Sensing Quality-aware Task Allocation for Multi-dimensional Vehicular Urban Sensing

Hosung Baek, Haneul Ko, Joonwoo Kim, Youbin Jeon, Sangheon Pack

Research output: Contribution to journalArticlepeer-review


Vehicular sensing has become attracting an increasing research interest for cost-effective monitoring in urban areas. Even though multiple types of sensing data are required to form a multi-dimensional sensing map in urban sensing applications, most of previous works have only considered the sensing quality of single sensor type. In this paper, we formulate an optimization problem of task allocation to improve the overall sensing quality in multi-dimensional vehicular urban sensing. To mitigate high complexity of the formulated problem, we prove the submodularity of the objective function and present a low-complexity heuristic algorithm called sensing quality-aware task allocation (SQTA) leveraging the property of submodular optimization. Extensive experiments have been conducted by using two real-world datasets, which demonstrate that SQTA can improve the average sensing quality of multiple sensor types and also guarantee sufficient levels of the sensing quality of all sensor types.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Internet of Things Journal
Publication statusAccepted/In press - 2023


  • Costs
  • Heuristic algorithms
  • multi-type sensor
  • Optimization
  • Resource management
  • sensing quality
  • Sensors
  • submodular function
  • task allocation
  • Task analysis
  • Trajectory
  • Vehicular urban sensing

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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