research-article

Towards Profit Optimization During Online Participant Selection in Compressive Mobile Crowdsensing

Authors:

MD Zakirul Alam Bhuiyan,

Pin LvAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 15, Issue 4

Article No.: 38, Pages 1 - 29

https://doi.org/10.1145/3342515

Published: 15 August 2019 Publication History

Abstract

A mobile crowdsensing (MCS) platform motivates employing participants from the crowd to complete sensing tasks. A crucial problem is to maximize the profit of the platform, i.e., the charge of a sensing task minus the payments to participants that execute the task. In this article, we improve the profit via the data reconstruction method, which brings new challenges, because it is hard to predict the reconstruction quality due to the dynamic features and mobility of participants. In particular, two Profit-driven Online Participant Selection (POPS) problems under different situations are studied in our work: (1) for S-POPS, the sensing cost of the different parts within the target area is the Same. Two mechanisms are designed to tackle this problem, including the ProSC and ProSC+. An exponential-based quality estimation method and a repetitive cross-validation algorithm are combined in the former mechanism, and the spatial distribution of selected participants are further discussed in the latter mechanism; (2) for V-POPS, the sensing cost of different parts within the target area is Various, which makes it the NP-hard problem. A heuristic mechanism called ProSCx is proposed to solve this problem, where the searching space is narrowed and both the participant quantity and distribution are optimized in each slot. Finally, we conduct comprehensive evaluations based on the real-world datasets. The experimental results demonstrate that our proposed mechanisms are more effective and efficient than baselines, selecting the participants with a larger profit for the platform.

References

[1]

Raghu K. Ganti, Fan Ye, and Hui Lei. 2011. Mobile crowdsensing: Current state and future challenges. IEEE Commun. Mag. 49, 11 (2011), 32--39.

[2]

Jim Cherian, Jun Luo, Hongliang Guo, Shen Shyang Ho, and Richard Wisbrun. 2016. ParkGauge: Gauging the occupancy of parking garages with crowdsensed parking characteristics. Proceedings of the 2016 IEEE International Conference on Mobile Data Management (MDM’16). 92--101.

[3]

Ioannis Koukoutsidis. 2018. Estimating spatial averages of environmental parameters based on mobile crowdsensing. ACM Trans. Sensor Netw. 14, 1 (2018), 2.

Digital Library

[4]

Shigeya Morishita, Shogo Maenaka, Daichi Nagata, Morihiko Tamai, Keiichi Yasumoto, Toshinobu Fukukura, and Keita Sato. 2015. SakuraSensor: Quasi-realtime cherry-lined roads detection through participatory video sensing by cars. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’15). 695--705.

Digital Library

[5]

Xiaoqiang Teng, Deke Guo, Yulan Guo, Xiaolei Zhou, Zeliu Ding, and Zhong Liu. 2017. IONavi: An indoor-outdoor navigation service via mobile crowdsensing. ACM Trans. Sensor Netw. 13, 2 (2017), 12.

Digital Library

[6]

Thomas Ludwig, Christian Reuter, and Volkmar Pipek. 2013. What you see is what I need: Mobile reporting practices in emergencies. Proceedings of the 13th European Conference on Computer Supported Cooperative Work (ECSCW’13). 181--206.

[7]

Zongjian He, Jiannong Cao, and Xuefeng Liu. 2015. High quality participant recruitment in vehicle-based crowdsourcing using predictable mobility. Proceedings of the 34th IEEE International Conference on Computer Communications (INFOCOM’15). 2542--2550.

[8]

Shenggong Ji, Yu Zheng, and Tianrui Li. 2016. Urban sensing based on human mobility. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’16). 1040--1051.

Digital Library

[9]

Leye Wang, Daqing Zhang, Animesh Pathak, Chao Chen, Haoyi Xiong, Dingqi Yang, and Yasha Wang. 2015. CCS-TA: Quality-guaranteed online task allocation in compressive crowdsensing. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’15). 683--694.

Digital Library

[10]

Francesco Restuccia, Nirnay Ghosh, Shameek Bhattacharjee, Sajal K. Das, and Tommaso Melodia. 2017. Quality of information in mobile crowdsensing: Survey and research challenges. ACM Trans. Sensor Netw. 13, 4 (2017), 34.

