大规模实证研究揭示：Wi-Fi连接耗时问题及其影响因素

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本文主要探讨了为什么在现实世界中的Wi-Fi网络连接过程中，用户常常会经历长时间的等待才能成功接入WiFi接入点（WiFi AP）。随着移动通信技术的发展，Wi-Fi已经成为数据传输的重要渠道，承载了大量流量。然而，尽管Wi-Fi网络的普及度很高，但其性能和质量仍然存在显著的改进空间，尤其是在连接速度和稳定性方面。研究者针对这一问题，对来自全球四大代表性城市的500万移动用户进行了深入的研究，这些用户在0.4亿次Wi-Fi会话中尝试连接700万个AP。他们利用一款市场领先、覆盖Android和iOS平台的移动应用——“WiFi Manager”，收集了大量的用户行为数据，这是目前同类研究中规模空前的一次分析。重点关注的两个关键指标是：Wi-Fi连接成功的概率以及连接设置过程所需的时间成本。这些指标直接关系到用户的上网体验，影响着他们对Wi-Fi服务满意度的评价。通过对大规模用户行为数据的分析，研究人员希望能够揭示影响Wi-Fi连接速度的潜在因素，包括但不限于信号强度、干扰、设备性能、网络负载以及用户位置等。通过实证研究，论文可能会探讨以下几点： 1. **连接成功率与环境因素**：分析不同地理位置、信号强度和干扰水平对连接成功率的影响，确定哪些条件可能阻碍快速连接。 2. **设备性能与连接时间**：探究设备硬件规格（如处理器能力、内存大小）与Wi-Fi连接速度的关系，以及如何优化设备设置以提高连接效率。 3. **网络负载与排队等待**：研究在高网络负荷下，用户连接AP所需的时间是否更长，以及如何通过网络管理策略减轻用户等待压力。 4. **用户行为模式**：了解用户的日常活动和连接习惯，以便预测和优化网络资源分配，减少连接设置的平均时间。 5. **未来技术趋势**：基于当前数据，提出针对Wi-Fi连接速度优化的建议，可能包括新技术的应用，如Wi-Fi 6或更快的协议，以及网络服务质量(QoS)管理。这篇研究论文旨在通过大规模数据驱动的方法，为提升Wi-Fi用户体验提供有价值的洞察，并为网络运营商和设备制造商提供策略指导，以解决实际场景中Wi-Fi连接设置时间过长的问题。

Why It Takes So Long to Connect to

a WiFi Access Point

Changhua Pei

†

, Zhi Wang

, Youjian Zhao

†

, Zihan Wang

, Yuan Meng

†

, Dan Pei

†∗

Yuanquan Peng

‡

, Wenliang Tang

‡

, Xiaodong Qu

‡

†

Tsinghua University

‡

Tencent

Graduate School at Shenzhen, Tsinghua University

†

Tsinghua National Laboratory for Information Science and Technology (TNList)

Abstract— 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 our data-driven

measurement and analysis, we reveal the insights as follows: (1)

Connection set-up failure and large connection set-up time cost

are common in today’s WiFi use. As large as 45% of the users

suffer connection set-up failures, and 15% (5%) of them have

large connection set-up time costs over 5 seconds (10 seconds). (2)

Contrary to the state-of-the-art work, scan, one of the subphase

of four phases in the connection set-up process, contributes the

most (47%) to the overall connection set-up time cost. (3) Mobile

device model and AP model can greatly help us to predict the

connection set-up time cost if we can make good use of the

hidden information. 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.

I. INTRODUCTION

In recent years wireless data trafﬁc has witnessed an expo-

nential rise due to the explosion of smart devices. Among

these wireless networks, 802.11 wireless LAN (WiFi) has

served a dominate fraction of today’s wireless trafﬁcs. In the

past decade, over 1 billion WiFi APs (Access Points) have

been sold and deployed to provide wireless connectivity [1].

WiFi hotspots that are found everywhere today have been used

even when users are using smart devices with 3G/4G cellular

networks supported.

However, network performance and user experience in WiFi

networks are still not satisfactory: based on our measurement

studies over 5 million users using WiFi networks in urban

areas, as large as 45% of mobile devices fail in establishing

∗

Dan Pei is the corresponding author.

a WiFi connection with the corresponding APs and 15%

(5%) of successful WiFi connections suffer connection set-up

time costs over 5 seconds (10 seconds). It is thus critical to

understand the WiFi connection set-up process, the ﬁrst step

to use a WiFi network, including how the WiFi connection

set-up performance is, why there is a signiﬁcant fraction

of connection failures and large connection set-up time cost

events, and what can be done to reduce the WiFi connection

set-up time cost.

Previous measurement studies on WiFi networks have been

focused on general user experience metrics (e.g., bandwidth

and latency experienced in WiFi networks) and few focus on

the performance of WiFi connection set-up process. [2] gives

the ﬁrst try to attract people’s attention on the connection set-

up time cost. They collected data from a handful of voluntary

Android smartphones in controlled environment and observed

that the large connection set-up time cost is mainly caused

by the loss of DHCP packets. However, the performance of

WiFi connection set-up processes in the wild still remain

unknown and there lacks thoroughly investigation in a larger

scale. In our paper, we aim to address this problem. We

collected connection log data from millions of mobile devices

which are equipped with a popular Android App called “WiFi

Manager”. Firstly, the “WiFi Manager” App enables us to

break down the connection set-up process to sub-phases to ﬁnd

some observations which are not discovered before. Secondly,

the large-scale data collection enables us to train a machine

learning model which can help us to predict the connection

set-up time cost right before the user’s connection attempt.

This model can be further used to help mobile users select the

best APs to reduce the connection set-up time costs.

Our contributions can be summarized as follows.

In the macro-level, we explore how the connection set-

up time cost looks like in the wild. We continuously col-

lect connection log from 5 million distinct mobile devices

equipped with “WiFi Manager” App and collect 0.4 billion

WiFi connection attempts. We ﬁnd 45% of connection attempts

fail. Among those successful connection attempts, 15% (5%)

are larger than 5 seconds (10 seconds). Based on the x-y vi-

sualization and correlation analysis (e.g., Relative Information

Gain), we discover that besides the well-known signal strength

which affects the connection set-up processes, knowing the AP

model and the mobile device model has great help to predict

the connection set-up time cost. This is mainly because APs

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