Quality-Aware and Fine-Grained Incentive Mechanisms for Mobile Crowdsensing

Jing Wang, Jian Tang, Dejun Yang, Erica Wang, Guoliang Xue

Research output: Chapter in Book/Report/Conference proceedingConference contribution

46 Citations (Scopus)

Abstract

Limited research efforts have been made for Mobile CrowdSensing (MCS) to address quality of the recruited crowd, i.e., quality of services/data each individual mobile user and the whole crowd are potentially capable of providing, which is the main focus of the paper. Moreover, to improve flexibility and effectiveness, we consider fine-grained MCS, in which each sensing task is divided into multiple subtasks and a mobile user may make contributions to multiple subtasks. In this paper, we first introduce mathematical models for characterizing the quality of a recruited crowd for different sensing applications. Based on these models, we present a novel auction formulation for quality-aware and fine-grained MCS, which minimizes the expected expenditure subject to the quality requirement of each subtask. Then we discuss how to achieve the optimal expected expenditure, and present a practical incentive mechanism to solve the auction problem, which is shown to have the desirable properties of truthfulness, individual rationality and computational efficiency. We conducted trace-driven simulation using the mobility dataset of San Francisco taxies. Extensive simulation results show the proposed incentive mechanism achieves noticeable expenditure savings compared to two well-designed baseline methods, and moreover, it produces close-to-optimal solutions.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE 36th International Conference on Distributed Computing Systems, ICDCS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages354-363
Number of pages10
Volume2016-August
ISBN (Electronic)9781509014828
DOIs
StatePublished - Aug 8 2016
Event36th IEEE International Conference on Distributed Computing Systems, ICDCS 2016 - Nara, Japan
Duration: Jun 27 2016Jun 30 2016

Other

Other36th IEEE International Conference on Distributed Computing Systems, ICDCS 2016
CountryJapan
CityNara
Period6/27/166/30/16

Fingerprint

Computational efficiency
Quality of service
Mathematical models

Keywords

  • Auction
  • Incentive Mechanism
  • Mobile Crowdsensing
  • Quality of Crowd
  • Smartphones

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Software

Cite this

Wang, J., Tang, J., Yang, D., Wang, E., & Xue, G. (2016). Quality-Aware and Fine-Grained Incentive Mechanisms for Mobile Crowdsensing. In Proceedings - 2016 IEEE 36th International Conference on Distributed Computing Systems, ICDCS 2016 (Vol. 2016-August, pp. 354-363). [7536534] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDCS.2016.30

Quality-Aware and Fine-Grained Incentive Mechanisms for Mobile Crowdsensing. / Wang, Jing; Tang, Jian; Yang, Dejun; Wang, Erica; Xue, Guoliang.

Proceedings - 2016 IEEE 36th International Conference on Distributed Computing Systems, ICDCS 2016. Vol. 2016-August Institute of Electrical and Electronics Engineers Inc., 2016. p. 354-363 7536534.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, J, Tang, J, Yang, D, Wang, E & Xue, G 2016, Quality-Aware and Fine-Grained Incentive Mechanisms for Mobile Crowdsensing. in Proceedings - 2016 IEEE 36th International Conference on Distributed Computing Systems, ICDCS 2016. vol. 2016-August, 7536534, Institute of Electrical and Electronics Engineers Inc., pp. 354-363, 36th IEEE International Conference on Distributed Computing Systems, ICDCS 2016, Nara, Japan, 6/27/16. https://doi.org/10.1109/ICDCS.2016.30
Wang J, Tang J, Yang D, Wang E, Xue G. Quality-Aware and Fine-Grained Incentive Mechanisms for Mobile Crowdsensing. In Proceedings - 2016 IEEE 36th International Conference on Distributed Computing Systems, ICDCS 2016. Vol. 2016-August. Institute of Electrical and Electronics Engineers Inc. 2016. p. 354-363. 7536534 https://doi.org/10.1109/ICDCS.2016.30
Wang, Jing ; Tang, Jian ; Yang, Dejun ; Wang, Erica ; Xue, Guoliang. / Quality-Aware and Fine-Grained Incentive Mechanisms for Mobile Crowdsensing. Proceedings - 2016 IEEE 36th International Conference on Distributed Computing Systems, ICDCS 2016. Vol. 2016-August Institute of Electrical and Electronics Engineers Inc., 2016. pp. 354-363
@inproceedings{22e34260828e46ed83d99cf8f0004f81,
title = "Quality-Aware and Fine-Grained Incentive Mechanisms for Mobile Crowdsensing",
abstract = "Limited research efforts have been made for Mobile CrowdSensing (MCS) to address quality of the recruited crowd, i.e., quality of services/data each individual mobile user and the whole crowd are potentially capable of providing, which is the main focus of the paper. Moreover, to improve flexibility and effectiveness, we consider fine-grained MCS, in which each sensing task is divided into multiple subtasks and a mobile user may make contributions to multiple subtasks. In this paper, we first introduce mathematical models for characterizing the quality of a recruited crowd for different sensing applications. Based on these models, we present a novel auction formulation for quality-aware and fine-grained MCS, which minimizes the expected expenditure subject to the quality requirement of each subtask. Then we discuss how to achieve the optimal expected expenditure, and present a practical incentive mechanism to solve the auction problem, which is shown to have the desirable properties of truthfulness, individual rationality and computational efficiency. We conducted trace-driven simulation using the mobility dataset of San Francisco taxies. Extensive simulation results show the proposed incentive mechanism achieves noticeable expenditure savings compared to two well-designed baseline methods, and moreover, it produces close-to-optimal solutions.",
keywords = "Auction, Incentive Mechanism, Mobile Crowdsensing, Quality of Crowd, Smartphones",
author = "Jing Wang and Jian Tang and Dejun Yang and Erica Wang and Guoliang Xue",
year = "2016",
month = "8",
day = "8",
doi = "10.1109/ICDCS.2016.30",
language = "English (US)",
volume = "2016-August",
pages = "354--363",
booktitle = "Proceedings - 2016 IEEE 36th International Conference on Distributed Computing Systems, ICDCS 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

