Utility-based Intelligent Network Selection in Beyond 3G ... - CiteSeerX

Utility-based Intelligent Network Selection in Beyond 3G Systems Olga Ormond, John Murphy

Gabriel-Miro Muntean

School of Computer Science and Informatics, University College Dublin, UCD Belfield, Dublin 4, Ireland

School of Electronic Engineering Dublin City University, DCU Glasnevin, Dublin 9, Ireland

Abstract—Development in wireless access technologies and multihomed personal user devices is driving the way towards a heterogeneous wireless access network environment. Success in this arena will be reliant on the ability to offer an enhanced user experience. Users will plan to take advantage of the competition and always connect to the network which can best service their preferences for the current application. They will rely on intelligent network selection decision strategies to aid them in their choice. The contribution of this paper is to propose an intelligent utility-based strategy for network selection in this multi-access network scenario. A number of utility functions are examined which explore different user attitudes to risk for money and delay preferences related to their current application. For example we show that risk takers who are willing to pay more money get a better service. Index Terms—User Utility Functions, Network Decisions, Heterogeneous Wireless Networks.

Selection

I. INTRODUCTION The success of next generation wireless networks is very much dependent on the users and their spending and usage patterns for services that they must find attractive, available and affordable. As consumer electronic devices merge and become one with wireless communications terminals there are many more opportunities for innovating popular main stream and niche services. These services will cater for communications, entertainment, news, and information needs of many different customers operating on a variety of different terminals with access through a multitude of different available wireless access technologies. User satisfaction, personalisation and expendable income are all important in a world where highly busy mobile people require access to services anywhere at any time. While it still remains to be seen exactly how fourth generation (4G) wireless systems will be run, it is evident that the revenue pie is beginning to be divided into two portions: access/transport provision and service/content offering. Service provision is no longer restricted to the operators and their few external collaborating content suppliers. The opening up of the service provision end of the market will see the emergence of a number of new service providers, all keen to maximise their potential share of the profits. A strong services portfolio is vital in drawing in the customers and their expendable income, contributing greatly to both the transport operators’ revenue This work is part funded by research grants from Enterprise Ireland Informatics Programme and Enterprise Ireland Research Innovation Fund. Professor Scott Jordan’s assistance is gratefully acknowledged.

and the earnings of the service providers. It is therefore in the best interest of the transport operators to cooperate with the service application and content suppliers to offer services which appeal to the users and best use the ever increasing technical abilities of their terminals. Figure 1 depicts the resulting three tier Service Oriented Heterogeneous Wireless Network Environment (SOHWNE) that consists of services offered by service providers, radio access networks (RAN) supplied by network operators and users with various devices. The user-centric vision for the future is one where users in a SOHWNE will be free to ‘shop around’ not only for the service they need, but also for the available access network which meets their current service needs. In this environment users will need an optimal intelligent network selection strategy, which will aid them in picking the ‘best’ available RAN solution with a minimal loss of time, energy, money, and user inconvenience. The decision involves a number of complex considerations and trade-offs for conflicting selection metrics. It needs to be a highly flexible strategy applicable to both the user’s professional and personal communications requirements in an ever-changing radio environment. Given the volatile nature of radio channel conditions, this decision will involve a certain amount of uncertainty and risk tolerance on the user’s part. Banking

Browsing

Gaming

Data Access

Shopping

SERVICES Private:

Private:

Public:

Public:

Public:

Public:

Home

Office

GSM

GPRS

UMTS

CDMA

WLAN

WLAN

Public: Hotspot

WLAN

RANS

USER DEVICES Figure 1. Service Oriented Heterogeneous Wireless Network Environment (SOHWNE)

This paper examines the tradeoffs between users’ risk taking attitudes and their corresponding network selection decisions. The aim of our work is to propose an intelligent

network selection strategy for each network selection decision users with a choice of RAN face. In the following section we explore the need for such a strategy. Section III discusses related work in this area. Three possible utility functions which are modeled on various users’ risk taking attitudes are proposed in section IV. These functions are then evaluated in a series of simulation-based tests and comparative results are presented in section V. Conclusions are drawn and future work directions are indicated in section VI. II.

