More Web Proxy on the site http://driver.im/

article

Dynamic pricing incentive for participatory sensing

Authors:

Baik HohAuthors Info & Claims

Pervasive and Mobile Computing, Volume 6, Issue 6

Pages 693 - 708

https://doi.org/10.1016/j.pmcj.2010.08.006

Published: 01 December 2010 Publication History

Abstract

User participation is one of the most important elements in participatory sensing application for providing adequate level of service quality. However, incentive mechanism and its economic model for user participation have been less addressed so far in this research domain. This paper studies the economic model of user participation incentive in participatory sensing applications. To stimulate user participation, we design and evaluate a novel reverse auction based dynamic pricing incentive mechanism where users can sell their sensing data to a service provider with users' claimed bid prices. The proposed incentive mechanism focuses on minimizing and stabilizing the incentive cost while maintaining adequate level of participants by preventing users from dropping out of participatory sensing applications. Compared with random selection based fixed pricing incentive mechanism, the proposed mechanism not only reduces the incentive cost for retaining the same number of participants but also improves the fairness of incentive distribution and social welfare. It also helps us to achieve the geographically balanced sensing measurements and, more importantly, can remove the burden of accurate price decision for user data that is the most difficult step in designing incentive mechanism.

References

[1]

Nokia Sensor Planet Project. http://www.sensorplanet.org.

[2]

Zhao, F., Sensornet 2.0: the new frontier. In: RTSS'06: Proceedings of the 27th IEEE International Real-Time Systems Symposium, IEEE, Washington, DC, USA.

[3]

Mobile Millennium. http://traffic.berkeley.edu.

[4]

Nokia Simple Context. https://alpha.nokoscope.com/eb2.

[5]

Intel Urban Atmosphere. http://www.urban-atmospheres.net/.

[6]

Bichler, M., The Future of e-Markets: Multidimensional Market Mechanism. 2001. Cambridge University Press.

[7]

J. Lee, B. Hoh, Sell your experiences: market mechanism based incentive for participatory sensing, in: IEEE International Conference on Pervasive Computing and Communications, PERCOM 2010, Mannheim, Germany, 2010.

[8]

B. Hoh, M. Gruteser, Computer ecology: responding to mobile worms with location-based quarantine boundaries, in: International Workshop on Research Challenges in Security and Privacy for Mobile and Wireless Networks, 2006.

[9]

B. Hull, V. Bychkovsky, Y. Zhang, K. Chen, M. Goraczko, A.K. Miu, E. Shih, H. Balakrishnan, S. Madden, CarTel: a distributed mobile sensor computing system, in: 4th ACM SenSys, Boulder, CO, November 2006.

Digital Library

[10]

A. Krause, E. Horvitz, A. Kansal, F. Zhao, Toward community sensing, in: ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN, April 2008.

[11]

Abdelzaher, T., Anokwa, Y., Boda, P., Burke, J., Estrin, D., Guibas, L., Kansal, A., Madden, S. and Reich, J., Mobiscopes for human spaces. IEEE Pervasive Computing-Mobile and Ubiquitous Systems. v6 i2.

[12]

B. Hoh, M. Gruteser, R. Herring, J. Ban, D. Work, J.C. Herrera, A.M. Bayen, M. An-navaram, Q. Jacobson, Virtual trip lines for distributed privacy-preserving traffic monitoring, in: MobiSys 2008, Breckenridge, CO.

[13]

Allen, M., Girod, L., Newton, R., Madden, S., Blumstein, D.T. and Estrin, D., Voxnet: an interactive, rapidly deployable acoustic monitoring platform. In: IPSN'08: Proceedings of the 2008 International Conference on Information Processing in Sensor Networks, IEEE Computer Society, Washington, DC, USA. pp. 371-382.

[14]

MIT Media Lab: The Owl Project. http://owlproject.media.mit.edu.

[15]

UCLA Center for Embedded Networked Sensing. http://research.cens.ucla.edu.

[16]

G. Danezis, S. Lewis, R. Anderson, How much is location privacy worth? WEIS'05: Proceedings of the Fourth Workshop on the Economics of Information Security, Harvard University 2005.

