A heterogeneous wireless network comprises of different wireless access technologies including IEEE 802.11 Wi-Fi, IEEE 802.16 WiMAX, 3G/4G, and satellite network, etc. On the other hand, a variety of mobile devices, including but not limited to smartphones, iPads, etc., have surfaced during the past few years and have been evolving rapidly while becoming smarter and more powerful. Most of these mobile devices are equipped with multiple interfaces in order to obtain services via different wireless access technologies in a heterogeneous environment. These technical advancements in the area of wireless communication and technology ultimately lead towards a ubiquitous and pervasive environment offering powerful and rich connectivity; a wireless environment where multiple access technologies are available and the end-users are served with anywhere, anytime networks, the so called “Always Best Connected” (ABC) networks.
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These ABCs have been designed to provide support for different types of services that can be consumed by end-users. These rich multimedia services are characterized by different traffic demands and diverse QoS requirements that must be fulfilled in order to guarantee end-users’ satisfaction.
Hence, selection of an ABC to meet specific application/service’s QoS requirements to maximize the end-user’s satisfaction is a very challenging task and involves several key attributes including RSS, QoS, traffic load, MS’s velocity, offered security, and network usage cost. Since all of these key parameters play a critical role in determining an ABC network, ranking the candidate networks based on a single criterion will not provide an optimal result while selecting a best target network.
Several MADM approaches [126] that rank alternatives by comparing them based on relative importance of multiple criteria have been developed. Since network selection has recently become a multi-criteria problem, application of different MADM algorithms to rank candidate networks in a preferential order is considered in this research work. In the following paragraphs, different algorithms, namely TOPSIS, Fuzzy TOPSIS, and Fuzzy VIKOR are discussed. These algorithms are successfully applied to implement the target network selection mechanism of VHITS. Figure 3.41 shows an overall design of the VHITS Target Network Selection scheme. A comparison of Figures 3.2 and 3.41 shows the presence of several common components utilized by both VHITS Handoff Necessity Estimation and VHITS Target Network Selection modules. Hence, in this section the details of some of these components that are already provided in Section 3.2 are intentionally skipped and only minor exceptions are noted.
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The VHITS target network selection scheme utilizes nine parameters including PRSS, individual QoS parameters (delay, jitter, PLR, and throughput), MS-velocity, traffic-load, security-preference and network-usage-cost. These nine parameters are measured from all available candidate networks that can provide coverage to the MS. As discussed in VHITS handoff necessity estimation module, we assume the availability of these system parameters to MS.
START
READ ATTRIBUTES FROM ALL NETWORKS IN RANGE
(WLAN, WMAN, WWAN)
WEIGHTS CALCULATIONS BASED ON TRAFFIC CLASS
RSS PREDICTION BASED ON
GREY PREDICTION THEORY
A NORMALIZATION OF ATTRIBUTES USING FUZZY TECHNIQUES VHO TARGET SELECTION SCHEME
(TOPSIS, FTOPSIS, FVIKOR)
TARGET SELECTION BASED ON THE RANKING OF ALTERNATIVES
FINISH A
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3.3.2 WEIGHTS CALCULATIONS FOR SYSTEM ATTRIBUTES
Due to the fact that the effects of multiple criteria on evaluating the available alternatives have natural variances, the relative importance of each of the attributes involved in ranking the available candidate networks based on users’ and/or operators’ preferences is decided by assigning weights to each of the utilized attributes. Any of the methods discussed in Section 3.2.2 can be utilized to calculate these weights. The TOPSIS based network selection method can work with any of these weighting schemes as all of them generate crisp values for final weights. However, these weight calculation schemes cannot be utilized with FTOPSIS and FVIKOR ranking methods, as these fuzzy based schemes require that their attributes and corresponding weights must be either Linguistic Variables or triangular fuzzy numbers. Hence, certain modifications are needed in these weighting schemes..
3.3.3 RSS PREDICTION USING GREY PREDICTION THEORY
A heterogeneous wireless network is characterized by a fast fading environment where the RSS of any available candidate network can fall below a threshold that is required to maintain the connectivity with the MS. The GPT is used to predict the future RSS values for all available candidate networks. This is to ensure that the networks with strong signals are available and will remain available at the time of handoff.
3.3.4 NORMALIZATION OF ATTRIBUTES USING FUZZY TECHNIQUES
The crisp values of all the required attributes, from all available candidate networks, are measured and normalized using the same process as detailed in Section 3.2.5; several FISs are utilized to determine the probability of selection/rejection for an
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MS to be connected to a given network type. After this process, all system attributes with different units and ranges are normalized to a common scale, which is required by all candidate-ranking algorithms.
The TOPSIS algorithm can then be used with these normalized parameters to obtain the rank of all available networks. On the other hand, since the measured data and their corresponding membership values are crisp, it cannot be utilized directly with Fuzzy TOPSIS and Fuzzy VIKOR schemes as they rely on Linguistic Variables and/or triangular fuzzy numbers. Hence, the proposed scheme utilizes a similar scale as presented in Table 3.22 to transform these crisp values into triangular fuzzy numbers that can be utilized by these fuzzy based methods.
3.3.5 SIGNIFICANCE OF INDIVIDUAL PARAMETERS
The VHITS handoff necessity estimation scheme relies on the degree of QoS for a specific traffic class that is obtained based on the values of QoS parameters provided by the current PoA. This degree is calculated using a weighted sum of membership values for all QoS parameters as shown in Equation (3.32). The assignment of weights is based on the type of current service consumed by the end-user.
The VHITS target network selection scheme cannot use this degree directly since at a given instance of time, measured values of these QoS parameters from multiple candidate networks are required during the network selection process. Hence, the proposed scheme measures individual QoS parameters from each of the available candidate networks, assigns weights to these parameters, based on the traffic class currently utilized or requested by the end-user, and then feeds them to the network
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ranking algorithm. This way the VHITS target network selection algorithm can rank the candidate networks based on the strength of individualized parameters to capture their significance in the final ranking order during the network selection process.
The target selection mechanism of VHITS relies on nine different parameters obtained from all available candidate networks (alternatives). These values are arranged together to create a decision matrix. The decision matrix, along with criteria weights, is then fed into the ranking algorithms that are discussed next.