Network Coding-Based Protection Strategy with Data Traffic Prediction

2011 ◽  
Author(s):  
Feibi Lv ◽  
Guochu Shou ◽  
Yihong Hu ◽  
Zhigang Guo
Keyword(s):  
Network Coding ◽  
Traffic Prediction ◽  
Data Traffic ◽  
Protection Strategy

scholarly journals CELLULAR NETWORK TRAFFIC PREDICTION USING EXPONENTIAL SMOOTHING METHODS

2018 ◽  
Author(s):  
Quang Thanh Tran ◽  
Li Jun Hao ◽  
Quang Khai Trinh
Keyword(s):  
Network Traffic ◽  
Cellular Network ◽  
Low Cost ◽  
Real Data ◽  
Exponential Smoothing ◽  
High Accuracy ◽  
Training Data ◽  
Traffic Prediction ◽  
Data Traffic ◽  
Smoothing Methods

Wireless traffic prediction plays an important role in network planning and management, especially for real-time decision making and short-term prediction. Systems require high accuracy, low cost, and low computational complexity prediction methods. Although exponential smoothing is an effective method, there is a lack of use with cellular networks and research on data traffic. The accuracy and suitability of this method need to be evaluated using several types of traffic. Thus, this study introduces the application of exponential smoothing as a method of adaptive forecasting of cellular network traffic for cases of voice (in Erlang) and data (in megabytes or gigabytes). Simple and Error, Trend, Seasonal (ETS) methods are used for exponential smoothing. By investigating the effect of their smoothing factors in describing cellular network traffic, the accuracy of forecast using each method is evaluated. This research comprises a comprehensive analysis approach using multiple case study comparisons to determine the best fit model. Different exponential smoothing models are evaluated for various traffic types in different time scales. The experiments are implemented on real data from a commercial cellular network, which is divided into a training data part for modeling and test data part for forecasting comparison. This study found that ETS framework is not suitable for hourly voice traffic, but it provides nearly the same results with Holt–Winter’s multiplicative seasonal (HWMS) in both cases of daily voice and data traffic. HWMS is presumably encompassed by ETC framework and shows good results in all cases of traffic. Therefore, HWMS is recommended for cellular network traffic prediction due to its simplicity and high accuracy.  

Analysis on Disequilibrium of Wireless Network Data Traffic

2015 ◽  
Vol 719-720 ◽  
pp. 687-690
Author(s):  
Jian Guan ◽  
Xin Zhou Cheng
Keyword(s):  
Research Methods ◽  
Wireless Network ◽  
Prediction Method ◽  
Traffic Prediction ◽  
Network Data ◽  
Data Traffic

This paper analyzes the disequilibrium of wireless network data traffic, gives analysis and research methods for regional disequilibrium and time disequilibrium characteristics, and proposes a data traffic prediction method for regional users in consideration of redundant traffic.

Parallel CNN based big data visualization for traffic monitoring

2020 ◽  
Vol 39 (3) ◽  
pp. 2679-2691
Author(s):  
G. Madhukar Rao ◽  
Dharavath Ramesh
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Temporal Dynamics ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Traffic Prediction ◽  
Data Traffic ◽  
Traffic Condition

In a real-time application such as traffic monitoring, it is required to process the enormous amount of data. Traffic prediction is essential for intelligent transportation systems (ITSs), traffic management authorities, and travelers. Traffic prediction has become a challenging task due to various non-linear temporal dynamics at different locations, complicated underlying spatial dependencies, and more extended step forecasting. To accommodate these instances, efficient visualization and data mining techniques are required to predict and analyze the massive amount of traffic big data. This paper presents a deep learning-based parallel convolutional neural network (Parallel-CNN) methodology to predict the traffic conditions of a specific region. The methodology of deep learning contains multiple processing layers and performs various computational strategies, which is used to learn representations of data with multilevel abstraction. The data has captured from the department of transportation; thus, the size of data is vast, and it can be analyzed to get the behavior of the traffic condition. The purpose of this paper is to monitor traffic behavior, which enables the user to make decisions to build the traffic-free cities. Experimental results show that the proposed methodology outperforms other existing methods such as KNN, CNN, and FIMT-DD.

Mobile Data Traffic Prediction by Exploiting Time-Evolving User Mobility Patterns

2021 ◽  
pp. 1-1
Author(s):  
Feiyang Sun ◽  
Pinghui Wang ◽  
Junzhou Zhao ◽  
Nuo Xu ◽  
Juxiang Zeng ◽  
...  
Keyword(s):  
Traffic Prediction ◽  
User Mobility ◽  
Data Traffic ◽  
Mobility Patterns ◽  
Mobile Data
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