Learning-Assisted Optimization in Mobile Crowd Sensing: A Survey

Abstract

Mobile crowd sensing (MCS) is a relatively new paradigm for collecting real-Time and location-dependent urban sensing data. Given its applications, it is crucial to optimize the MCS process with the objective of maximizing the sensing quality and minimizing the sensing cost. While earlier studies mainly tackle this issue by designing different combinatorial optimization algorithms, there is a new trend to further optimize MCS by integrating learning techniques to extract knowledge, such as participants' behavioral patterns or sensing data correlation. In this paper, we perform an extensive literature review of learning-Assisted optimization approaches in MCS. Specifically, from the perspecti..