Handling Network Uncertainty in Heterogeneous Wireless Networks

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by various wireless technologies, and derive crisp values for the imprecise network parameters. ... of connected MCs with active interfaces from different net-.
This paper was presented as part of the main technical program at IEEE INFOCOM 2011

Handling Network Uncertainty in Heterogeneous Wireless Networks Talmai Oliveira, Srisudha Mahadevan and Dharma P. Agrawal School of Computing Sciences and Informatics, Center for Distributed and Mobile Computing University of Cincinnati, Cincinnati, OH 45221-0030, USA [email protected], [email protected], [email protected]

Abstract—With the constant evolution of wireless communication access technologies, there is a clear trend for mobile clients (MC) to be equipped with multiple interfaces for simultaneous access to different types of networks. This has been denominated a heterogeneous wireless networks. However, in order to achieve a desired quality of service while satisfying the user’s requirements, MCs must take advantage of the inherent characteristics of these access technologies, and rely on adaptable decision making mechanism for data forwarding. Unfortunately, route selection satisfying multiple constraints has proven to be NP-Complete, and although various heuristic algorithms have been proposed, they assume that the network state information is static and both the user’s and networks constraints are clearly specified. This paper focuses on the imprecise and dynamic nature of the network conditions while satisfying multiple - often contradicting - constraints. A fuzzy logic model is proposed which aims at translating the uncertainty factor of the network conditions to accurate values using the fuzzy logic tools and techniques. We then perform a thorough analysis of the metric values offered by various wireless technologies, and derive crisp values for the imprecise network parameters. A sensitivity analysis reflecting the performance of, and relative importance of metrics on each network is carried out. These results are shown to impact user’s decision in handing over data to the appropriate interface. Index Terms—Fuzzy Logic, Heterogeneous Multiple Attribute Decision Making, Multiple Constraints, SAW, TOPSIS, Uncertainty, Wireless Networks.

I. I NTRODUCTION With constant evolution of wireless communication access technologies, including wireless local area networks (IEEE 802.11 protocols), cellular networks (GSM, UMTS, HSPA, LTE), and broadband wireless networks (IEEE 802.16 WiMAX protocols, LTE), there is a clear trend for mobile clients (MC) to be equipped with multiple interfaces for simultaneous access to the different networks. Such heterogeneity in different access technologies, architecture and specifications coined the idea of a “Heterogeneous Wireless Network” (HWN), and, as far back as the work of Katz [1], it has been studied in situations where MCs could migrate between homogeneous and heterogeneous network structures through Mobile IP [2]. Obviously, a number of options/paths are made available to facilitate communication between MCs. However, although multiple interfaces are available, they are normally deployed on the MCs in a traditional way, where interfaces tend to

be used separately and independently of each other, not fully utilizing the full potential of a HWN. We consider a network of connected MCs with active interfaces from different network technologies with multiple ongoing communication traffic flows. Specifically, MCs with multiple network interfaces capable of simultaneously connecting to GSM/GPRS, UMTS, 802.11 WiFi b/g/n, and 802.16 WiMAX networks. In such a scenario, whenever a MC receives data from an active flow, it must be able to decide to which interface it should forward to in a distributed and independent manner. Solving this decision problem requires each MC to be able to deal with multiple dynamic constraints: network conditions may drastically change, operating parameters may fluctuate, radio interference may become unbearable, and users and applications requirements may not follow a well known distribution and even be difficult to predict. The number of attributes are very large and may range from the authentication mechanism, to the access technology, to the cost per byte or to security guarantees. The multitude of conditional requirements makes the resolutions of this task hard. In fact, the basic problem of finding a path that satisfies multiple constraints has been proven to be NP-complete [3]. Not surprisingly, a large number of solutions exist to solve this problem. One possible approach consists of defining a single utility function from multiple parameters. By taking into consideration many different measured sets of information, a single measure can be produced and used during the decision process. For example, link quality may be estimated by incorporating factors like multi-channel diversity, interference, and congestion. In fact, significant throughput gains have been demonstrated when these attributes have been considered [4]. Single mixed metrics, however, even when considering stochastic elements, does not contain sufficient information to assess whether multiple requirements can be met or not, and can only be used as an indicator at best [3]. Alternatively, multiple metrics can be used to represent the requirements more accurately. Mostly due to ease of implementation and deterministic in nature, a lot of work has been done using conventional Multiple Attribute Decision Making (MADM) methods where a decision is made based on a selected subset of attributes. Unfortunately, decision making under uncertainty is a com-