Digital Library

[11]

Yiran Shen, Wen Hu, Rajib Rana, and Chun Tung Chou. 2013. Nonuniform compressive sensing for heterogeneous wireless sensor networks. IEEE Sensors J. 13, 6 (2013), 2120--2128.

[12]

Rajarshi Middya, Nabajit Chakravarty, and Mrinal Kanti Naskar. 2017. Compressive sensing in wireless sensor networks--a survey. IETE Techn. Rev. 34, 6 (2017), 642--654.

[13]

Leye Wang, Daqing Zhang, Dingqi Yang, Animesh Pathak, Chao Chen, Xiao Han, Haoyi Xiong, and Yasha Wang. 2017. SPACE-TA: Cost-effective task allocation exploiting intradata and interdata correlations in sparse crowdsensing. Trans. Intell. Syst. Technol. 9, 2 (2017).

Digital Library

[14]

Suining He and Kang G. Shin. 2018. Steering crowdsourced signal map construction via bayesian compressive sensing. Proceedings of the 37th IEEE Conference on Computer Communications (INFOCOM’18). 1016--1024.

[15]

Linghe Kong, Mingyuan Xia, Xiao-Yang Liu, Guangshuo Chen, Yu Gu, Min-You Wu, and Xue Liu. 2013. Data loss and reconstruction in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 25, 11 (2013), 2818--2828.

[16]

Hanshang Li, Ting Li, Fan Li, Weichao Wang, and Yu Wang. 2016. Enhancing participant selection through caching in mobile crowd sensing. Proceedings of the IEEE/ACM 24th International Symposium on Quality of Service (IWQoS’16). 1--10.

[17]

Shuo Yang, Fan Wu, Shaojie Tang, Tie Luo, Xiaofeng Gao, Linghe Kong, and Guihai Chen. 2016. Selecting most informative contributors with unknown costs for budgeted crowdsensing. Proceedings of the IEEE/ACM 24th International Symposium on Quality of Service (IWQoS’16). 1--6.

[18]

Xiaoyan Yin, Yanjiao Chen, and Baochun Li. 2017. Task assignment with guaranteed quality for crowdsourcing platforms. Proceedings of the IEEE/ACM 25th International Symposium on Quality of Service (IWQoS’17). 1--10.

[19]

Yueyue Chen, Deke Guo, and Ming Xu. 2018. ProSC+: Profit-driven online participant selection in compressive mobile crowdsensing. Proceedings of the IEEE/ACM 26th International Symposium on Quality of Service (IWQoS’18).

[20]

Yin Zhang, Matthew Roughan, Walter Willinger, and Lili Qiu. 2009. Spatio-temporal compressive sensing and internet traffic matrices. Network 20, 3 (2009), 267--278.

Digital Library

[21]

Yueyue Chen, Pin Lv, Deke Guo, Tongqing Zhou, and Ming Xu. 2018. A survey on task and participant matching in mobile crowd sensing. J. Comput. Sci. Technol. 33, 4 (2018), 768--791.

[22]

Kai Han, He Huang, and Jun Luo. 2018. Quality-aware pricing for mobile crowdsensing. IEEE/ACM Trans. Netw. 26, 4 (2018), 1728--1741.

Digital Library

[23]

Yi Gao, Wei Dong, Kai Guo, Xue Liu, Yuan Chen, Xiaojin Liu, Jiajun Bu, and Chun Chen. 2016. Mosaic: A low-cost mobile sensing system for urban air-quality monitoring. In Proceedings of the 35th Annual IEEE International Conference on Computer Communications (INFOCOM’16). IEEE, 1--9.

Digital Library

[24]

Yueyue Chen, Pin Lv, Deke Guo, Tongqing Zhou, and Ming Xu. 2017. Trajectory segment selection with limited budget in mobile crowd sensing. Pervas. Mobile Comput. 40 (2017), 123--138.

Digital Library

[25]

Yongxin Tong, Jieying She, Bolin Ding, Libin Wang, and Lei Chen. 2016. Online mobile micro-task allocation in spatial crowdsourcing. Proceedings of the 2016 IEEE 32nd International Conference on Data Engineering (ICDE’16). 49--60.

