SocialRecruiter: Dynamic Incentive Mechanism for Mobile Crowdsourcing Worker Recruitment with Social Networks

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Zhibo Wang ◽  
Yuting Huang ◽  
Xinkai Wang ◽  
Ju Ren ◽  
Qian Wang ◽  
...  
Keyword(s):  
Social Networks ◽  
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 Task Allocation Model Based on Worker Friend Relationship for Mobile Crowdsourcing

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 921 ◽  
Author(s):  
Bingxu Zhao ◽  
Yingjie Wang ◽  
Yingshu Li ◽  
Yang Gao ◽  
Xiangrong Tong
Keyword(s):  
Social Networks ◽  
Task Allocation ◽  
Location Privacy ◽  
Rapid Development ◽  
Human Beings ◽  
Allocation Mechanism ◽  
Allocation Model ◽  
Mobile Crowdsourcing ◽  
System A ◽  
The Relationship

With the rapid development of mobile devices, mobile crowdsourcing has become an important research focus. According to the task allocation, scholars have proposed many methods. However, few works discuss combining social networks and mobile crowdsourcing. To maximize the utilities of mobile crowdsourcing system, this paper proposes a task allocation model considering the attributes of social networks for mobile crowdsourcing system. Starting from the homogeneity of human beings, the relationship between friends in social networks is applied to mobile crowdsourcing system. A task allocation algorithm based on the friend relationships is proposed. The GeoHash coding mechanism is adopted in the process of calculating the strength of worker relationship, which effectively protects the location privacy of workers. Utilizing synthetic dataset and the real-world Yelp dataset, the performance of the proposed task allocation model was evaluated. Through comparison experiments, the effectiveness and applicability of the proposed allocation mechanism were verified.

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

Acceptance-Aware Mobile Crowdsourcing Worker Recruitment in Social Networks

2021 ◽  
pp. 1-1
Author(s):  
Liang Wang ◽  
Dingqi Yang ◽  
Zhiwen Yu ◽  
Qi Han ◽  
En Wang ◽  
...  
Keyword(s):  
Social Networks ◽  
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.

Task Execution Quality Maximization for Mobile Crowdsourcing in Geo-Social Networks

2021 ◽  
Vol 5 (CSCW2) ◽  
pp. 1-29
Author(s):  
Liang Wang ◽  
Zhiwen Yu ◽  
Dingqi Yang ◽  
Tian Wang ◽  
En Wang ◽  
...  
Keyword(s):  
Social Networks ◽  
Task Execution ◽  
Mobile Crowdsourcing
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