scholarly journals Contract-Based Incentive Mechanism for Mobile Crowdsourcing Networks

Algorithms ◽  
2017 ◽  
Vol 10 (3) ◽  
pp. 104 ◽  
Author(s):  
Nan Zhao ◽  
Menglin Fan ◽  
Chao Tian ◽  
Pengfei Fan
Keyword(s):  
Incentive Mechanism ◽  
Mobile Crowdsourcing

scholarly journals Multi-Dimensional Incentive Mechanism in Mobile Crowdsourcing with Moral Hazard

2018 ◽  
Vol 17 (3) ◽  
pp. 604-616 ◽  
Author(s):  
Yanru Zhang ◽  
Yunan Gu ◽  
Miao Pan ◽  
Nguyen H. Tran ◽  
Zaher Dawy ◽  
...  
Keyword(s):  
Moral Hazard ◽  
Incentive Mechanism ◽  
Mobile Crowdsourcing

scholarly journals Multistrategy Repeated Game-Based Mobile Crowdsourcing Incentive Mechanism for Mobile Edge Computing in Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Chuanxiu Chi ◽  
Yingjie Wang ◽  
Yingshu Li ◽  
Xiangrong Tong
Keyword(s):  
Game Theory ◽  
Internet Of Things ◽  
Incentive Mechanism ◽  
Repeated Game ◽  
Edge Computing ◽  
Quality Data ◽  
Mobile Edge Computing ◽  
High Quality ◽  
Mobile Crowdsourcing

With the advent of the Internet of Things (IoT) era, various application requirements have put forward higher requirements for data transmission bandwidth and real-time data processing. Mobile edge computing (MEC) can greatly alleviate the pressure on network bandwidth and improve the response speed by effectively using the device resources of mobile edge. Research on mobile crowdsourcing in edge computing has become a hot spot. Hence, we studied resource utilization issues between edge mobile devices, namely, crowdsourcing scenarios in mobile edge computing. We aimed to design an incentive mechanism to ensure the long-term participation of users and high quality of tasks. This paper designs a long-term incentive mechanism based on game theory. The long-term incentive mechanism is to encourage participants to provide long-term and continuous quality data for mobile crowdsourcing systems. The multistrategy repeated game-based incentive mechanism (MSRG incentive mechanism) is proposed to guide participants to provide long-term participation and high-quality data. The proposed mechanism regards the interaction between the worker and the requester as a repeated game and obtains a long-term incentive based on the historical information and discount factor. In addition, the evolutionary game theory and the Wright-Fisher model in biology are used to analyze the evolution of participants’ strategies. The optimal discount factor is found within the range of discount factors based on repeated games. Finally, simulation experiments verify the existing crowdsourcing dilemma and the effectiveness of the incentive mechanism. The results show that the proposed MSRG incentive mechanism has a long-term incentive effect for participants in mobile crowdsourcing systems.

scholarly journals An Optimization and Auction-Based Incentive Mechanism to Maximize Social Welfare for Mobile Crowdsourcing

2019 ◽  
Vol 6 (3) ◽  
pp. 414-429 ◽  
Author(s):  
Yingjie Wang ◽  
Zhipeng Cai ◽  
Zhi-Hui Zhan ◽  
Yue-Jiao Gong ◽  
Xiangrong Tong
Keyword(s):  
Social Welfare ◽  
Incentive Mechanism ◽  
Mobile Crowdsourcing

Reputation-Based Dynamic Contract Incentive Mechanism in Mobile Crowdsourcing Network

2020 ◽  
Author(s):  
Minghu Wu ◽  
Qixuan Wan ◽  
Xuan Zheng ◽  
Yuhan Jiang ◽  
Nan Zhao
Keyword(s):  
Incentive Mechanism ◽  
Impact Factors ◽  
Optimization Strategy ◽  
Crowd Sourcing ◽  
Process Data ◽  
Mobile Crowdsourcing ◽  
Dynamic Contract ◽  
The Incentive Mechanism ◽  
The Impact

Abstract Mobile crowdsourcing network is a promising technology utilizing the mobile ter- minal’s sensing and computing capabilities to collect and process data. However, because the mobile users (MUs) have selfish characteristics, the MUs only aim at maximizing their benefits. Therefore, how to design an appropriate long-term incentive mechanism for the service provider (SP) in dynamic environments is an urgent problem. In this work, we investigate the reputation-based dynamic contract for mobile crowdsourcing network. A two-period dynamic contract is first investi- gated to deal with the asymmetric information problem in the long-term crowd- sourcing tasks. Reputation strategy is introduced to attract the MUs to complete the long-term tasks. The incentives of the contract and the implicit incentives of the reputation strategy are used together to encourage MUs to complete the long-term crowdsourcing tasks. The optimization strategy is formulated by adjust- ing the reputation coefficient to maximize the SP’s utility. The impact of MUs’ risk attitude and reputation impact factors on the incentive mechanism is studied through experiments. Numerical simulation results demonstrate that the optimal reputation-based contract design scheme is efficient in the Mobile crowdsourcing networks.