NEED FOR A NETWORK SELECTION STRATEGY

Accustomed to broadband data networks with flat-rate pricing in wired networks – users have grown a dependency pattern on having a high quality broad service range available at a low price. Outlook on the desired service priority, quality and budget requirements will change depending on the user profile and current context. A user may be sitting with their laptop in a café with time to wait for long downloads expecting a high quality presentation of requested data on their large screen, alternatively they could be rushing down a busy street with low battery power requiring only minimal presentation of their requested data on a handset. In common users will seek value for money and will have a patience limitation on their willingness-to-wait for mobile system response to their requests. If a user considers a particular application as expensive they will be discouraged from using it, and keep their consumption of this wireless data service to a minimum. Consumers choose the products and services and that will give them the most satisfaction. In the world of economics utility functions are often used to describe this user satisfaction. In our case the utility function depicts the preference relation for different available service and access options. Users want the best obtainable value for their budget allowance; they seek cost effective solutions to meet their communications expectations. Users aim to maximise their utility, and to this end they will compare marginal benefits against marginal costs. As long as marginal benefit exceeds marginal cost and it is within their monetary means they will consume the commodity. If the price that the user pays for the transfer is less than the value they were willing-to-pay then the user has saved money. This difference between the monetary value of the data to the user and the actual price charged is known in microeconomic terms as the Consumer Surplus (CS). In our strategy, users aim to maximise their positive CS while meeting their transfer completion time deadline. User behaviour is strongly affected by the charging scheme employed, the quality of service on offer and the other available connection choices open to them. Different networks may employ different charging schemes, from charging a simple fixed price per Kbytes transferred to a more complicated scheme (e.g. congestion based pricing as will be investigated in future model versions). User preference combinations of network and service metrics depend on many tradeoffs, which will vary for each user. These tradeoffs depend on many different factors including the user’s previous experiences with available communications capabilities. Users may remember networks that continually let them down or charged them more than expected and decide not to use them anymore. New and experienced or frequent users may differ in their views on perceived quality of service. As terminal

technology advances it may be possible for users to run multiple applications simultaneously, while connected either to one network at a time or to multiple networks at the same time. This means that making the choice is even more complicated. Therefore there is a need for a user-oriented intelligent network selection mechanism such is the one we propose. III. RESEARCH APPROACHES TO NETWORK SELECTION The focus of our work is the user-centric decision making problem of which available network to choose for data transfer. Decision metrics and need for decision policy design are outlined by McNair and Zhu [1] in the context of vertical handoff for a single mobile user running multiple communication sessions. These metrics are also relevant to the initial network selection decision for any user with a choice in available networks. Much of the work in RAN selection policy design investigates the access network selection problem as part of the seamless handover venture. Ylitalo et al. in [2] look at how to facilitate a user making a network interface selection decision. They concentrate on a possible architecture for the end terminal and not on any particular strategy, but they do mention an Always Cheapest (AC) network selection strategy. Bircher and Braun [3] propose an agent-based architecture with a user agent decision function. Customers compare and select services with the best performance/price ratio, negotiate with providers for offered services and pre-reserve the resources for an agreed price. Details of the exact negotiation terms are not covered but they are based on differently weighted QoS parameters such as delay, bandwidth, packet loss, etc. In [4] H. J. Wang et al. describe a handover ‘policy’ for heterogeneous wireless networks, which is used to select the ‘best’ available network and time for handover initiation. They consider the cost of using a particular network to be in terms of the sum of weighted functions of bandwidth, power consumption and cost. Bandwidth is determined either by use an agent in each RAN which estimates and broadcasts the current network load, or in the case of commercial networks, by the ‘typical’ value of bandwidth advertised by these networks. The network which is consistently calculated to have lowest cost is chosen as the target network. A randomised waiting scheme based on the impact of the estimated handover delay is used to achieve stability and load-balancing in the system, and to avoid handover synchronisation. A number of papers, all of which reference [4] use similar cost functions. A smart decision model for vertical handoff is the focus of Chen et al. in [5]. Their proposed scheme relies on a score function which, is based on functions of allocated bandwidth (transfer completion time), battery power consumption and cost charged by the available networks. The value or benefit function of offered link capacity is a concave increasing function following the economic assumption of diminishing marginal utility, i.e. value will increase for each unit of added bandwidth up to certain point after which the gain in value for extra capacity is marginal as described as in Murphy et al. [6]. The bandwidth in this case is measured using a probing tool. Both [4] and [5] collect current information on bandwidths available on all local networks – this requires heavy power consumption and introduces a certain delay, factors which should possibly be added to the cost of

implementing the suggested strategies. The HTTP handoff decision model presented by Kammann et al. in [7] and the work in [1] also use a cost function which is used to compare available networks from the list of options and establish the network to handoff to according to the importance weightings associated with different metrics. The user network selection decision strategy will be influenced by the pricing scheme employed in the available networks. Le Bodic et al. [8] look at networks with an auction based pricing scheme and employ two different strategies based on user preference for low service charge, or user preference for networks with a good reputation. Many new pricing schemes are being proposed for RANs [9], the majority focus on network-centric benefit. In this paper we assume all available networks employ a fixed price per byte pricing scheme, as i-mode users are currently charged. Utility-based functions are commonly used to describe user preference rating relationship for a number of metrics. X. Wang et al. [10] consider user preferences to be represented quantitatively through a utility function when comparing two congestion pricing schemes. In [11] Das et al. consider users to choose a pricing plan based on their data delay considerations, described by a user utility function. They use their understanding of user behaviour to maximise network gains, however they do not consider user-centric SOHWNEs but rather look at efficient network resource management with the goal of reducing customer churn and maximizing wireless network operator revenue. Gazis et al. [12] look at the complexity of being ‘Always Best Connected’. The decision of which network to select is user-centric. It involves identifying the network, or combination of access networks, from the available candidates which will best satisfy the current user requirements in their current circumstance. The complexity of the access network selection decision problem is mapped to an NP-hard optimisation problem. Heuristics may solve this combinatorial optimisation problem to produce optimal or near optimal solutions on some input instances. IV.

There are different possible network selection strategies or policies for the user. For example a user may choose to always stick with one particular network regardless of its current characteristics, or to always select the cheapest network, or to go for random network selection. However we believe that an intelligent selection based on user willingness to pay, their file completion time constraints and estimated access network delivery time should be used instead. We propose a utilitybased algorithm that accounts for user time constraints, estimates complete file delivery time (for each available access network) and then selects the most promising access network based on consumer surplus difference. B. Consumer Surplus-based Nework Selection Algorithm While the strict time restrictions imposed by real-time traffic do not apply to non real-time data, it is assumed that every user has a patience limit, a threshold value for the duration that they are willing-to-wait for completed transfer of their data. Beyond this value they become dissatisfied and less willing-to-pay. This threshold is defined as the maximum transfer completion time for the file, and is denoted as Tc2. Once the data transfer time for a file exceeds Tc2 the user is unwilling to pay any money for the file delivery.

DECISION STRATEGY & THE UTILITY FUNCTION

A. Decision Strategy The focus of our work is to find an intelligent solution to the RAN selection decision problem for non real-time data services. This decision strategy will rely heavily on the charging schemes implemented by the candidate networks, as users make every effort to get the best value for their money. The scheme also depends on the utility function employed to describe the user’s preference relation over their willingnessto-pay verses other decision metrics. This paper considers the scenario where a user wishing to FTP non real-time data (a file) is in an area of overlapping RAN coverage with their intelligent multi-mode wireless terminal. The user has the choice of the available access networks, each of which employ a fixed price per byte pricing scheme but each charging different prices. Also each network has varying capability to transfer data. Naturally the user wants timely delivery of their data at the lowest possible price. The problem is to select the network that has the biggest chance to achieve this goal.

