Exploring learning-assisted optimization for mobile crowd sensing

Introducing sensing mobile crowds (SMC), a novel paradigm for real-time location-dependent urban sensing data collection. It is critically important to optimize the SMC process such that it provides the highest sensing quality at the lowest feasible cost due to its practical use. As an alternative to the combinatorial optimization algorithms utilized in previous research, a new approach to SMC optimization is to apply learning approaches to extract knowledge, such as patterns in participants’ behavior or correlations in sensing data. In this work, we thoroughly research learning-assisted optimization approaches for SMC. Using the existing literature as a starting point, we will describe various learning and optimization methods and evaluate them from the perspectives of the task and the participant. How to combine different approaches to get a complete solution is also discussed. Lastly, we point out the limitations that exist at the moment, which might lead to research directions in the future.