an improved vertical handoff algorithm for 4g heterogeneous wireless ...

ISSN 2454-3349 Int. Journal of Philosophies in Computer Science Vol. 1, No. 1, (2015), pp. 1 – 12

AN IMPROVED VERTICAL HANDOFF ALGORITHM FOR 4G HETEROGENEOUS WIRELESS NETWORKS Mesala Sravani1 Shivali Sonwani1 and D. P. Acharjya1 1

School of Computing Science and Engineering, VIT University, Vellore, India e-mail address: {sravani.meesala, shiwali.sonwani, dpacharjya}@gmail.com

ABSTRACT In modern Heterogeneous 4G wireless networks, the essential criterion is un interrupted call service and continuity of the call. Handoff is the best technique to achieve continuous call service. In heterogeneous wireless networks the key challenging aspect is continuous call connection among different networks like WiFi, Wi-Max, WLAN, and CDMA etc.. In this paper various handoff decision algorithms are discussed and their performance is analyzed. A modified algorithm is proposed and the results compared with the performance of other vertical handoff algorithms. The analysis and results show that the modified algorithm gives better results in minimizing the processing delay during the handoff process. The best network among available networks is chosen using the technique of order preference by similarity to ideal solution (TOPSIS) method.

KEYWORDS Next generation networks, handoff, wireless networks, vertical handoff, decision scheme, TOPSIS.

1.

INTRODUCTION

In modern day Next Generation wireless systems, most challenging area is continuous service for the mobile moving in an area where there is overlapping of networks. The main aim of any next generation cellular network is best connectivity to every user at any time and anywhere [1]. In the last few years, plenty of research work has been focused on this challenging issue of mobility management process in heterogeneous wireless systems. When the mobile user is in continuous motion, there is a need for handoff to be performed from one network to the other network, keeping in view, the requirement of the user in future. Hand off mechanism deals with the concept of changing over the channels associated with the current connection when a call is in progress. Vertical handoff (VHO) is most prominently used technique to support continuing call between different networks having different air interface techniques during internetwork movements. “Handover” is a process of redirecting the services of a mobile in a network to a new network. Handoff mechanism helps in selecting the best suitable network to which the user has to be connected after the execution of handoff. The main constrain in hand off is minimum processing delay. Research work is concentrated on designing and implementing many new algorithms with the main aim of providing the required quality of service (QOS) over a wide range of applications [2].The heterogeneous networks are utilized by many users on the basis of preference given to various QOS parameter such as real time interactive traffic, less delay, low jitter, high availability, high bandwidth (BW), Low bit error rate (BER). Vertical handoff is necessary for better performance and high availability reasons. The main parameter like capability of the network, cost of the network, handoff latency, conditions prevailing in the network, user preference and consumption of power are to be considered during vertical handoff. The process of handoff mechanism is shown in Figure.1. The mechanism of handoff has four different phases namely initiation of handoff, handoff decision making, selection of network, and execution of handoff process. Handoff decided by some quality of

Sravani, Shivali and Acharjya service (QOS) parameter like strength of the signal and quality of the network link etc. is known as initiation of handoff. In handoff decision making, the signal strength and the QOS parameters of the neighboring networks are measured and a decision is taken to select the best network suitable for performing handoff. Network selection phase identifies the best suitable network among all the available networks chosen to perform handoff. Execution of handoff deals with establishment of connection, release of connection, and network security aspects.

Downlink

Mobile Terminal

Handoff Uplink

Base Station

Cell 1

Cell 2 Figure 1:

Handoff Mechanism

Hand off mechanism is classified into horizontal handoff (HHO) and vertical handoff (VHO). The diagrammatic representation is given in Figure.2. Horizontal handoff is a technique where the handoff is performed between two networks having the same architecture. For example, handoff between WiFi to Wi-Fi connectivity is considered as horizontal handoff. Vertical handoff is the handoff between networks having different technology and different architecture. For example, Wi-Fi to Wi-Max connectivity is considered as vertical handoff. It is the most prominently used handoff mechanism. This paper emphasizes on the phase of hand off decision making where the focus is laid on the decision of choosing the best network amongst all the available networks. TOPSIS algorithm based on the concept of multiple attribute decision making (MADM) is adapted to select the best network and redirect the connection to the mobile terminal. Base Station Vertical Handoff

