An analytic network process and trapezoidal interval-valued fuzzy technique for order preference by similarity to ideal solution network access selection method (presentation)

International Journal of Communication Systems (IJCS) 2014, Wiley
An analytic network process and
trapezoidal interval-valued fuzzy
technique for order preference by
similarity to ideal solution network access
selection method
Emmanouil Skondras1,2, Aggeliki Sgora1,2, Angelos Michalas2 and
Dimitrios D. Vergados1
1Department of Informatics, University of Piraeus, 80, Karaoli and Dimitriou St.,
GR-18534, Piraeus, Greece
2Department of Informatics Engineering, Technological Educational Institute of
Western Macedonia, GR-52100, Kastoria, Greece
1

Outline
• Introduction.
• Contributions.
• Related work.
• The proposed Network Selection Method.
• Simulation Setup and Results.
• Conclusions.
• References.
2

Introduction

Introduction (1/8)
• Next generation wireless networks
– Consist of many heterogeneous access technologies.
• Support various service types with different quality of
service (QoS) constraints, as well as user, requirements and
provider policies.
– Growing rapidly integrating multiple network
technologies.
• Aiming to support multimedia services in addition to voice
and data with high data rates and guaranteed QoS [1].
4

Introduction (2/8)
• End users devices (such as mobile phone or
netbook) are equipped with multiple radio
interfaces.
– Allowing connectivity to the most suitable network
environment.
• Based on users requirements and operators policies [2, 3].
• Need for network selection mechanisms that
consider multiple factors must be addressed.
5

Introduction (3/8)
• According to the always best connection
principle of the 4G wireless networks:
– Users of mobile services should be provided with
connectivity to the best access technology at
anytime [4, 5].
• Need for efficient vertical handover (VHO)
mechanisms to be applied.
6

Introduction (4/8)
• The handover process is supposed to be:
– Successful, infrequent, and imperceptible.
• To enable telecommunication providers meet the QoS
requirements of the users [6].
• In the case of heterogeneous networks:
– Seamless interworking among the different
technologies is also needed [7].
– Special attention to the VHO process should be
given [8].
7

Introduction (5/8)
• The VHO procedure consists of three main steps:
– Handover initiation.
• Contains the required procedures to identify the available access
networks and select the time of handover in respect of network
conditions and user mobility.
– Network selection.
• Selection of the most appropriate network alternative based on the
available network characteristics, user preferences, and applications
requirements.
– Handover execution.
• Completes the handover process by seamlessly connecting the terminal
to the selected network.
• This work deals with the network selection step of the VHO
process. 8

Introduction (6/8)
• Existing handover network selection schemes:
– Employ multi attribute decision-making methods (MADM),
fuzzy logic, neural networks, and utility functions [9].
• Because the selection of an access network depends
on several parameters with different relative
importance:
– The access network selection problem is usually looked at
from the aspect of multi-criteria analysis.
• More specifically by applying different MADM algorithms.
9

Introduction (7/8)
• A network selection method is proposed by
employing two MADM algorithms:
– The Analytic Network Process (ANP).
• Extension of the Analytic Hierarchy Process (AHP) for
criteria weights calculation
– A fuzzy version of the technique for order
preference by similarity to ideal solution (TOPSIS).
• For accomplishing the ranking of the candidate
networks.
10

Introduction (8/8)
• The proposed method considers the following factors to
provide advanced connection services:
– Network QoS characteristics and policies.
– Application requirements.
– Different types of users service-level agreements (SLAs).
• Linguistic values.
– Are used to characterize the performance of selection
criteria.
• Which are represented by interval-valued trapezoidal fuzzy
numbers.
11

Contributions

Contributions (1/3)
• Complex relationships allowed
– Within and among clusters of selection criteria
• By applying the ANP method
– Does not use an hierarchical framework as AHP but a
network model of dependencies.
– Eliminates the index consistency requirement of AHP.
» i.e., in AHP the relative importance of decision factors need to
be redefined in case index consistency value is more than 0.1.
• In our case:
– As clusters of selection criteria are considered the network QoS
characteristics and the network policies characteristics.
13

Contributions (2/3)
• Imprecise information of performance
selection criteria
– Can be better expressed.
• For different application types and users SLAs.
– By applying linguistic values and interval-valued fuzzy
numbers.
 Interval value fuzzy numbers.
– Can efficiently present uncertain information.
• By minimum maximum membership interval.
• Rather than by single membership values.
14

Contributions (3/3)
• Selection of the best network access technology
– Considering contradictory selection criteria.
• Facilitating the provision of high quality services.
– Satisfying different types of users SLAs.
• Fuzzy version of TOPSIS.
– Trapezoidal interval-valued Fuzzy TOPSIS (TFT).
• It resolves the case of having several services of different QoS constraints
running simultaneously on a terminal.
– Network selection is performed in a way satisfying multiple groups of
criteria per user.
– The ranking abnormality problem experienced in the original TOPSIS is
discarded [10].
• To avoid inconsistencies.
– When a new network is available or an existing network is removed from the
alternatives. 15

