Task Assignment on Multi-Skill Oriented Spatial Crowdsourcing (1510.03149v3)

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.

Overview of Task Assignment on Multi-Skill Oriented Spatial Crowdsourcing

The paper addresses the challenge of efficiently assigning tasks to workers in a multi-skill spatial crowdsourcing system. This involves scenarios where both workers and tasks are subject to spatial constraints and demand a set of specific skills. The Multi-Skill Spatial Crowdsourcing (MS-SC) problem presented is characterized by assigning these spatial tasks to workers such that skill sets required by tasks are adequately covered by workers, while also maximizing the assignment score within the bounds of budget constraints. Proving that this problem is NP-hard, the authors propose three heuristic approaches to efficiently approximate solutions: a greedy algorithm, a $g$-divide-and-conquer strategy, and a cost-model-based adaptive method.

Key Concepts and Methodologies

The MS-SC problem is defined within the framework of spatial crowdsourcing where tasks require specific skill sets to be completed by workers who must reach task locations within time constraints and whose skills match the task requirements. Recognizing the problem as NP-hard, the research develops three heuristic algorithms:

1. Greedy Algorithm: This approach incrementally builds a solution by selecting the worker-to-task assignment that yields the highest score increase at each step. It leverages pruning strategies to efficiently rule out suboptimal assignments. The greedy algorithm is computationally set in $O(m \cdot n^2)$, where $m$ and $n$ denote the number of tasks and workers respectively.
2. $g$-Divide-and-Conquer (D&C) Approach: This method breaks the problem into smaller subproblems and recursively solves them. The task assignments are first decomposed, subsequently handled via recursion, and merge conflicts are addressed through a specific reconciliation process. It offers a balance between granularity of divided problems and solution optimality.
3. Cost-Model-Based Adaptive Algorithm: This adaptive strategy decides dynamically between applying the greedy or the $g$-D&C approach based on a cost model that evaluates the computational expense of both strategies, allowing for more flexibility in algorithmic decision-making.

Numerical Results and Implications

Extensive experiments are conducted using both real-world data from Meetup and synthetic data to validate the effectiveness and efficiency of the proposed algorithms. The results demonstrate that the adaptive algorithm presents a suitable trade-off between runtime and accuracy, often outperforming static strategies like the greedy or entirely divide-and-conquer methods. The paper confirms that as task budgets and worker speed constraints increase, the assignment scores likewise increase, affirming a key relationship between task parameters and optimal worker-to-task pairing.

Implications and Future Directions

The MS-SC framework and findings carry implications for enhancing the efficacy of spatial crowdsourcing platforms. By introducing sophisticated assignment algorithms that consider multi-dimensional constraints—such as skill coverage, distance, time, and budget—the work advances the computational approaches available for real-world deployment. Future research may consider incorporating dynamic pricing models, exploring more complex spatial dynamics, or integrating more advanced machine learning techniques to anticipate task requirements and worker supply, hence offering more robust adaptive strategies.

This work emphasizes the complexities and computational challenges involved in orchestrating task assignments in contemporary spatial crowdsourcing environments. As spatial crowdsourcing continues to expand within a wide array of applications, including urban logistics and dynamic workforce management, these insights contribute foundational methods and perspective-altering findings to the field.