TY - GEN
T1 - QUAC
T2 - 14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2017
AU - Li, Ming
AU - Lin, Jian
AU - Yang, Dejun
AU - Xue, Guoliang
AU - Tang, Jian
N1 - Funding Information:
Li, Lin, and Yang are with Colorado School of Mines, Golden, CO 80401. E-mail: {mili, djyang, jilin}@mines.edu. Xue is with the Arizona State University, Tempe, AZ 85287. E-mail: xue@asu.edu. Tang is with Syracuse University, Syracuse, NY 13210. E-mail: jtang02@syr.edu. This research was supported in part by NSF grants 1421685, 1444059, 1525920, 1717197, and 1717315. The information reported here does not reflect the position or the policy of the funding agencies.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/14
Y1 - 2017/11/14
N2 - Crowdsensing is a sensing method which involves participants from general public to collect sensed data from their mobile devices, and also contribute and utilize a common database. To ensure a crowdsensing system to operate properly, there must be certain effective and efficient incentive mechanism to attract users and stimulate them to submit sensing data with high quality. Intuitively, the agreement on the qualities and payments in crowdsensing systems can be best modeled as a contract. However, none of existing incentive mechanisms consider data quality through effective contract design. In this paper, we design two quality-aware contract-based incentive mechanisms for crowdsensing, named QUAC-F and QUAC-I, under full information model and incomplete information model, respectively, which differ in the level of users' information known to the system. Both QUAC-F and QUAC-I are guaranteed to maximize the platform utility while satisfying individual rationality and incentive compatibility. We evaluate the performance of our designed mechanisms based on a real dataset.
AB - Crowdsensing is a sensing method which involves participants from general public to collect sensed data from their mobile devices, and also contribute and utilize a common database. To ensure a crowdsensing system to operate properly, there must be certain effective and efficient incentive mechanism to attract users and stimulate them to submit sensing data with high quality. Intuitively, the agreement on the qualities and payments in crowdsensing systems can be best modeled as a contract. However, none of existing incentive mechanisms consider data quality through effective contract design. In this paper, we design two quality-aware contract-based incentive mechanisms for crowdsensing, named QUAC-F and QUAC-I, under full information model and incomplete information model, respectively, which differ in the level of users' information known to the system. Both QUAC-F and QUAC-I are guaranteed to maximize the platform utility while satisfying individual rationality and incentive compatibility. We evaluate the performance of our designed mechanisms based on a real dataset.
UR - http://www.scopus.com/inward/record.url?scp=85040638849&partnerID=8YFLogxK
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U2 - 10.1109/MASS.2017.45
DO - 10.1109/MASS.2017.45
M3 - Conference contribution
AN - SCOPUS:85040638849
T3 - Proceedings - 14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2017
SP - 72
EP - 80
BT - Proceedings - 14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 October 2017 through 25 October 2017
ER -