Access Point

Mobile Terminal WLAN Horizontal Handoff WiMax Figure 2:

Horizontal and Vertical Handoff

Additionally, vertical handoff is categorized into two types such as upward and downward handoffs; and hard and soft handoffs. Based on the coverage area of home and target networks; vertical handoff is classified as upward and downward vertical handoffs. If the switching of the mobile is from a small coverage area to a large coverage area network, it is termed as upward handoff. On the other hand, if switching is in the reverse direction, i.e., from a larger coverage area to a smaller coverage area network it is termed as downward handoff. Similarly, the vertical handoff process where a mobile node associates with the new base station after getting disconnected from the previous base station is termed as hard handoff or break before make. On the other hand, in soft handover a mobile node maintains the connection with the previous base station till its association with the new base station is completed. This process is also termed as make before break and the mobile node maintains simultaneous connections with both the base stations during the interim period. Soft handoffs are preferable compared to hard handoffs as they eliminate the problem of disruption of service. In

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Vertical Handoff Algorithm for 4G Heterogeneous Wireless Networks addition, there are several other vertical handoffs available in the computer network. Keeping in mind the length of the paper, these are excluded. The following Figure 3 depicts the vertical handoff taxonomy. MCHO

Upward

NCHO

Downward Vertical Handoff

MCNA

Soft

NCMA

Hard Imperative Figure 3:

Alternative

Taxonomy of Vertical Handoff

In the following section 2 we discuss the literature review pertaining to vertical handoff. Section 3 discusses various techniques used in vertical handoff techniques. In section 4, the proposed technique for improvement in performance is discussed. A case study is presented in section 5 including analysis of all the cases. Finally, we conclude with a conclusion in section 6.

2.

LITERATURE R EVIEW

Fourth Generation networks (4G) mainly visualizes the concept of vertical handoff. The concept of being always best connected (ABC) has been described by Gustafsson and Jonsson [2] and have highlighted different aspects of ABC criterion that will expand the technology and business platform of next generation communication. A survey based on various issues like research, future challenges, and various approaches that are possible to tackle the challenges of ABC for handoff over heterogeneous wireless networks is carried out by Ekram et al. [6]. Savitha and Chandrasekar discussed broader overall overview of vertical handoff decision algorithms [7]. An analytic hierarchy process (AHP) algorithm is used as a decision making tool to decide the best possible network [8, 9]. An algorithm based on signal to interference plus noise Ratio (SINR), AHP and entropy weight method for vertical handoff is proposed by Liu et al. [9]. From the literature review it is clear that the algorithms have been designed and proposed to find QOS parameters of the circuit like bandwidth, jitter, bit error rate, traffic, delay etc.. In addition, the algorithms are tested in two network environments. Validity of the algorithms is not tested for more than two random area networks (RANs). Additionally, impact of network traffic load (NTL) on the optimum random access network selection is not explored. This paper mainly focuses on the design of a novel VHO decision algorithm which involves simple mathematical calculation and yet selects the optimum RAN considering multiple criteria. Many handoff decision algorithms have been proposed in the literature in [1]. A comparison is done among various algorithms like simple additive weight (SAW), grey relational analysis (GRA) and multiplicative exponent weighting (MEW), technique for order preference by similarity to ideal solution (TOPSIS) for deciding when to perform a vertical handoff. A vertical handover decision algorithm for heterogeneous wireless network is discussed in [3], where the problem is based on Markov decision process algorithm. A fuzzy multiple attribute decision making (MADM) for vertical handover decision is formulated by Attaullah et al. [5]. A novel vertical handover decision scheme to avoid the processing delay and power consumption and to reduce the overload and the processing delay is proposed in [8]. A vertical handover decision scheme to avoid the processing delay which uses the MADM method is stated in [8]. An algorithm to make a trust handover decision and to reduce processing delay in a heterogeneous wireless environment using T-DVHD is discussed in [9]. A novel distributed vertical handoff decision algorithm using the simple additive weight method with a distributed manner to avoid the drawbacks is discussed. All these proposed works mainly lay their

