Paper Title
Hiring A Team From Social Network: Incentive Mechanism Design For Two-Tiered Social Mobile Crowd Sourcing

Abstract
Conventional mobile crowd sourcing systems follow a direct platform-user interaction model, where users complete assigned tasks in exchange for compensation. However, these systems suffer from several challenges that hinder their efficiency and scalability. One of the primary issues is low user participation, as inadequate or poorly structured incentive mechanisms fail to attract and retain users, leading to reduced task completion rates. Additionally, there is a fundamental trade-off between quantity and quality, where many systems emphasize completing a high volume of tasks rather than ensuring the accuracy and reliability of collected data. This results in inconsistent and often low-quality contributions, affecting the overall utility of the system. Another significant limitation is the high evaluation overhead, as verifying the relevance, validity, and authenticity of user submissions requires substantial computational resources, increasing operational costs and making large-scale implementation challenging. Furthermore, traditional crowdsourcing models lack an effective mechanism to handle the cold-start problem, making it difficult to initiate task distribution and user engagement in new or underutilized systems. Additionally, the absence of a structured recruitment model results in inefficient task dissemination, limiting the reach and participation of potential contributors. These constraints limit the overall effectiveness of traditional crowdsourcing models, highlighting the need for a more advanced and incentivized approach that enhances user engagement, ensures high-quality data collection, minimizes fraudulent contributions, and optimizes resource allocation for sustainable scalability. Keywords - Crowdsourcing, Direct Platform-User Interaction, User Participation, Incentive Mechanism, Data Quality, Evaluation Overhead, Cold-Start Problem, Task Dissemination, Computational Costs, Scalability, Fraud Prevention, Resource Optimization.