A Vertical Handover Approach Using GTMA in Wireless Networks

Kaur, Gaganpreet; Goyal, Raman Kumar; Mehta, Rajesh

doi:10.1007/978-981-19-2821-5_50

Cited by 1 publication

(13 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The weight calculation used in the classical MADM methods influences the ranking abnormality. 1,3,5,7,8,10,11,[13][14][15][16][17][18][19][20] The handover triggering has not been utilized in existing techniques 3,5,7,8,10-12,14-16,18-20 which leads to unnecessary handovers. The key parameters such as throughput, 1,3,5,11,13,15,17,19 price, 1,10,11,[13][14][15]17,19,20 packet loss rate, 11,[13][14][15]17,19,20 delay 1,11,[13][14][15]17,19 and jitter 11,14,15,[17]…”

Section: Related Workmentioning

confidence: 99%

“…The key parameters such as throughput, 1,3,5,11,13,15,17,19 price, 1,10,11,[13][14][15]17,19,20 packet loss rate, 11,[13][14][15]17,19,20 delay 1,11,[13][14][15]17,19 and jitter 11,14,15,[17][18][19] are not considered while making handover decision. The basic performance metric of MADM methods such as number of handovers 12,15,16,19 and ranking abnormality 1,5,11,[13][14][15]17,19,20 are not analyzed. The performance of the proposed technique is not compared with the traditional MADM methods such as A...…”

Section: Related Workmentioning

confidence: 99%

“…The basic performance metric of MADM methods such as number of handovers 12,15,16,19 and ranking abnormality 1,5,11,[13][14][15]17,19,20 are not analyzed. The performance of the proposed technique is not compared with the traditional MADM methods such as AHP 1,7,8,[10][11][12][13][14][15][16]19,20 and TOPSIS. 5,7,8,15,16,19 During performance analysis, the network traffic types such as conversational, background, interactive and streaming 1,8,[11][12][13][14][15][16]19,20 are not taken in to consideration.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, the MADM method can be utilized for the selection of the best network from all the available alternative networks on the basis of the multiple parameters. 1 Various MADM methods 1,[3][4][5]7,8,[10][11][12][13][14][15][16][17][18][19] have been proposed by the researchers in the literature to handle and optimize the vertical handover problems.…”

Section: Introductionmentioning

confidence: 99%

“…Various researchers have proposed different approaches in literature, which includes MADM methods 3,4,7,[10][11][12][13][14][15][16][17][18][19] and MADM in combination with fuzzy-logic 6,13 to improve the issues involved in mobility management such as handover triggering 1,13 reducing the number of handovers, and ranking abnormality. 3,4,7,8,[10][11][12] From the literature, it is observed that resolving unnecessary handovers and ranking abnormality is still a challenging issue in this field.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Reducing unnecessary handovers and improving ranking abnormality based on multi‐attribute decision making graph theory and matrix approach with Euclidean distance in heterogeneous wireless networks

Kaur

Goyal

Mehta

2023

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

SummaryNew mobile devices offer multiple network interfaces to allow the users to connect to the best available network. The heterogeneous networks can provide better internet connectivity to the users by means of vertical handover. The handover must be triggered at a suitable point of time to avoid mobility issues such as unnecessary handovers and handover ping‐pongs. The network selection during handover is usually done using classical multi‐attribute decision making (MADM) methods. However, ranking abnormality is one of the prominent issues of the classical MADM methods. To address these challenges, a graph theory and matrix approach (GTMA) with Euclidean distance is proposed for vertical handover in wireless networks. GTMA is used for the selection of the appropriate network and Euclidean distance is utilized for the handover triggering. The simulation results reveal that the proposed method has eliminated the ranking abnormality issue. This proposed technique without triggering has also reduced the number of handovers up to 75.61%, 85.71%, and 66.67% as compared to the traditional MADM methods such as AHP, GRA, and TOPSIS respectively. The use of Euclidean distance for handover triggering has further reduced the number of handovers of the proposed technique as well as traditional techniques for all the traffic types.

show abstract