Related work

Related work (1/37)
• Multi attribute decision-making methods
– Are used to select the best alternative network
• Among candidate networks
– Given a set of criteria with different importance weights.
• MADM algorithms
– Are able to evaluate criteria of different value ranges.
• Sometimes even contradictory, using multi- criteria analysis.
– Widely used methods include:
• Analytic Hierarchy Process (AHP) [11, 12].
• Simple additive weighting (SAW) [12, 13].
• Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) [12–14].
• Fuzzy AHP-ELECTRE (FAE) [15].
• Gray relational analysis (GRA) [12, 13].
• Multiplicative exponent weighting (MEW) [12, 13].
• Distance to ideal alternative (D2I) [12].
• Analytic Network Process (ANP) [16].
17

Related work (2/37)
• Various weighting methods are used.
– Provide suitable criteria weights for each alternative.
• Several research studies use MADM methods for
network selection.
• Sharma and Khola [14]
– Presented a network selection algorithm.
• Based on the TOPSIS algorithm.
• Besides the usual parameters
– i.e., QoS, bandwidth, and cost
– It also takes a prediction of the Received Signal Strength (RSS) into
account.
18

Related work (3/37)
• Shi and Zhu [11]
– Employed two MADM methods
• Combined with the group decision-making algorithm to perform
network selection.
– The proposed procedure
• Defines two types of weights (namely the objective weights)
– Consider:
» The current attributes of candidate networks.
» The subjective weights specified according to the subscribers and traffic
class preferences.
– Weight vectors.
• Objective weights vector.
– Determined using the entropy weighting method.
• Subjective weights vector.
– Evaluated using the AHP.
19

Related work (4/37)
• Shi and Zhu [11]…
– The group decision-making method
• Employs both vector types to produce a synthesized vector
• The ranking of alternatives is the sum of the product of the
normalized attribute values with their respective weights.
– Compatibility of the integrated decision.
• It is finally checked to ensure the effectiveness of the proposed
solution.
– Results showed that the proposed method:
• Reduces the number of handoffs
• Improves QoS characteristics of conversational and interactive traffic
flows
 Compared with entropy weighting and GRA approaches.
20

Related work (5/37)
• Lassoued et al. [13]
– Described an evaluation framework of VHO mechanisms,
which emulates:
• Application characteristics.
• Mobile terminals context.
• User and operators preferences.
– The model provides user traces containing information
about:
• The location of the users.
• The QoS performance of the networks.
– Current network characteristics are obtained from:
• A mobility simulator emulating network access technologies.
• Location of access points.
• User mobility.
21

Related work (6/37)
• Lassoued et al. [13]…
– The proposed methodology:
• Used to compare the efficiency of various MADM network
selection algorithms in a dynamic environment, including:
– SAW, TOPSIS, GRA, MEW and their own proposed scheme
called Ubique [17].
– Simulation results
• Showed that the examined algorithms achieve good
performance,
• Ubique is less flexible to changes of delay and cost criteria
weights than the other approaches.
22

Related work (7/37)
• Lahby et al. [12]
– Proposed a network selection scheme
• It is based on the AHP method and the Mahalanobis distance.
– Mahalanobis distance is used to measure the distance of
alternatives.
» From the correlation of criteria
» The optimal network satisfying the QoS, security and cost
criteria is selected.
– Simulation results
• Both the ranking abnormality problem and the number of
handoffs in the proposed method
– Are reduced compared with the decision algorithms SAW, MEW,
TOPSIS and distance to ideal alternative.
23

Related work (8/37)
– Proposed a technique for network selection using:
• ANP to estimate the weights of selection criteria
• GRA to rank the alternative networks.
– Selection criteria include network related attributes
• The preference of users is expressed by evaluating different
criteria weights for each access network.
– By applying the ANP.
» Evaluates the criteria weights of each access network separately
• Based on users preferences; in that way, unique criteria weights
exist for each network.
24

Related work (9/37)
• Lahby et al. [16]…
– Simulation results.
• Indicated that this method reduces:
– The ranking abnormality problem.
– The number of handoffs.
 Compared with other method variants.
25

Related work (10/37)
• Sheng-mei et al. [18]
– Presented a network selection algorithm making
use of:
• The AHP and the entropy weight method.
– To evaluate the weights of network and user related criteria.
– Candidate access networks.
• Identified on the basis of their signal-to-interference-
plus-noise ratio (SINR) values.
– TOPSIS is used for the final ranking of the network
alternatives.
26