An incentive mechanism with privacy protection in mobile crowdsourcing systems

2016 ◽  
Vol 102 ◽  
pp. 157-171 ◽  
Author(s):  
Yingjie Wang ◽  
Zhipeng Cai ◽  
Guisheng Yin ◽  
Yang Gao ◽  
Xiangrong Tong ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Incentive Mechanism ◽  
Mobile Crowdsourcing

Topic-aware Incentive Mechanism for Task Diffusion in Mobile Crowdsourcing through Social Network

2022 ◽  
Vol 22 (1) ◽  
pp. 1-23
Author(s):  
Jia Xu ◽  
Yuanhang Zhou ◽  
Gongyu Chen ◽  
Yuqing Ding ◽  
Dejun Yang ◽  
...  
Keyword(s):  
Social Network ◽  
Diffusion Model ◽  
Large Scale ◽  
Incentive Mechanism ◽  
Estimation Algorithm ◽  
Task Completion ◽  
Incentive Mechanisms ◽  
Mobile Crowdsourcing ◽  
Real World Datasets ◽  
Budget Feasible

Crowdsourcing has become an efficient paradigm to utilize human intelligence to perform tasks that are challenging for machines. Many incentive mechanisms for crowdsourcing systems have been proposed. However, most of existing incentive mechanisms assume that there are sufficient participants to perform crowdsourcing tasks. In large-scale crowdsourcing scenarios, this assumption may be not applicable. To address this issue, we diffuse the crowdsourcing tasks in social network to increase the number of participants. To make the task diffusion more applicable to crowdsourcing system, we enhance the classic Independent Cascade model so the influence is strongly connected with both the types and topics of tasks. Based on the tailored task diffusion model, we formulate the Budget Feasible Task Diffusion ( BFTD ) problem for maximizing the value function of platform with constrained budget. We design a parameter estimation algorithm based on Expectation Maximization algorithm to estimate the parameters in proposed task diffusion model. Benefitting from the submodular property of the objective function, we apply the budget-feasible incentive mechanism, which satisfies desirable properties of computational efficiency, individual rationality, budget-feasible, truthfulness, and guaranteed approximation, to stimulate the task diffusers. The simulation results based on two real-world datasets show that our incentive mechanism can improve the number of active users and the task completion rate by 9.8% and 11%, on average.

scholarly journals An Incentive Mechanism in Mobile Crowdsourcing Based on Multi-Attribute Reverse Auctions

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3453 ◽  
Author(s):  
Ying Hu ◽  
Yingjie Wang ◽  
Yingshu Li ◽  
Xiangrong Tong
Keyword(s):  
Incentive Mechanism ◽  
Budget Balance ◽  
Selection Algorithm ◽  
Reverse Auction ◽  
Mobile Crowdsourcing ◽  
Trust Degree ◽  
Worker Selection ◽  
Bidding Price ◽  
Good Reputation

In order to avoid malicious competition and select high quality crowd workers to improve the utility of crowdsourcing system, this paper proposes an incentive mechanism based on the combination of reverse auction and multi-attribute auction in mobile crowdsourcing. The proposed online incentive mechanism includes two algorithms. One is the crowd worker selection algorithm based on multi-attribute reverse auction that adopts dynamic threshold to make an online decision for whether accept a crowd worker according to its attributes. Another is the payment determination algorithm which determines payment for a crowd worker based on its reputation and quality of sensing data, that is, a crowd worker can get payment equal to the bidding price before performing task only if his reputation reaches good reputation threshold, otherwise he will get payment based on his data sensing quality. We prove that our proposed online incentive mechanism has the properties of computational efficiency, individual rationality, budget-balance, truthfulness and honesty. Through simulations, the efficiency of our proposed online incentive mechanism is verified which can improve the efficiency, adaptability and trust degree of the mobile crowdsourcing system.

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