Figure 2. Network Selection Strategy

A suitable user utility function, Ui, is chosen to describe the user’s flexibility to delay and how they expect the cost to decrease with increase in delay for file i. Users must be realistic in their cost expectations, given the current prices charged in their present location. For each file transfer the user will aim to maximise their consumer surplus (CS) while meeting their delay conditions for the current scenario. The bigger the CS is - the more satisfied the user will be, provided that the transaction meets the transfer completion time deadline set by the user. The proposed approach is that at the time of access network selection decision the user’s terminal will survey the radio

interface and determine a list of current available access networks. The terminal will employ an algorithm to predict the current transfer rates on offer in each of the listed RANs. It will then apply the predicted rates and the user’s utility function to determine which network is predicted to meet the data time deadline and offer the best value for money. Figure 2 presents the proposed Consumer Surplus-based Network Selection algorithm in detail. We first calculate the predicted completion time (Tc) and thus the predicted utility (Ui) and CS for each candidate network. The network with the best predicted CS, which is also predicted to meet the completion deadline, is then selected as the most suitable network for the data transaction. Tc1 denotes the user’s best expectation for transfer completion time and Tc2 the maximum transfer completion time that a user is willing to wait. A model has been developed in NS2 [17] to analyse the proposed strategy. C. Estimation of Transfer Completion Time The actual transfer rate determines the transfer completion time and therefore the value of the data to the user. Access discovery is part of a common three-phase handover procedure. In this phase the wireless terminal seeks information on available access networks. The amount of information supplied should be minimal. Receiving current network condition information may be resource consuming and wasteful in the ever changing unreliable wireless environment. In this model we employed a simple rate prediction scheme to forecast the available rates on offer. The method used considers available information on the networks’ recent history, using an average of the previous five rates experienced in a particular network to predict the transfer completion time for that network. If previous rates are unavailable a default rate (that is the minimum acceptable rate which will meet the time deadline) is used. If the prediction produces a bad rate we only consider this prediction for a number of decisions before we decide that the information is stale and replace it with the default rate. One drawback in doing this is that if either network suffered congestion a number of times in succession, the user would continue to select the other network until they got the same (lower) throughput or the information is deemed stale and is replaced. The first network may well have recovered from its slump in transfer rate before this time. D. Possible Utility Function Choosing an appropriate utility function to model human user preferences under the uncertainty of the radio environment is a challenge. We start with a reasonable set of assumptions and conditions to help us to determine a possible shape and threshold values for the utility function, and then compare the resulting utility function with two variations. This utility function relies on the zone-based structure introduced in user studies [13]. There are three zones of user’s willingness to wait: satisfaction zone, tolerance zone and frustration zone. These correspond to zones 1, 2 and 3 respectively in Figure 3. User’s willingness-to-pay or utility Ui for a file i depends on the required transfer completion time (Tc). Timely arrival of the file anywhere within zone 1 is worth the highest value, Umax. We denote completion time Tc1, as the threshold of the satisfaction zone. After waiting Tc1 seconds the value of completed transfer decreases with increase in delay, until the

time exceeds the user defined maximum tolerated delay, Tc2. Any file arriving after this specified maximum completion time is worth 0 cent to the frustrated user. There are a number of possible shapes to describe the utility function for zone 2 or the tolerated transfer completion delay zone. In the notes on attitude to risk taking [14] Kirkwood describes three different attitudes to risk and their associated utility function shapes. We adopted these three shapes in our discovery of the best possible utility function. The attitudes in terms of our function metrics are: • risk neutral - user equally prefers paying less to experiencing less delay • risk seeking - user prefers alternative of less delay to assured money saving • risk adverse - user prefers to be certain of paying less These three utility functions are described below and are graphically represented in Figure 3.

