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A Reliable Multi-task Allocation Based on Reverse Auction for Mobile Crowdsensing

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Wireless Algorithms, Systems, and Applications (WASA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12384))

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Abstract

Mobile crowdsensing (MCS) has been widely studied with the popularization of mobile intelligent devices, while a large number of sensing data can be obtained by using existing mobile devices. Multi-task allocation is an important problem for MCS. In this paper, we propose a highly reliable multi-task allocation (RMA) framework that considers the offline stage and online stage. In the offline stage, we propose a network flow model based on reverse auction, while adding the bid price compensation (BPC) strategy to ensure workers we recruit are reliable. In the online stage, we propose the asynchronous k-secretary strategy to achieve dynamic real-time recruitment of workers and prove the competitive ratio is \(1/e^k\). Experimental results over the Rome taxi track data set show that our two-stage framework has better coverage and budget consumption, as well as good computational efficiency.

P. Li—This work is partially supported by the NSF of China (No. 61802286), the Hubei Provincial Natural Science Foundation of China (No. 2018CFB424).

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Authors and Affiliations

  1. College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei, China

    Junlei Xiao, Peng Li & Lei Nie

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, Hubei, China

    Junlei Xiao, Peng Li & Lei Nie

Authors
  1. Junlei Xiao
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  2. Peng Li
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  3. Lei Nie
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Corresponding author

Correspondence to Peng Li .

Editor information

Editors and Affiliations

  1. Shandong University, Qingdao, China

    Dongxiao Yu

  2. TU Berlin, Berlin, Germany

    Falko Dressler

  3. Qilu University of Technology, Jinan, China

    Jiguo Yu

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Xiao, J., Li, P., Nie, L. (2020). A Reliable Multi-task Allocation Based on Reverse Auction for Mobile Crowdsensing. In: Yu, D., Dressler, F., Yu, J. (eds) Wireless Algorithms, Systems, and Applications. WASA 2020. Lecture Notes in Computer Science(), vol 12384. Springer, Cham. https://doi.org/10.1007/978-3-030-59016-1_44

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  • DOI: https://doi.org/10.1007/978-3-030-59016-1_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59015-4

  • Online ISBN: 978-3-030-59016-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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