TY - CHAP
T1 - Collaborative Incentive Mechanism for Mobile Crowdsensing
AU - Li, Youqi
AU - Li, Fan
AU - Yang, Song
AU - Zhang, Chuan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.
PY - 2024
Y1 - 2024
N2 - In this chapter, we propose PTASIM, an incentive mechanism that explores cooperation with POI-tagging App for Mobile Edge Crowdsensing (MEC). PTASIM requests the App to tag some edges to be POI (Points-of-Interest), which further guides App users to perform tasks at that location. We further model the interactions of users, a platform, and an App by a three-stage decision process. The App first determines the POI-tagging price to maximize its payoff. Platform and users subsequently decide how to determine tasks reward and select edges to be tagged, and how to select the best task to perform, respectively. We analyze the optimal solution in those stages. Specifically, we prove greedy algorithm could provide the optimal solution for the platform’s payoff maximization in polynomial time. The numerical results show that: (1) the cooperation with App brings long-term and sufficient participation; the optimal strategies reduce the platform’s tasks cost as well as improve App’s revenues.
AB - In this chapter, we propose PTASIM, an incentive mechanism that explores cooperation with POI-tagging App for Mobile Edge Crowdsensing (MEC). PTASIM requests the App to tag some edges to be POI (Points-of-Interest), which further guides App users to perform tasks at that location. We further model the interactions of users, a platform, and an App by a three-stage decision process. The App first determines the POI-tagging price to maximize its payoff. Platform and users subsequently decide how to determine tasks reward and select edges to be tagged, and how to select the best task to perform, respectively. We analyze the optimal solution in those stages. Specifically, we prove greedy algorithm could provide the optimal solution for the platform’s payoff maximization in polynomial time. The numerical results show that: (1) the cooperation with App brings long-term and sufficient participation; the optimal strategies reduce the platform’s tasks cost as well as improve App’s revenues.
KW - POI-tagging App
KW - Participation rate guarantee
KW - Stackelberg game
KW - Third-party collaboration
KW - Three-stage decision process
UR - http://www.scopus.com/inward/record.url?scp=85182862443&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-6921-0_4
DO - 10.1007/978-981-99-6921-0_4
M3 - Chapter
AN - SCOPUS:85182862443
T3 - SpringerBriefs in Computer Science
SP - 71
EP - 93
BT - SpringerBriefs in Computer Science
PB - Springer
ER -