## 1 Introduction
### Contributions  
In spite of the advancements made in prior research on the allocation of tasks to crowdworkers, examination of their privacy, trustworthiness in contributing information to crowdsourcers, and the recruitment of crowdworkers, the issue of facilitating crowdworkers to make selections of crowdsourcers based on provided incentives remains largely unexplored. Additionally, addressing externalities in the decision-making process of selecting a crowdsourcer, influenced by the decisions of other crowdworkers, poses an even more formidable challenge.  
This paper aims to address these challenges. Specifically, our research introduces a crowdsourcing matching mechanism, named CROWDMTCH, aimed at empowering crowdworkers to select a suitable crowdsourcer and contribute information to a crowdsourcing task, taking into account the provided incentives, the availability and cost of information, and decisions made by other crowdworkers. An Approximate CROWDMTCH mechanism is initially developed, drawing on matching theory principles, to eliminate externalities from the decisions of other crowdworkers, allowing each crowdworker to maximize its utility while selecting a crowdsourcer. Simultaneously, all crowdsourcers also maximize their benefits. Subsequently, the Accurate CROWDMTCH mechanism is introduced, based on coalition game-theoretic principles, refining the selection process by accounting for externalities stemming from other crowdworkers' decisions and incentives provided by other crowdsourcers. The distinctive contributions of this research, setting it apart from the existing literature, are succinctly outlined below:  
1. The CROWDMTCH system model is introduced, comprising a set of crowdsourcers such as Amazon Mechanical Turk, Upwork, CrowdFlower, Google Maps, etc., and a set of crowdworkers. Operational characteristics, including the availability and cost of information, provided incentives, and the assessment of crowdworkers' benefits and crowdsourcers' incentive costs, are encapsulated in utility functions that reflect the advantages derived by both crowdworkers and crowdsourcers in the context of the crowdsourcing process;
2. The Approximate CrowDMATCH mechanism is initially devised based on matching theory principles, taking into account the absence of externalities in the decision-making process of crowdworkers as they select the most suitable crowdsourcer to contribute information to a corresponding crowdsourcing task. This mechanism converges to a stable matching of crowdworkers to crowdsourcers, optimizing the utility for both entities, and serves as input for the Accurate CrowDMATCH mechanism;
3. Addressing the system's externalities, the Accurate CrowDMATCH mechanism is proposed, based on coalition game-theoretic principles, to ascertain an optimal matching between crowdworkers and crowdsourcers, maximizing their respective utilities. The existence of a Nash-Individually stable matching of crowdworkers is analytically demonstrated;
4. Comprehensive simulation results demonstrate the operational advantages of the CrowDMATCH mechanism, its scalability in large-scale crowdsourcing systems, and its superior performance compared to existing methods that empower crowdworkers in the selection of crowdsourcers.