A MADM method for network selection in heterogeneous wireless networks

TL;DR

This paper introduces a modified MADM method based on SAW to improve network selection in heterogeneous wireless networks, addressing rank reversal issues and enhancing decision accuracy for better connectivity.

Contribution

The paper proposes a modified SAW-based MADM method that overcomes rank reversal problems in network selection, improving performance in heterogeneous wireless environments.

Findings

The proposed method outperforms existing MADM approaches in simulations.

It effectively addresses rank reversal issues in network selection.

Performance improvements are demonstrated across various scenarios.

Abstract

The coexistence of different Radio Access Technologies (RATs) in the same area has enabled the researchers to get profit from the available networks by the selection of the best RAT at each moment to satisfy the user requirements. The challenge is to achieve the Always Best Connected (ABC) concept; the main issue is the automatic choice of the suitable Radio Access Technology (RAT) from the list of the available RATs. This decision is called network selection (NS). In this paper, we propose a modified Simple Additive Weigh (modified-SAW) function to deal with the drawbacks of the existing solutions. Indeed, the existing Multiple Attribute Decision Making (MADM) methods suffer mainly from the famous problem of rank reversal once an alternative is added or removed, other problems occur in the legacy MADMs. We modify the SAW method intelligently and we use it to solve the NS problem. Finally, we compare the performance of our solution with the previous works in different scenarios; the simulations show that our proposal outperforms the other existing methods