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mon occurrence in wireless networks [5], and classical MADM methods are unable to efficiently address this decision problem when presented with imprecise data [6]. Therefore, no optimal algorithm is known to exist that is capable of dealing with imprecise network parameters and constraints. The uncertainty factor in our problem can be mapped to a fuzzy logic model, and for this reason, we propose to use fuzzy logic to treat input variables that are not clearly defined. Inputs like available bandwidth, network delays, and transmission costs - which are inherently imprecise, vague and difficult to accurately measure - can be represented through simple qualitative terms such as low cost or medium delay. With the help of fuzzy logic, these are then converted to crisp values, allowing the MCs to deal with approximate reasoning. Previous research have resorted to using fuzzy theory to model QoS measurement and monitoring, and the reader may question the originality and contribution of our work. For instance, [6] discusses in depth on the handover decision using fuzzy multiple attribute decision making methods like Simple Additive Weighted method (SAW), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and other MADM methods. But, while arriving at the decision to proceed or not with the handover, only the user’s uncertainty is considered, while the network’s imprecise measured data is ignored. Similarly, the work described in [7] considers the fuzzy logic model, provides an analysis of the expected inputs and describes a set of fuzzy rules. But, specific process of defuzzification leading to crisp values is not discussed at length, and the work does not provide useful parameter ranges for implementation in real networks. There are even researchers that map multi-constrained QoS inputs into a fuzzy controller, but unfortunately are too specific to a single technology and considers only specific communication rule sets [8]. While the principles of fuzzy logic have been understood for some time, the hardest part of designing the fuzzy controller is identifying the most meaningful input parameters that lead to realistic fuzzy sets. Our contributions in this paper are three-fold. First of all, a number of metrics are considered that play a critical role on the performance of a chosen path. However, there are some overlapping metrics that indirectly influence others. Therefore, a thorough study is performed, taken from existing state-of-theart research papers and standards, and the key parameters are isolated and clearly identified. Then, for the specified networks, we have pinpointed the working ranges of the parameters considered and modeled them into a fuzzy number conversion scale. This allows us to carry out a sensitivity analysis reflecting the performance of metrics on each network. Finally, we describe and propose a fuzzy based network selection mechanism where a novel solution is presented for the inherent dependency of these metrics. Additionally, we describe a fuzzy logic model which aims at considering the uncertainty factor of the network conditions to prioritize the networks. This leads to

the wonderful result of allowing any decision making method, involving multiple criteria, to be able to deal with uncertain measured values in making a decision. Fuzzy inference rules are described, and defuzzyfication values are calculated for all the networks. II. I DENTIFYING K EY PARAMETERS FOR I NTERFACE S ELECTION M ETRIC For a MC, a good interface selection metric should accurately capture the state and quality of all available network links, and be capable of ranking them so that a forwarding decision can be made. The most traditional ranking metric is the hop count that determines paths with the fewest number of hops. However, this metric treats all the links to be alike in the network. However, an effective multichannel HWN metric needs to consider other factors such as link reliability, channel diversity, load of the nodes, interference, etc. [9]. By examining published works [10, 11], and the references therein, we analyzed a large number of metrics proposed in the literature. This study helped us identify a diverse set of criteria and factors that do affect a wireless transmission. However, there may be overlapping objectives which makes choosing the smallest subset extremely hard. For example: • Comprehending the wireless interface behavior in the face of interference influences other performance measures such as delay and throughput due to retransmission attempts and ongoing contention for the wireless medium. • Available bandwidth is a good indicator of the network traffic conditions, and is extremely important for any real-time or delay-sensitive applications. The bandwidth parameter also helps avoid overload and prevents contention for any bottleneck. But, a high bandwidth along an unstable path is useless, as this will reduce the QoS. • The higher the transmission rate, a link is capable of providing, the smaller the transmission time. Consequently, this is an interesting criteria on which to rank, since the best connection would outperform other links. However, the transmission rate of neighboring MCs impact the quality of the communication due to contention among shared wireless medium. Clearly this affects both the transmission time as well as the transmission rate of that interface. To makes matters worse, some metrics are hard to correctly measure - if not impossible. For example, interference can be of 3 types: intra-flow (when the same channel is shared by neighboring MCs, forwarding the same flow of data), interflow (when competing flows share the channels) and external (which can be caused by environmental factors, uncontrollable signals emitted in the same frequency range and so on). Even if correctly measured, interference may quickly change due to changing environment. For this reason, instead of proposing an analytical formulation that considers so many factors, we simplified the key 2

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TABLE I K EY C OMPONENT B OUNDS BY N ETWORK T YPE

Node Density Transmission Rate Packet Loss Rate Link Stability Bandwidth Latency Achievable Throughput