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Sravani, Shivali and Acharjya emphasis on the handoff decision making and calculating the criteria for handoff decision making on the mobile terminal end and the proposed algorithms are utilized for reducing the delay due to processing by doing the required calculations using MADM in a distributed manner. Keeping view to all these techniques, the present work proposes an algorithm to analyze and select the best network amongst several visitor networks for the vertical decision schemes and the ranking of the network optimizes the best available network in the constraint of multiple network environments.

3.

VERTICAL HANDOFF A LGORITHMS

In this section, we discuss various handoff algorithms discussed in the literature by the researchers. These are weighted sum method (WSM), weighted product method (WPM), technique for order preference by similarity to ideal solution (TOPSIS). In the following section, we discuss briefly about these techniques.

3.1

Weighted Sum Method (WSM)

Weighted sum method technique for vertical handoff is also named as simple additive weight or scoring method. It is a simplified and most prominently adapted multi attribute decision mechanism. The basis of this algorithm is based on weighted average technique. A score of evaluation is calibrated for each alternative by using the product of the scaled value assigned to the alternative of that particular attribute and the weights of relative importance directly assigned by decision maker followed by addition of the multiplication for all criteria. The use of WSM scoring is dependent on identifying the objectives and alternatives, evaluating the alternatives, determining the sub-objective weights, additive aggregation of weighted partial preference values, sensitive analysis. It makes use of direct rating on the standardised scales exclusively in pure qualitative attributes. For numerical attributes, score are found by normalized values to match the standardised scale. The WSM is a scale that is comparative for every element in the decision matrix. Mathematically, it is defined as below.

vij =

or

vij =

xij

(1)

x max j x min j

(2)

xij

In the above equations, xij indicates the performance rating of the i th alternative with respect to the j th attribute. From this, it is clear that, the multiple criteria decision making problems can be concisely written in the matrix format of order (n × m) , where n is the number of networks and m is the total number of attributes taken into consideration for decision making. The WSM method, the alternative score of each network i is computed by adding the contributions from each attribute vij

multiplied by the importance weight of each attribute w j . We denote the score of network i as Vi . Mathematically, it is defined as: Vi = ∑ w j vij

for j = 1, 2,⋯, m and i = 1, 2,⋯, n

(3)

j

It is to be noted that, the value of such weights should represent the different levels of importance of a parameter for the decision maker. The set of importance weights has to satisfy the constraint condition Σw j = 1 for j = 1, 2,⋯, m .

3.2

Weighted Product Method (WPM)

Weighted product method is another multiple attribute decision making method and is very similar to WSM method. The prime difference is that, instead of addition, there is multiplication [4]. It was initially proposed for vertical handoff in [6]. In WPM, the scores of the networks are determined by the weighted product of the attributes. The score of the i th network, Vi is computed as discussed in

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Vertical Handoff Algorithm for 4G Heterogeneous Wireless Networks the following equation (4), where meaning of vij and w j remains same as discussed in WSM approach. w

Vi = ∏ vij j

for j = 1, 2,⋯, m and i = 1, 2,⋯, n

(4)

j w

In above equation (4), w j is a positive exponent for benefit metrics, xij j , and a negative exponent for −w

cost metrics, xij j . Since parameter normalization is not required, the network score formulated by WPM does not contain an upper bound [4], thus it is convenient to compare the score of each network with the score of the positive ideal network. This particular network is decided as a network with the most suitable and best values in each of the metric. The best value in benefit metric is the largest and, the best value is the lowest for a cost metric.

