Using Fuzzy Gray Relational Analysis in the Vertical Handover Process in Wireless Networks

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

“…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.…”

“…Then, we tried the combination of MADM methods and fuzzy sets in the network selection context [18]. The fuzzy theory was developed to deal with the vagueness and uncertainties of the metrics in the industry, so as to make more precise and efficient decisions [8,10].…”

“…In this contribution, we present a new fuzzy MADM method, based on Manhattan distance, named fuzzy Manhattan distance to the ideal alternative (FMDIA). This method is compared through simulation with other MADM methods such as the technique for ordering the preferences by similarity to ideal solution (TOPSIS) or grey relational analysis (GRA) [13] and fuzzy GRA (FGRA) [18], and the results demonstrate that this new method outperforms those compared.…”

A Use of Fuzzy TOPSIS to Improve the Network Selection in Wireless Multiaccess Environments

“…We found in the literature more complex theories, applied in the network selection context, such as fuzzy logic [5], gaming theory [6], neural networks, and Multiattribute Decision-Making (MADM) methods [7]. Other works propose hybrid methods that combine more than one theory to make the decision in different contexts [8][9][10][11]. MADM methods are the best match techniques, for the network selection problem, because they can consider multiple attributes in the decision.…”

“…ey also concluded that fuzzy weighting techniques are better because they consider the uncertainties and vagueness in the judgments. e authors of [9] use fuzzy GRA (FGRA) in the VH context; they conclude that FGRA enables the MT to make steadier decisions, minimizing the overall VH number, while avoiding unnecessary VHs.…”