1

2

Tc1

3 Tc2

Figure 3. User Utility Functions for 100 Kbyte Payload

1) Utility Function 1, U1i: Utility function 1 takes the shape of the risk neutral users in zone 2, this results in a piece-wise linear user utility function to describe the relationship between the user’s time and budget limitations. We call this utility function 1, U1i it is described by the equations in (1). In our initial strategy development and testing [15, 16] our strategy that used this utility function, U1i, was compared to a strategy where the user always selected the cheapest network. Testing results have shown that our strategy worked significantly better. U max, T c ≤ T c1 ~ zone 1  U 1 i (T c ) = U var − T c , T c 1 < T c ≤ T c 2 ~ zone 2 (1) 0 Tc > Tc 2 ~ zone 3  ,

2) Utility Function 2, U2i: Risk seeking user preferences are described by utility function 2, U2i. The user is more interested in the alternative of low transfer completion time delay as opposed to being certain of saving money. The utility function U2i, presented in (2), is expressed as in (1) for zones 1 and 3 and differs for zone 2. Tc ≤ Tc1 ~ zone 1 U max,   − T 3c (2) U 2 i (T c ) =  T c1 < T c ≤ T c 2 ~ zone 2 − q,  w Tc > Tc 2 ~ zone 3 0 , 

V.

TEST SETUP AND RESULTS

A. Testing Setup and Scenarios The scenario considered contains two partly overlapping WLANs, each with a number of terminals generating background traffic. On WLAN0 this traffic starts at a low level (5 nodes generating CBR traffic over UDP, 1000 byte packets at a range of rates, 100kbps to 900kbps), increases to a high level (18 CBR sources) and finishes at the starting level (low). On WLAN1 background traffic starts and ends on a high level (18 CBR sources), and exhibits low level (5 CBR sources) in the middle of each simulation run. The price per Kbyte in WLAN0 is set at 0.1 cent/Kbyte, whereas WLAN1 is twice as expensive. Both LANs are connected to a wired network which hosts the sink for all application data. Our intelligent user terminal is located in the overlap area and has a choice of the two RANs for FTP file transfer. During successive tests file size is varied from 10 Kbytes to 1.5 Mbytes. Tests are repeated with each utility function: U1i, U2i and U3i. In the case of each network selection decision the intelligent node will shortlist the networks based on delivery time prediction to meet Tc2. The file is then transmitted over the short-listed RAN which is predicted to maximise the CS. The simulation model was developed in NS21 version 2.27 with IEEE 802.11b wireless LAN parameter settings (data rate 11 Mbps), the NOAH (No Ad-hoc routing) extension, and our 1

Network Simulator-2, [Online]. Available: http://www.isi.edu/nsnam/ns

B. Testing Results The three possible utility functions that model various users’ risk taking attitudes are used during testing. The comparative results correspond with each user’s approach to risk taking: • Risk neutral users see their delay and cost results sit between the results for the other two utilities. • Risk seeking users pay more, but experience less delay. • Risk adverse users pay less, but experience more delay. The results are shown both as graphs in Figures 4, 5 and 6 and tables in Table I, II and III. Figure 4 shows how the user employing U3i experiences the greatest average delay per file transferred, whereas Figure 5 clearly indicates that the same user pays on average the least per file transfer. This result contrasts those for the user employing U2i, who has less average delay, but higher costs. For example when 100K files are transmitted by risk adverse users, they will arrive in on average in 1.16 seconds and will cost the user on average 2.15 cent. Unfortunately 6.46% of the files will arrive after the time deadline causing the user much frustration and money loss (i.e. 6.46% of the files they paid for were worth 0 cent to them). When a risk seeking user transmits 100K files they will pay on average 2.53 cent but 99.2% of the files transferred will arrive before the deadline, keeping this user in the satisfied and tolerant zones. The results suggest that the difference between these users increases with the file size. That is as the file size increases, the associated delay and costs gains are larger, and therefore the value of employing an appropriate utility function for a given user’s preferences rises with increase in file size. 40

U tility 1 U tility 2

Average Tc (sec)

E. Thresholds To determine reasonable values for the user utility function we took into account a large range of possible throughput rates available in existing and emerging RANs, and a range of likely file sizes. We compared the expected transfer completion times for each and consider any delay below 1 second to be negligible, whereas delays greater than 30 seconds are breaching the end-users expectation for good service. This algorithm considers a bit-rate of 212 Kbps as the threshold for user acceptability. This value was chosen from the table of possible rates as one which would provide a medium transport service rate. The associated user utility function for an FTP application transferring a payload of 100 Kbytes is shown in Figure 3. The graph indicates how any completion time up to Tc1 = 1 seconds is worth the highest price to the user, whereas files arriving after Tc2 = 4 seconds are worth 0 cent. In between Tc1 and Tc2 the value decreases with time dependent on the user’s attitude to delay and money conservation.

application which simulates a multi-homed terminal with inbuilt CS based network selection strategy. The topology consists of a wired network connected to two APs; each AP has 19 associated wireless nodes. Wired links are high bandwidth with negligible delay such that end-to-end delay is mainly dependent on the performance of the chosen wireless network.

U tility 3

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Figure 4. Average Transfer Completion Time Per File 50

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3) Utility Function 3, U3i: The third utility function describes a risk adverse user. This user’s attitude is that they are reluctant to pay out money, and prefer to be certain of spending less than experiencing less delay. Their associated utility function U3i, for transfer of file i is expressed in (3). T c ≤ T c1 ~ zone 1 U max,  T  U 3 i (T c ) =  a ⋅ exp( − c ), T c1 < T c ≤ T c 2 ~ zone 2 (3) b  Tc > Tc 2 ~ zone 3  0 ,

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Figure 5. Average Cost Per File Transfer

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Figure 6. Percentage of File Transfers That Exceed Tc2 TABLE I.

File Size (KBytes) 10 20 50 100 200 500 1000 1500

UTILITY FUNCTION 1, U1I

Total Average Files Tc Sent (sec) 5294 0.19 2743 0.36 1570 0.63 743 1.33 364 2.68 142 6.93 51 19.23 31 31.52

% over Tcmax {zone3} 8.29 7.84 6.62 8.34 8.24 10.56 27.45 25.81

Total Ave cost cost per file (cent) (cent) 1237.05 0.23 1251.30 0.46 1701.75 1.08 1743.00 2.35 1539.00 4.23 1537.50 10.83 1290.00 25.29 1282.50 41.37

environment. Our proposed solution is a user-centric RAN selection strategy based on maximizing consumer surplus subject to meeting user-defined constraints in terms of transfer completion time. This paper presents our exploration of a number of possible utility functions based on different user’s attitudes to risk. The simulations produced results that correspond to the inputted user utility descriptions. The risk taker ends up paying more, but enjoying less delay. The risk adverse use pays less money but experiences more delay and money waste. For smaller file sizes the price difference between U3i and U2i seems negligible, making U2i the choice on account of the smaller percentage of transfers exceeding the time deadline. For large files the price difference may deter low budget users from this choice. Future work aims are to further improve our strategy, and examine the impact of multiple intelligent users operating in the same region. Especially the case when users in a particular location are homogenous in their profile (e.g. students on a university campus) and are expected to have similar behaviour. REFERENCES [1] [2] [3] [4]

TABLE II.

File Size (KBytes) 10 20 50 100 200 500 1000 1500

Total Average Files Tc Sent (sec) 7726 0.13 5578 0.18 1849 0.53 994 0.99 489 2.01 160 6.13 54 18.14 46 21.37 TABLE III.