3.3

Techniques for Order Preference by Similarity to Ideal Solution (TOPSIS)

A technique for order preference by similarity to ideal solution is a multiple attribute decision making instrument for measuring relative efficiency of alternatives. It determines the preference order on the grounds of the similarity to a positive ideal solution and the worst similarity to a negative solution. The ideal solution is a hypothetical solution with the best values in each parameter whereas the negative ideal solution is the opposite. The following steps are used in this technique. We present the steps in form of an algorithm. Algorithm (TOPSIS)

Input: Multiple attribute decision making matrix. Output: Top selected network 1. Compute the normalised decision matrix from the multiple attribute decision matrix by using the following equation, where the symbols have the same meaning as discussed in WSM method. xij for i = 1, 2,⋯, n (5) vij = (Σ xij2 )1 2 2. Construct the weighted normalized decision matrix by using the following equation, where w j represents same meaning as discussed in WSM method.

Vij = w j vij

(6) + j

3. Determine the two main solutions such as positive ideal solution ( V ) and negative ideal solution ( V j− ) as discussed below. Positive ideal solution V j+ = {(max Vij | j ∈ J ),(min Vij | j ∈ J ′)}

(7)

Negative ideal solution V j− = {(min Vij | j ∈ J ),(max Vij | j ∈ J ′)}

(8)

i

i

i

i

where J is the set of benefit parameters and J ′ is the set of cost parameters. 4. Compute the distance measure between the networks, and the positive and negative ideal solution as below. d i+ =

∑ (V

ij

− V j+ ) 2

and

− V j− ) 2

for i = 1, 2,⋯, n

j

di− =

∑ (V

ij

(9)

j

5. Finally compute the relative closeness, Vi , to the ideal solution by using the equation as below. d− Vi = + i − (10) (d i + d i ) 6. The top selected network is the network that has maximum value of Vi . Also, one can see the networks either in the ascending or descending order.

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Sravani, Shivali and Acharjya

4.

PROPOSED TECHNIQUE FOR VERTICAL HANDOFF A LGORITHMS

The major drawback of all the algorithms is that, they have considered the minimum value for cost parameters and used equation (2) while normalizing the matrix whereas other parameters are normalized by considering maximum value and using equation (1). But, in real life situations it may not be true always. For example, consider a situation with parameters delay, bandwidth, cost and jitter. From the parameters it is clear that, the parameters delay and cost is to be minimized whereas the parameters bandwidth and jitter is to be maximized. Therefore, it is clear that, instead of only cost some other parameter may be minimized also. Therefore, in our analysis while normalizing the matrix, initially we have identified the parameters which are to be minimized and which are to be maximized. Accordingly, we have used equation (1) and (2) for normalizing the matrix. Surprisingly, it is seen that in all the techniques, the performances are better as compared to the existing techniques. To analyse all the cases, in the following section we have considered a case study and show how analysis can be performed.

5.

A C ASE STUDY

This section outlines a case study in which we analyze all the vertical handoff algorithms discussed in this paper. Consider a case of a mobile terminal currently connected to a Wi-Fi cell and has to make decision among six candidate networks A1 , A2 , A3 , A4 , A5 , and A6 . It is to be noted that the networks A3 and A4 are Wi-Fi cells and other are Wi-Max cells. Assume the vertical handoff criteria as delay, bandwidth, cost, and jitter. We use the notation for the criteria as X 1 , X 2 , X 3 , and X 4 . Let us assume the multiple criteria decision matrix (M) as below. Let us assume the importance weight of each criterion as W = [ w1 , w2 , w3 , w4 ] = [0.3, 0.2, 0.2, 0.3] .

X1

X2

X3

X4

A1 0.00062 8 9 0.411 A2  0.00063 1.5 8 0.762  A 0.00062 15 12 0.057  M = 3  A4  0.00063 7 6 0.939  A5 0.00062 11 10 0.103    A6  0.00061 1 9 0.247 

5.1

Results and Analysis in Accordance with WSM

According to WSM, initially multi attribute decision matrix is to be normalized by using equation (1) and (2). The normalized decision matrix ( M ′ ) is presented below. The criteria, cost is normalized by using equation (2) whereas other criteria are normalized by using equation (1). X1 A1  0.984 A2  1 A  0.984 M′= 3  A4  1 A5  0.984  A6  0.968

