Fine-Grained Multitask Allocation for Participatory Sensing With a Shared Budget

Jiangtao Wang, Yasha Wang, Daqing Zhang, Leye Wang, Haoyi Xiong, Abdelsalam Helal, Yuanduo He, Feng Wang

Research output: Contribution to journalArticlepeer-review

61 Citations (Scopus)


For participatory sensing, task allocation is a crucial research problem that embodies a tradeoff between sensing quality and cost. An organizer usually publishes and manages multiple tasks utilizing one shared budget. Allocating multiple tasks to participants, with the objective of maximizing the overall data quality under the shared budget constraint, is an emerging and important research problem. We propose a fine-grained multitask allocation framework (MTPS), which assigns a subset of tasks to each participant in each cycle. Specifically, considering the user burden of switching among varying sensing tasks, MTPS operates on an attention-compensated incentive model where, in addition to the incentive paid for each specific sensing task, an extra compensation is paid to each participant if s/he is assigned with more than one task type. Additionally, based on the prediction of the participants' mobility pattern, MTPS adopts an iterative greedy process to achieve a near-optimal allocation solution. Extensive evaluation based on real-world mobility data shows that our approach outperforms the baseline methods, and theoretical analysis proves that it has a good approximation bound.
Original languageEnglish
Pages (from-to)1395 - 1405
Number of pages11
JournalIEEE Internet of Things Journal
Issue number6
Early online date9 Sept 2016
Publication statusPublished - 1 Dec 2016
Externally publishedYes


  • Fine-grained
  • multitask allocation
  • participatory sensing (PS)


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