WiMAX (802.16 e) 20-70 % 20-75 Mbps 2-6.5 % 5-20 % 5-18 MHz 20-40 ms 12-18 Mbps

WiFi (802.11b) 10-60 % 4-11 Mbps 0.5-1.5 % 30-80 % 8-20 MHz 30-70 ms 4-11 Mbps

WiFi (802.11g) 12-60 % 12-54 Mbps 1.5-5 % 20-50 % 8-20 MHz 10-50 ms 15-22 Mbps

components that can be used to compose an ideal set of metrics. Of course, a number of arguments can be given as to adding or replacing factors from this set. However, we believe that this reduced set truly represents the essential parameters that illustrates the complexity of wireless communication. The list includes node density, transmission rate, link stability, bandwidth and packet loss rate. Interference, for example, can be represented by a combination of almost all of the listed components. A metric such as throughput can be attributed to packet loss and transmission rate (which in inherently affected by loads of the contending neighboring MCs, endto-end forwarding delay and link quality). By following this procedure among all identified factors, we worked on the list of components until we reached the reasonable sub-set. According to [12–15], HWN’s with interfaces capable of connecting to GSM/GPRS, UMTS, 802.11 WiFi b/g/n, and 802.16 WiMAX networks would be bound within the ranges shown in Table I, due to physical characteristics and network protocols. The following metrics are then considered as final constraints in our work: link stability, latency and throughput. In our opinion, by considering these three constraints, any set of links, belonging to one of more wireless technologies, can be ranked and compared. Furthermore, this limited set is capable of effectively representing all the key components listed above. Link stability is based on the delivery ratio, which accounts for the throughput and the effects of link losses in both the directions of communication. It also reflects hop count since longer path tends to have lower throughput due to path length and intra-flow interference. Relative mobility can also influence link stability, as faster relative movement may cause frequent link failure as MCs could move out of the transmission range. Time sensitive traffic is highly affected by the delay and jitter. Obviously, if the bandwidth is insufficient, queueing delays will increase and could lead to congestion. This forces retransmissions, which result in even higher delays. On the other hand, traffic tolerant to delays is also sensitive to packet loss rates. When an interface starts to drop packets, the network performance may decrease to an intolerable level. Involved with all these elements is the transmission rate, which, depending on the situation, needs to be increased or decreased in order to achieve a good communication performance. User-based and application-provoked demands can then be

WiFi (802.11n) 15-70 % 60-150 Mbps 3-5.5 % 30-70 % 20-40 MHz 10-35 ms 40-100 Mbps

GSM/GPRS (2.5G) 7-30 % 0.02-0.09 Mbps 2-3 % 20-50 % 0.2-1 MHz 120-500 ms 0.02-0.04 Mbps

UMTS (3G) 7-30 % 1-2.5 Mbps 2-4 % 20-50 % 1-5 MHz 90-250 ms 0.05-1 Mbps

met by also mapping the list of requirements against such constraints. Additionally, instead of having to convey demands in regards to the five key components, they would only need to be informed in regards to the three constraints. One problem still remains, however, in the fact that demands still need to be precisely stated. In the next section, with the introduction of fuzzy logic, we will eliminate this dilemma. III. F UZZY L OGIC M ODELING The key component set and the interface metrics, presented in the previous section, distinctly represents the minimal subset of factors that influence the routing metrics. Items from that list are then used as input parameters towards selecting measurable metrics that can be used to make the forwarding decision. However, the method in determining these metrics need to be tolerant to uncertainty and imprecise values. Machine learning techniques seem most promising, and most popular choices include fuzzy logic and artificial neural networks based techniques. While there are many options, we have chosen to use fuzzy logic since it is easy to understand, while still powerful enough to analyze systems that may be too complex to describe a mathematical model accurately. Fuzzy logic allows encoding of qualitative expert knowledge as an extremely flexible algorithm, using natural language to expose the classification rules. This also permits network operators to change their preferences or even their decision criteria for various applications depending on the deployment scenario or the environment. Fuzzy logic is a multi-valued logic that maps imprecise terms into crisp values. A fuzzy controller is composed of the inference system that includes a rule set, the input membership functions and the output variable. The input values go through a process of fuzzification, where they are converted in terms of the membership functions of the fuzzy sets. These sets are defined over the range of the fuzzy input values, and linguistically describe the variable’s universe of variation. Following the fuzzification step, the inference system determines the fuzzy output using the rules that are described in the form of IFTHEN rules. De-fuzzification is finally used to translate the fuzzy output to a crisp value. We refer the reader to [16] for further details. 3

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A. Fuzzy Classification Model

variable is linguistic-term then an action is carried out. Fuzzy IF-THEN structures can be combined with fuzzy operators to formulate rules which forms the basis of our fuzzy logic model. The output comprising the then part is a value between 0 and 1. Design of fuzzy inference rules are based on the common knowledge and many times, are purely subjective, based on the experience of the designer. In this work, however, as previously indicated, we have chosen values according to a vast array of existing research work. The fuzzy rule matrix for each of the subsystems can be seen in the data contained in Table II.