X2

X3

X4

0.533 0.667 0.438  0.1 0.75 0.812  1 0.5 0.061  0.467 1 1  0.733 0.6 0.119   0.667 0.667 0.263 

The users running application was voice. On considering the importance of weight for each criterion as W = [ w1 , w2 , w3 , w4 ] = [0.3, 0.2, 0.2, 0.3] , the score of each network Vi , i = 1, 2, 3 , and 4 is computed by using equation (3). The results obtained are given below and plotted in the Figure 4. From the figure it is clear that, the best network is A4 and is selected to connect the mobile terminal for service continuity with minimum processing delay.

6

Vertical Handoff Algorithm for 4G Heterogeneous Wireless Networks V1 = 0.6666 , V2 = 0.7136 , V3 = 0.6135 , V4 = 0.8934 , V5 = 0.5975 , and V6 = 0.6361

Figure 4

5.2

Network selections in accordance with WSM

Results and Analysis in Accordance with WPM

Unlike WSM, in WPM, initially multi attribute decision matrix is to be normalized by using equation (1) and (2). The normalized decision matrix ( M ′ ) is presented below. The criteria, cost is normalized by using equation (2) whereas other criteria are normalized by using equation (1). X1 A1  0.984 A2  1 A  0.984 M′= 3  A4  1 A5  0.984  A6  0.968

X2

X3

X4

0.533 0.667 0.438  0.1 0.75 0.812  1 0.5 0.061  0.467 1 1  0.733 0.6 0.119   0.667 0.667 0.263 

The users running application was voice. On considering the importance of weight for each criterion as W = [ w1 , w2 , w3 , w4 ] = [0.3, 0.2, 0.2, 0.3] , the score of each network Vi , i = 1, 2, 3 , and 4 is computed by using equation (4). The results obtained are given below and plotted in the Figure 5. From the figure it is clear that, the best network is A4 and is selected to connect the mobile terminal for service continuity with minimum processing delay. V1 = 0.6317 , V2 = 0.5596 , V3 = 0.3744 , V4 = 0.8587 , V5 = 0.4459 , and V6 = 0.5642

Figure 5

Network selections in accordance with WPM

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Sravani, Shivali and Acharjya 5.3

Results and Analysis in Accordance with TOPSIS

According to this algorithm, initially, the normalized matrix M ′ of the matrix M is obtained by using equation (5). The normalized matrix is given below. X1 A1  0.4071 A2  0.4137 A  0.4071 M′= 3  A4  0.4137 A5  0.4071  A6  0.4005

X2

X3

X4

0.3375 0.3812 0.3146  0.0633 0.4286 0.5832  0.6332 0.2857 0.0438   0.2957 0.5714 0.7183  0.4641 0.3429 0.0855   0.4223 0.3812 0.1889 

The users running application was voice. On considering the importance of weight for each criterion as W = [ w1 , w2 , w3 , w4 ] = [0.3, 0.2, 0.2, 0.3] , construct the weighted normalized decision matrix by using equation (6). The weighted normalized decision matrix, V, is given as below. X1 A1  0.1221 A2  0.1241 A  0.1221 V = 3 A4  0.1241 A5  0.1221  A6  0.1201

X2

X3

X4

0.0675 0.0762 0.0944  0.0127 0.0857 0.1750  0.1266 0.0571 0.0131  0.0591 0.1143 0.2155  0.0928 0.0686 0.0256   0.0845 0.0762 0.0567 

Compute the positive ideal solution ( V j+ ) and negative ideal solution ( V j− ) by using equations (7) and (8) and is given below. V j+ = [0.1241, 0.0127, 0.1143, 0.2155] and

V j− = [0.1201, 0.1266, 0.0571, 0.0131] The distance measure between the networks and the positive and negative ideal solution is computed as below on using equation (9). d1+ = 0.138289 ;

d 2+ = 0.049580;

d3+ = 0.239196;

d 4+ = 0.0464;

d 5+ = 0.211117;

d 6+ = 0.178438;

d1− = 0.102329 ;

d 2− = 0.200047;

d3− = 0.002;

d 4− = 0.220929;

d5− = 0.037881;

d6− = 0.063547

The relative closeness, Vi , to the ideal solution is computed by using the equation (10) as below. V1 = 0.425275, V2 = 0.801383, V3 = 0.008292, V4 = 0.826431,

V5 = 0.152134,

V6 = 0.262607

From the analysis it is clear that, the best network is A4 and is selected to connect the mobile terminal for service continuity with minimum processing delay.

