Probabilistic Control of Dynamic Crowds Toward Uniform Spatial-Temporal Coverage

Yukio Ogawa, Go Hasegawa, Masayuki Murata

Research output: Contribution to journalArticlepeer-review


Vehicular mobility and connectivity vary significantly over space and time when vehicular crowd sensing covers a city-wide area for a long time period, but it is important to achieve sufficiently uniform data coverage to satisfy the requirements of an environmental monitoring scenario. Our goal is thus to ensure uniform spatial-temporal coverage of sensed data over a city-wide area despite such vehicle dynamics. For a large area, trajectory-based approaches must deal with a great number and variety of participant mobility patterns. Hence, we propose a probabilistic control mechanism that adaptively adjusts the incentive to each participant, without using any prior information about participants. We provide a mathematical analysis that ensures stability of the number of participants with assigned tasks (called workers), and we evaluate the mechanism's robustness by using 24-hr vehicle trace data from a city-wide area. Our results demonstrate that, when the number of participants is up to 1500 times higher than the required number of workers, sensing actions result in a distribution with a mean of about 1 and an interquartile range of around 4 for a required sensing interval; moreover, the mean increases by 2% when 30% of communication messages are randomly lost.

Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalIEEE Transactions on Mobile Computing
Publication statusAccepted/In press - 2022


  • Mathematical models
  • Probabilistic logic
  • Resource management
  • Robot sensing systems
  • Sensors
  • Task analysis
  • Trajectory
  • environmental monitoring
  • probabilistic control
  • spatial-temporal coverage
  • vehicular crowd sensing

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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