERFORMANCE EVALUATION To present the behavior of our proposed algorithm, we now analyze a sample test with three users moving from region A to region B, through the same route at a pedestrian speed. For the sake of simplicity users are equipped with the same multi-mode terminal. John is a private user, who has to access a service of Instant Messaging. Susan is a working user, who initiates a videoconference service with her remote colleagues. Last, Mark is a businessman, who visits the city; at the beginning he accesses the network to download urgent


e-mail with attachments, suddenly (instant 350 in the graph of Fig. 2) he needs to start an economic transaction. Considered networks are UMTS, WLAN1 and WLAN2. Their coverage/availability with reference to region A and is shown in Fig. 1. UMTS operator charges all the three users with a sort of flat monthly rate. UMTS bandwidth changes according to the user mobility. WLAN1 is a very secure network, with a bandwidth of 5Mbps, that charges the users, while WLAN2 provides free access, 2Mbps of bandwidth, but it guarantees a low level of security. According to the different needs and preferences of the users, our cost function will select different radio access networks for them. Reference values of user preferences for our test scenario are shown in Table 2. Preferences change from user to user (user-awareness) and from session to session (situation & location-awareness), for the same user, as in the case of Mark.

UMTS

WLAN1

WLAN2

Region A

Region B

Figure 1: Wireless overlay networks scenario.

Table 2: cost parameters declared by users in our test Private

User Working User

Business Man

From 0 to 600 s

From 0 to 600 s

From 0 to 350 s

From 350 to 600 s

Monetary cost Low High High NONE Bandwidth Medium High High High Security Low Medium Medium very High

Curves reported, show the access network selected by each user while roaming across A and B regions during a 10 minutes time interval. The graph shows that the network selected by the middleware for John, accessing a service without particular QoS and security constraints but with a keen interest in reducing the monetary cost, is UMTS. This selection holds until John moves to the area under the coverage of WLAN2, which is completely free of charge, and a vertical handoff is automatically triggered by our system. Perceived QoS is more significant to Susan; this is why she is willing to afford a higher monetary cost. This is why her connection remains in WLAN1 as much as possible and handover is performed only when the coverage from WLAN1 is not possible. Mark initially requests a high QoS level (e_mail download with many attachments); therefore, his connection remains in WLAN1. Subsequently, the system makes his terminal perform handover towards WLAN2, instead of UMTS. When he completes the document download, he performs an economic transaction and the cost function, with reference to this new exigency, selects UMTS network which results to be more trusty in terms of security. From this simple test performed by our platform it can be noticed that had we used a selection algorithm only based on

the minimization of the monetary cost of the service, then Susan and Mark constraints would have not been satisfied. Differently, had the criteria chosen the network with the greatest current resource availability, then John in region A would not have used UMTS, more convenient to him, but Wi-Fi, this implying a higher expense.

V. CONCLUSIONS We proposed a novel multi-criteria selection algorithm which has been implemented into a middleware platform suitably conceived for being used in 4G scenarios. We have assessed through a comprehensive test campaign (only a sample is illustrated due to length constraints) that a more effective criteria for selecting the access network is achieved by associating to each cost parameter in the selection function a weight dynamically adapted to user preferences and profile.

Figure 2: Selected Networks (network 1: UMTS; network 2:Wi-Fi

WLAN 1; network 3: WiFi WLAN 2).

REFERENCES [1] V. Gazis, et. al., Toward a Generic “Always Best Connected”

Capability in Integrated WLAN/UMTS Cellular Mobile Networks (and Beyond), IEEE Wireless Communications, June 2005

[2] Gustafsson et. al., Always Best Connect, IEEE Wireless Communications, February 2003

[3] Wei et al., Context-aware handover based on active network technology, IFIP TC6 5th International Workshop, IWAN 2003

[4] Prehofer, et. al., A framework for Context-aware Handover Decision”, Proc, PIMRC 2003, China, 2003 .

[5] Suciu et. al., A survey of multi-criteria network selection algorithms, Global Mobile Congress, 2004 Shanghais

[6] O’ Droma, Wireless, Mobile and Always Best Connected, Proc. of the 1st International ANWIRE Workshop, 2003

[7] McNair et. al., Vertical Handoffs in Fourth-Generation Multinetwork Environments, IEEE Wireless Comm., June 2004

[8] Wang et. al., Policy-enabled handoffs across heterogeneous wireless networks, IEEE PIMRC, 2000

[9] Chen et. al., A smart decision Model for Vertical Handoff, Proceedings of ANWIRE 2004, Greece, 2004

[10] A. Iera et. al., New Concept Platforms for QoS Management in Future Telecommunication Scenarios, invited submission to International Journal of Wireless Information Networks, to appear

[11] Cavalieri, Microeconomia politica, Chapter 3: Le preferenze dei consumatori e le funzioni di domanda: ipotesi teoriche, pag. 91-130, Giuffrè Editore, 1979

[12] A.Iera, et. al., Adaptively Controlling the QoS of Multimedia Wireless Applications Through ``User Profiling'' Techniques, IEEE J-SAC, 4th quarter 2003

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