5.4

Results and Analysis in Accordance with Proposed Scheme

According to this algorithm, initially, the normalized matrix M ′ of the matrix M is obtained by using equations (1) and (2) as per requirement of the parameter. The normalized matrix is given below.

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Vertical Handoff Algorithm for 4G Heterogeneous Wireless Networks X1 A1  0.983 A2  0.968 A  0.983 M′= 3  A4  0.968 A5  0.983  A6  1

X2

X3

X4

0.533 0.667 0.138 0.1 0.75 0.074 1 0.5 1   0.467 1 0.060 0.733 0.6 0.553  0.667 0.667 0.230

Further analysis is carried out as discussed earlier in all the techniques. The results obtained through original algorithm (D1) and modified algorithm (D2) for WPM is presented in the Figure 6 given below. Similarly, the results obtained through original and modified algorithm in accordance with WSM method is presented in Figure 7 given below.

Figure 6

Results in accordance with original and modified WPM algorithm

Figure 7

Results in accordance with original and modified WSM algorithm

Finally, the results obtained through original algorithm (D1) and modified algorithm (D2) for TOPSIS is presented in the Figure 8 given below. A summary of the ranking order based on different methods of multiple attribute decision making are discussed in Figure 9. In figure 9, we have plotted the graph for WSM, modified WSM, WPM, modified WPM, TOPSIS, and modified TOPSIS. The performance of TOPSIS is better as compared to WSM and WPM. Again, modified TOPSIS presents better performance as compared to other modified WSM and modified WPM. The network selection according to all modified WSM, WPM, and TOPSIS is given in Table 1, whereas modified efficiency

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Sravani, Shivali and Acharjya for all the modified WSM, WPM, and TOPSIS is presented in Table 2. It is found that modified TOPSIS has efficiency of 33.94 whereas the efficiency of WSM and WPM is 10.82 and 16.80 respectively.

Figure 8

Figure 9

Results in accordance with original and modified TOPSIS algorithm

Performance analyses of all the techniques for vertical handoff Table 1

Ranking order comparison

Network

Rank 1

Rank 2

Rank 3

Rank 4

Rank 5

Rank 6

Modified WSM

A4

A2

A1

A6

A3

A5

Modified WPM

A4

A1

A6

A2

A5

A3

Modified TOPSIS

A3

A5

A6

A1

A2

A4

10

Vertical Handoff Algorithm for 4G Heterogeneous Wireless Networks Table 2

6.

Efficiency order comparison Efficiency

Network

Modified WSM

10.82

A4

Modified WPM

16.80

A4

Modified TOPSIS

33.94

A3

CONCLUSIONS

In this paper we have considered the handover decision problem, which is complex due to the availability of different access networks. Next generation networks not only offer the choice of a variety of access networks but also a myriad of applications to users. A key feature of the applications designed for NGNs is their adaptability to the diverse networks offered. The overall user experience depends not only on the performance of the applications, but also on the cost benefits and the device performance. We have presented a generic framework for handover decision in NGNs using the concept of technique of order preference by similarity to ideal solution. The performance of proposed algorithm is analyzed and compared with the performance of WSM, WPM algorithm. The simulated results and analysis show that the TOPSIS algorithm can achieve excellent performance in terms of efficiency as shown in figure .8 and meets the requirements of the traffic for user and network with good QOS. Through examples we showed that our framework can be utilized by any existing handover decision algorithm for horizontal or vertical handover.

