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.

scholarly journals Tensor Decomposition for Spatial—Temporal Traffic Flow Prediction with Sparse Data

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6046
Author(s):  
Funing Yang ◽  
Guoliang Liu ◽  
Liping Huang ◽  
Cheng Siong Chin
Keyword(s):  
Traffic Flow ◽  
Prediction Method ◽  
Sparse Data ◽  
Time Of Day ◽  
Traffic Prediction ◽  
Traffic Surveillance ◽  
Data Sparsity ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Sparsity Problem

Urban transport traffic surveillance is of great importance for public traffic control and personal travel path planning. Effective and efficient traffic flow prediction is helpful to optimize these real applications. The main challenge of traffic flow prediction is the data sparsity problem, meaning that traffic flow on some roads or of certain periods cannot be monitored. This paper presents a transport traffic prediction method that leverages the spatial and temporal correlation of transportation traffic to tackle this problem. We first propose to model the traffic flow using a fourth-order tensor, which incorporates the location, the time of day, the day of the week, and the week of the month. Based on the constructed traffic flow tensor, we either propose a model to estimate the correlation in each dimension of the tensor. Furthermore, we utilize the gradient descent strategy to design a traffic flow prediction algorithm that is capable of tackling the data sparsity problem from the spatial and temporal perspectives of the traffic pattern. To validate the proposed traffic prediction method, case studies using real-work datasets are constructed, and the results demonstrate that the prediction accuracy of our proposed method outperforms the baselines. The accuracy decreases the least with the percentage of missing data increasing, including the situation of data being missing on neighboring roads in one or continuous multi-days. This certifies that the proposed prediction method can be utilized for sparse data-based transportation traffic surveillance.

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.  

Data Security in Wired and Wireless Systems

2019 ◽  
pp. 1213-1240
Author(s):  
Abhinav Prakash ◽  
Dharma Prakash Agarwal
Keyword(s):  
Wireless Network ◽  
Data Security ◽  
Security Protocols ◽  
Wireless Systems ◽  
Main Tool ◽  
Security Protocol ◽  
Network Data ◽  
Wireless Technologies ◽  
Secured Communication ◽  
Different Parts

The issues related to network data security were identified shortly after the inception of the first wired network. Initial protocols relied heavily on obscurity as the main tool for security provisions. Hacking into a wired network requires physically tapping into the wire link on which the data is being transferred. Both these factors seemed to work hand in hand and made secured communication somewhat possible using simple protocols. Then came the wireless network which radically changed the field and associated environment. How do you secure something that freely travels through the air as a medium? Furthermore, wireless technology empowered devices to be mobile, making it harder for security protocols to identify and locate a malicious device in the network while making it easier for hackers to access different parts of the network while moving around. Quite often, the discussion centered on the question: Is it even possible to provide complete security in a wireless network? It can be debated that wireless networks and perfect data security are mutually exclusive. Availability of latest wideband wireless technologies have diminished predominantly large gap between the network capacities of a wireless network versus a wired one. Regardless, the physical medium limitation still exists for a wired network. Hence, security is a way more complicated and harder goal to achieve for a wireless network (Imai, Rahman, & Kobara, 2006). So, it can be safely assumed that a security protocol that is robust for a wireless network will provide at least equal if not better level of security in a similar wired network. Henceforth, we will talk about security essentially in a wireless network and readers should assume it to be equally applicable to a wired network.

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