The use of MADM methods in the vertical handover decision making context

Mansouri, Mouad; Leghris, Cherkaoui

doi:10.1109/wincom.2017.8238169

Cited by 13 publications

(11 citation statements)

References 9 publications

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“…based on the received signal strength, the available bandwidth etc.) [5], game theory algorithms [6], genetic algorithms [7], artificial intelligence and fuzzy logic [8][9][10], neural networks, multi-attribute decision-making (MADM) methods [11][12][13][14][15], and hybrid methods combining more than one methodology [16][17][18][19]. However, the majority of these algorithms are either basic and thus inefficient or too complex that it could cause additional computational overhead, which could, in turn, break the current session.…”

Section: Vh Negotiationmentioning

confidence: 99%

“…By definition, the network selection problem is a MADM problem, and MADM methods are the best to be used in this kind of situation. Thus, we decided to focus in our research works on the use of MADM algorithms in the VH context [11]. Besides their efficiency to resolve this kind of problem, these algorithms are relatively simple to implement and do not require extra computational resources.…”

Section: Vh Negotiationmentioning

confidence: 99%

“…Some authors prefer to apply one type of algorithm for the decision-making issues (e.g. MADM methods) [3,11,23,24]. In [23], the authors present a review and a classification of the most significant MADM algorithms in the context of network selection in heterogeneous wireless networks environments, focusing on the positive and negative points of each decision-making scheme.…”

Section: Related Workmentioning

confidence: 99%

“…They also discussed the criteria choice and its impact on decision making, and finally, they give an overview of the current research trend in the application of MADM algorithms to VH decision problems. In [11], the authors present an overview and a comparative study between different combinations of MADM methods in the VH context, separating the different existing methods used in the different MADM steps, and conclude that TOPSIS and GRA methods are the best to be used in this context when using the appropriate normalisation method. In [24], the authors present an overview of the VH decision schemes using MADM methods and compare them in different terms such as the bandwidth and the packet delay, taking into consideration multiple criteria (e.g.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

New Manhattan distance‐based fuzzy MADM method for the network selection

Mansouri

Leghris

2019

IET Communications

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Section: Vh Negotiationmentioning

confidence: 99%

Section: Vh Negotiationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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New Manhattan distance‐based fuzzy MADM method for the network selection

Mansouri

Leghris

2019

IET Communications

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“…Fuzzy Multi-Attribute Decision is a method for finding the optimal alternative from some alternatives to a particular criterion [14]. FMADM is the core of determining the weight value for each attribute and the ranking of the process that will select the given alternatives.…”

Section: Fmadmmentioning

confidence: 99%

Collaboration FMADM And K-Means Clustering To Determine The Activity Proposal In Operational Management Activity

Awangga

Pane

Tunnisa³

2019

emitter

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Indonesian government agencies under the Ministry of Energy and Mineral Resources still use manual methods in determining and selecting proposals for operational activities to be carried out. This study uses the Decision Support System (DSS) method, namely Fuzzy Multiple Attribute Decision Decision (Fmadm) and K-Means Clustering method in managing Operational Plan activities. Fmadm to select the best alternative from a number of alternatives, alternatives from this study proposed activity proposals, then ranking to determine the optimal alternative. The K-Means Clustering Method to obtain cluster values for alternatives on the criteria for activity dates, types of activities, and activity ceilings. The last iteration of the Euclidian distance calculation data on k-means shows that alternatives that have the smallest centroid value are important proposal criteria and the largest centroid value is an insignificant proposal criteria. The results of the collaboration of the Fmadm and K-Means Clustering methods show the optimal ranking of activities (proposal activities) and the centroid value of each alternative.

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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

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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.

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The use of MADM methods in the vertical handover decision making context

Cited by 13 publications

References 9 publications

New Manhattan distance‐based fuzzy MADM method for the network selection

New Manhattan distance‐based fuzzy MADM method for the network selection

Collaboration FMADM And K-Means Clustering To Determine The Activity Proposal In Operational Management Activity

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

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