Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the popularity of mobile devices,  spatial crowdsourcing  is rising as a new framework that enables human workers to solve tasks in the physical world. With spatial crowdsourcing, the goal is to crowdsource a set of spatiotemporal tasks (i.e., tasks related to time and location) to a set of workers, which requires the workers to physically travel to those locations in order to perform the tasks. In this article,  focus on one class of spatial crowdsourcing, in which the workers send their locations to the server and thereafter the server assigns to every worker tasks in proximity to the worker’s location with the aim of maximizing the overall number of assigned tasks.  formally define this  maximum task assignment (MTA)  problem in spatial crowdsourcing, and identify its challenges.  propose alternative solutions to address these challenges by exploiting the spatial properties of the problem space, including the spatial distribution and the travel cost of the workers. MTA is based on the assumptions that all tasks are of the same type and all workers are equally qualified in performing the tasks. Meanwhile, different types of tasks may require workers with various skill sets or expertise. Subsequently,  extend MTA by taking the expertise of the workers into consideration.  refer to this problem as the  maximum score assignment (MSA)  problem and show its practicality and generality. Extensive experiments with various synthetic and two real-world datasets show the applicability of our proposed framework.
['Hien To', 'Cyrus Shahabi', 'Leyla Kazemi']
I am writing a research paper on a idea A Server-Assigned Spatial Crowdsourcing Framework
[{"from": "human", "value": "I am writing a research paper on a idea A Server-Assigned Spatial Crowdsourcing Framework"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the popularity of mobile devices,  spatial crowdsourcing  is rising as a new framework that enables human workers to solve tasks in the physical world. With spatial crowdsourcing, the goal is to crowdsource a set of spatiotemporal tasks (i.e., tasks related to time and location) to a set of workers, which requires the workers to physically travel to those locations in order to perform the tasks. In this article,  focus on one class of spatial crowdsourcing, in which the workers send their locations to the server and thereafter the server assigns to every worker tasks in proximity to the worker\u2019s location with the aim of maximizing the overall number of assigned tasks.  formally define this  maximum task assignment (MTA)  problem in spatial crowdsourcing, and identify its challenges.  propose alternative solutions to address these challenges by exploiting the spatial properties of the problem space, including the spatial distribution and the travel cost of the workers. MTA is based on the assumptions that all tasks are of the same type and all workers are equally qualified in performing the tasks. Meanwhile, different types of tasks may require workers with various skill sets or expertise. Subsequently,  extend MTA by taking the expertise of the workers into consideration.  refer to this problem as the  maximum score assignment (MSA)  problem and show its practicality and generality. Extensive experiments with various synthetic and two real-world datasets show the applicability of our proposed framework."}]