TY - GEN

T1 - Quality-Aware and Fine-Grained Incentive Mechanisms for Mobile Crowdsensing

AU - Wang, Jing

AU - Tang, Jian

AU - Yang, Dejun

AU - Wang, Erica

AU - Xue, Guoliang

PY - 2016/8/8

Y1 - 2016/8/8

N2 - Limited research efforts have been made for Mobile CrowdSensing (MCS) to address quality of the recruited crowd, i.e., quality of services/data each individual mobile user and the whole crowd are potentially capable of providing, which is the main focus of the paper. Moreover, to improve flexibility and effectiveness, we consider fine-grained MCS, in which each sensing task is divided into multiple subtasks and a mobile user may make contributions to multiple subtasks. In this paper, we first introduce mathematical models for characterizing the quality of a recruited crowd for different sensing applications. Based on these models, we present a novel auction formulation for quality-aware and fine-grained MCS, which minimizes the expected expenditure subject to the quality requirement of each subtask. Then we discuss how to achieve the optimal expected expenditure, and present a practical incentive mechanism to solve the auction problem, which is shown to have the desirable properties of truthfulness, individual rationality and computational efficiency. We conducted trace-driven simulation using the mobility dataset of San Francisco taxies. Extensive simulation results show the proposed incentive mechanism achieves noticeable expenditure savings compared to two well-designed baseline methods, and moreover, it produces close-to-optimal solutions.

AB - Limited research efforts have been made for Mobile CrowdSensing (MCS) to address quality of the recruited crowd, i.e., quality of services/data each individual mobile user and the whole crowd are potentially capable of providing, which is the main focus of the paper. Moreover, to improve flexibility and effectiveness, we consider fine-grained MCS, in which each sensing task is divided into multiple subtasks and a mobile user may make contributions to multiple subtasks. In this paper, we first introduce mathematical models for characterizing the quality of a recruited crowd for different sensing applications. Based on these models, we present a novel auction formulation for quality-aware and fine-grained MCS, which minimizes the expected expenditure subject to the quality requirement of each subtask. Then we discuss how to achieve the optimal expected expenditure, and present a practical incentive mechanism to solve the auction problem, which is shown to have the desirable properties of truthfulness, individual rationality and computational efficiency. We conducted trace-driven simulation using the mobility dataset of San Francisco taxies. Extensive simulation results show the proposed incentive mechanism achieves noticeable expenditure savings compared to two well-designed baseline methods, and moreover, it produces close-to-optimal solutions.

KW - Auction

KW - Incentive Mechanism

KW - Mobile Crowdsensing

KW - Quality of Crowd

KW - Smartphones

UR - http://www.scopus.com/inward/record.url?scp=84985952123&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84985952123&partnerID=8YFLogxK

U2 - 10.1109/ICDCS.2016.30

DO - 10.1109/ICDCS.2016.30

M3 - Conference contribution

AN - SCOPUS:84985952123

VL - 2016-August

SP - 354

EP - 363

BT - Proceedings - 2016 IEEE 36th International Conference on Distributed Computing Systems, ICDCS 2016

PB - Institute of Electrical and Electronics Engineers Inc.

ER -