• Sheng-mei et al. [18]…
– The proposed method achieves:
• Higher throughput.
• Reduced number of vertical handoffs.
• For various traffic classes.
 Compared with:
– Combined SINR-based vertical handoff algorithms [19]
– Multi-dimensional adaptive SINR-based vertical handoff
algorithms [20].
27

• Alkhawlani et al. [21]
– Proposed a VHO decision system.
• Integrates fuzzy logic and TOP- SIS method.
– Network and user related criteria
• Processed by parallel fuzzy logic control (FLC)
subsystems.
– TOPSIS is applied to perform the selection of the
best network choice.
28

• Alkhawlani et al. [21]…
– Simulation results showed that the proposed
solution:
• Reduces handover rate and handover failure.
• Increases the percentage of users assigned to networks
of their preference.
• Increases the utilization of inexpensive networks.
29

• Alkhawlani and Mohsen [22]
– Presented a network selection system.
• Suitable for tightly coupled wireless network environments.
– It takes into account:
• The network choice proposed by the user
• Criteria imposed by the operator such as:
– Network policies.
– QoS characteristics.
– System capacity.
– Utilization.
30

• Alkhawlani and Mohsen [22]…
– Two modules:
• The user software module.
– Evaluates the best network alternative based on selection criteria set by the
user including reliability, security, battery power, and price.
• The operator software module.
– Resides at the coordinator of the radio access technologies and performs the
final selection decision.
– It uses:
» The FLC subsystems of [18].
• To evaluate the performance of criteria.
» The AHP method.
• To assess the FLC subsystems outputs and select the best possible
network.
31

• Alkhawlani and Mohsen [22]…
• The proposed network selection scheme
– Achieves better performance.
» In terms of user preferences satisfaction, QoS fulfillment and operator
benefits improvement.
– Compared with four different reference algorithms performing:
i. Random selection.
ii. Selection based on terminal speed.
iii. Selection based on service type.
iv. Selection based on the availability of resources, respectively.
32

• Vasu et al. [23]
– Proposed a fuzzy rule based decision algorithm
• For vertical handoff in wireless heterogeneous
networks.
– The algorithm uses:
• QoS performance values as decision parameters.
• Triangular fuzzy membership functions.
– For the fuzzification of the input parameters and the
defuzzification of the output result.
33

• Vasu et al. [23]…
– Evaluation of the proposed model.
• A non-birth Markov chain with states corresponding to
available access networks is used.
– Simulation experiments.
• Comparing the proposed approach against various
MADM methods
• The proposed method improves the performance of
delay sensitive applications.
34

• Fuzzy logic for network selection
– Requires the definition of logic rules from
specialists
• With thorough knowledge of the behavior of the
available access networks in various conditions.
– As the number of selection criteria and the
available networks increase.
• Rules become more complex struggling to:
– Define effective policies.
– Evaluate the best alternative.
35

• Fuzzy logic for network selection…
– Use of fuzzy logic based solutions.
• Limited to handover decision schemes.
– With reduced number of networks and selection criteria.
– Some network selection methods combine fuzzy
logic with neural networks.
• To rate the alternative access networks.
36

• Gowrishankar et al. [24]
– Created an artificial neural network multi-criteria decision
analysis system.
• Performs network selection using network related attributes.
– Expressed either in crisp or in fuzzy linguistic values.
– Sensitivity analysis.
• Among the proposed solution, the TOPSIS and the SAW methods.
• Carried out in a network environment consisting of four overlaid
networks, where:
– Weights of different criteria change.
– Connections of four traffic types exist.
37

• Gowrishankar et al. [24]…
– Results.
• The proposed method is:
– Less stable than TOPSIS.
– More stable than SAW.
 In respect to criteria weights changes.
– Neural network approaches.
• Replace the complex logic rules of fuzzy logic approaches.
• But they still suffer from scalability issues.
– Because of the required large number of the processing elements at
their hidden layers.
» As the complexity of criteria and the number of networks increase.
38

• Several network selection schemes make use of
utility/cost functions.
– To provide performance metrics for different types of
criteria.
• Rodriguez et al. [25]
– Use a cost function for the network selection
• Includes the rules and policies for selecting the best candidate
network or for adapting ongoing session parameters.
39

• Wu et al. [26]
– Used a set of utility functions to quantify selection criteria
including:
• Link quality (RSS).
• Battery power.
• Average throughput.
• Network delay.
• Monetary cost.
• Application type.
– The relative weights of criteria are calculated according to the
AHP method.
– The candidate networks are ranked using the weighted product
method.
• The proposed scheme:
– Improves network performance.
– Reduces power consumption of users terminals.
40

