The Journal of China Universities of Posts and Telecommunications ›› 2020, Vol. 27 ›› Issue (1): 26-37.doi: 10.19682/j.cnki.1005-8885.2020.0007
Previous Articles Next Articles
Ying-Lin HOU1,Wei-Qing CHENG2
Received:
2019-09-02
Revised:
2019-12-11
Online:
2020-02-28
Published:
2020-02-28
Contact:
Wei-Qing CHENG
E-mail:chengweiq@njupt.edu.cn
Supported by:
CLC Number:
Ying-Lin HOU Wei-Qing CHENG. Task allocation based on profit maximization for mobile crowdsourcing[J]. The Journal of China Universities of Posts and Telecommunications, 2020, 27(1): 26-37.
Add to citation manager EndNote|Ris|BibTeX
URL: https://jcupt.bupt.edu.cn/EN/10.19682/j.cnki.1005-8885.2020.0007
1. Buettner R. A systematic literature review of crowdsourcing research from a human resource management perspective. Proceedings of the 48th Annual Hawaii International Conference on System Sciences, Jan 5-8, 2015, Kauai, HI, USA. Piscataway, NJ, USA: IEEE, 2015: 4609-4618. 2. Brodt-Giles D. OpenEI—An open energy data and information exchange for international audiences. World Renewable Energy Forum (WREF) 2012, May 13-17, 2012, Golden, CO, USA. 2012 3. Aitamurto T. Motivation factors in crowdsourced journalism: Social impact, social change and peer-learning. International Journal of Communication, 2015, 9: 3523-3543. 4. Aitamurto T. Crowdsourcing as a knowledge-search method in digital journalism: Ruptured ideals and blended responsibility. Digital Journalism, 2016, 4(2): 280-297 5. Maisonneuve N, Stevens M, Niessen M E, et al. Noise tube: Measuring and mapping noise pollution with mobile phones. Information Technologies in Environmental Engineering, Berlin, Germany: Springer, 2009: 215-228 6. Panteras G, Cervone G. Enhancing the temporal resolution of satellite-based flood extent generation using crowdsourced data for disaster monitoring. International Journal of Remote Sensing, 2018, 39(5):1459-1474 7. Matti T, Zhu Y T , Xu K. Financial fraud detection using social media crowdsourcing. Proceedings of the 33rd International Performance Computing and Communications Conference (IPCCC’14), Dec 5-7, 2014, Austin, TX, USA. Piscataway, NJ, USA: IEEE, 2014 8. Neal S B , Kantikar G , Fujimoto R. Power consumption of data distribution management for on-line simulations. Proceedings of the 2nd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (SIGSIM PADS'14), May 18-21, 2014, Denver, CO, USA. Piscataway, NJ, USA: IEEE,2014: 197-204 9. Chen L , Lee D W , Milo T. Data-driven crowdsourcing: Management, mining, and applications. Proceedings of the IEEE 31st International Conference on Data Engineering (ICDE’), Apr 13-17, 2015, Seoul, Republic of Korea. Piscataway, NJ, USA: IEEE, 2015 10. Levine B N, Shields C, Margolin N B. A survey of solutions to the Sybil attack. TR 2006-052. Amerst, MA, USA: University of Massachusetts Amherst, 2006 11. Zheng Y. Liu F R, Hsieh H P. U-air: When urban air quality inference meetsbig data. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'13), Aug 11-14, 2013, Chicago, IL, USA. New York, NY, USA: ACM, 2013: 1436-1444 12. Guo B, Chen H H, Yu Z W, et al. TaskMe: Toward a dynamic and quality-enhanced incentive mechanism for mobile crowd sensing. International Journal of Human-Computer Studies, 2016, 102: 14-26 13. Yan Z S, Liu Q, Zhang T, et al. CrowdDBS: A crowdsourced brightness scaling optimization for display energy reduction in mobile video. IEEE Transactions on Mobile Computing, 2018, 17(11): 2536-2549 14. Luo T, Kanhere S S, Das S K, et al. Incentive mechanism design for heterogeneous crowdsourcing using all-pay contests. IEEE