QoS-guarantee Method for Public Wireless LAN Access Environments

Matsumoto, M.; Itoh, T.

doi:10.1109/wirles.2005.1549392

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…Ryan W Thomas et al, [14] identified three dominant factors which are required to be considered for forming a cognitive network. These factors are the characteristics of the decisionmaking elements, the details of computational state accessible to those entities, and the extent of control they posses for estimating the consequences of its design tradeoffs, the author have developed a metric known as "price of a feature."…”

Section: Literature Surveymentioning

confidence: 99%

Reinforcement based Cognitive Algorithms to Detect Malicious Node in Wireless Networks

Sunilkumar¹,

J²,

Venugopal³

et al. 2015

IJCA

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The growth of wireless communication technologies and its applications leads to many security issues. Malicious node detection is one among the major security issues. Adoption of cognition can detect and Prevent malicious activities in the wireless networks. To achieve cognition into wireless networks, we are using reinforcement learning techniques. By using the existing reinforcement techniques, we have proposed GreedyQ cognitive (GQC) and SoftSARSA cognitive (SSC) algorithms for malicious node detection and the performances among these algorithms are evaluated and the result shows SSC algorithm is best algorithm. The proposed algorithms perform better in malicious node detection as compared to the existing algorithms.

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Section: Literature Surveymentioning

confidence: 99%