[26]

Tianshu Song, Yongxin Tong, Libin Wang, Jieying She, Bin Yao, Lei Chen, and Ke Xu. 2017. Trichromatic online matching in real-Time spatial crowdsourcing. Proceedings of the 2017 International Conference on Data Engineering (ICDE’17). 1009--1020.

[27]

Jieying She, Yongxin Tong, Lei Chen, and Tianshu Song. 2017. Feedback-aware social event-participant arrangement. Proceedings of the 2017 ACM International Conference on Management of Data (SIGMOD’17). 851--865.

Digital Library

[28]

Yun Song, Gaobo Yang, Hongtao Xie, Dengyong Zhang, and Sun Xingming. 2017. Residual domain dictionary learning for compressed sensing video recovery. Multimedia Tools Appl. 76, 7 (2017), 10083--10096.

Digital Library

[29]

Dengyong Zhang, Ting Yin, Gaobo Yang, Ming Xia, Leida Li, and Xingming Sun. 2017. Detecting image seam carving with low scaling ratio using multi-scale spatial and spectral entropies. J. Visual Commun. Image Represent. 48 (2017), 281--291.

Digital Library

[30]

Liwen Xu, Xiaohong Hao, Nicholas D. Lane, Xin Liu, and Thomas Moscibroda. 2015. Cost-aware compressive sensing for networked sensing systems. Proceedings of the 14th International Conference on Information Processing in Sensor Networks (IPSN’15). 130--141.

Digital Library

[31]

Xiaohong Hao, Liwen Xu, Nicholas D. Lane, Xin Liu, and Thomas Moscibroda. 2017. Density-aware compressive crowdsensing. Proceedings of the 16th International Conference on Information Processing in Sensor Networks (IPSN’17). 29--39.

Digital Library

[32]

Yu Zheng, Tong Liu, Yilun Wang, Yanmin Zhu, Yanchi Liu, and Eric Chang. 2014. Diagnosing New York city’s noises with ubiquitous data. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’14). ACM, 715--725.

Digital Library

[33]

Shengzhong Liu, Zhenzhe Zheng, Fan Wu, Shaojie Tang, and Guihai Chen. 2017. Context-aware data quality estimation in mobile crowdsensing. Proceedings of the IEEE 36th International Conference on Computer Communications (INFOCOM’17). 802--810.

[34]

Xiaomei Zhang, Yibo Wu, Lifu Huang, Heng Ji, and Guohong Cao. 2017. Expertise-aware truth analysis and task allocation in mobile crowdsourcing. Proceedings of the IEEE 37th International Conference on Distributed Computing Systems (ICDCS’17).

[35]

Tongqing Zhou, Bin Xiao, Zhiping Cai, Ming Xu, and Xuan Liu. 2018. From uncertain photos to certain coverage: A novel photo selection approach to mobile crowdsensing. Proceedings of the IEEE 37th International Conference on Computer Communications (INFOCOM’18). 1979--1987.

Digital Library

[36]

Jianping Fan, Rong Wang, Liming Zhang, Dingjia Xing, and Fuxi Gan. 1996. Image sequence segmentation based on 2D temporal entropic thresholding. Pattern Recogn. Lett. 17, 10 (1996), 1101--1107.

Digital Library

[37]

Yilun Wang, Yu Zheng, and Yexiang Xue. 2014. Travel time estimation of a path using sparse trajectories. In Proceedings of the 20th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD’14). ACM, 25--34.

Digital Library

[38]

Lu Lin, Jianxin Li, Feng Chen, Jieping Ye, and Jinpeng Huai. 2018. Road traffic speed prediction: A probabilistic model fusing multi-source data. IEEE Trans. Knowl. Data Eng. 30, 7 (2018), 1310--1323.

[39]

François Ingelrest, Guillermo Barrenetxea, Gunnar Schaefer, Martin Vetterli, Olivier Couach, and Marc Parlange. 2010. Sensorscope: Application-specific sensor network for environmental monitoring. ACM Trans. Sensor Netw. 6, 2 (2010), 17.

Digital Library

[40]

Yu Zheng, Furui Liu, and Hsun-Ping Hsieh. 2013. U-air: When urban air-quality inference meets big data. Proceedings of the 19th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD’13). 1436--1444.

