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Encounter Probability Aware Task Assignment in Mobile Crowdsensing

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Abstract

Wireless Sensor Networks (WSNs) have become essential parts in various smart city projects. However, the application-specific WSN deployment is constrained by its high cost, low flexibility and hard management. To address these limitations, a complementary promising solution, known as mobile crowdsensing, is proposed. Mobile crowdsensing leverages the surge of mobile devices as well as the sensors attached to them to opportunistically and cooperatively conduct sensing tasks. Thanks to the crowdness and mobility of mobile devices, mobile crowdsensing is able to enlarge the sensing scale and granularity. Existing mobile crowdsensing techniques are usually centralized methods and rely on infrastructure communications. Witnessing the development of Device-to-Device (D2D) communications, it is ideal to explore such abilities such that the sensing tasks can be conducted in a distributed manner as well as an infrastructureless way. Via D2D, all participated nodes can directly assign tasks to encountered nodes. In this paper, aided by the encounter relationship among mobile nodes, we study the time minimization task assignment problem in mobile crowdsensing. Specially, we propose offline and online algorithms based on historic encounter information and real-time assigned task execution time, respectively. Real-world trace based experiments validate the efficiency of our proposal.

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Notes

  1. http://crawdad.org/intel/home/20060416/

  2. http://crawdad.org/upmc/rollernet/20090202/

  3. http://crawdad.org/cambridge/haggle/20090529/

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Acknowledgments

This research was supported by the NSF of China (Grant No. 61673354, 61672474, 61402425, 61272470, 61305087, 61440060, 61501412), the Provincial Natural Science Foundation of Hubei (Grant No. 2015CFA065). This paper has been subjected to Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China. It was also supported by Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing (KLIGIP201603 and KLIGIP201607).

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Authors and Affiliations

  1. Hubei Key Laboratory of Intelligent Geo-Information Processing, School of Computer Science, China University of Geosciences, Wuhan, 430074, China

    Hong Yao, Muzhou Xiong, Chao Liu & Qingzhong Liang

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  1. Hong Yao
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  2. Muzhou Xiong
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  3. Chao Liu
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  4. Qingzhong Liang
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Correspondence to Qingzhong Liang.

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Yao, H., Xiong, M., Liu, C. et al. Encounter Probability Aware Task Assignment in Mobile Crowdsensing. Mobile Netw Appl 22, 275–286 (2017). https://doi.org/10.1007/s11036-016-0794-5

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