[17]

Lee, J. and Szymanski, B.K., Auctions as a dynamic pricing mechanism for E-services. In: Hsu, C. (Ed.), Service Enterprise Integration, Kluwer. pp. 131-156.

[18]

McAfee, R. and McMillan, P.J., Auction and bidding. Journal of Economic Literature. v25. 699-738.

[19]

J. Lee, B.K. Szymanski, A novel auction mechanism for selling time-sensitive E-services, in: 7th International IEEE Conference on E-Commerce Technology 2005, CEC'05, Munich, Germany, 2005.

[20]

Waze: real-time maps and traffic information based on the wisdom of the crowd. http://www.waze.com.

[21]

Lee, J. and Szymanski, B.K., Auction as a dynamic pricing mechanism for E-services. In: Hsu, Cheng (Ed.), Service Enterprise Integration, Springer Science + Business Media, LLC, New York.

[22]

J. Lee, B.K. Szymanski, A participation incentive market mechanism for allocating heterogeneous network services, in: IEEE Global Communications Conference, GLOBECOM 2009, Honolulu, Hawaii, USA, December 2009.

[23]

Hoh, B., Gruteser, M., Xiong, H. and Alrabady, A., Enhancing security and privacy in traffic-monitoring systems. IEEE Pervasive Computing Magazine. iOctober-December.

[24]

S. Saroiu, A. Wolman, I am a sensor, and I approve this message, in: HotMobile 2010: Proceedings of the Eleventh Workshop on Mobile Computing Systems and Applications, New York, USA, 2010.

[25]

P. Gilbert, L.P. Cox, J. Jung, D. Wetherall, Toward trustworthy mobile sensing, in: HotMobile 2010: Proceedings of the Eleventh Workshop on Mobile Computing Systems and Applications, New York, USA, 2010.

[26]

M. Bichler, M. Kaukal, A. Segev, Multi-attribute auctions for electronic procurement, in: Proc. 1st IBM IAC Workshop on Internet Based Negotiation Technologies, Yorktown Heights, NY, 1999.

[27]

J. Grossklags, A. Acquisti, When 25 cents is too much: an experiment on willingness-to-sell and willingness-to-protect personal information, in: Sixth Workshop on the Economics of Information Security, WEIS 2007, Pittsburgh, PA, 2007.

[28]

Amazon MTurk. https://www.mturk.com/mturk/welcome.

[29]

T. Yan, V. Kumar, D. Ganesan, CrowdSearch: exploiting crowds for accurate real-time image search on mobile phones, in: Proceeding of 8th Annual International Conference on Mobile Systems, Applications and Services, MobiSys 2010, San Francisco, USA, 2010.

[30]

N. Eagle, TextEagle. http://txteagle.com/.

[31]

C. Chou, N. Bulusu, S. Kanhere, Sensing data market, in: Proceedings of the International Conference on Distributed Computing in Sensor Systems, DCOSS 2008, Santorini, Greece, June 2008.

[32]

B. Hubermann, E. Adar, A market for secrets, First Monday, vol. 6, October 2001. http://www.firstmonday.org/issues/issue6_8/adar/index.html.

Cited By

Li YChang LLi LBao XGu T(2021)Key Research Issues and Related Technologies in Crowdsourcing Data CollectionWireless Communications & Mobile Computing10.1155/2021/87458972021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/8745897
Dong LWang XLiu BZheng TWang Z(2021)Information Acquisition Incentive Mechanism Based on Evolutionary Game TheoryWireless Communications & Mobile Computing10.1155/2021/55257912021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/5525791
Hu JWang ZWei JLv RZhao JWang QChen HYang D(2020)Towards Demand-Driven Dynamic Incentive for Mobile Crowdsensing SystemsIEEE Transactions on Wireless Communications10.1109/TWC.2020.298827119:7(4907-4918)Online publication date: 9-Jul-2020
https://dl.acm.org/doi/10.1109/TWC.2020.2988271
Show More Cited By

Recommendations

Multidimensional Dynamic Pricing for Welfare Maximization
EC '17: Proceedings of the 2017 ACM Conference on Economics and Computation

We study the problem of a seller dynamically pricing d distinct types of indivisible goods, when faced with the online arrival of unit-demand buyers drawn independently from an unknown distribution. The goods are not in limited supply, but can only be ...
Dynamic and Nonuniform Pricing Strategies for Revenue Maximization
^† Special Section on the Fiftieth Annual IEEE Symposium on Foundations of Computer Science (FOCS 2009)

We consider the item pricing problem for revenue maximization, where a single seller with multiple distinct items caters to multiple buyers with unknown subadditive valuation functions who arrive in a sequence. The seller sets the prices on individual items, ...
Dynamic pricing with limited supply
EC '12: Proceedings of the 13th ACM Conference on Electronic Commerce

We consider the problem of designing revenue maximizing online posted-price mechanisms when the seller has limited supply. A seller has k identical items for sale and is facing n potential buyers ("agents") that are arriving sequentially. Each agent is ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Pervasive and Mobile Computing

Pervasive and Mobile Computing Volume 6, Issue 6

December, 2010

119 pages

ISSN:1574-1192

Issue’s Table of Contents

Copyright © Elsevier B.V. © 2010.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 December 2010

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

46
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Li YChang LLi LBao XGu T(2021)Key Research Issues and Related Technologies in Crowdsourcing Data CollectionWireless Communications & Mobile Computing10.1155/2021/87458972021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/8745897
Dong LWang XLiu BZheng TWang Z(2021)Information Acquisition Incentive Mechanism Based on Evolutionary Game TheoryWireless Communications & Mobile Computing10.1155/2021/55257912021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/5525791
Hu JWang ZWei JLv RZhao JWang QChen HYang D(2020)Towards Demand-Driven Dynamic Incentive for Mobile Crowdsensing SystemsIEEE Transactions on Wireless Communications10.1109/TWC.2020.298827119:7(4907-4918)Online publication date: 9-Jul-2020
https://dl.acm.org/doi/10.1109/TWC.2020.2988271
Jaimes LCalderon J(2020)An UAV-based incentive mechanism for Crowdsensing with budget constraints2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC)10.1109/CCNC46108.2020.9045642(1-6)Online publication date: 10-Jan-2020
https://dl.acm.org/doi/10.1109/CCNC46108.2020.9045642
Yonezawa TSakamura MKawaguchi NNakazawa J(2020)Civic Crowdsensing Through Location-Aware Virtual MonstersDistributed, Ambient and Pervasive Interactions10.1007/978-3-030-50344-4_33(464-476)Online publication date: 19-Jul-2020
https://dl.acm.org/doi/10.1007/978-3-030-50344-4_33
Yu RCao JLiu RGao WWang XLiang J(2019)Participant Incentive Mechanism Toward Quality-Oriented SensingACM Transactions on Sensor Networks10.1145/330370315:2(1-25)Online publication date: 11-Feb-2019
https://dl.acm.org/doi/10.1145/3303703
Gummidi SXie XPedersen T(2019)A Survey of Spatial CrowdsourcingACM Transactions on Database Systems10.1145/329193344:2(1-46)Online publication date: 15-Mar-2019
https://dl.acm.org/doi/10.1145/3291933
Neiat ABouguettaya AMistry S(2019)Incentive-Based Crowdsourcing of Hotspot ServicesACM Transactions on Internet Technology10.1145/322904719:1(1-24)Online publication date: 29-Jan-2019
https://dl.acm.org/doi/10.1145/3229047
Dobakhshari DNaghizadeh PLiu MGupta V(2019)A Reputation-Based Contract for Repeated Crowdsensing With Costly VerificationIEEE Transactions on Signal Processing10.1109/TSP.2019.295205067:23(6092-6104)Online publication date: 25-Nov-2019
https://dl.acm.org/doi/10.1109/TSP.2019.2952050
Niu XWang JYe QZhang Y(2018)A Privacy-Preserving Incentive Mechanism for Participatory Sensing SystemsSecurity and Communication Networks10.1155/2018/25935372018Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1155/2018/2593537
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents