A Dynamic Cooperative Monitor Node Selection Algorithm in Wireless Mesh Networks

Yao, Yu; Ning, Zhaolong; Song, Qingyang; Guo, Lei; Liu, Hongyan

doi:10.1109/hpcc-css-icess.2015.118

Cited by 4 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors in [17] proposed a dynamic cooperative monitoring interface-selection mechanism based on social network analysis, which can effectively reduce the network cost of direct measurement. Besides, the authors in [18] studied the problem of selecting a set of monitoring interfaces, and proposed a Cross-Layer Security Monitoring selection algorithm based on Traffic Prediction (CLSM-TP). The authors in [19] proposed a mechanism to support the Software Defined Network (SDN) to distribute the monitoring load on multiple switches in the network by monitoring traffic in a distributed manner.…”

Section: Related Workmentioning

confidence: 99%

Traffic Measurement Optimization Based on Reinforcement Learning in Large-Scale ITS-Oriented Backbone Networks

et al. 2020

View full text Add to dashboard Cite

The end-to-end network traffic information is the basis of network management for a large-scale intelligent transportation systems-oriented backbone network. To obtain exact network traffic data, a prevalent idea is to deploy NetFlow or sFlow on all routers of the network. However, this method not only increases operational expenditures, but also affects the network load. Motivated by this issue, we propose an optimized traffic measurement method based on reinforcement learning in this paper, which can collect most of the network traffic data by activating NetFlow on a subset of interfaces of routers in a network. We use the Q-learning-based approach to deal with the problem of the interface-selection. We propose an approach to compute the reward, furthermore a modified Q-learning approach is proposed to handle the problem of interface-selection. The method is evaluated by the real data from the Abilene and GÉANT backbone networks. Simulation results show that the proposed method can improve the efficiency of traffic measurement distinctly. INDEX TERMS Network measurement, reinforcement learning, intelligent transportation systems, IP backbone network.

show abstract

Section: Related Workmentioning

confidence: 99%

Traffic Measurement Optimization Based on Reinforcement Learning in Large-Scale ITS-Oriented Backbone Networks

et al. 2020

View full text Add to dashboard Cite

show abstract

“…2) Confidentiality [4] .Data confidentiality dictates that only the two communicating entities can read the interchanged data. To ensure this our proposed scheme uses encryption along with key-pair LKMN while interchanging data.…”

Section: ) Security and Privacy Requirementsmentioning

confidence: 99%

Preventing & Isolating Distributed Denial of Service (DDOS) attack in Wireless Mesh Networks (WMN's)

2019

IJITEE

View full text Add to dashboard Cite

Wireless Mesh networks (WMN’s) are prone to a number of attacks & these attacks compromise the security of these networks. Attaining security in these networks is a challenging task. It is logical to consider that there are many types of scripts in the internet. The virus can either be a key logger or somebody else's mischief. With this script we can steal any information. Since the existence of virus cannot be ignored, therefore the authors have tried to present their work on first detecting it and later on fixing it. With the help of different protocols present in the Application Layer, a hacker takes information out of the script. The authors have used Covert Channel, which has been mentioned in many essays. Now with the help of this channel, the information will go to all and it will not go to any of the informatics. This research proposal envisions a methodology to first detect the selfish node in the network & later on provides a technique for mitigation of the same.NS2 simulator has been used to simulate & analyze the performance of our proposed methodology for Open Shortest Path First (OSPF) protocol in WMN’s.

show abstract

A Cross-Layer Security Monitoring Selection Algorithm Based on Traffic Prediction

et al. 2018

View full text Add to dashboard Cite

A Dynamic Cooperative Monitor Node Selection Algorithm in Wireless Mesh Networks

Cited by 4 publications

References 16 publications

Traffic Measurement Optimization Based on Reinforcement Learning in Large-Scale ITS-Oriented Backbone Networks

Traffic Measurement Optimization Based on Reinforcement Learning in Large-Scale ITS-Oriented Backbone Networks

Preventing & Isolating Distributed Denial of Service (DDOS) attack in Wireless Mesh Networks (WMN's)

A Cross-Layer Security Monitoring Selection Algorithm Based on Traffic Prediction

Contact Info

Product

Resources

About