.png){kind=icon}
.png){kind=icon}
Return
Pairwise-Based Multi-Attribute Decision Making Approach for Wireless Network
.png){kind=icon}
OA
AI
DOI：10.1109/TNET.2021.3074002.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In wireless network applications, such as routing decision, network selection, etc., the Multi-Attribute Decision Making (MADM) is widely used. The MADM approach can address the multi-objective decision making issues effectively. However, when the parameters vary greatly, the traditional MADM algorithm is not effective anymore. To solve this problem, in this paper, we propose the pairwise-based MADM algorithm. In the PMADM, only two nodes' utilities are calculated and compared at each time. The PMADM algorithm is much more accurate than the traditional MADM algorithm. Moreover, we also prove that the PMADM algorithm is sensitive to the parameters which vary seriously and insensitive to the parameters which change slightly. This property is better than that of the traditional MADM algorithm. Additionally, the PMADM algorithm is more stable than traditional MADM algorithm. For reducing the computational complexity of the PMADM algorithm, we propose the low-complexity PMADM algorithm. For analyzing the computational complexity of the l PMADM algorithm, we propose the tree-based decomposing algorithm in this paper. The l PMADM algorithm has the same properties and performances as that of the PMADM algorithm; however, it is simpler than the PMADM algorithm. The simulation results show that the PMADM and l PMADM algorithms are much more effective than the traditional MADM algorithm.
Keywords:
Multi-attribute decision making
routing
network selection
data center network
wireless network
pairwise-based
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
3.6
Papers:
4.4K
Citations:
9.5K