Embedded Crowdsensing for Pavement Monitoring and Its Incentive Mechanisms
Ten novel game-theoretical incentive mechanisms for crowdsensing platforms, evaluated across 11 million scenarios.
April 2020 – May 2021 · First-author book chapter, De Gruyter (2023)
I had the honor of being published as the first author for my work developing ten novel game-theoretical incentive mechanisms in crowdsensing platforms. My research included two use-cases for supervised learning in budget prediction and one use-case for an unsupervised learning algorithm for path optimization.
To evaluate each incentive mechanism, I developed a crowdsensing simulation in C++, which allowed me to test a total of 11 million scenarios. After running these simulations, I visualized and analyzed the evaluation metrics using Matplotlib in Python.
The work appears as a chapter in Machine Learning under Resource Constraints – Applications (De Gruyter, 2023).
