Home > Research > Publications & Outputs > Learning-Assisted Optimization in Mobile Crowd ...

Associated organisational unit


Text available via DOI:

View graph of relations

Learning-Assisted Optimization in Mobile Crowd Sensing: A Survey

Research output: Contribution to journalJournal articlepeer-review

  • Jiangtao Wang
  • Yasha Wang
  • Daqing Zhang
  • Jorge Goncalves
  • Denzil Ferreira
  • Aku Visuri
  • Sen Ma
<mark>Journal publication date</mark>1/01/2019
<mark>Journal</mark>IEEE Transactions on Industrial Informatics
Issue number1
Number of pages8
Pages (from-to)15-22
Publication StatusPublished
Early online date4/09/18
<mark>Original language</mark>English


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.