We model the three constraints as fuzzy subsystems with input from the key component set. Figure 1 depicts our proposed fuzzy classification model (FCM). The subsystems are the fuzzy based constraints that are calculated using the input parameters influencing the performance. As explained in the previous section, the constraints in our work are link stability, latency and throughput. The input variables influencing the link stability are the node density and the transmission rate. Since transmission rate also influences the throughput, the output value serves as input for the throughput subsystem and the output of the link stability subsystem is fed as input into the latency subsystem. However, as previously argued, there is a very tight inter-dependence between the metrics, and therefore an interesting pattern of cascade effect can be noticed, where each subsystem is linked to another in some way and the constraints are tightly connected.

Fig. 1.

TABLE II F UZZY R ULE M ATRIX FOR S UBSYSTEMS

These rule matrices are constructed by using the common knowledge of the network conditions and the various parameters influencing them [10]. The input parameters in the if part of the inference rules are combined using the fuzzy union (AND) operator. For instance, considering a rule in the latency subsystem:

Fuzzy Classification Model

Although a number of approaches have been suggested to deal with network constraints directly, they assume that the network state information is static. However, in practical scenarios, the network is dynamic and the measured values keep fluctuating. By defining a fuzzy based model, we assume that the network state values can be anywhere in a given range, like for example ‘low’ or ‘medium’ (as given by the bounds taken from Table I). We argue then that any value within the required range, is good enough to satisfy the application’s operating constraints. By applying fuzzy methods to uncertain input variables, and translating them into crisp values, our proposed solution is supplemented with an inherent capacity of imprecise measured and/or dynamically fluctuating inputs.

if Link Stability is low and Bandwidth is low then Latency is high

This rule clearly shows how we map the linguistic terms of the input variable using AND operator to the output which is also a fuzzy term. A membership function is used to measure the magnitude of participation of each input variable to the fuzzy sets. This function takes each point on the input space and maps it to their respective degree of membership which lies in the range [0,1]. Various shapes for membership functions are defined such as triangular, trapezoidal, gaussian, bell, sigmoidal and s-shaped functions. Selection of membership function greatly impacts the fuzzy model, but has also been shown to be purely subjective. The use of the triangular membership function is often made due to its simplicity of implementation [8]. For the same reasons, and to gain maximum efficiency, we employ the triangular membership function for every input parameter. An example of the fuzzy membership function for WiMAX’s bandwidth inputs is shown in Figure 2. Note how the possible input range is mapped over the range of the fuzzy input values and linguistically describe the variable’s universe of variation. So, for example, if the measured bandwidth for the

B. Fuzzification, Rule set and Membership Functions In the world of fuzzy logic, an uncertainty is represented by linguistic term or fuzzy variable. Our work describes the linguistic variable for every metric as low, medium and high. Although a larger number of variables could have been used, like for example, five variables defined as very low, low, medium, high and very high, or different linguistic terms, like average instead of medium, in order to simplify our work and achieve the desired results, we restrict to three linguistic variables that are commonly used and makes it easy to establish bounds. Fuzzy inference rules are statements using the conditional IF-THEN structure on the input variables, where if input4

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TABLE III F UZZY T RIANGULAR M EMBERSHIP F UNCTIONS VALUES

WiMAX interface is 8 MHz, then that would be fuzzified to the ‘medium’ set with membership of 1.0. While if it that value is 12 MHz, it would have membership in both the ‘medium’ as well as the ‘high’ sets.

the network metrics, and we take full advantage of the fuzzy logic to solve the impreciseness which is inherent in practical situations. Of course, if the environment and network characteristic could introduce too much variability, our approach still produces significantly lower performance results. However, to our best knowledge, this approach of combining the range of values to come up with one single value, while taking into account network uncertainty and the various factors influencing the constraints, has never been ventured upon and our novelty lies in this aspect.

