The proliferation of smart mobile devices has spurred an explosive growth of mobile crowd-learning services, where service providers rely on the user community to voluntarily collect, report, and share real-time information for a collection of scattered points of interest. A critical factor affecting the future large-scale adoption of such mobile crowd-learning applications is the freshness of the crowd-learned information, which can be measured by a metric termed “age-of-information” (AoI). However, we show that the AoI of mobile crowd-learning could be arbitrarily bad under selfish users’ behaviors if the system is poorly designed. This motivates us to design efficient reward mechanisms to incentivize mobile users to report information in time, with the goal of keeping the AoI and congestion level of each PoI low. In this talk, we will consider a simple linear AoI-based reward mechanism and analyze its AoI and congestion performances in terms of price of anarchy (PoA), which characterizes the degradation of the system efficiency due to selfish behavior of users. This is the joint work with Jia Liu at Iowa State University.
Bin Li received his B.S. degree in Electronic and Information Engineering in 2005, M.S. degree in Communication and Information Engineering in 2008, both fromXiamen University, and Ph.D. degree in Electrical and Computer Engineering from TheOhio State Universityin May 2014. Between June 2014 and August 2016, he was a Postdoctoral Researcher working with Prof.R. Srikantin theCoordinated Science Labat theUniversity of Illinois at Urbana-Champaign. In August 2016, he joined theUniversity of Rhode Islandas an assistant professor in theDepartment of Electrical, Computer and Biomedical Engineering. His research spans wireless networks, virtual and augmented reality, fog computing, and data centers. In particular, his research utilizes mathematical tools from stochastic processes, optimization, control, and algorithms to understand fundamental performance limits of complex network systems, and develop efficient, adaptable, and scalable algorithms for diverse applications.
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