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1. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
TASK ASSIGNMENT ON MULTI-SKILL ORIENTED SPATIAL
CROWDSOURCING
ABSTRACT
With the rapid development of mobile devices and crowdsourcing platforms,
the spatial crowdsourcing has attracted much attention from the database
community. Specifically, the spatial crowdsourcing refers to sending location-
based requests to workers, based on their current positions. In this paper, we
consider a spatial crowdsourcing scenario, in which each worker has a set of
qualified skills, whereas each spatial task (e.g., repairing a house, decorating a
room, and performing entertainment shows for a ceremony) is time-
constrained, under the budget constraint, and required a set of skills. Under
this scenario, we will study an important problem, namely multi-skill spatial
crowdsourcing (MS-SC), which finds an optimal worker-and-task assignment
strategy, such that skills between workers and tasks match with each other,
and workers’ benefits are maximized under the budget constraint. We prove
that the MS-SC problem is NP-hard and intractable. Therefore, we propose
three effective heuristic approaches, including greedy, g-divide-and-conquer
and cost-model-based adaptive algorithms to get worker-and-task
assignments. Through extensive experiments, we demonstrate the efficiency
and effectiveness of our MS-SC processing approaches on both real and
synthetic data sets.

2. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
CONCLUSION
In this paper, we propose the problem of the multi-skill oriented spatial
crowdsourcing (MS-SC), which assigns the time-constrained and multi-skill-
required spatial tasks with dynamically moving workers, such that the required
skills of tasks can be covered by skills of workers and the assignment score is
maximized. We prove that the processing of the MSSC problem is NP-hard, and
thus we propose three approximation approaches (i.e., greedy, g-D&C, and
cost-model-based adaptive algorithms), which can efficiently retrieve MS-SC
answers. Extensive experiments have shown the efficiency and effectiveness of
our proposed MS-SC approaches on both real and synthetic data sets.
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3. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
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