scholarly journals Enhancing network transmission capacity by efficiently allocating node capability

2011 ◽  
Vol 390 (2) ◽  
pp. 387-391 ◽  
Author(s):  
Guo-Qing Zhang ◽  
Shi Zhou ◽  
Di Wang ◽  
Gang Yan ◽  
Guo-Qiang Zhang
Keyword(s):  
Transmission Capacity ◽  
Network Transmission

Optimal topologies for maximizing network transmission capacity

2018 ◽  
Vol 495 ◽  
pp. 191-201 ◽  
Author(s):  
Zhenhao Chen ◽  
Jiajing Wu ◽  
Zhihai Rong ◽  
Chi K. Tse
Keyword(s):  
Transmission Capacity ◽  
Network Transmission

Analysis of Packet Transmission Delay in Vehicular DC-ARQ

2013 ◽  
Vol 336-338 ◽  
pp. 1722-1725
Author(s):  
Suo Ping Li ◽  
Yong Wei
Keyword(s):  
Information Transmission ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Packet Transmission ◽  
Transmission Delay ◽  
Transmission Capacity ◽  
Analytical Expression ◽  
Transmission Cycle ◽  
Delay Performance ◽  
Network Transmission

Considering the DC-ARQ mechanism, this paper discussed the information transmission cycle of between vehicle nodes in the vehicle and road environment, and obtained the analytical expression of delay performance for GBN-ARQ retransmission protocol. The results obtained in the paper provide a way to improve vehicle ad-hoc network transmission capacity.

Multi-priority routing algorithm based on source node importance in complex networks

2019 ◽  
Vol 30 (07) ◽  
pp. 1940010
Author(s):  
Yu Bai ◽  
Ding-Ding Han ◽  
Ming Tang
Keyword(s):  
Routing Algorithm ◽  
Large Degree ◽  
Transmission Capacity ◽  
Source Node ◽  
Normal Operation ◽  
Data Packets ◽  
Node Importance ◽  
Routing Strategy ◽  
Network Transmission ◽  
Important Nodes

Network transmission capacity is an important guarantee for the normal operation of the network. The effective routing strategy avoids the use of nodes with large degree value, which leads to low utilization of nodes and failure to consider the priorities of different packets. On this basis, a routing algorithm based on packet source node classification is proposed. This strategy introduces an adjustable parameter. By adjusting this parameter, the data packets generated at the important nodes are transferred to the nodes with higher degree, which is to say they can reach the destination faster. The data packets generated at the sub-important nodes are transmitted by nodes with smaller degrees, thus reaching the destination relatively slowly. The routing strategy is evaluated in terms of order parameters, average routing time and node utilization. Compared with nonclassified routing and randomly classified routing strategy, the network transmission capacity was increased by 19% and 38%, respectively. Each node in the network was used more evenly. At the same time, the network transmission capacity under different parameters is analyzed theoretically through a series of derivations. In order to explore the performance of routing strategy in actual networks, this paper selects the actual network of web-EPA for simulation. The experimental results show that the proposed routing strategy is 7% and 17% higher than the nonclassified routing and random classified routing, respectively.

scholarly journals Improvement of the Intelligent Tutor by Identifying the Face of the E-Learner's

2019 ◽  
Vol 6 (2) ◽  
pp. 112-119
Author(s):  
El-Kaber Hachem
Keyword(s):  
Image Representation ◽  
Transmission Capacity ◽  
Facial Image ◽  
Learning Platform ◽  
The Face ◽  
Processing Module ◽  
Network Transmission ◽  
Intelligent Tutor ◽  
Client Device

As part of our project which aims at the realization of a system named ASTEMOI In this article, we display a new and productive facial image representation based on the Local Sensitive Hash (LSH). This technique makes it possible to recognize the learners who follow their training in our learning platform. Once recognized, the student must be oriented towards an appropriate profile that takes into account his strengths and weaknesses. We also use a light processing module on the client device with a compact code so that we don’t need a lot of bandwidth, a lot of network transmission capacity to send the feature over the network, and to be able to index many pictures in a huge database in the cloud.

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