1 low high medium

0.9 0.8

Membership

0.7 0.6 0.5 0.4 0.3

IV. PAYOFF M ATRIX FOR W IRELESS N ETWORK I NTERFACE USING D EFUZZIFICATION Defuzzification is the final step in our fuzzy logic modeling where the linguistic terms of the output constraint are transformed into crisp values. Various methods exist pertaining to defuzzification such as center of gravity, weighted mean and mean of maximum [17]. Our work derives the crisp values for the output constraints by first decomposing each interface’s metrics into the linguistic terms as ‘low’, ‘medium’ and ‘high’. Then, we proceed to classify each metric as beneficial and nonbeneficial [6]. The beneficial constraints are those where the higher value represents a better performance. On the other hand, nonbeneficial constraints are denominated by cost factors, and achieve maximal efficiency with lower values. In this work, link stability and throughput are classified as beneficial while latency as a non-beneficial constraint. We further consider the MC’s attitude while selecting the network interface to handover data. Some of the decision maker’s attitudes proposed in literature are [18]: • Pessimistic Attitude - The decision maker possessing this attitude selects the worst possible outcome for each choice of alternatives, and then selects the best among the worst alternatives. Also known as maximum strategy, this reflects the pessimistic attitude of the user.

0.2 0.1 0

Fig. 2.

0

2

4

6

8 10 12 WiMax Bandwidth (MHz)

14

16

18

WiMAX Bandwidth Fuzzy Membership Function

Albeit this is an extremely simple procedure, there is a very serious problem: the uncertainty of the measured value. In the dataset of sensed values, we have a collection of possible measures that represent (with an unknown probability) the true value. That means that, for example, the measured delay may be completely incorrect at any moment. Furthermore, even if the measured value is a true representation of that physical attribute, there is almost no guarantee that it will remain that way. For this reason, in our FCM, we consider all possible combinations of input metric values inside the measured range, and apply the AND operator to come up with a range of possible output metric values. By doing this, we are able to consider the dynamic and uncertain network conditions at each instant of time. For example, if the measured attribute is fuzzy-mapped to ‘low’ or a ‘medium’ performance, then our approach considers the fact that the network values keep varying due to external factors and any value in a particular range is bound to satisfy that application requirements and demands. Our focus is to give a weight, or a pay-off value, to each linguistic term of 5

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TABLE IV PAYOFF M ATRIX

Optimistic Attitude - The decision maker with this attitude selects the best possible outcome for each choice of alternatives, and then selects the best among the best alternatives. Also known as maximax strategy, this exhibits the optimism of the user. We base our work on the pessimistic attitude of the MC. We assume that, if the network conditions can satisfy the user’s pessimistic needs efficiently, then anything better than the worst case scenario will only result in increased performance. Hence, we focus on the worst case strategy. We begin by defining lower, middle and upper bound values for the ‘low’, ‘medium’ and ‘high’ terms of each input metric. The complete list of values used for the input parameters are shown in Table III, where for each metric, the three point values used in the triangular membership function are provided. One may also match values with that available in Figure 2 for a more complete understanding. Applying the fuzzy inference rule in combination with the AND operator to the set of inputs, we take the minimum value of their membership degrees. For example,

take the same approach, and use the min operator. In case of non-beneficial constraints like latency, we use the max operator. We repeat the above process for all possible inference rules defined for each metric. In this manner, we obtain three sets of low, medium and high for each metric. We reduce them to one single value for each set by applying once again the max operator (beneficial constraints) and min operator (non beneficial constraints). This completes the pessimistic attitude strategy by selecting the best among the worst possible outcome.

•

µlink min[µnode

stability high (x)

F inal High value max (or min) [high1, high2, high3] ∗ centroid of output membership f unction

=

Now, considering all possible values in the low range of node density and transmission rate, we get a range of output values for high range of the link stability using the operation defined above. We then employ the pessimistic attitude and select the worst possible outcome amongst the high link stability values derived using a single inference rule. This way of considering all possible combination of values ensures that we leverage the dynamic network conditions where the wireless network interface’s state can change at any instant of time. So, when considering a high link stability, rather than restricting to a particular value of node density and transmission rate, we take into consideration all possible values in the low range of the two input parameters. High Link Stability

(2)

In the above Equation 2, high1, high2 and high3 represent the values obtained using Equation 1 for each inference rule shown in Table II. Similarly, we repeat the process for the final medium and low values, for each of the proposed subsystems. Using the range of values in Table I, we rely on our FCM and go through the steps described above for each network. The results of all these calculations results in a matrix of values, or payoffs, that have been attributed to each network, given the linguistic variable that they have been classified as. This matrix, seen in Table IV, annotates the values for each metric’s ‘low’, ‘medium’ and ‘high’ ranges. A simple example can be stated using the data from the WiFi 802.11g column: if the latency subsystem has classified the link stability of the WiFi 802.11g network as low, then one can attribute the value of 0.3 to that metric. This is a very important aspect that we would like to emphasize. Note that the values of the chosen constraints, that is, of link stability, latency, and throughput, were derived from the respective fuzzy subsystems. These subsystems were informed values that were measured, but that may have been inaccurate. However, due to our FCM, a single numerical value can be obtained for posterior ranking and ordering. Additonally, the proposed FCM, with the help of any MADM method, is now capable of efficiently identifying the appropriate network through which any MC should forward any data, while being immune to uncertainty. This means that, from a decision making point of view, the MC may now specify their needs and preferences (weights) and, by using any existing MADM method, rank the available networks. By using a range

density low (x), µtransmission rate low (x)].

min[range of high link stability values]

=

= (1)

Equation 1 shows that one can employ the min operator and select the worst possible outcome using a single inference rule. In case of beneficial constraints like link stability, we 6

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of values, rather than an inaccurate single measure, we add flexibility to both the network as well as the MC so that they may adapt to the dynamic nature of the network conditions with greater ease and less fluctuation.

appropriate network is selected to carry out the forwarding process. Weights are derived from these fuzzy values for these constraints based on existing knowledge of the application parameters, and using fuzzy logic tools and techniques. Due to space constraints, we do not provide further details on the derivation process of these values, but refer the reader to [23]. The weights used in the simulation work are:

V. D ECISION M AKING U NDER U NCERTAINTY Decision making under uncertainty is a very important topic. It deals with the problem of selecting the most adequate action, in the face of uncertainty with respect to the environment [19]. In a multiple attribute decision process, the selection of an alternative is not an easy task. The evaluation of each alternative, and the ranking of the possible actions, is done by the mean of several and eventually conflicting metric and criteria, which may have different preferences (or weights) in the selection process [20]. There exists a large number of MADM algorithms [21], the most famous being Analytical Hierarchical Process (AHP), Grey Relational Analysis (GRA), Simple Additive Weighting Method (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). While we do not intend to expand on these MADM algorithms, in this Section we will describe the simulation results of utilizing TOPSIS and SAW algorithms, and comparing their pure results, to those obtained with the same algorithms enhanced with our FCM. Simulations were carried out depicting the significance of our proposed approach by implementing these strategies, under the following networks: GSM/GPRS, UMTS, 802.11 WiFi b/g/n, and 802.16 WiMAX. The TOPSIS algorithm is based on the assumption that the chosen solution has the shortest distance from the best solution, but has the longest distance from the worst solution. It is relatively simple and easy to understand. The SAW method is probably the best known and most widely used. The total score for each alternative is computed by multiplying the comparable rating (or utility) for each attribute by the importance weight assigned to the attribute and then summing these products over all the attributes. The preference on the constraints is modelled as weights assigned by the user. User needs are based on the application requirements, and in our simulation, we consider two popular and significant ones: VoIP and Web browsing. Each has an entirely different set of constraints with regards to the network performance. VoIP’s primary criteria is to forward packets with minimum delay, while web browsing demands improved throughput and error free transmission. The QoS requirements of these network applications have been well studied [22], and thus we define the user’s constraints for these applications. The constraints are in the order of [link stability, latency and throughput].

V oIP W eb

= =

[0.4 0.2 0.4] [0.35 0.3 0.35]

Utilizing the above values, we implement TOPSIS and SAW with values from the payoff matrix (as shown in Table IV). We obtain the ranking order of the different networks considering VoIP and web browsing demands. Since we want to focus on the adaptability to uncertainty of the decision making process, our simulations introduce variability to the measured constraints and metrics in order to see how this affects performance. Results are shown in Figures 3 and 4, where the three metrics are varied from little or no variability/uncertainty, to higher levels of imprecision. For each sub-graph, only the specified metric is varied, while the others are maintained constant. However, we note that the variability is bounded by the range (specified in Table I) for that specific network, and within the requirement of the user’s preference. So, for example, for VoIP, which has a latency required of ‘low’ by the user, the variability is bounded to values that are still acceptable from the MADM decision making algorithm point of view. This was done on purpose (so as to clearly show that, even with very low variability that is, variability that still allows for that metric to be useful, given the user’s preference), there are accentuated changes in the decision ranking order.