Transactions on Mobile Computing, 2016, 15(9): 2234-2246 15. Zhang Y, van der Schaar. Reputation-based incentive protocols in crowdsourcing applications. Proceedings of the 31st Annual Joint Conference of the IEEE Computer and Communications (INFOCOM’12), Mar 25-30, 2012, Orlando, FL, USA. Piscataway, NJ, USA: IEEE, 2012: 2140-2148 16. Krontiris I, Albers A. Monetary incentives in participatory sensing using multi-attributive auctions. International Journal of Parallel Emergent and Distributed Systems, 2012, 27(4): 317-336 17. Yang D J, Xue G L, Fang X, et al. Crowdsourcing to smartphones: Incentive mechanism design for mobile phone sensing. Proceedings of the 18th Annual International Conference on Mobile Computing and Networking (MobiCom'12), Aug 22-26, 2012, Istanbul, Turkey. New York, NY, USA: ACM, 2012: 173-184 18. Lease M, Alonso O. Crowdsourcing and human computation, introduction. Encyclopedia of Social Network Analysis and Mining, Berlin, Germany: Springer, 2014: 304-315 (Chapter 107) 19. Zhang B, Liu C H, Ren Z Y, et al. Crowdsourcing energy-efficient participants to ensure quality-of-information. Proceedings of the IEEE 26th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC’15), Aug 30-Sept 2, 2015, Hong Kong, China. Piscataway, NJ, USA: IEEE, 2015: 1606-1610 20. Yin X Y, Chen Y J, Li B C. Task assignment with guaranteed qualityfor crowdsourcing platforms. Proceedings of the IEEE/ACM 25th International Symposium on Quality of Service (IWQoS’17), Jun 14-16, 2017, Vilanova, Spain. Piscataway, NJ, USA: IEEE, 2017: 10p 21. Miao C Y, Yu H, Shen Z Q, et al. Balancing quality and budget considerations in mobile crowdsourcing. Decision Support Systems, 2016, 90: 56-64 22. Ye B, Wang Y, Liu L. Crowd trust: A context-aware trust model for worker selection in crowdsourcing environments. Proceedings of the 2015 IEEE International Conference on Web Services, Jun 27-Jul 2, 2015, New York, NY, USA. Piscataway, NJ, USA: IEEE, 2015: 121-128 23. Hardisty D J, Appelt K C, Weber E U. Good or bad, we want it now: Fixed-cost present bias for gains and losses explains magnitude asymmetries in intertemporal choice. Journal of Behavioral Decision Making, 2013, 26(4): 348-361. 24. Wang W B, Gao H, Liu C H, et al. Credible and energy-aware participant selection with limited task budget for mobile crowd sensing. Ad Hoc Networks, 2016, 43: 56-70 25. Zhang B, Song Z, Liu C H, et al. An event-driven QOI-aware participatory sensing framework with energy and budget constraints. ACM Transactions on Intelligent Systems and Technology, 2015, 6(3): Article 42 26. Champaign J, Cohen R, Zhang J, et al. The validation of an annotations approach to peer tutoring through simulation incorporating the modeling of reputation. Proceedings of the 19th International Conference on Computer in Education (ICCE’11), Nov 28-Dec 2, 2011, Chiang Mai, Thailand: Asia-Pacific Society for Computers in Education, 2011: 5p 27. He S B, Shin D H, Zhang J S, et al. An exchange market approach to mobile crowdsensing: Pricing, task allocation, and walrasian equilibrium, IEEE Journal on Selected Areas in Communications, 2017, 35 (4): 921-934 28. Yang D J, Xue G L, Fang X, et al. Crowdsourcing to smartphones: Incentive mechanism design for mobile phone sensing. Proceedings of the 18th Annual International Conference on Mobile Computing and Networking (MobiCom’12), Aug 22-26, 2012, Istanbul, Turkey. New York, NY, USA: ACM, 2012: 173-184. 29. Boyd S, Parikh N, Chu E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 2011, 3(1): 1-122 30. Zhan Y F, Xia Y Q, Zhang J H. Quality-aware incentive mechanism based on payoff maximization for mobile crowdsensing. Ad Hoc Networks, 2018, 72: 44-55 |
