IEEE802.11,known as WiFi has proliferated in the last decade.It can be found in smartphones,laptops,smart TVs and surveillance cameras.This popularity has revealed many issues in health,data privacy and security.In th...IEEE802.11,known as WiFi has proliferated in the last decade.It can be found in smartphones,laptops,smart TVs and surveillance cameras.This popularity has revealed many issues in health,data privacy and security.In this work,a WiFi measurement study has been conducted in Amman,the capital city of Jordan.An Android App has been written to harvest WiFi information of the transmitted frames of any surrounding Access points(APs).More than 240,000 APs information has been harvested in this work.The harvested data have been analyzed to find statistics ofWiFi devices in this city.Moreover,three power distribution models have been derived from the data for three different areas,closed,open and hybrid areas.In addition,the collected data revealed that the SSID can be leveraged as a landmark for the access points(APs).To this end,SSIDtrack algorithm is proposed to track shoppers/walkers in closed areas,such as malls to find their walking route utilizing only the SSID information collected from the surrounding area.The algorithm has been tested in two different malls that consist of four different floors.The accuracy recorded for the algorithm acceded 95%.展开更多
Wi-Fi technology has evolved significantly since its introduction in 1997,advancing to Wi-Fi 6 as the latest standard,with Wi-Fi 7 currently under development.Despite these advancements,integrating machine learning in...Wi-Fi technology has evolved significantly since its introduction in 1997,advancing to Wi-Fi 6 as the latest standard,with Wi-Fi 7 currently under development.Despite these advancements,integrating machine learning into Wi-Fi networks remains challenging,especially in decentralized environments with multiple access points(mAPs).This paper is a short review that summarizes the potential applications of federated reinforcement learning(FRL)across eight key areas of Wi-Fi functionality,including channel access,link adaptation,beamforming,multi-user transmissions,channel bonding,multi-link operation,spatial reuse,and multi-basic servic set(multi-BSS)coordination.FRL is highlighted as a promising framework for enabling decentralized training and decision-making while preserving data privacy.To illustrate its role in practice,we present a case study on link activation in a multi-link operation(MLO)environment with multiple APs.Through theoretical discussion and simulation results,the study demonstrates how FRL can improve performance and reliability,paving the way for more adaptive and collaborative Wi-Fi networks in the era of Wi-Fi 7 and beyond.展开更多
基金This work is funded by Al-Zaytoonah University of Jordan under project name“miniature distributed architecture for massive data processing”with the grant number 15/12/2019-2020。
文摘IEEE802.11,known as WiFi has proliferated in the last decade.It can be found in smartphones,laptops,smart TVs and surveillance cameras.This popularity has revealed many issues in health,data privacy and security.In this work,a WiFi measurement study has been conducted in Amman,the capital city of Jordan.An Android App has been written to harvest WiFi information of the transmitted frames of any surrounding Access points(APs).More than 240,000 APs information has been harvested in this work.The harvested data have been analyzed to find statistics ofWiFi devices in this city.Moreover,three power distribution models have been derived from the data for three different areas,closed,open and hybrid areas.In addition,the collected data revealed that the SSID can be leveraged as a landmark for the access points(APs).To this end,SSIDtrack algorithm is proposed to track shoppers/walkers in closed areas,such as malls to find their walking route utilizing only the SSID information collected from the surrounding area.The algorithm has been tested in two different malls that consist of four different floors.The accuracy recorded for the algorithm acceded 95%.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia,grant number RG-2-611-42(A.O.A.).
文摘Wi-Fi technology has evolved significantly since its introduction in 1997,advancing to Wi-Fi 6 as the latest standard,with Wi-Fi 7 currently under development.Despite these advancements,integrating machine learning into Wi-Fi networks remains challenging,especially in decentralized environments with multiple access points(mAPs).This paper is a short review that summarizes the potential applications of federated reinforcement learning(FRL)across eight key areas of Wi-Fi functionality,including channel access,link adaptation,beamforming,multi-user transmissions,channel bonding,multi-link operation,spatial reuse,and multi-basic servic set(multi-BSS)coordination.FRL is highlighted as a promising framework for enabling decentralized training and decision-making while preserving data privacy.To illustrate its role in practice,we present a case study on link activation in a multi-link operation(MLO)environment with multiple APs.Through theoretical discussion and simulation results,the study demonstrates how FRL can improve performance and reliability,paving the way for more adaptive and collaborative Wi-Fi networks in the era of Wi-Fi 7 and beyond.
文摘为了提高W PAN应用环境下IEEE802.11b的吞吐量,首先分析IEEE802.11b DCF协议应用时的数据包传输时序,得到IEEE802.11b系统的吞吐量性能与包长及包错误概率的关系,然后对W PAN干扰网络存在时包错误概率与PLCP服务数据单元(PSDU,PLCP service data u-nit)值之间的关系进行建模.在理论上证明了在一定的W PAN干扰强度下,IEEE802.11b系统存在一个最佳的数据包长度,可以使得系统的吞吐量最大.在此基础上提出了W PAN干扰网络存在时的自适应包长方案.仿真结果表明,所提方案可以有效地提高网络吞吐量.