Fig. 3. U ser Constraints f or V oIP U ser Constraints f or W eb Browsing

= =

[M ED LOW M ED] [HIGH M ED HIGH]

VoIP network ranking under uncertainty with TOPSIS

From Figure 3, where VoIP applications results are shown, we notice that as uncertainty is introduced, the ranking order actually changes, depending on the metric and on the variability. For instance, as uncertainty in the measured throughput has

As with any MADM algorithm, the user specifies the preferences of the network performance, and based on that, the 7

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little uncertainty of 1% to 8% at any given moment, then the user is provided with a ranking that prefers WiMAX to 802.11 b selection. However, if the actual imprecision is greater, close to 15%, the user is provided with an entirely new set of network choices (the order actually changes) to forward data packets that might cause unnecessary re-routing, and could fail to satisfy the user demands. Our approach, however, leverages this fact and hence considers all possible range of values which we encompass into one single payoff value for the network. This ensures that negligible changes in the network conditions under practical scenario does not affect the user’s way of selecting the networks and the multiple constraints are guaranteed to be satisfied. The ranking order obtained by our method remains stable irrespective of the changes made to the network conditions within the particular range specified by the application requirement. For instance, if VoIP requires medium link stability, then through our FCM, we provide a certain ‘immunity’ to the network by specifying a range for the medium link stability that deals with variations to the measured value within that range, such it will not impact the ranking order of the wireless interfaces. Hence, the network selection remains stable and eliminates the need to know the exact value of the network condition at each instance. Figure 4 illustrates the web applications and the order of interface selection under these constraints. The only observation we make is regarding the ranking values for latency, which show little variation with respect to GSM/GPRS. This is due to the fact that the latency value for this particular network is very high, and hence the low range defined is by itself of little impact. Our scale lies quite close to the lower bound of the low range defined for this network, and therefore, the variability we introduce has insignificant impact. However, as the user requirements for web browsing latency is ‘medium’, GSM/GPRS has a very low ranking value.

constraints running the two applications, VoIP and web browsing. Once again, as uncertainty in presented to the ranking algorithm, the ranking order fluctuates at different points.

Fig. 5.

However, results of TOPSIS show that the ranking values vary gradually. While using SAW, the variations in the ranking score are vividly seen, and the overlapping values show that ranking orders changed during different levels of input. We observe that the classical SAW is significantly more sensitive to variations than the SAW enhanced with our FCM, where stability is ensured in the network selection process and guarantees user satisfaction.

Fig. 6.

Fig. 4.

VoIP network ranking under uncertainty with SAW

Web browsing network ranking under uncertainty with SAW

These simulations also demonstrate how each attribute influences the network ranking and aids the decision maker. Also, compared to the link stability and throughput, the latency shows relatively small changes in the ranking order. This is due to the fact that latency is influenced by link stability (according to our classification model), and therefore the impact of varying latency alone is much smaller than when varying latency alongside with link stability. We plan on analyzing multiple constraint uncertainty to identify which are more sensitive to

Web browsing network ranking under uncertainty with TOPSIS

We have also implemented similar simulation scenarios using SAW, and results can be seen in Figures 5 and 6. We repeated our simulation experiments with the same user 8

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uncertainty. As a side note, these results demonstrate how SAW and TOPSIS rank networks differently, given the same constraints and weights. Hence, even under the same set of conditions and input parameters, different ranking approaches give different outcomes and the choice of the ranking scheme is left to the decision maker’s discretion and desirability. Our proposed approach can easily be adapted to any classic decision making problem efficiently, and the simulation results of TOPSIS and SAW justify this argument. With our proposed FCM, decision making can now be accomplished with imprecise data, and the problem of uncertainty in MADM is avoided.

