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 perspective of the participant and the task, we organize the existing work into a conceptual framework, present different learning and optimization methods, and describe their evaluation. Furthermore, we discuss how different techniques can be combined to form a complete solution. In the end, we point out existing limitations, which can inform and guide future research directions.
Wang Jingtao, Wang Yasha, Zhang Daqing, Goncalves Jorge, Ferreira Denzil, Visuri Aku, Ma Sen
A2 Review article in a scientific journal
Place of publication:
Wang, J., Wang, Y., Zhang, D., Goncalves, J., Ferreira, D., Visuri, A., Ma, S. (2019) Learning-Assisted Optimization in Mobile Crowd Sensing: A Survey. IEEE Transactions on Industrial Informatics, 15 (1), 15-22. doi:10.1109/TII.2018.2868703
Read the publication here: