Abstract
We present a new spatio-temporal incentive-based approach to achieve a geographically balanced coverage of crowdsourced services. The proposed approach is based on a new spatio-temporal incentive model that considers multiple parameters including location entropy, time of day, and spatio-temporal density to encourage the participation of crowdsourced service providers. We present a greedy network flow algorithm that offers incentives to redistribute crowdsourced service providers to improve the crowdsourced coverage balance within an area. A novel participation probability model is also introduced to estimate the expected number of crowdsourced service providers’ movement based on spatio-temporal features. Experimental results validate the efficiency and effectiveness of the proposed approach.
- Maria J. Antikainen and Heli K. Vaataja. 2010. Rewarding in open innovation communities—How to motivate members. International Journal of Entrepreneurship and Innovation Management 11, 4 (2010), 440--456.Google ScholarCross Ref
- Eran Ben-Elia and Dick Ettema. 2009. Carrots versus sticks: Rewarding commuters for avoiding the rush-hour: A study of willingness to participate. Transport Policy 16, 2 (2009), 68--76.Google ScholarCross Ref
- Eran Ben-Elia and Dick Ettema. 2011. Rewarding rush-hour avoidance: A study of commuters travel behavior. Transportation Research Part A: Policy and Practice 45, 7 (2011), 567--582.Google ScholarCross Ref
- Michiel C. J. Bliemer, Matthijs Dicke-Ogenia, and Dick Ettema. 2010. Rewarding for avoiding the peak period: A synthesis of four studies in the Netherlands. (2010).Google Scholar
- Athman Bouguettaya, Munindar Singh, Michael Huhns, Quan Z. Sheng, Hai Dong, Qi Yu, Azadeh Ghari Neiat et al. 2017. A service computing manifesto: The next 10 years. Communications of the ACM 60, 4 (2017), 64--72. Google ScholarDigital Library
- Athman Bouguettaya, Surya Nepal, Wanita Sherchan, Xuan Zhou, Jemma Wu, Shiping Chen, Dongxi Liu, Lily Li, Hongbing Wang, and Xumin Liu. 2010. End-to-end service support for mashups. IEEE Transactions on Services Computing 3, 3 (2010), 250--263. Google ScholarDigital Library
- Peter Cohen, Robert Hahn, Jonathan Hall, Steven Levitt, and Robert Metcalfe. 2016. Using Big Data to Estimate Consumer Surplus: The Case of Uber. Technical Report. National Bureau of Economic Research.Google Scholar
- Justin Cranshaw, Eran Toch, Jason Hong, Aniket Kittur, and Norman Sadeh. 2010. Bridging the gap between physical location and online social networks. In Proceedings of the 12th ACM International Conference on Ubiquitous Computing. ACM, 119--128. Google ScholarDigital Library
- Shreya Das and Debapratim Pandit. 2013. Importance of user perception in evaluating level of service for bus transit for a developing country like India: A review. Transport Reviews 33, 4 (2013), 402--420.Google ScholarCross Ref
- Edward Deci and Richard M. Ryan. 1985. Intrinsic Motivation and Self-Determination in Human Behavior. Springer Science 8 Business Media.Google Scholar
- Dingxiong Deng, Cyrus Shahabi, and Linhong Zhu. 2015. Task matching and scheduling for multiple workers in spatial crowdsourcing. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 21. Google ScholarDigital Library
- Hongwei Dong, Liang Ma, and Joseph Broach. 2016. Promoting sustainable travel modes for commute tours: A comparison of the effects of home and work locations and employer-provided incentives. International Journal of Sustainable Transportation 10, 6 (2016), 485--494.Google ScholarCross Ref
- Hossein Falaki, Ratul Mahajan, Srikanth Kandula, Dimitrios Lymberopoulos, Ramesh Govindan, and Deborah Estrin. 2010. Diversity in smartphone usage. In Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services. ACM, 179--194. Google ScholarDigital Library
- Shibo He, Dong-Hoon Shin, Junshan Zhang, and Jiming Chen. 2014. Toward optimal allocation of location dependent tasks in crowdsensing. In Proceedings of the IEEE Conference on Computer Communications. IEEE, 745--753.Google ScholarCross Ref
- Luis G. Jaimes, Idalides Vergara-Laurens, and Alireza Chakeri. 2014. Spread, a crowd sensing incentive mechanism to acquire better representative samples. In Proceedings of the 2014 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops). IEEE, 92--97.Google ScholarCross Ref
- Luis G. Jaimes, Idalides Vergara-Laurens, and Miguel A. Labrador. 2012. A location-based incentive mechanism for participatory sensing systems with budget constraints. In Proceedings of the 2012 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, 103--108.Google Scholar
- Luis G. Jaimes, Idalides J. Vergara-Laurens, and Andrew Raij. 2015. A survey of incentive techniques for mobile crowd sensing. Internet of Things Journal, IEEE 2, 5 (2015), 370--380.Google Scholar
- Leyla Kazemi and Cyrus Shahabi. 2012. Geocrowd: Enabling query answering with spatial crowdsourcing. In Proceedings of the 20th International Conference on Advances in Geographic Information Systems. ACM, 189--198. Google ScholarDigital Library
- Samir Khuller, Anna Moss, and Joseph Seffi Naor. 1999. The budgeted maximum coverage problem. Information Processing Letters 70, 1 (1999), 39--45. Google ScholarDigital Library
- Ioannis Krontiris and Andreas Albers. 2012. Monetary incentives in participatory sensing using multi-attributive auctions. International Journal of Parallel, Emergent, and Distributed Systems 27, 4 (2012), 317--336. Google ScholarDigital Library
- Juong-Sik Lee and Baik Hoh. 2010a. Dynamic pricing incentive for participatory sensing. Pervasive and Mobile Computing 6, 6 (2010), 693--708. Google ScholarDigital Library
- Juong-Sik Lee and Baik Hoh. 2010b. Sell your experiences: A market mechanism based incentive for participatory sensing. In Proceedings of the 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, 60--68.Google ScholarCross Ref
- Diego Mendez and Miguel A. Labrador. 2012. Density maps: Determining where to sample in participatory sensing systems. In Proceedings of the 2012 3rd FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing (MUSIC). IEEE, 35--40. Google ScholarDigital Library
- Azadeh Ghari Neiat, Athman Bouguettaya, and Timos Sellis. 2015. Spatio-temporal composition of crowdsourced services. In Service-Oriented Computing (ICSOC). Springer, 373--382.Google Scholar
- Azadeh Ghari Neiat, Athman Bouguettaya, Timos Sellis, and Hai Dong. 2014. Failure-proof spatio-temporal composition of sensor cloud services. In Service-Oriented Computing (ICSOC). Springer, 368--377.Google Scholar
- Azadeh Ghari Neiat, Athman Bouguettaya, Timos Sellis, and Sajib Mistry. 2017. Crowdsourced coverage as a service: Two-level composition of sensor cloud services. IEEE Transactions on Knowledge and Data Engineering 29, 7 (2017), 1384--1397.Google ScholarDigital Library
- Michal Piorkowski, Natasa Sarafijanovic-Djukic, and Matthias Grossglauser. 2009. CRAWDAD dataset epfl/mobility (v. 2009-02-24). Retrieved February 2009 from http://crawdad.org/epfl/mobility/20090224.Google Scholar
- Sasank Reddy, Deborah Estrin, and Mani Srivastava. 2010. Recruitment framework for participatory sensing data collections. In International Conference on Pervasive Computing. Springer, 138--155. Google ScholarDigital Library
- Anne C. Rouse. 2010. A preliminary taxonomy of crowdsourcing. In Proceedings of ACIS 2010 76 (2010), 1--10.Google Scholar
- John P. Rula, Vishnu Navda, Fabián E. Bustamante, Ranjita Bhagwan, and Saikat Guha. 2014. No one-size fits all: Towards a principled approach for incentives in mobile crowdsourcing. In Proceedings of the 15th Workshop on Mobile Computing Systems and Applications. ACM, 3. Google ScholarDigital Library
- Wen Sun and Chen-Khong Tham. 2015. A spatio-temporal incentive scheme with consumer demand awareness for participatory sensing. In Proceedings of the 2015 IEEE International Conference on Communications (ICC). IEEE, 6363--6369.Google ScholarCross Ref
- Niwat Thepvilojanapong, Kai Zhang, Tomoya Tsujimori, Yoshikatsu Ohta, Yunlong Zhao, and Yoshito Tobe. 2013. Participation-aware incentive for active crowd sensing. In Proceedings of the 2013 IEEE 10th International Conference on High Performance Computing and Communications 8 2013 IEEE International Conference on Embedded and Ubiquitous Computing (HPCC_EUC). IEEE, 2127--2134.Google Scholar
- Dejun Yang, Guoliang Xue, Xi Fang, and Jian Tang. 2012. Crowdsourcing to smartphones: Incentive mechanism design for mobile phone sensing. In Proceedings of the 18th Annual International Conference on Mobile Computing and Networking. ACM, 173--184. Google ScholarDigital Library
- Xinglin Zhang, Zheng Yang, Wei Sun, Yunhao Liu, Shaohua Tang, Kai Xing, and Xufei Mao. 2016. Incentives for mobile crowd sensing: A survey. IEEE Communications Surveys 8 Tutorials 18, 1 (2016), 54--67.Google Scholar
Index Terms
- Incentive-Based Crowdsourcing of Hotspot Services
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