About the Speaker：

Lingjie Duan is an Associate Professor at Singapore University of Technology and Design (SUTD), which was established in collaboration with MIT. He received the PhD degree from the Chinese University of Hong Kong, in 2012. In 2011, he was a visiting scholar with the University of California, Berkeley. His current research interests include network economics and game theory, cognitive and cooperative networking, energy harvesting wireless communications, and mobile crowdsourcing.

His recent works appeared in both top-tier engineering and business journals including 5 ESI Highly Cited Papers. He was a recipient of the 2016 SUTD Excellence in Research Award, the 10th IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award in 2015, and the Hong Kong Young Scientist Award (Finalist in Engineering Science track) in 2014. Regarding professional services, he is an Editor of both the IEEE Transactions on Wireless Communications and the IEEE Communications Surveys and Tutorials since 2017. In 2019, he was a Guest Editor of IEEE Transactions on Cognitive Communications and Networking. In 2016, he was a Guest Editor of the IEEE Journal on Selected Areas in Communications - special issue on human-in-the-loop mobile networks, and was also a Guest Editor of the IEEE Wireless Communications Magazine for feature topic of sustainable green networking and computing in 5G systems. He is also a regular TPC member of top conferences in wireless communications and networking (e.g., INFOCOM, WiOPT, MobiHoc, and SECON).

Abstract：

Fuelled by the rapid development of communication networks and sensors in portable devices, today many mobile users are invited by content providers to sense and send back real-time useful information (e.g., traffic observations and sensor data) to keep the freshness of the online platforms’ content updates. However, due to the sampling cost in sensing and transmission, an individual may not have the incentive to contribute the real-time information to help a platform reduce the age of information (AoI). Accordingly, we propose dynamic pricing for the platform to offer age-dependent monetary returns and encourage users to sample information at different rates over time. This dynamic pricing design problem needs to balance the monetary payments to users and the AoI evolution over time, and is challenging to solve especially under the incomplete information about users’ arrivals and their private sampling costs. For analysis tractability, we linearize the nonlinear AoI evolution in the constrained dynamic programming problem, by approximating the dynamic AoI reduction as a time-average term and solving the approximate dynamic pricing in closed-form.

This economics problem becomes more interesting by further considering the stage after sampling, where more than one platform coexists in sharing the content delivery network of limited bandwidth, and one selfish platform’s update may jam or pre-empt the other’s under negative network externalities. We formulate the platforms’ interaction as a non-cooperative Bayesian game and show that they want to over-sample to reduce their own AoI, causing the price of anarchy (PoA) to be infinity. To remedy this huge efficiency loss, we propose a non-monetary trigger mechanism of punishment in a repeated game to enforce the platforms’ cooperation to approach the social optimum.