[1] | Shi Jinjing, Wang Wenxuan, Xiao Zimeng, Mu Shuai, Li Qin. Quantum classifier with parameterized quantum circuit based on the isolated quantum system [J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(4): 21-31. |
[2] | Liu Hailing, Zhang Jie, Qin Sujuan, Gao Fei. Quantum algorithm for soft margin support vector machine with hinge loss function [J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(4): 32-41. |
[3] | Wang Jian, Qiao Kuoyuan, Yuan Yanlei, Liu Xiaole, Yang Jian. Adaptive learning path recommendation model for examination-oriented education [J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(4): 77-88. |
[4] | Meng Wei, Wang Liting, Lu Meng. Summary of research on recommendation system based on serendipity [J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(4): 89-105. |
[5] | Jia Wei, Gong Chao. Precise and efficient Chinese license plate recognition in the real monitoring scene of intelligent transportation system [J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(3): 1-14. |
[6] | Song Yue, Wu Chengmao, Tian Xiaoping, Song Qiuyu. Enhanced kernel-based fuzzy local information clustering integrating neighborhood membership [J]. The Journal of China Universities of Posts and Telecommunications, 2021, 28(6): 65-81. |
[7] | Ming Yue, Li Wenmin, Xu Siya, Gao Lifang, Zhang Hua, Shao Sujie, Yang Huifeng. Liveness detection of occluded face based on dual-modality convolutional neural network [J]. The Journal of China Universities of Posts and Telecommunications, 2021, 28(4): 1-12. |
[8] | Xue Chenzi, Wei Yifei, Zhang Yong. Performance optimization for smart grid blockchain integrated with fog computing using DDQN [J]. The Journal of China Universities of Posts and Telecommunications, 2021, 28(2): 68-78. |
[9] | Guo Hairu, Meng Xueyao, Liu Yongli, Liu Shen. Improved HHO algorithm based on good point set and nonlinear convergence formula [J]. The Journal of China Universities of Posts and Telecommunications, 2021, 28(2): 48-67. |
[10] | Wu Chengmao, Cao Zhuo. Entropy-like distance driven fuzzy clustering with local information constraints for image segmentation [J]. The Journal of China Universities of Posts and Telecommunications, 2021, 28(1): 24-40. |
[11] | Zhao Guosheng, Liu Dongmei, Wang Jian. Cloud security situation prediction method based on grey wolf optimization and BP neural network [J]. The Journal of China Universities of Posts and Telecommunications, 2020, 27(6): 30-41. |
[12] | Shan Rui, Jiang Lin, Deng Junyong, Cui Pengfei, Zhang Yuting, Wu Haoyue, Xie Xiaoyan. Parallel design of convolutional neural networks for remote sensing images object recognition based on data-driven array processor [J]. The Journal of China Universities of Posts and Telecommunications, 2020, 27(6): 87-100. |
[13] | Liu Kun, Wang Hui , Shen Zihao. Prediction of network attack profit path based on NAPG model [J]. The Journal of China Universities of Posts and Telecommunications, 2020, 27(5): 91-102. |
[14] | Wang Zhaoying, Zhou Junhua, Liao Zhonghua, Zhai Xiang, Zhang Lianping. Semantic segmentation of track image based on deep neural network [J]. The Journal of China Universities of Posts and Telecommunications, 2020, 27(5): 23-33. |
[15] | Chen Faquan, Fan Jun. Real-time prediction of the motion tendency of human lower limbs during gait [J]. The Journal of China Universities of Posts and Telecommunications, 2020, 27(4): 1-7. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||