A utility based access point selection method for IEEE 802.11 wireless networks with enhanced quality of experience

Author(s):  
Jason B. Ernst ◽  
Stefan Kremer ◽  
Joel J. P. C. Rodrigues
Author(s):  
S. Vasudevan ◽  
K. Papagiannaki ◽  
C. Diot ◽  
J. Kurose ◽  
D. Towsley

2010 ◽  
Vol 2010 ◽  
pp. 1-15
Author(s):  
Mina Malekzadeh ◽  
Abdul Azim Abdul Ghani ◽  
Shamala Subramaniam

In wireless network communications, radio waves travel through free space; hence, the information reaches any receiving point with appropriate radio receivers. This aspect makes the wireless networks vulnerable to various types of attacks. A true understanding of these attacks provides better ability to defend the network against the attacks, thus eliminating potential threats from the wireless systems. This work presents a series of cyberwar laboratory exercises that are designed for IEEE 802.11 wireless networks security courses. The exercises expose different aspects of violations in security such as confidentiality, privacy, availability, and integrity. The types of attacks include traffic analysis, rogue access point, MAC filtering, replay, man-in-the-middle, and denial of service attacks. For each exercise, the materials are presented as open-source tools along with descriptions of the respective methods, procedures, and penetration techniques.


Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 318
Author(s):  
Merima Kulin ◽  
Tarik Kazaz ◽  
Eli De Poorter ◽  
Ingrid Moerman

This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.


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