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

Wang, Zhen; Huang, Yuting; Wang, Xinkai; Ren, Ju; Wang, Qian; Wu, Libing

doi:10.1109/tmc.2020.2973958

Cited by 42 publications

(7 citation statements)

References 35 publications

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“…Inspired by the success of social networks (e.g., Facebook, WeChat, Twitter, etc.) in recent years, several studies [ 26 , 27 , 28 , 29 , 30 ] have proposed task assignment mechanisms that recruit users with the help of social networks. A novel task allocation algorithm is proposed in [ 26 ] that distributes sensory tasks fairly to users while using the Social Internet of Things (SIoT) to assess the reputation level of each member of the network.…”

Section: Related Workmentioning

confidence: 99%

“…A novel task allocation algorithm is proposed in [ 26 ] that distributes sensory tasks fairly to users while using the Social Internet of Things (SIoT) to assess the reputation level of each member of the network. A dynamic task allocation algorithm for social recruiters was proposed in [ 27 ] to encourage users on the MCS platform to spread tasks through social networks and use social networks to recruit employees to complete tasks when the number of users is insufficient, while expanding the workforce. Location-based social networks (LBSNs) to obtain crowdfunding data are used in [ 28 ] to study the task allocation problem under influence maximization in LBSNs.…”

Section: Related Workmentioning

confidence: 99%

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Preference-Matched Multitask Assignment for Group Socialization under Mobile Crowdsensing

Zhang

Chen

Wei

et al. 2023

Sensors

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Mobile crowdsensing (MCS) has been an emerging sensing paradigm in recent years, which uses a sensing platform for real-time processing to support various services for the Internet of Things (IoT) and promote the development of IoT. As an important component of MCS, how to design task assignment algorithms to cope with the coexistence of multiple concurrent heterogeneous tasks in group-oriented social relationships while satisfying the impact of users’ preferences on heterogeneous multitask assignment and solving the preference matching problem under heterogeneous tasks, is one of the most pressing issues. In this paper, a new algorithm, group-oriented adjustable bidding task assignment (GO-ABTA), is considered to solve the group-oriented bilateral preference-matching problem. First, the initial leaders and their collaborative groups in the social network are selected by group-oriented collaboration, and then the preference assignment of task requesters and users is modeled as a stable preference-matching problem. Then, a tunable bidding task assignment process is completed based on preference matching under budget constraints. Finally, the individual reasonableness, stability, and convergence of the proposed algorithm are demonstrated. The effectiveness of the proposed algorithm and its superiority to other algorithms are verified by simulation results.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Preference-Matched Multitask Assignment for Group Socialization under Mobile Crowdsensing

Zhang

Chen

Wei

et al. 2023

Sensors

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“…Now the question is, how to inform others about the ongoing event? To address the above-discussed realistic scenario, some works have been carried out in the past [12,17,18]. A two-tiered social crowdsourcing architecture is proposed in [12] that allows the task executors to forward the floated tasks that are to be executed by their neighbors in the social connections.…”

Section: Introductionmentioning

confidence: 99%

“…For the discussed set-up a truthful budget feasible mechanism is developed that takes into account the enhanced classic independent cascade model. In [18] an effort has been made to design a dynamic incentive mechanism that transfers information about the task execution process through the social connections of the task executors. Motivated by the above discussed crowdsourcing scenarios, in this paper, one of the scenarios of IoT-based crowdsourcing is studied as a two-tiered process in strategic setting as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Influence before Hiring: A Two-tiered Incentive Compatible Mechanism for IoT-based Crowdsourcing in Strategic Setting

Chattu,

Singh

2023

Preprint

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In this article, a crowdsourcing scenario with multiple heterogeneous tasks and multiple IoT devices is studied as a two-tiered process. In the first tier, it is assumed that a substantial number of IoT devices are not aware of the hiring process and are made aware by utilizing their social connections. Each IoT device reports a cost (private) and is strategic. For the first tier, a truthful budget feasible mechanism is proposed. In the second tier, a set of quality IoT devices is determined and each of them reports private valuation that satisfies gross substitute criteria along with its favorite bundle of tasks. The truthful mechanisms are designed independently for determining the quality of IoT devices, and for hiring them and deciding their payment. Theoretical analysis shows that the proposed mechanisms are computationally efficient, truthful, correct, and budget feasible. The simulation is done to compare the proposed mechanisms with the existing mechanisms.

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“…The authors in [27] adopted the deep reinforcement learning scheme enabling each CSP to study the reward policy from the interaction history, requiring none of users' privacy information. In [72,73], the authors studied the insufficient participation problem without a preassumed large user Chapter 2. Literature Review pool, by first selecting a small set of users and then leveraging the social influence to encourage more users.…”

Section: Incentive Mechanisms Design With Multiple Cspsmentioning

confidence: 99%

Towards sustainable socially-aware mobile crowdsensing : leveraging hierarchical game for incentive mechanisms

Nie¹

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

Cited by 42 publications

References 35 publications

Preference-Matched Multitask Assignment for Group Socialization under Mobile Crowdsensing

Preference-Matched Multitask Assignment for Group Socialization under Mobile Crowdsensing

Influence before Hiring: A Two-tiered Incentive Compatible Mechanism for IoT-based Crowdsourcing in Strategic Setting

Towards sustainable socially-aware mobile crowdsensing : leveraging hierarchical game for incentive mechanisms

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