Dynamic data-sharing based user recruitment in mobile crowdsensing

被引：0
作者：
Chen S. [1 ,2 ]
Liu M. [1 ]
Sun S. [1 ,2 ]
Jiao Z. [1 ]
机构：
[1] State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing
[2] University of Chinese Academy of Sciences, Beijing
来源：
High Technology Letters | 2019年 / 25卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Data sharing; Dynamic decision; Mobile crowdsensing (MCS); Mobility prediction; User recruitment;
D O I：
10.3772/j.issn.1006-6748.2019.01.002
中图分类号：
学科分类号：
摘要：
Mobile crowdsensing (MCS) has become an emerging paradigm to solve urban sensing problems by leveraging the ubiquitous sensing capabilities of the crowd. One critical issue in MCS is how to recruit users to fulfill more sensing tasks with budget restriction, while sharing data among tasks can be a credible way to improve the efficiency. The data-sharing based user recruitment problem under budget constraint in a realistic scenario is studied, where multiple tasks require homogeneous data but have various spatio-temporal execution ranges, meanwhile users suffer from uncertain future positions. The problem is formulated in a manner of probability by predicting user mobility, then a dynamic user recruitment algorithm is proposed to solve it. In the algorithm a greedy-adding-and-substitution (GAS) heuristic is repeatedly implemented by updating user mobility prediction in each time slot to gradually achieve the final solution. Extensive simulations are conducted using a real-world taxi trace dataset, and the results demonstrate that the approach can fulfill more tasks than existing methods. Copyright © by HIGH TECHNOLOGY LETTERS PRESS.
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页码：8 / 16
页数：8
相关论文
共 22 条
[1]  
Ganti R.K., Ye F., Lei H., Mobile crowdsensing: Current state and future challenges, IEEE Communication Magazine, 49, 11, pp. 32-39, (2011)
[2]  
Thiagarajan A., Ravindranath L., LaCurts K., Et al., Vtrack: accurate, energy-aware road traffic delay estimation using mobile phones, Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, pp. 85-98, (2009)
[3]  
Nawaz S., Efstratiou C., Mascolo C., Parksense: A smartphone based sensing system for on-street parking, Proceedings of the 19th ACM Annual International Conference on Mobile Computing & Networking, pp. 75-86, (2013)
[4]  
Dutta P., Aoki P.M., Kumar N., Et al., Common sense: participatory urban sensing using a network of handheld air quality monitors, Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, pp. 349-350, (2009)
[5]  
Cheung M.H., Southwell R., Hou F., Et al., Distributed time-sensitive task selection in mobile crowdsensing, Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 16-157, (2015)
[6]  
Zhang D., Xiong H., Wang L., Et al., Crowdrecruiter: Selecting participants for piggyback crowdsensing under probabilistic coverage constraint, Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 703-714, (2014)
[7]  
Xiong H., Zhang D., Chen G., Et al., Crowdtasker: maximizing coverage quality in piggyback crowdsensing under budget constraint, Proceedings of the 2015 IEEE International Conference on Pervasive Computing and Communications, pp. 55-62, (2015)
[8]  
Song Z., Liu C.H., Wu J., Et al., Qoi-aware multitaskoriented dynamic participant selection with budget constraints, IEEE Transactions on Vehicular Technology, 63, 9, pp. 4618-4632, (2014)
[9]  
Wang J., Wang Y., Zhang D., Et al., Fine-grained multi-task allocation for participatory sensing with a shared budget, IEEE Internet of Things Journal, 3, 6, pp. 1395-1405, (2016)
[10]  
Wu W., Wang J., Li M., Et al., Energy-efficient transmission with data sharing in participatory sensing systems, IEEE Journal on Selected Areas in Communications, 34, 12, pp. 4048-4062, (2016)
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