Why It Takes So Long to Connect to a WiFi Access Point

Today's WiFi networks deliver a large fraction of traffic. However, the performance and quality of WiFi networks are still far from satisfactory. Among many popular quality metrics (throughput, latency), the probability of successfully connecting to WiFi APs and the time cost of the WiFi connection set-up process are the two of the most critical metrics that affect WiFi users' experience. To understand the WiFi connection set-up process in real-world settings, we carry out measurement studies on $5$ million mobile users from $4$ representative cities associating with $7$ million APs in $0.4$ billion WiFi sessions, collected from a mobile "WiFi Manager" App that tops the Android/iOS App market. To the best of our knowledge, we are the first to do such large scale study on: how large the WiFi connection set-up time cost is, what factors affect the WiFi connection set-up process, and what can be done to reduce the WiFi connection set-up time cost. Based on the measurement analysis, we develop a machine learning based AP selection strategy that can significantly improve WiFi connection set-up performance, against the conventional strategy purely based on signal strength, by reducing the connection set-up failures from $33\%$ to $3.6\%$ and reducing $80\%$ time costs of the connection set-up processes by more than $10$ times.Comment: 11pages, conferenc

Location and product bundling in the provision of WiFi networks

WiFi promises to revolutionise how and where we access the internet. As WiFi networks are rolled out around the globe, access to the internet will no longer be through fixed networks or unsatisfactory mobile phone connections. Instead access will be through low cost wireless networks at speeds of up to 11Mbps. It is hard not to be impressed by the enthusiasm with which WiFi has been embraced. GREEN, ROSENBUSH, CROKETT and HOLMES (2003) assert that WiFi is a disruptive technology akin to telephones in the 1920s and network computers in the 1990s. WiFi is seen as both an opportunity in its own right, as well as an enabler of opportunities for others. Computer manufacturers are hoping that WiFi will increases sales of their laptops, whilst Microsoft feels that WiFi will result in users upgrading their operating systems to Windows XP. This paper seeks to understand why three companies have sought to provide WiFi

Ruin Theory for Dynamic Spectrum Allocation in LTE-U Networks

LTE in the unlicensed band (LTE-U) is a promising solution to overcome the scarcity of the wireless spectrum. However, to reap the benefits of LTE-U, it is essential to maintain its effective coexistence with WiFi systems. Such a coexistence, hence, constitutes a major challenge for LTE-U deployment. In this paper, the problem of unlicensed spectrum sharing among WiFi and LTE-U system is studied. In particular, a fair time sharing model based on \emph{ruin theory} is proposed to share redundant spectral resources from the unlicensed band with LTE-U without jeopardizing the performance of the WiFi system. Fairness among both WiFi and LTE-U is maintained by applying the concept of the probability of ruin. In particular, the probability of ruin is used to perform efficient duty-cycle allocation in LTE-U, so as to provide fairness to the WiFi system and maintain certain WiFi performance. Simulation results show that the proposed ruin-based algorithm provides better fairness to the WiFi system as compared to equal duty-cycle sharing among WiFi and LTE-U.Comment: Accepted in IEEE Communications Letters (09-Dec 2018

THE RELATIONSHIP BETWEEN THE INTENSITY AND INTERESTS OF SCHOOL FACILITIES WIFI UTILIZATION WITH ICT SUBJECTS LEARNING PERFORMANCE ON STUDENTS OF 1st STATE HIGH SCHOOL (SMAN 1) JETIS BANTUL ON ACADEMIC YEAR OF 2011/2012

This study was conducted to determine the relationship of the intensity on school wifi utilization on learning performance, the relationship between interest on school wifi utilization with learning performance and the relationship between the intensity and interest on the wifi school facilities utilization on learning performance. This study was a corelational research and using quantitative research methodology. The population were the X, XI, and XII class in the Academic Year of 2011/2012 in SMA N 1 Jetis Bantul totaling of 576 students, divided into 18 classes, which were than sampled of 93 students according to Suharsimi Arikunto and using purposive sampling technique. The test instrument was conducted on 30 respondents in the study population beyond the sample. Methods for collecting data were using questionnaires and documentation. Questionnaire method was using to collect the variable data for interests on wifi school facilities utilization. While the documentation a method was using to collect the variable data for intensity on wifi school utilization and the value of students’ learning performance data. The techniques of data analysis was product moment correlation and multiple regression analysis. The criteria for rejection and acceptance of hypothesis test was using a significance level of 5%. The results showed that there was a positive and significant relationship between the intensity of wifi school facilities utilization with students’ learning performance on ICT subjects, it mean that the higher intensity on wifi school facilities utilization, the higher students’ learning performance. There was a positive and significant relationship between the interest of wifi school facilities utilization with the students’ learning performance on ICT subjects, it mean that the higher interest of wifi school facilities utilization, the higher students’ learning performance. There was a positive and significant relationship between the intensity and interest of wifi school facilities utilization with the students’ learning performance on ICT subjects. It could be seen from the determinant coefficient of R2 for 0327. This might imply that the intensity and interest in the use of wifi facilities contribute to the students’ success on ICT subjects of 32% while the remaining was explained by other factors. Keywords: The intensity of wifi school facilities utilization, the interest of wifi school facilities utilization and the learning performance of ICT subject

Tracking Human Mobility using WiFi signals

We study six months of human mobility data, including WiFi and GPS traces recorded with high temporal resolution, and find that time series of WiFi scans contain a strong latent location signal. In fact, due to inherent stability and low entropy of human mobility, it is possible to assign location to WiFi access points based on a very small number of GPS samples and then use these access points as location beacons. Using just one GPS observation per day per person allows us to estimate the location of, and subsequently use, WiFi access points to account for 80\% of mobility across a population. These results reveal a great opportunity for using ubiquitous WiFi routers for high-resolution outdoor positioning, but also significant privacy implications of such side-channel location tracking