The PI’s research philosophy is to conduct research that has theoretic foundation and impact on emerging social networking research and wireless network standards. In recent years, the PI focuses on network simulation platform, game theoretic model for wireless networks, and social learning framework for social networks. The aim of our research is to achieve both academic value and industrial impact.

Game Theory for Wireless Networks

The PI has been applying game theoretic model to communication and networking problems, and is one of the leading experts in Taiwan working on this area. We studied the emerging new concepts in next-generation wireless networks, such as cognitive radio, heterogeneous networks, and device-to-device communications from game-theoretic perspective. In these works, we identified various potential threats from the undesired behaviors of selfish participants, such as mobile users, access points, or self-organized femtocells. In general, we observed that the undesired behaviors cause performance degradations or critical failures to existing protocol and systems if they are not regulated in advance. We therefore utilized game-theoretic approaches to analyze the existing or emerging wireless systems, and proposed novel solutions to prevent undesired performance degradation from such behaviors.

Information Diffusion in Social Networks

The PI also has been applying game theoretic model to social networks. Due to people’s eagerness for interactions with others, online social network has been one of the most popular Internet services in the world. However, the most frustrating element is the overwhelming information users facing while using these applications on social networks. In this research, we investigate the effect of overloaded information on users’ behaviors in terms of the network formation of online social networks. A real-case study on how information is diffused through Twitter under different diffusion mechanisms is presented.

Pioneering Social Learning Framework

The PI established a new game-theoretic model called Chinese Restaurant Game, which is one of the first game-theoretic framework capable of analyzing the social learning and decision making behaviors in networks with externalities. In a social network, agents are intelligent and have the capability to make decisions to maximize their utilities. They can either make wise decisions by taking advantages of other agents’ experiences through learning (social learning), or make decisions earlier to avoid competitions from huge crowds (network externality). While there are existing works on either social learning or negative network externality, a general study on considering both effects is still limited. The proposed Chinese Restaurant Game is capable of handling both effects while addressing the rationality of agents. We derived the optimal strategy of each agent and provided a recursive method to achieve the optimal strategy. We first illustrated the spectrum access problem in cognitive radio networks as one of the application of Chinese restaurant game. We found that the proposed Chinese restaurant game theoretic approach indeed helps users make better decisions and improves the overall system performance. Furthermore, the proposed Chinese Restaurant Game framework was applied to resolve challenges in various networks involving rational agents, such as cooperative sensing in cognitive radio networks, network access, and video multicasting system. All studies showed that the proposed Chinese Restaurant Game framework improves the user experiences, social welfare of the system, and profit of the service providers.