[2] A.-E. M. Taha, H. S. Hassanein, and H. T. Mouftah, “On robust allocation policies in wireless heterogeneous networks,” in Proceedings of the First International Conference on Quality of Service in Heterogeneous Wired/Wireless Networks, (Washington, DC, USA), pp. 198–205, IEEE Computer Society, 2004. [3] Z. Wang and J. Crowcroft, “Quality-of-service routing for supporting multimedia applications,” IEEE Journal on Selected Areas in Communications, vol. 14, no. 7, pp. 1228–1234, 1996. [4] T. Salonidis, M. Garetto, A. Saha, and E. Knightly, “Identifying high throughput paths in 802.11 mesh networks: a model-based approach,” IEEE International Conference on Network Protocols, pp. 21–30, 2007. [5] A. L. Wilson, A. Lenaghan, and R. Malyan, “Optimising wireless access network selection to maintain QoS in heterogeneous wireless environments,” in 8th International Symposium on Wireless Personal Multimedia Communications (L. Heinzl and N. Prasad, eds.), (Tokyo, Japan), pp. 1236–1240, NICT, September 2005. [6] W. Zhang, “Handover decision using fuzzy madm in heterogeneous networks,” in IEEE Wireless Communications and Networking Conference, vol. 4, pp. 653–658, 2004. [7] Z. Jing, C. Xuefen, L. Guan, and L. Hongxia, “Service-aware multiconstrained routing protocol with qos guarantee based on fuzzy logic,” in AINAW ’08: Proceedings of the 22nd International Conference on Advanced Information Networking and Applications - Workshops, (Washington, DC, USA), pp. 762–767, IEEE Computer Society, 2008. [8] P. Alipio, S. R. Lima, and P. Carvalho, “A unified metric for quality of service quantification,” in Simutools ’09: Proceedings of the 2nd International Conference on Simulation Tools and Techniques, (ICST, Brussels, Belgium, Belgium), pp. 1–7, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2009. [9] H. Zhou, C. Huang, Y. Cheng, and G. Wang, “A new multi-metric qos routing protocol in wireless mesh network,” International Conference on Networks Security, Wireless Communications and Trusted Computing, vol. 1, pp. 459–467, 2009. [10] B. K. Addagada, V. Kisara, and K. Desai, A Survey: Routing Metrics for Wireless Mesh Networks. 2009. URL: http://bit.ly/aeiezH. [11] X. Yan, S¸ekercio˘glu Y. Ahmet, and S. Narayanan, “A survey of vertical handover decision algorithms in fourth generation heterogeneous wireless networks,” Comput. Netw., vol. 54, no. 11, pp. 1848–1863, 2010. [12] W.-T. Chen and Y.-Y. Shu, “Active application oriented vertical handoff in next-generation wireless networks,” in Proceedings of the 2005 IEEE Wireless Communications and Networking Conference (WCNC’05), pp. 1383–1388, IEEE Computer Society, 2005. [13] K. Piamrat, A. Ksentini, J.-M. Bonnin, and C. Viho, “Radio resource management in emerging heterogeneous wireless networks,” Computer Communications, February 2010. [14] “Ieee standard 802.16-2009. ieee standard for local and metropolitan area networks - part 16 : Air interface for fixed broadband wireless access systems,” May 2009. [15] “Ieee standard 802.11: Wireless lan medium access control (mac) and physical layer (phy) specifications,” 2007. [16] L. A. Zadeh, “Fuzzy logic and approximate reasoning,” Synthese, vol. 30, no. 3-4, pp. 407–428, 1975. [17] G. Klir and B. Yuan, Fuzzy Sets and Fuzzy Logic, Theory and Applications. 1995. [18] S. B. Richmond, Operations Research for Management Decisions. 1968. [19] R. R. Yager, “Decision making using minimization of regret,” International Journal of Approximate Reasoning, vol. 36, no. 2, pp. 109–128, 2004. [20] A. B. Nacef and N. Montavont, “A generic end-host mechanism for path selection and flow distribution,” in PIMRC 2008: Proceedings of the IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1–5, 2008. [21] C.-L. Hwang and K. Yoon, Multiple Attribute Decision Making. SpringerVerlag, 1981. [22] T. F. Yan Chen and N. Ye, QoS Requirements of Network Applications on the Internet, vol. 4. IOS Press, January 2004. [23] S.-J. Chen, C.-L. Hwang, and F. P. Hwang, Fuzzy multiple attribute decision making: methods and applications. Springer-Verlag, 1992.

VI. C ONCLUSION In this paper we have considered the imprecise and dynamic nature of the network conditions, while satisfying multiple constraints in heterogeneous wireless networks, where MCs are equipped with multiple network interfaces capable of simultaneously connecting to GSM/GPRS, UMTS, 802.11 WiFi b/g/n, and 802.16 WiMAX networks. For this, we propose a fuzzy based mechanism that allows for MADM algorithms, to efficiently deal with uncertainty. Many strategies have been proposed in literature that deal with QoS routing and employ fuzzy logic to deal with multiple criteria or imprecise data. However, there is no particular work that exists which can deal with uncertain network parameters by considering a heterogeneous wireless scenario, while also considering multiple application requirements at the same time. A number of metrics are considered that play a critical role on the performance of the traffic forwarding, and for the specified networks, we have pinpointed the working ranges of the parameters considered and modeled them into a fuzzy number conversion scale. Fuzzy inference rules as described, and defuzzyfication values are calculated for all networks. Our proposed mechanism allows any decision making method involving multiple criteria to be able to deal with uncertain measured values before making a decision. That means that, even if the measured values are inaccurate, due to our proposed mechanism, a single numerical value can be obtained for posterior ranking and ordering. Consequently, from a decision making point of view, the mobile client may now specify their needs and preferences (weights) and, by using any existing MADM method, rank the available networks. Simulation results indicate that our approach gracefully handles negligible changes in network conditions. Under practical scenarios it does not affect the user’s way of selecting the networks, while still satisfying the multiple constraints. The ranking order obtained by our method remains stable irrespective of the changes made to the network conditions within the particular range specified by the application requirement. R EFERENCES [1] R. H. Katz, Y. H. Katz, and E. A. Brewer, “The case for wireless overlay networks,” pp. 77–88, 1996.

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