Digital Library

Cited By

Peng RHuang WXu HPi MLiu J(2022)“Follower of the Reference Point”: Platform Utility-Oriented Incentive Mechanism in CrowdsensingElectronics10.3390/electronics1116260911:16(2609)Online publication date: 20-Aug-2022
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Tan MYuan FYu JWang GGu X(2022)Fine-grained Image Classification via Multi-scale Selective Hierarchical Biquadratic PoolingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/349222118:1s(1-23)Online publication date: 25-Jan-2022
https://dl.acm.org/doi/10.1145/3492221
Yan CMeng LLi LZhang JWang ZYin JZhang JSun YZheng B(2022)Age-Invariant Face Recognition by Multi-Feature Fusionand Decomposition with Self-attentionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/347281018:1s(1-18)Online publication date: 25-Jan-2022
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Index Terms

Towards Profit Optimization During Online Participant Selection in Compressive Mobile Crowdsensing
1. Applied computing
  1. Computer forensics
    1. Data recovery
2. Human-centered computing
  1. Collaborative and social computing
  2. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing theory, concepts and paradigms
      1. Mobile computing

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Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 15, Issue 4

November 2019

373 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/3352582

Editor:
Yunhao Liu
Tsinghua University, China

Issue’s Table of Contents

Copyright © 2019 ACM.

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 15 August 2019

Accepted: 01 June 2019

Revised: 01 April 2019

Received: 01 November 2018

Published in TOSN Volume 15, Issue 4

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Tianjin Science and Technology Foundation
National Program for Support of Top-Notch Young Professionals of National Program for Special Support of Eminent Professionals
National Natural Science Foundation of China
Guangdong Provincial Natural Science Foundation

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Cited By

Peng RHuang WXu HPi MLiu J(2022)“Follower of the Reference Point”: Platform Utility-Oriented Incentive Mechanism in CrowdsensingElectronics10.3390/electronics1116260911:16(2609)Online publication date: 20-Aug-2022
https://doi.org/10.3390/electronics11162609
Tan MYuan FYu JWang GGu X(2022)Fine-grained Image Classification via Multi-scale Selective Hierarchical Biquadratic PoolingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/349222118:1s(1-23)Online publication date: 25-Jan-2022
https://dl.acm.org/doi/10.1145/3492221
Yan CMeng LLi LZhang JWang ZYin JZhang JSun YZheng B(2022)Age-Invariant Face Recognition by Multi-Feature Fusionand Decomposition with Self-attentionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/347281018:1s(1-18)Online publication date: 25-Jan-2022
https://dl.acm.org/doi/10.1145/3472810
Zohar N(2022)Divide and Conquer: Detecting and Tracking Passive RFID Tags in Retail Spaces2022 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC51071.2022.9771839(1443-1448)Online publication date: 10-Apr-2022
https://dl.acm.org/doi/10.1109/WCNC51071.2022.9771839
Li LShi DZhang XHou RYue HLi HPan M(2022)Privacy Preserving Participant Recruitment for Coverage Maximization in Location Aware Mobile CrowdsensingIEEE Transactions on Mobile Computing10.1109/TMC.2021.305014721:9(3250-3262)Online publication date: 1-Sep-2022
https://doi.org/10.1109/TMC.2021.3050147
Yan CHao YLi LYin JLiu AMao ZChen ZGao X(2022)Task-Adaptive Attention for Image CaptioningIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2021.306744932:1(43-51)Online publication date: 10-Jan-2022
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Fukushima YSuda TMurase TTarutani YYokohira T(2022)Minimizing the monetary penalty and energy cost of server migration serviceTransactions on Emerging Telecommunications Technologies10.1002/ett.451133:9Online publication date: 10-Apr-2022
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Zhang YZhang X(2021)Price Learning-based Incentive Mechanism for Mobile Crowd SensingACM Transactions on Sensor Networks10.1145/344762217:2(1-24)Online publication date: 25-Jun-2021
https://dl.acm.org/doi/10.1145/3447622
Hu YLiu XZhang BHan JCao X(2021)Alignment Enhancement Network for Fine-grained Visual CategorizationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/344620817:1s(1-20)Online publication date: 31-Mar-2021
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