Entire network load‐aware cooperative routing algorithm for video streaming over mobile ad hoc networks

Kwon, Oh Chan; Oh, Hyung Rai; Lee, Zae-Kwun; Lee, GyeongCheol; Song, Hwangjun

doi:10.1002/wcm.1169

Cited by 5 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Literature [4] mainly discusses the application of two kinds of neural networks in information routing optimization. In document [5], LEE and CHANG applied ANN to study the…”

Section: Since December 2019 Wuhan City Hubeimentioning

confidence: 99%

An ad-hoc network routing algorithm based on improved neural network under the influence of COVID-19

Kai

2020

IFS

View full text Add to dashboard Cite

Under the influence of COVID-19, an efficient Ad-hoc network routing algorithm is required in the process of epidemic prevention and control. Artificial neural network has become an effective method to solve large-scale optimization problems. It has been proved that the appropriate neural network can get the exact solution of the problem in real time. Based on the continuous Hopfield neural network (CHNN), this paper focuses on the study of the best algorithm path for QoS routing in Ad-hoc networks. In this paper, a new Hopfield neural network model is proposed to solve the minimum cost problem in Ad-hoc networks with time delay. In the improved version of the path algorithm, the relationship between the parameters of the energy function is provided, and it is proved that the feasible solution of the network belongs to the category of progressive stability by properly selecting the parameters. The calculation example shows that the solution is not affected by the initial value, and the global optimal solution can always be obtained. The algorithm is very effective in the prevention and control in COVID-19 epidemic.

show abstract

“…Literature [4] mainly discusses the application of two kinds of neural networks in information routing optimization. In document [5], LEE and CHANG applied ANN to study the…”

Section: Since December 2019 Wuhan City Hubeimentioning

confidence: 99%

An ad-hoc network routing algorithm based on improved neural network under the influence of COVID-19

Kai

2020

IFS

View full text Add to dashboard Cite

show abstract

“…This method reduces the time slots while assuring the necessary time slots at all the adjacent nodes along the route. In addition, the introduced routing method can allocate the network load across the whole network [12].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Routing for Improving the Lifetime of Wireless Ad-Hoc Networks

Unnikrishnan¹,

Das²

2022

IJASIS

View full text Add to dashboard Cite

In Wireless Ad-hoc Networks (WANET), route detection is the main issue. In the usual route detection method, the sender itself discovers the route to the receiver based on the shortest path. In this path, the sender node does not require knowledge of the in-between nodes, and the sender node transmits the information to the in-between nodes. The in-between nodes transmit the data to the near node that receives it. This procedure will be maintained till the information reaches the receiver node. The main disadvantage of usual route detection is that the node is highly moved; thus, the transmitted data packet will be dropped. A Cooperative Routing for Improving Lifetime (CRIL) in WANET is introduced to solve these issues. This approach aims to enhance the WANET lifetime and minimize the cost of route discovery. This approach uses the fresher encounter algorithm with energyefficient routing to improve network lifetime. It is a simple algorithm to efficiently discover the routes in WANET.

show abstract

“…In heterogeneous wireless networks where different networks are available concurrently , network selection is of great importance for improving users' communication quality . Most of existing approaches compare the networks' performance and select the best one to access , known as “always best connected.” Note that the networks' performance is evaluated on the basis of network parameters such as bandwidth, delay, and price.…”

Section: Introductionmentioning

confidence: 99%

Learning with handoff cost constraint for network selection in heterogeneous wireless networks

Yang

2014

Wireless Communications

View full text Add to dashboard Cite

In heterogeneous wireless networks, network selection algorithms provide the user with the optimum network access choice. The optimal network is evaluated according to network parameters. Considering that the network parameters are dynamic and unavailable for the user in realistic heterogeneous wireless network environments, most existing network selection algorithms cannot work effectively. Learning‐based algorithms can address the problem of uncertain network parameters, while they commonly need considerable network handoff, resulting in unbearable handoff cost. In order to tackle the uncertainty of network parameters, we formulate the network selection problem as a multi‐armed bandit problem. Moreover, two online learning‐based network selection algorithms with a special consideration on reducing network handoff cost are proposed. By updating in a block manner, both algorithms achieve optimal logarithmic‐order regret and limited network handoff cost. The simulation indicates that the two algorithms can significantly reduce the network handoff cost and improve the transmission performance compared with existing algorithms, simultaneously. Copyright © 2014 John Wiley & Sons, Ltd.

show abstract

Entire network load‐aware cooperative routing algorithm for video streaming over mobile ad hoc networks

Cited by 5 publications

References 27 publications

An ad-hoc network routing algorithm based on improved neural network under the influence of COVID-19

An ad-hoc network routing algorithm based on improved neural network under the influence of COVID-19

Cooperative Routing for Improving the Lifetime of Wireless Ad-Hoc Networks

Learning with handoff cost constraint for network selection in heterogeneous wireless networks

Contact Info

Product

Resources

About