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Online task dispatching and pricing for quality-of-service-aware sensing data collection for mobile edge clouds

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CCF Transactions on Networking

Abstract

The proliferation of mobile devices equipped with rich sensing and computing resources has pushed the emergence of a new cloud paradigm, mobile edge clouds, where tasks are dispatched from the centralized cloud to the network edge. By taking the advantage of widely-distributed mobile devices, urban monitoring-oriented crowdsourcing services can be provided by a mobile edge cloud, where fine-grained monitoring data over time are crowdsourced by mobile devices and then useful information is extracted. However, as considerable costs are incurred on mobile devices, there exists a major problem that a high financial budget is required to guarantee the quality of service. Fortunately, we observe that real-world sensing data exhibit strong spatial and temporal correlations, and advanced inference methods can be employed to efficiently recover missing data. Motivated by the observation, we provide a near-optimal online task dispatching approach for crowdsourcing services provided by a mobile edge cloud, aiming to minimize the total cost incurred on devices while guarantee the quality of service in the meantime. Besides, considering strategic device users with private cost information, we also propose a truthful pricing policy. Extensive simulations based on real datasets show that our approach outperforms other competing schemes, producing a high quality of service with a much lower budget.

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Notes

  1. Note that there should always exist \(\delta _1>\max \{\delta _2,\delta _3\}\), otherwise (4) can be satisfied directly when (5) or (6) is satisfied.

  2. Because VCG only works when the optimal selection is achieved. Obviously, random selection cannot achieve the minimum total cost.

  3. Note that these two baseline algorithms cannot guarantee the spatial coverage constraint and the temporal coverage constraint are satisfied.

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Acknowledgements

This research is supported in part by 973 Program (No. 2014CB340303), Shanghai Sailing Program 18YF1408200, and NSFC (Nos. 61772341, 61472254, 61572324, 61170238, and 61802245). This work is also supported by the Program for the Program for Changjiang Young Scholars in University of China, the Program for China Top Young Talents, and the Program for Shanghai Top Young Talents.

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China

    Tong Liu

  2. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China

    Yanmin Zhu & Jiadi Yu

  3. Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, 11794, USA

    Yuanyuan Yang & Fan Ye

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  1. Tong Liu
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  2. Yanmin Zhu
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  4. Fan Ye
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  5. Jiadi Yu
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Correspondence to Yanmin Zhu.

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Liu, T., Zhu, Y., Yang, Y. et al. Online task dispatching and pricing for quality-of-service-aware sensing data collection for mobile edge clouds. CCF Trans. Netw. 2, 28–42 (2019). https://doi.org/10.1007/s42045-018-0008-8

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