REFERENCES [1]

E. Gustafsson, and A. Jonsson, “Always Best Connected”, IEEE Wireless Communications Magazine, Vol.10 (1), pp. 49-55, (2003).

[2]

E. Steven Navarro, V. W. S. Wong, and Yuxia, Lin, “A Vertical Handover Decision Algorithm for Heterogeneous Wireless Networks”, Proceedings of the IEEE Wireless Communications and Networking Conference, IEEE Xplore, pp. 3199-3204, (2007).

[3]

E. Steven Navarro, and V. W. S. Wong, “Comparison between Vertical Handover Decision Algorithms for Heterogeneous Wireless Networks”, Proceedings of the IEEE 63rd Vehicular Technology Conference, IEEE Xplore, Vol. 2, pp. 947 – 951, (2006).

[4]

R. Tawil, J. Demerjain, and G. Pujolle, “A Trusted Handover Decision Scheme for the Next Generation Wireless Networks”, International Journal of Computer Science Network Security, Vol. 8 (6), pp. 174-182, (2008).

[5]

H. Attaullah, F. Iqbal, and M. Y. Javed, “Intelligent Vertical Handover Decision Model to Improve QoS”, Proceedings of the Third International Conference on Digital Information Management, IEEE Xplore, pp. 119-124, (2008).

[6]

E. Hossain, G. Chow, Victor C. M. Leung, Robert D. McLeod, Jelena Misic, Vincent W. S. Wong, Oliver Yang, “Vehicular Telematics over Heterogeneous Wireless Networks: A survey”, Computer Communications, Vol. 33 (7), pp. 775–793, (2010)

[7]

K. Savitha, and C. Chandrasekar, “Network Selection using TOPSIS in Vertical Handover Decision Schemes for Heterogeneous Wireless Networks”, International Journal of Computer Science Issues, Vol. 8 (3), pp. 400-406, (2011)

[8]

K. Savitha, and C. Chandrasekar, “Comparison of Vertical Handoff Decision Scheme in Heterogeneous Wireless Network”, Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research, IEEE Xplore, pp.1-5, (2010).

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Sravani, Shivali and Acharjya [9]

Liu Sheng Mei, Pan Su, and Xu Ming-hai, “An Improved TOPSIS Vertical Handoff Algorithm for Heterogeneous Wireless Networks”, Proceedings of the 12th IEEE International Conference on Communication Technology, IEEE Xplore, pp. 750-754, (2010).

Authors Biography Mesala Sravani received her B. Tech. degree in Computer Science and Engineering from Jawaharlal Nehru Technical University, Kakinada, India in 2014. Presently, she is pursuing her M. Tech. in Computer Science and Engineering from VIT University, Vellore, Tamilnadu, India. She has presented two papers in international conference. Her area of interest includes computer network, and cloud computing.

Shiwali Sonwani received her B. Tech. degree in Computer Science and Engineering from Sri Govindaram Seksaria Institute of Technology and Science, Indoor, MP, India. She is presently pursuing her M. Tech. in Computer Science and Engineering from VIT University, Vellore, Tamilnadu, India. She has presented two papers in international conference. Her area of interest includes computer network, and cloud computing.

D P Acharjya received his Ph. D. in Computer Science from Berhampur University, India; M. Tech. degree in Computer Science from Utkal University, India; M. Phil. from Berhampur University, India; and M. Sc. from NIT, Rourkela, India. He has been awarded with Gold Medal in M. Sc. Currently he is working as a Professor in the School of Computing Science and Engineering, VIT University, Vellore, India. He has authored more than 40 national, international journal, conference papers, and Book chapters to his credit. In addition, he has written four books to his credit. Also, he has edited three books to his credit. He has chaired many international conferences and delivered keynote addresses. He is in the editorial board member of many international journals. He is reviewer of many international journals such as Fuzzy Sets and Systems, Knowledge Based Systems, Applied Journal of Soft Computing, and International Journal of Artificial Intelligence and Soft Computing etc. His research interests include rough sets, knowledge representation, computational intelligence, neural network, and data analytics.

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