A Game-Theoretic Analysis of User Behaviors in Crowdsourced Wireless Community Networks

The Network Communications and Economics Lab (NCEL) led by Prof. Jianwei Huang, Department of Information Engineering, CUHK, has recently made a comprehensive analysis of the user behaviors in crowd-sourced Wi-Fi community networks. The research team co-authored by Miss Qian MA, Dr. Lin GAO, and Prof. Jianwei Huang demonstrated that such a novel Wi-Fi network scenario can help to expand the Wi-Fi coverage with a low cost, by incentivizing individual users share their private home Wi-Fi Access Points (APs) with each other. This work won the Best Student Paper Award in IEEE WiOpt 2015, a leading wireless conference focusing on modeling and optimization of wireless networks. 

Driven by the explosive growth of smart mobile device (such as smartphones and tablets) and bandwidth-hunger applications (such as mobile video streaming and Web/File/VoIP), Wi-Fi networks are playing an increasingly important role in carrying a significant amount of mobile data traffic. According to the forecast of Cisco VNI, by the year of 2019, the amount of traffic from smartphones carried by Wi-Fi networks will be 54%, and the amount of traffic from tablets carried by Wi-Fi networks will be 70%.The fast growth of Wi-Fi technology and network is due to several factors, including the low costs of Wi-Fi APs, simple installation, easy management, and high transmission data rates. However, the deployment of large-scale and seamless Wi-Fi networks is often restricted by the limited coverage of each single Wi-Fi AP (typically tens of meters indoors). Hence, despite of the low cost of each Wi-Fi AP, it is often very expensive to deploy enough Wi-Fi APs to entirely cover a large area such as a city or a nation.

The crowd-sourced Wi-Fi community network turns out as a promising solution to expand the Wi-Fi coverage with a low cost. The key idea is to encourage individuals (users) to share their private owned Wi-Fi APs with each other, hence crowdsource the coverage of these private Wi-Fi APs. Such a novel network scenario can fully utilize the capacity of millions of private Wi-Fi APs already installed, hence reducing the requirement of new installations by any single operator. Meanwhile, each user also benefits from joining such a community network, as he can use not only his own AP when staying at home, but also other users' APs when traveling.

One prominent commercial example of such a Wi-Fi community networks is FON, the world largest Wi-Fi operator, which has more than 15 million member Wi-Fi APs globally by May 2015. In FON, the operator incentivizes its customers (users) to share their private home APs with others, by using two different incentive schemes, corresponding to two kinds of memberships: Linus and Bill. As a Linus, a user can use other FON members' APs free of charge, and cannot receive any compensation when other users access his AP. As a Bill, a user needs to pay for using other APs, and meanwhile can receive certain compensation when other users access his AP. Moreover, the above community network is also open for users without owning APs, often called Aliens, who needs to pay for using any AP in the FON network.

Clearly, the success of such a crowd-sourced Wi-Fi network greatly depends on the active participations and contributions of many individual users with private Wi-Fi APs, and hence requires the careful design of a proper economic incentive mechanism. Through the study of user behaviors in crowd-sourced wireless community networks, Prof. Jianwei Huang and his team hope to reveal insight into the underlying economic principles in the crowd-sourced wireless community networks, provide some guideline for the operator to design pricing and incentive mechanism, and eventually promote the long-term and sustainable development of such a novel network scenario.

User Behavior Analysis in the Crowd-sourced Wi-Fi Community Network

A comprehensive analysis of user behaviors is essential for the success of a crowd-sourced Wi-Fi community network. The CUHK research team proposes a two-stage dynamic game model to study user behaviors, where stage I is the users’ membership selections and stage II is the users’ Wi-Fi connection time decisions. In this two-stage dynamic game model proposed by Prof. Huang and his team, users choose the memberships of Linus or Bill in stage I, by comparing the achievable benefits under the two different memberships. Then in stage II, users decide the Wi-Fi connection time on each Wi-Fi AP that he is traveling, taking the network congestion into consideration. The study explores how different users choose different decisions in their membership selections and network connections. The results show that a user with a more popular home location, a smaller travel time, or a smaller network access evaluation is more likely to choose the Bill membership type. The results also show that the Wi-Fi AP with a larger data rate or a smaller price will attract users to connect to it for a longer time.

Through the two-stage dynamic game model, users are able to make the best choices of their memberships when joining the crowd-sourced network, and the best choices of their Wi-Fi connection times when roaming at others’ APs considering the network congestion level. The community network operator is able to design the best pricing and incentive mechanism, hence achieving a win-win situation.

About Network Communications and Economics Lab

The Network Communications and Economics Lab (NCEL) was formed in 2007 by Prof. Jianwei Huang, focusing on the interdisciplinary research among communications, networking, and economics.  The NCEL team has published around 180 papers in top international journals and conferences, with a total citation of around 5000 times. The NCEL's research results have received 8 Best Papers Awards in international venues, including the 2011 IEEE Marconi Prize Paper Award in Wireless Communications from IEEE Communications Society and IEEE Signal Processing Society. Four papers from NCEL are among the ESI Highly Cited Papers in the field of Computer Science, which are the 1% top papers in terms of citations within the field according to Essential Science Indicators from Web of Science. 

The co-authors of this awarding winning work also include Ms. Qian Ma, Dr. Lin Gao, and Prof. Yafeng Liu (from Chinese Academy of Science). Ms. Ma is a PhD student under the supervision of Prof. Jianwei Huang. Dr. Lin Gao is a Postdoc Research Fellow in Prof. Jianwei Huang’s team, and received the Best Paper Awards from IEEE WiOpt in 2015, 2014, and 2013.