• Wang et al. [27]
– The concepts of fuzzy logic, neural network, and utility
functions.
• Are combined to perform network selection.
– The proposed method.
• Uses a fuzzy neural network.
– Obtains network, user, and terminal related input criteria.
• Evaluates the performance of each access network.
– Attributes of criteria.
• Defined through utility functions.
• Processed through the fuzzification, interference and
defuzzification layers of the neural network.
41

• Wang et al. [27]…
– A fuzzy version of the particle swarm optimization is used.
• For neural network training.
– It is not clear how expected network performance degrees are specified
during the learning process.
• The proposed method achieves better performance in terms of:
– Access blocking probability.
– Packet drop probability.
– Average throughput.
Compared with other network selection algorithms including GRA,
AHP and game theoretic.
42

• There is a rate of uncertainty in characterizing
performance measurements as well as rates of
influence of performance metrics.
• Fuzzy MADM methods.
– Expressing uncertain quantities by fuzzy numbers.
• Received the interest of many researchers in decision theory.
• Several fuzzy MADM network selection methods.
– Are suggested utilizing linguistic variables, triangular fuzzy
numbers, trapezoidal fuzzy numbers, etc.
• To model network attributes and their respective weights.
43

• Chamodrakas and Martakos [10]
– Proposed a method for network selection that considers:
• Network conditions.
• QoS constraints.
• Energy consumption requirements.
– User preferences.
• Indicating the relative importance of criteria in different applications.
• Expressed using linguistic expressions.
– Transformed to triangular fuzzy numbers.
– Graded mean integration method.
• Used for the defuzzification of fuzzy numbers into crisp values.
– Utility functions.
• Used to model.
– QoS requirements.
– Energy consumption characteristics.
 Of different applications.
44

• Chamodrakas and Martakos [10]…
– The fuzzy set representation version of TOPSIS.
• Used to combine selection criteria and weights.
– To perform the rating of the available networks.
• Resolves possible inconsistencies.
– Because of conflicting criteria.
» Such as bandwidth and energy consumption.
• The proposed method accomplishes a trade-off between QoS
requirements and energy consumption.
45

• Sasirekha and Ilanzkumaran [28]
– Described two methods to perform network selection.
– Both methods use a fuzzy version of the AHP technique.
• To obtain the weights of selection criteria specifying networks
performance.
– Relative importance matrix.
• Resulting from the pairwise comparison of criteria .
• Fuzzified using triangular fuzzy numbers.
– With membership functions representing the scale of importance of five
levels.
– Relative importance values.
• Turned into crisp values using the geometric mean operator.
– While the rest of the steps of the AHP method follow.
46

• Sasirekha and Ilanzkumaran [28]…
– The former network selection method.
• Uses TOPSIS to evaluate the best alternative network based on:
– The weights from AHP.
– The criteria values of each alternative network.
– The latter network selection method.
• Combines the fuzzy AHP with VIKOR method.
– Which has less complexity and performs equally well as TOPSIS.
– Evaluation.
• Both methods succeed to select the best network alternative.
47

• Kaleem et al. [29]
– Presented a VHO decision algorithm.
• Based on network performance measurements to evaluate.
– The necessity of making a handoff.
– The best network alternative in case that handoff is required.
– To determine the handoff decision.
• A handoff factor is evaluated and compared with a constant
threshold.
– Network selection is performed using fuzzy TOPSIS.
48

• Kaleem et al. [29]
– User preferences are defined.
• In the form of criteria weights.
– Ratings of selection criteria and criteria weights are expressed as
trapezoidal or triangular fuzzy numbers.
– Numerical examples and simulation experiments.
• Present the competence of the proposed approach.
– For various traffic classes.
– In heterogeneous network access technologies.
49

– Compared the weighting algorithms of:
• AHP, fuzzy AHP, ANP and fuzzy ANP.
– For assigning weights to network dependent criteria used by
MADM algorithms performing network selection.
– The TOPSIS method is used.
• To evaluate the effects of the weighting algorithms.
50

• Lahby et al. [30]…
– Results.
• All algorithms achieve similar results concerning the
network selected.
• The ranking abnormality of TOPSIS is reduced.
– When the ANP weighting method is used for:
» Background traffic.
» Conversational traffic.
» Interactive traffic.
» Streaming traffic.
51

• Zhang [31]
– Performed an analysis of MADM methods for
handover decision.
– Uncertain linguistic terms of decision criteria such as
sojourn time and seamlessness.
• Converted to fuzzy data.
– Which in turn are converted to crisp values.
52

• Zhang [31]…
– SAW and TOPSIS.
• Suggested to perform the final ranking of the candidate
networks.
– Sensitivity analysis.
» TOPSIS is more sensitive to the criteria performance and
their weights.
– The paper identifies the handover decision case.
• In which several applications are running simultaneously on
a terminal as a group decision problem.
– Although its solution is not being addressed.
53