File Size (KBytes) 10 20 50 100 200 500 1000 1500

UTILITY FUNCTION 2, U2I

% over Tcmax {zone3} 0.23 0.29 0.38 0.80 1.43 5.63 16.67 10.87

Total Ave cost cost per file (cent) (cent) 1926.30 0.25 3059.40 0.55 2202.00 1.19 2511.00 2.53 2406.00 4.92 1905.00 11.91 1395.00 25.83 2025.00 44.02

UTILITY FUNCTION 3, U3I

Total Average Files Tc Sent (sec) 6970 0.14 3259 0.30 1654 0.59 851 1.16 313 3.15 136 7.24 44 22.24 24 40.40

% over Tcmax {zone3} 6.26 6.60 6.17 6.46 13.74 13.24 27.27 45.83

Total Ave cost cost per file (cent) (cent) 1500.90 0.22 1399.20 0.43 1790.25 1.08 1833.00 2.15 1524.00 4.87 1530.00 11.25 1095.00 24.89 922.50 38.44

VI. CONCLUSIONS AND FURTHER WORK The ultimate goal is to find the best user-centric network selection strategy for non real-time data transfer in the next generation service oriented heterogeneous wireless network

[5] [6] [7] [8] [9] [10]

[11] [12] [13] [14] [15] [16]

J. McNair, F. Zhu, “Vertical Handoffs in Fourth-Generation Multinetwork Environments” IEEE Wireless Comms, pp. 8-15, June 2004. J. Ylitalo, T. Jokikyyny, T. Kauppinen, A. J. Tuominen, J. Laine, “Dynamic Network Interface Selection in Multihomed Mobile Hosts”, 36th Hawaii Intl. Conf. on System Sciences, January 2003. E. Bircher, T. Braun, “An Agent-Based Architecture for Service Discovery and Negotiations in Wireless Networks”, 2nd Intl. Conf. on Wired/Wireless Internet Communications, February 2004. H. J. Wang, R. H. Katz, J. Giese, “Policy-enabled Handoffs Across Heterogeneous Wireless Networks”, Second IEEE Workshop on Mobile Computing Systems and Applications (WMCSA'' 99), February 1999. L.-J. Chen, T. Sun, B. Chen, V. Rajendran, M. Gerla. "A Smart Decision Model for Vertical Handoff." The 4th ANWIRE International Workshop on Wireless Internet and Reconfigurability (ANWIRE), May 2004. J. Murphy, L. Murphy, "Bandwidth Allocation By Pricing In ATM Networks", IFIP Transactions C-24, pp. 333-351, Broadband Communications II, March 1994. M. Angermann, J. Kammann, “Cost Metrics For Decision Problem In Wireless Ad Hoc Networking,” IEEE CAS Workshop on Wireless Communications and Networking, 2002. G. Le Bodic, J. Irvine, D. Girma, J. Dunlop, "Dynamic 3G Network Selection for Increasing the Competition in the Mobile Communications Market", IEEE Vehicular Technology Conference, Sept. 2000. M. Falkner, M. Devetsikiotis, I. Lambadaris, “An Overview of Pricing Concepts for Broadband IP Networks”, IEEE Communication Surveys and Tutorials Q3, 2000. X. Wang, H. Schulzrinne, “An Integrated Resource Negotiation, Pricing, and QoS Adaptation Framework for Multimedia Applications”, IEEE Journal on Selected Areas in Communications (JSAC), Special Issue on Internet QoS, vol. 18, December 2000. S. Das, H. Lin, M. Chatterjee, “An Econometric Model for Resource Management in Competitive Wireless Data Networks”, IEEE Network Magazine, pp. 20-26, Nov/Dec 2004. V. Gazis, N. Houssos, N. Alonistioti, L. Merakos “On the Complexity of “Always Best Connected” in 4G Mobile Networks”, Vehicular Technology Conference (VTC-Fall) 2003. P. J. Sevcik, “Understanding How Users View Application Performance”, Business Comms. Review, Vol. 32, No. 7, pp. 8-9, 2002. C. Kirkwood “Notes on Attitude Toward Risk Taking and the Exponential Utility Function”, Working paper, Arizona State University, Tempe, AZ, USA, 1997 O. Ormond, G.-M. Muntean, J. Murphy, “Network Selection Strategy in Heterogeneous Wireless Networks”, to appear Information Technology and Telecommunications Conference (IT&T), October 2005. O. Ormond, P. Perry, J. Murphy “Network Selection Decision in Wireless Heterogeneous Networks”, IEEE Intl. Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), September 2005.