Performance Evaluation of a Self-organized Hierarchical Topology for Update of Replicas in a Large Distributed System

2005 ◽  
pp. 90-101
Author(s):  
Jesús Acosta-Elias ◽  
B. Pineda Reyes ◽  
E. Chavez Leos ◽  
Alejandro Ochoa-Cardiel ◽  
Mario Recio ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Distributed System ◽  
Hierarchical Topology ◽  
Self Organized

Comparative Performance Analysis of Flat and Hierarchical Routing in Wireless Sensor Network

2014 ◽  
Vol 685 ◽  
pp. 587-590
Author(s):  
Qing Tan ◽  
Xiao Jing Yue
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Routing Protocols ◽  
Wireless Sensor ◽  
Topology Structure ◽  
Comparative Performance ◽  
Hierarchical Routing ◽  
Advantages And Disadvantages ◽  
Hierarchical Topology ◽  
Self Organized

Wireless sensor network is composed of a set of sensors in a wireless network of self-organized. Research on topological structure of wireless sensor network is focused in two directions, namely plane topology structure and hierarchical topology structure. This paper gives a detailed analysis of wireless sensor networks in the flat routing protocols and hierarchical routing protocols, and compares the advantages and disadvantages of the performance of two kinds of protocol by simulation experiment.

scholarly journals The comparison of Self-organized map and k-means algorithms used for 4G network performance evaluation

2021 ◽  
Author(s):  
Shaoxuan Wang
Keyword(s):  
Performance Evaluation ◽  
Mobile Networks ◽  
Network Performance ◽  
Clustering Algorithms ◽  
Correlation Coefficients ◽  
Clustering Methods ◽  
Data Set ◽  
Self Organized ◽  
Degree Correlation ◽  
Significant Difference

Abstract With the increasing complexity of mobile networks, it has become more and more difficult to perform effective management of mobile networks, which has led to more data to be evaluated and optimized. This article focuses on the performance evaluation of Long Term Evolution (LTE) networks by using two unsupervised learning techniques. Besides, this paper aims to identify the pros and cons of these two clustering algorithms as well. To achieve the above goals, different dimensional datasets for learning a process based on two classic unsupervised clustering methods are introduced to this work. A Self-organized map (SOM) neural network and k-means are as a comparison algorithm and the sample data with three different degree correlation coefficients features with 63 LTE cells, which is from a major European city. The purpose behind using these two methods is to see how different dimensions of the datasets can be used for testing clustering effectiveness and we propose a method based on the features extracted from key performance indicators (KPIs) and Euclidean distance is used as the evaluation standard for the distance between different clusters and samples within clusters. The comparing results show that k-means has a better cluster performance in low dimension data set, whereas the SOM’s performance unsatisfactory. On the other hand, the SOM’s clustering performance is better than k-means in high dimension and big data set and it could visualize results. It was verified that there is a significant difference in the obtained results using different clustering algorithms.

Sign in / Sign up

Export Citation Format

Share Document