Intelligent Network-Flow Solutions with Risks at Transportation of Products

2021 ◽  
pp. 417-439
Author(s):  
Vassil Sgurev ◽  
Lyubka Doukovska ◽  
Stanislav Drangajov
Keyword(s):  
Network Flow ◽  
Intelligent Network

Power generation scheduling through use of generalised network flow programming

1991 ◽  
Vol 138 (1) ◽  
pp. 39 ◽  
Author(s):  
R.E. Rice ◽  
W.M. Grady ◽  
W.G. Lesso ◽  
A.H. Noyola ◽  
M.E. Connolly
Keyword(s):  
Power Generation ◽  
Network Flow ◽  
Generation Scheduling ◽  
Network Flow Programming

Reliability Comparison Between Redundant Repairable Network Flow Systems

2014 ◽  
Vol 1 (1) ◽  
pp. 42-59
Author(s):  
Ibrahim Yusuf ◽  
◽  
Bashir Yusuf
Keyword(s):  
Network Flow ◽  
Flow Systems

Novel Network Flow Control Model on Multi-Team Game Theory

2010 ◽  
Vol 32 (2) ◽  
pp. 267-271 ◽  
Author(s):  
Hui-bin Feng ◽  
Shun-yi Zhang ◽  
Chao Liu ◽  
Jue-fu Liu
Keyword(s):  
Game Theory ◽  
Flow Control ◽  
Network Flow ◽  
Control Model ◽  
Team Game

Virtual Private Network Flow Detection in Wireless Sensor Networks using Machine Learning Techniques

2021 ◽  
Vol 10 ◽  
Author(s):  
S. Phani Praveen ◽  
T. Bala Murali Krishna ◽  
Sunil K. Chawla ◽  
CH Anuradha
Keyword(s):  
Machine Learning ◽  
Traffic Flow ◽  
Network Flow ◽  
Virtual Private Network ◽  
Machine Learning Techniques ◽  
Protocol Security ◽  
Learning Techniques ◽  
Private Network ◽  
Hyper Text Transfer Protocol ◽  
Transfer Protocol

Background: Every organization generally uses a VPN service individually to leather the actual communication. Such communication is actually not allowed by organization monitoring network. But these institutes are not in a position to spend huge amount of funds on secure sockets layer to monitor traffic over their computer networks. Objective: Our work suggests simple technique to block or detect annoying VPN clients inside the network activities. This method does not requires the network to decrypt or even decode any network communication. Method: The proposed solution selects two machine learning techniques Feature Tree and K-means as classifiction techniques which work on time related features. First, the DNS mapping with the ordinary characteristic of the transmission control protocol / internet protocol computer network stack is identified and it is not to be considered as a normal traiffic flow if the domain name information is not available. The process also examines non-standard utilization of hyper text transfer protocol security and also conceal such communication from hyper text transfer protocol security dependent filters in firewall to detect as anomaly in largely. Results: we define the trafic flow as normal trafic flow and VPN traffic flow. These two flows are characterized by taking two machine learning techniques Feature Tree and K-means. We have executed each experment 4 times. As a result, eight types of regular traffics and eight types of VPN traffics were represented. Conclusion: Once trafic flow is identified, it is classified and studied by machine learning techniques. Using time related features, the traffic flow is defined as normal flow or VPN traffic flow.

Cross-provincial Cross-region Power Trading Optimization Modeling

2020 ◽  
Vol 13 (8) ◽  
pp. 1183-1189
Author(s):  
Jing-wen Chen ◽  
Yan Xiao ◽  
Hong-she Dang ◽  
Rong Zhang
Keyword(s):  
Network Flow ◽  
Optimal Allocation ◽  
Route Optimization ◽  
Regional Power ◽  
Power Resources ◽  
Gray Theory ◽  
Power Trading ◽  
Study Results ◽  
Multi Agent ◽  
Regional Economic Disparities

Background: China's power resources are unevenly distributed in geography, and the supply-demand imbalance becomes worse due to regional economic disparities. It is essential to optimize the allocation of power resources through cross-provincial and cross-regional power trading. Methods: This paper uses load forecasting, transaction subject data declaration, and route optimization models to achieve optimal allocation of electricity and power resources cross-provincial and cross-regional and maximize social benefits. Gray theory is used to predict the medium and longterm loads, while multi-agent technology is used to report the power trading price. Results: Cross-provincial and cross-regional power trading become a network flow problem, through which we can find the optimized complete trading paths. Conclusion: Numerical case study results has verified the efficiency of the proposed method in optimizing power allocation across provinces and regions.

User-Driven Network Selection during Vertical Handover in 4G Networks

2017 ◽  
Vol 10 ◽  
pp. 1
Author(s):  
Abubakar Muhammad Miyim ◽  
Mahamod Ismail ◽  
Rosdiadee Nordin
Keyword(s):  
Blocking Probability ◽  
Service Selection ◽  
Vertical Handover ◽  
Network Selection ◽  
Selection Strategy ◽  
Intelligent Network ◽  
Decision Algorithm ◽  
Radio Access ◽  
Service Application ◽  
Multi Access

The importance of network selection for wireless networks, is to facilitate users with various personal wireless devices to access their desired services via a range of available radio access networks. The inability of these networks to provide broadband data service applications to users poses a serious challenge in the wireless environment. Network Optimization has therefore become necessary, so as to accommodate the increasing number of users’ service application demands while maintaining the required quality of services. To achieve that, the need to incorporate intelligent and fast mechanism as a solution to select the best value network for the user arises. This paper provides an intelligent network selection strategy based on the user- and network-valued metrics to suit their preferences when communicating in multi-access environment. A user-driven network selection strategy that employs Multi-Access Service Selection Vertical Handover Decision Algorithm (MASS-VHDA) via three interfaces; Wi-Fi, WiMAX and LTE-A is proposed, numerically evaluated and simulated. The results from the performance analysis demonstrate some improvement in the QoS and network blocking probability to satisfy user application requests for multiple simultaneous services.

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