A Multi-Attribute Intuitionistic Fuzzy Group Decision Method For Network Selection In Heterogeneous Wireless Networks Using TOPSIS

• Sanjeev Prakash,
• R. B. Patel,
• V. K. Jain,

Vol. 10, No. 11, November 29, 2016
10.3837/tiis.2016.11.002, Download Paper (Free):

• Intuitionistic Fuzzy Sets (IFS)
• Mobile Node (MN)
• Decision Makers (DMs)
• Multi-Attribute Intuitionistic Fuzzy Group Decision Method (MAIFGDM)
• Multiple Attribute Group Decision (MAGD)

Abstract

With proliferation of diverse network access technologies, users demands are also increasing and service providers are offering a Quality of Service (QoS) to satisfy their customers. In roaming, a mobile node (MN) traverses number of available networks in the heterogeneous wireless networks environment and a single operator is not capable to fulfill the demands of user. It is crucial task for MN for selecting a best network from the list of networks at any time anywhere. A MN undergoes a network selection situation frequently when it is becoming away from the home network. Multiple Attribute Group Decision (MAGD) method will be one of the best ways for selecting target network in heterogeneous wireless networks (4G). MAGD network selection process is predominantly dependent on two steps, i.e., attribute weight, decision maker's (DM’s) weight and aggregation of opinion of DMs. This paper proposes Multi-Attribute Intuitionistic Fuzzy Group Decision Method (MAIFGDM) using TOPSIS for the selection of the suitable candidate network. It is scalable and is able to handle any number of networks with large set of attributes. This is a method of lower complexity and is useful for real time applications. It gives more accurate result because it uses Intuitionistic Fuzzy Sets (IFS) with an additional parameter intuitionistic fuzzy index or hesitant degree. MAIFGDM is simulated in MATLAB for its evaluation. A comparative study of MAIFDGM is also made with TOPSIS and Fuzzy-TOPSIS in respect to decision delay. It is observed that MAIFDGM have low values of decision time in comparison to TOPSIS and Fuzzy-TOPSIS methods.

Statistics

Cite this article