TY - JOUR
T1 - A Multi-Leader Multi-Follower Game-Based Analysis for Incentive Mechanisms in Socially-Aware Mobile Crowdsensing
AU - Nie, Jiangtian
AU - Luo, Jun
AU - Xiong, Zehui
AU - Niyato, Dusit
AU - Wang, Ping
AU - Poor, H. Vincent
N1 - Funding Information:
Manuscript received April 28, 2020; revised August 4, 2020 and September 28, 2020; accepted October 11, 2020. Date of publication November 3, 2020; date of current version March 10, 2021. This work was supported in part by the AcRF Tier 1 Grant RG17/19, in part by the National Research Foundation (NRF), Singapore, through Singapore Energy Market Authority (EMA), Energy Resilience, under Grant NRF2017EWT-EP003-041, in part by the Singapore under Grant NRF2015-NRF-ISF001-2277, in part by the Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure NSoE under Grant DeST-SCI2019-0007, in part by the A*STAR-NTU-SUTD Joint Research Grant on Artificial Intelligence for the Future of Manufacturing under Grant RGANS1906, in part by the Wallenberg AI, Autonomous Systems, and Software Program and Nanyang Technological University (WASP/NTU) under Grant M4082187 (4080), in part by the Singapore Ministry of Education (MOE) Tier 1 under Grant RG16/20, in part by the Alibaba Group through Alibaba Innovative Research (AIR) Program, in part by the National Natural Science Foundation of China under Grant 62071343, in part by the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China, under Grant ICT20044, in part by the Canada NSERC Discovery under Grant RGPIN-2019-06375, and in part by the U.S. National Science Foundation under Grant CCF-1908308. The associate editor coordinating the review of this article and approving it for publication was X. Wang. (Corresponding author: Zehui Xiong.) Jiangtian Nie is with the ERI@N, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore 639798, and also with the School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798.
Publisher Copyright:
© 2002-2012 IEEE.
PY - 2021/3
Y1 - 2021/3
N2 - The mobile crowdsensing paradigm facilitates a broad range of emerging sensing applications by leveraging ubiquitous mobile users to cooperatively perform certain sensing tasks with their smart devices. As this paradigm involves data collection from users, the issue of designing rewards to incentivize users is fundamentally important to ensure participation in crowdsensing. In this paper, we revisit this issue in the context of socially-aware crowdsensing which integrates crowdsensing into social networks. For example, in healthcare-based crowdsensing services, the fun of tracking daily nutrition information for a certain user can be promoted by comparing her nutritional information with that contributed and shared by her socially-connected friends. To be more general and practical, we study the incentive mechanisms in presence of multiple crowdsensing service providers and multiple users. Understanding the behaviors of users and service providers in socially-aware crowdsensing is of paramount importance for incentive mechanisms. With this focus, we propose a multi-leader and multi-follower Stackelberg game approach to model the strategic interactions among service providers and users, where the social influence of users and the strategic interconnections of service providers are jointly and formally integrated into the game modeling. Through backward induction methods, we theoretically prove the existence and uniqueness of the Stackelberg equilibrium. We conduct extensive simulations to investigate game equilibrium properties, and the real-world dataset is applied to evaluate and demonstrate the performance effectiveness of the proposed game model.
AB - The mobile crowdsensing paradigm facilitates a broad range of emerging sensing applications by leveraging ubiquitous mobile users to cooperatively perform certain sensing tasks with their smart devices. As this paradigm involves data collection from users, the issue of designing rewards to incentivize users is fundamentally important to ensure participation in crowdsensing. In this paper, we revisit this issue in the context of socially-aware crowdsensing which integrates crowdsensing into social networks. For example, in healthcare-based crowdsensing services, the fun of tracking daily nutrition information for a certain user can be promoted by comparing her nutritional information with that contributed and shared by her socially-connected friends. To be more general and practical, we study the incentive mechanisms in presence of multiple crowdsensing service providers and multiple users. Understanding the behaviors of users and service providers in socially-aware crowdsensing is of paramount importance for incentive mechanisms. With this focus, we propose a multi-leader and multi-follower Stackelberg game approach to model the strategic interactions among service providers and users, where the social influence of users and the strategic interconnections of service providers are jointly and formally integrated into the game modeling. Through backward induction methods, we theoretically prove the existence and uniqueness of the Stackelberg equilibrium. We conduct extensive simulations to investigate game equilibrium properties, and the real-world dataset is applied to evaluate and demonstrate the performance effectiveness of the proposed game model.
KW - Socially-aware crowdsensing
KW - incentive scheme
KW - multi-leader multi-follower game
KW - reward design
UR - http://www.scopus.com/inward/record.url?scp=85102759058&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102759058&partnerID=8YFLogxK
U2 - 10.1109/TWC.2020.3033822
DO - 10.1109/TWC.2020.3033822
M3 - Article
AN - SCOPUS:85102759058
SN - 1536-1276
VL - 20
SP - 1457
EP - 1471
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
IS - 3
M1 - 9247464
ER -