The proposed Network Selection
Method

The proposed Network Selection
Method
• Our proposed method consists of two MADM
algorithms:
– ANP
• Calculates the relative importance of the selection
criteria.
– Trapezoidal Fuzzy TOPSIS (TFT)
• Accomplishes the ranking of the candidate networks.
• Represents the performance of selection criteria using
interval-valued trapezoidal fuzzy numbers.
55

Analytic Network Process (ANP)
(1/2)
• The ANP was also introduced by Saaty [32].
– To deal with decision problems that criteria and
alternatives depend on each other.
• It is actually the generalization of the AHP.
• A decision problem that is analyzed with the ANP.
– Can be designed either as a control hierarchy or as a
nonhierarchical network.
56

Analytic Network Process (ANP)
(2/2)
• Nodes of the network represent components (or
clusters) of the system.
– Arcs denote interactions between them.
• Inner dependencies.
– Interactions and feedbacks within clusters.
• Outer dependencies.
– Interactions and feedbacks between clusters.
57

The ANP steps (1/6)
• Step 1. Model construction and problem
structuring.
– The problem is analyzed and decomposed into a
rational system.
• Such as a network.
• Step 2. Pairwise comparison matrices and
priority vectors.
– The pairwise comparison matrix, as in AHP, is
derived using Saaty’s nine-point importance scale.
58

The ANP steps (2/6)
• Analytic Hierarchy Process (AHP).
59

The ANP steps (3/6)
• Step 3. Supermatrix formation.
– Represents the inner and the outer dependencies of
the network.
– It is actually a partitioned matrix.
• Each matrix segment represents a relationship between two
clusters in the network.
– To construct the supermatrix.
• The local priority vectors obtained in step 2 are grouped and
placed in the appropriate positions in a supermatrix.
– Based on the flow of influence from one cluster to another, or
from a cluster to itself, as in the loop.
60

The ANP steps (4/6)
• Step 3. Supermatrix formation…
– Then, the supermatrix is transformed to a stochastic
one.
• The weighted supermatrix.
– The weighted supermatrix.
• Raised to limiting powers.
– Until all the entries converge to calculate the overall priorities.
» The cumulative influence of each element on every other
element with which it interacts is obtained [34].
– At this point, all the columns of the new matrix, the
limit supermatrix are the same.
• Their values show the global priority of each element of
network.
61

The ANP steps (5/6)
• For example, if we assume a network with n clusters, where each cluster
Ck , k = 1, 2,…, n, and has mn elements, denoted as ek1 , ek2 ,…, ekmk , then
the standard form for a supermatrix can be expressed as:
62

The ANP steps (6/6)
• Step 4. Selection of the best alternatives:
– If the supermatrix formed in step 3 covers the whole network:
• The priority weights of the alternatives can be found in the column of
alternatives in the normalized supermatrix.
– Otherwise:
• Additional calculations are required.
– In order to obtain the overall priorities of the alternatives.
– The alternative with the largest overall priority should be
selected.
• As it is the best alternative as determined by the calculations made
using matrix operations.
63

The trapezoidal Interval-Valued
Fuzzy Numbers (1/7)
• The concept of fuzzy logic was introduced by Zadeh
[35].
– It is used to make a decision from indeterminate and
approximate information.
• A fuzzy number represented by:
– A set of real values representing an uncertain quantity.
– A convex normalized continuous function
• Which estimates the degree of membership for each value in the
subset.
• Triangular or trapezoidal fuzzy numbers.
– Frequently used to represent uncertain information.
64

Fuzzy Numbers (2/7)
• Trapezoidal fuzzy number
– Can be defined as a vector x = (x1, x2, x3, x4, υÂ)
with membership function:
 x1 < x2 < x3 < x4 and υÂ ∈[0,1].
65

Fuzzy Numbers (3/7)
• Interval-Valued Fuzzy Number (IVFN).
– Introduced by Sambuc [36]
– Defined as A = [ΑL, AU].
• Consisting of the lower AL and the upper AU fuzzy numbers.
– Replace the crisp membership values by intervals in
[0, 1].
– Fuzzy information can be better expressed by intervals
than by single values.
66

Fuzzy Numbers (4/7)
• Liu and Jin [37] and Cornelis et al. [38]
– Suggest that IVFNs are useful:
• In multiple criteria decision-making problems.
• Particularly in cases where attribute values are in the form of
linguistic expressions.
• Ashtiani et al. [39]
– Propose an extension of the fuzzy TOPSIS method.
• Using interval-valued triangular fuzzy numbers.
67

Fuzzy Numbers (5/7)
• Liu and Jin [37]
– propose a decision-making method
• using weighted geometric aggregation operators
– on attribute values expressed in the form of IVFNs.
• According to the definition in [39], an IVFN A
is defined as follows:
68

Fuzzy Numbers (6/7)
• The IVFNs:
69

Fuzzy Numbers (7/7)
• Interval-valued trapezoidal fuzzy number [40].
– The most general form of fuzzy number.
– Can be represented as:
A = [AL, AU] = [(x1
L, x1
L, x1
L, x1
L, υA
L), (x1
U, x1
U, x1
U, x1
U, υA
U))],
where 0 ≤ x1
L ≤ x2
L ≤ x3
L ≤ x4
L ≤ 1, 0 ≤ x1
U ≤ x2
U ≤ x3
U ≤ x4
U ≤ 1and AL ⊂ AU
– The operational rules of the interval-valued trapezoidal
fuzzy numbers are defined in [40].
70

The trapezoidal interval-valued
fuzzy TOPSIS algorithm (1/15)
• The TOPSIS introduced by Hwang and Yoon [41].
– It is based on the concept that:
• The best alternative should have the shortest distance from
the positive ideal solution.
• The longer distance from the negative ideal solution.
• In the present work:
– Network selection is performed using a proposed fuzzy
version of TOPSIS, namely Trapezoidal Fuzzy TOPSIS (TFT).
• Linguistic values of criteria attributes.
– Represented by interval-valued trapezoidal fuzzy numbers.
71

• A = {A1, A2,…, An}
– The set of possible alternatives.
• C = {C1, C2,…, Cn}
– The set of criteria.
• w1, w2, …, wm
– The weights of each criterion.
• The steps of the method are as follows.
72

• Step 1. Construction of the decision matrix:
– Each xij element of the n x m decision matrix D is an
interval-valued trapezoidal fuzzy number.
• Expresses the performance of alternative i for criterion j.
73

• In case there are Q decision makers.
– The decision matrix and the criteria weights.
• Include the average of the performance values and
weights, respectively, of the decision makers.
• Assuming that for the kth decision maker.
– xijk is the performance of alternative i for criterion j.
– wjk is the importance weight for criterion j.
74

• The average of the performance values and weights are
given by:
75

• Step 2. Normalization of the decision matrix:
– Consider that Ωb is the set of benefits attributes and Ωc is the set of costs
attributes.
– The elements of the normalized decision matrix are computed as:
76

• Step 3. Construction of the weighted
normalized decision matrix:
– It is constructed by multiplying each element of
the normalized decision matrix rij.
• With the respective weight wj according to the formula:
77

• Step 4. Determination of the positive and negative
ideal solution:
– The positive ideal solution is defined as:
78

• The negative ideal solutions are defined accordingly
as:
79

• Step 5. Measurement of the distance of each
alternative from the ideal solutions:
– The distances of each alternative from the positive ideal
solution are evaluated as follows:
80

• The distances of each alternative from the negative
ideal solution are estimated:
81

• Consequently, similar to [39]:
– The distance of the alternatives from the positive
and negative ideal solutions are expressed by
intervals such as [di1
+, di2
+] and [di1
-, di2
-].
• Instead of single values.
– In this way, less information is lost.
82

• Step 6. Calculation of the relative closeness:
– The relative closeness of the distances from the
ideal solutions are computed as:
83

• Compound relative closeness.
– Obtained from the average of the aforementioned
values:
84

• Step 7. Alternatives ranking:
– The alternatives are ranked according to their RCi
values.
– The best alternative is the one with the highest RCi
value.
85

Simulation Setup and Results

Simulation Setup and Results (1/2)
• Ranking of the networks alternatives
– Performed using the TFT algorithm.
• Weights of network selection criteria
– Obtained from the ANP.
• Heterogeneous network environment.
– Consisting of a number of long-term evolution (LTE),
WiMAX and WiFi networks.
87

Simulation Setup and Results (2/2)
• Each network can provide at least one of the
following five service types:
– Voice-over-Internet protocol (VoIP).
– Conversational video (CVideo).
– Buffered streaming (BStreaming).
– Real time gaming (RTGaming).
– Web browsing.
88

QoS class mapping and Service-
Level Agreements (1/4)
89
• In order to allow service continuity.
– QoS mapping among the QoS classes of the
different access technologies is required.

• Four SLAs are defined.
– SLA1 has the highest service priority
– SLA4 has the lowest service priority.
• SLA1.
– Supports all service types.
– Provides the best values for QoS and policy decision criteria.
• SLA2.
– Supports less service types.
– It does not provide support for the VoIP and real time gaming services.
– Provides slightly worse decision criteria values than those offered by
the SLA1.
90

• SLA3.
– Supports only the buffered streaming and the Web
browsing services.
– Satisfactory QoS characteristics and policies.
• SLA4.
– Low price.
– Supports only the Web browsing service.
– Acceptable decision criteria values.
91

• The ANP method is applied
in order to estimate the
weights of network
selection criteria per
service type and SLA.
• The ANP network model:
92

The ANP network model
93
• Criteria are classified into two groups:
– The QoS characteristics.
• Contains network performance related criteria including:
– Throughput.
– Delay.
– Jitter.
– Packet loss.
– The policy characteristics.
• Contains operator defined rules including:
– Price.
– Security.
– Service reliability.
» Determines the ability for service constraints satisfaction and
optimization of performance when a network is congested.

Pairwise comparison decision
matrices
• Pairwise comparison decision matrices.
– Created on the basis of relations among the seven
selection criteria.
94

Relations of criteria
95
• The pairwise comparison decision matrices are used:
– To evaluate the priority vectors of criteria.
– To form the supermatrix per service type and SLA.
• Subsequently, the weighted supermatrices and,
finally, the limit supermatrices are obtained.
• Indicatively, for the SLA1 VoIP service:
– The initial, the weighted and the limit supermatrices are
presented in the following slides.

The ANP supermatrix for SLA1
VoIP service
96

The ANP weighted supermatrix
for SLA1 VoIP service
97

The ANP limit supermatrix for
SLA1 VoIP service
98

Criteria weights
• The criteria weights per service and SLA obtained by the limit
supermatrices are presented in the following slides.
• The weights are proportional to the constraints of each service as
well as to the agreements of each SLA.
• The weight of the price criterion is low for SLA1.
– The service reliability and the network QoS characteristics are
considered as the most important factors.
• In SLA2, the price criterion is more important than in SLA1.
– The respective weight is greater than that of SLA1.
• The weights of the service reliability and QoS characteristics criteria
in SLA2 are lower compared to the relative weights of SLA1.
• In SLA3, the weights of price and service reliability criteria are
balanced.
– As they are almost of equivalent importance.
• In SLA4, the price is the most important criterion.
– Resulting in a high estimated weight value.
99

Criteria weights for SLA1
100

101

102

103

Linguistic terms and the corresponding
interval-valued trapezoidal fuzzy numbers
• Linguistic terms for the criteria attributes
– Represented by interval-valued trapezoidal fuzzy
numbers as shown in the following slide.
• Network policy specifications are expressed
directly using linguistic terms.
104

Linguistic terms and the corresponding
interval-valued trapezoidal fuzzy numbers
105

Relation of the network QoS
characteristics and linguistic terms
• Crisp values of network QoS characteristics are
converted into linguistic terms
– Which correspond to specific ranges of values per
service type.
• The following table presents a relative example
for the VoIP service.
– Illustrating the correspondence between:
• Ranges of network QoS characteristics values.
• Linguistic terms.
106

Relation of the network QoS
characteristics and linguistic terms for VoIP
107

Available networks
• The available-candidate networks in our simulations
at the time of network selection per service and SLA,
as well as, their specifications expressed by linguistic
terms, are depicted in the following slides.
• The case of having several services of different QoS
constraints running at the user site is being
addressed.
– Network selection is performed in a way satisfying multiple
groups of criteria per user.
108

Available networks of SLA1
109

110

111

112

Users
• We consider the case where nine users need
to select a network that:
– Satisfies the requirements of their services.
– Complies with their respective SLA agreements.
113

Required services per user
114

TFT results (1/4)
• The proposed TFT algorithm is applied for
each user.
– The available networks are ranked as shown in the
following slide.
• Positive and negative ideal solutions.
– Represented by unary and null trapezoidal fuzzy
numbers, respectively
• To eliminate the ranking abnormality problem.
115

TFT results (2/4)
116

TFT results (3/4)
• Ranking of the network alternatives is in
accordance with the users expectations.
• Example:
– User 1 requiring increased QoS provisioning selects
LTE 1 network.
• Which guarantees the best QoS characteristics and
service reliability.
• LTE 1 achieves higher ranking than the other networks
– Because of the high values of the QoS characteristics and
service reliability factors bearing higher importance according
to the relative ANP weights in SLA1.
117

TFT results (4/4)
• Example:
– On the contrary, user 9:
• Whose prior selection criterion is the price of the
service.
• Selects the WiFi 1 network.
– Which satisfies his or her requirements in respect of his or her SLA
agreement.
118

Performance evaluation of the
TFT algorithm (1/8)
• The performance of TFT algorithm was evaluated
against:
– The original TOPSIS method
– The FAE method [15].
• Calculates the criteria weights using the fuzzy AHP
• Performs the network selection by applying the ELECTRE
algorithm.
• We consider the scenario of the nine users
described in the previous slides.
119

TFT algorithm (2/8)
• Critical weakness of the TOPSIS and FAE
– They do not support users with more than one
service.
• In these cases, they consider only the most demanding
service of the user.
• Specifically:
– For users 2 and 3
» They applied only for the VoIP service.
– For user 7
» They applied only for the BStreaming service.
– For the rest of the users
» They applied for each single user service.
120

TFT algorithm (3/8)
• The following slide presents the networks
classification performed by the:
– TFT.
– TOPSIS.
– FAE.
121

122
• Networks classification in respect of TFT, TOPSIS (T) and Fuzzy
AHP–ELECTRE (FAE) results:
TFT algorithm (4/8)

TFT algorithm (5/8)
• When a user has only one service.
– The methods usually provide similar results.
• When a user requires multiple services.
– The TFT accomplishes more reliable results than the TOPSIS
and FAE.
• Because it considers the weights of each service.
123

TFT algorithm (6/8)
• The results concerning user 1 using only the VoIP
service .
– Are similar for TFT and TOPSIS methods with the exception
of the evaluation sequence of the WiFi 1 and WiFi 2
networks.
– FAE accomplishes quite similar network rates with the TFT
and TOPSIS methods for this user.
124

TFT algorithm (7/8)
• TFT succeeds more reliable results for user 4.
– Only the RTGaming service is used
• And the most important criteria are service reliability, throughput and
delay.
– TFT selects the WiMAX 2 network
• Which provides AG for service reliability, VG for throughput, and AG for
delay criterion.
– TOPSIS selects the LTE 1 network
• Which has similar values with the WiMAX 2 for service reliability and delay
criteria.
– But worse performance for throughout criterion by providing G instead of VG.
– FAE does not provide a clear choice for user 4
• Results to equal evaluation sequence for both WiMAX 2 and LTE 1
networks.
125

TFT algorithm (8/8)
• The classification of networks obtained from
the three methods is quite different for user 7
– Who requests both BStreaming and Web browsing
services
– The TFT accomplishes more reliable results by
taking into account the weights of both services.
126

Sensitivity analysis of the TFT
algorithm (1/3)
• The sensitivity of the TFT is evaluated when the number of the
available access networks changes frequently.
• We consider three different network configuration scenarios for
the nine users defined in the previous slides.
• In the first scenario.
– All networks defined in the previous slides are available.
• In the second and third scenarios.
– The LTE 1 and the WiFi 2 networks, respectively, are not reachable.
• The following graphs include three column types of different
pattern.
– Indicating the ranking of network alternatives in each case.
127

algorithm (2/3)
• First case.
– User 1 selects the LTE 1 network.
• Second case.
– The remaining networks improve their ranking order thus
user 1 selects the WiMAX 2 network.
• Third case.
– Only the last rated WiFi 3 network increases its rank.
• Because the WiFi 2 network preceded WiFi 3 in the other two
cases.
128

algorithm (3/3)
• Similar behavior is observed in the ranking of
network alternatives for the other users.
• Ranking results of the proposed method:
– Normally adjusted with respect to the heterogeneous
network environment changes.
• Highlighting the methods sensitivity.
129

TFT’s networks ranking in case of
networks environment changes (1/5)
130

131

132

133

134

Conclusions

Conclusions (1/5)
• Network selection in heterogeneous networks
is a complex task.
– Because it takes into account different parameters
with different relative importance, such as the:
• Network and the application characteristics.
• User preferences.
• Service cost.
136

Conclusions (2/5)
• The proposed network selection method.
– Takes into account the:
• Network QoS characteristics policies.
• Application requirements.
• Different types of users SLAs.
– Selects the optimal network that satisfy
simultaneously:
• Applications’ requirements.
• User’s preferences running on a mobile user’s device.
137

Conclusions (3/5)
• More specifically, the proposed method employs two MADM
algorithms:
– The ANP for criteria weights calculation.
– The TFT for accomplishing the overall rating of the network technologies.
• The ANP is selected to determine the relative importance and the
dependence of the criteria.
• As selection criteria are considered the:
– Network QoS parameters.
– Service constraints.
– User requirements.
– Provider policies.
• These criteria are easily configured and represented by interval-
valued trapezoidal fuzzy numbers. 138

Conclusions (4/5)
• The TFT algorithm is applied to calculate the
overall rating of the available networks.
• Performance evaluation:
– When a user has only one service.
• TFT provides similar results to the original TOPSIS and FAE
methods.
– When a user requires multiple services.
• TFT performs better by satisfying multiple groups of criteria
per user.
– Because the original TOPSIS and FAE methods cannot support
more than one services.
139

Conclusions (5/5)
• Sensitivity analysis:
– TFT does not suffer from the ranking abnormality
problem.
– The results are normally adjusted to the
heterogeneous network environment changes.
140

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