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Task Allocation in Semi-Opportunistic Mobile Crowdsensing: Paradigm and Algorithms

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Abstract

Mobile crowdsensing paradigms can be categorized into two classes: opportunistic sensing and participatory sensing, each of which has its advantage and disadvantage. The high flexibility of worker mobility in participatory sensing leads to high task coverage but also high worker employment fee. The little human involvement in opportunistic sensing results in low worker employment fee but also low task coverage. In this paper, we propose a new mobile crowdsensing paradigm, named semi-opportunistic sensing, aiming to achieve both high task coverage and low worker employment fee. In this paradigm, each worker can provide multiple candidate moving paths for his/her trip, among which the service platform chooses one for the worker to undertake task(s). The platform selects workers and assigns tasks to them with an objective to optimize total task quality under the platform’s incentive budget and workers’ task performing time constraints. In this paper, we formulate the task allocation problem, prove its NP-hardness, and present two efficient heuristic algorithms. The first heuristic, named Best Path/Task first algorithm (BPT), always chooses the best path and task in a greedy manner. The second heuristic, named LP-Relaxation based algorithm (LPR), assigns paths and tasks with the largest values according to the LP-relaxation. We conduct extensive experiments on synthetic dataset and real-life traces. Experiment results show that the proposed semi-opportunistic sensing paradigm can significantly improve total task quality compared with opportunistic sensing. Moreover, the experiment results also validate the high efficiency of our proposed task allocation algorithms.

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References

  1. Bradai S, Khemakhem S, Jmaiel M (2018) Real-time and energy aware opportunistic mobile crowdsensing framework based on people’s connectivity habits. Comput Netw 142:179–193

    Article  Google Scholar 

  2. Du D-Z, Ko K-I, Hu X (2012) Design and analysis of approximation algorithms. Springer, New York

    Book  Google Scholar 

  3. Flores H, Hui P, Nurmi P, Lagerspetz E, Tarkoma S, Manner J, Kostakos V, Li Y, Su X (2018) Evidence-aware mobile computational offloading. IEEE Trans Mob Comput 17(8):1834–1850

    Article  Google Scholar 

  4. Ganti RK, Ye F, Lei H (2011) Mobile crowdsensing: current state and future challenges. IEEE Commun Mag 49(11):32–39

    Article  Google Scholar 

  5. Gong W, Zhang B, Li C (2017) Location-based online task scheduling in mobile crowdsensing. In: Proceedings of the IEEE GLOBECOM 2017, pp 1–6

  6. Gong W, Zhang B, Li C (2018a) Task assignment for semi-opportunistic mobile crowdsensing. In: Proceedings of the Adhocnets 2018, pp 1–12

  7. Gong W, Zhang B, Li C (2018b) Task assignment in mobile crowdsensing: present and future directions. IEEE Netw 32(4):100–107

    Article  Google Scholar 

  8. Gong W, Zhang B, Li C (2019) Location-based online task assignment and path planning for mobile crowdsensing. IEEE Trans Veh Technol 68(2):1772–1783

    Article  Google Scholar 

  9. Gong Z, Li C, Jiang F (2018) Auv-aided joint localization and time synchronization for underwater acoustic sensor networks. IEEE Signal Process Lett 25(4):477–481

    Article  Google Scholar 

  10. Guo B, Liu Y, Wu W, Yu Z, Han Q (2017) Activecrowd: a framework for optimized multitask allocation in mobile crowdsensing systems. IEEE Transactions on Human-Machine Systems 47(3):392–403

    Article  Google Scholar 

  11. Han S, Xu S, Meng W, Li C (2018a) Dense-device-enabled cooperative networks for efficient and secure transmission. IEEE Netw 32(2):100–106

    Article  Google Scholar 

  12. Han S, Zhang Y, Meng W, Chen H (2018b) Self-interference-cancelation-based SLNR precoding design for full-duplex relay-assisted system. IEEE Trans Veh Technol 67(9):8249–8262

    Article  Google Scholar 

  13. Han S, Huang Y, Meng W, Li C, Xu N, Chen D (2019) Optimal power allocation for SCMA downlink systems based on maximum capacity. IEEE Trans Commun 67(2):1480–1489

    Article  Google Scholar 

  14. Jiang C, Gao L, Duan L, Huang J (2018) Scalable mobile crowdsensing via peer-to-peer data sharing. IEEE Trans Mob Comput 17(4):898–912

    Article  Google Scholar 

  15. Kang Y, Miao X, Liu K, Chen L, Liu Y (2015) Quality-aware online task assignment in mobile crowdsourcing. In: Proceedings of IEEE MASS, pp 127–135

  16. Liu Y, Guo B, Wang Y, Wu W, Yu Z, Zhang D (2016) Taskme: multi-task allocation in mobile crowd sensing. In: Proceedings of IEEE MASS, pp 403–414

  17. Song Z, Liu CH, Wu J, Ma J, Wang W (2014) Qoi-aware multitask-oriented dynamic participant selection with budget constraints. IEEE Trans Veh Technol 63(9):4618–4632

    Article  Google Scholar 

  18. To H, Fan L, Luan T, Shahabi C (2016) Real-time task assignment in hyperlocal spatial crowdsourcing under budget constraints. In: Proceedings of IEEE PerCom, pp 1–8

  19. Tong Y, She J, Ding B, Wang L, Chen L (2016) Online mobile micro-task allocation in spatial crowdsourcing. In: Proceedings of the IEEE ICDE’16, pp 49–60

  20. Tsai TC, Chan HH (2015) NCCU trace: social-network-aware mobility trace. IEEE Commun Mag 53 (10):144–149

    Article  Google Scholar 

  21. Wang E, Yang Y, Wu J, Liu W, Wang X (2018a) An efficient prediction-based user recruitment for mobile crowdsensing. IEEE Trans Mob Comput 17(1):16–28

    Article  Google Scholar 

  22. Wang J, Wang L, Wang Y, Zhang D, Kong L (2018b) Task allocation in mobile crowd sensing: State-of-the-art and future opportunities. IEEE Internet of Things Journal 5(5):3747–3757

    Article  Google Scholar 

  23. Wang L, Liu W, Zhang D, Wang Y, Wang E, Yang Y (2018c) Cell selection with deep reinforcement learning in sparse mobile crowdsensing. In: Proceedings of IEEE ICDCS, pp 1543–1546

  24. Xiong H, Zhang D, Chen G, Wang L, Gauthier V, Barnes L (2016) iCrowd: near-optimal task allocation for piggyback crowdsensing. IEEE Trans Mob Comput 15(8):2010–2022

    Article  Google Scholar 

  25. Zhang C, Li C, Chen Y (2018) A markov model for batch-based opportunistic routing in multi-hop wireless mesh networks. IEEE Trans Veh Technol 67(12):12:025–12:037

    Article  Google Scholar 

  26. Zhang M, Yang P, Tian C, Tang S, Gao X, Wang B, Xiao F (2016) Quality-aware sensing coverage in budget-constrained mobile crowdsensing networks. IEEE Trans Veh Technol 65(9):7698–7707

    Article  Google Scholar 

  27. Zhang X, Yang Z, Gong Y, Liu Y, Tang S (2017) Spatialrecruiter: maximizing sensing coverage in selecting workers for spatial crowdsourcing. IEEE Trans Veh Technol 66(6):5229–5240

    Article  Google Scholar 

  28. Zheng Y, Xie X, Ma WY (2010) Geolife: a collaborative social networking service among user, location and trajectory. IEEE Data Engineering Bulletin 33(2):32–40

    Google Scholar 

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Acknowledgements

This work was supported in part by the NSF of China under Grant Nos. 61872331, 61531006, 61471339, the Natural Sciences and Engineering Research Council (NSERC) of Canada (Discovery Grant RGPIN-2018-03792), and the InnovateNL SensorTECH Grant 5404-2061-101.

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Authors and Affiliations

  1. Research Center of Ubiquitous Sensor Networks, University of Chinese Academy of Sciences, Beijing, 100049, China

    Wei Gong, Baoxian Zhang & Zheng Yao

  2. School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin, 300384, China

    Cheng Li

  3. Faculty of Engineering and Applied Science, Memorial University, St. John’s, NL, A1B 3X5, Canada

    Cheng Li

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  1. Wei Gong
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  2. Baoxian Zhang
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  3. Cheng Li
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  4. Zheng Yao
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Correspondence to Zheng Yao.

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Gong, W., Zhang, B., Li, C. et al. Task Allocation in Semi-Opportunistic Mobile Crowdsensing: Paradigm and Algorithms. Mobile Netw Appl 25, 772–782 (2020). https://doi.org/10.1007/s11036-019-01299-3

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