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 |