Reliable Diversity-Based Spatial Crowdsourcing by Moving Workers (1412.0223v5)

Abstract: With the rapid development of mobile devices and the crowdsourcig platforms, the spatial crowdsourcing has attracted much attention from the database community, specifically, spatial crowdsourcing refers to sending a location-based request to workers according to their positions. In this paper, we consider an important spatial crowdsourcing problem, namely reliable diversity-based spatial crowdsourcing (RDB-SC), in which spatial tasks (such as taking videos/photos of a landmark or firework shows, and checking whether or not parking spaces are available) are time-constrained, and workers are moving towards some directions. Our RDB-SC problem is to assign workers to spatial tasks such that the completion reliability and the spatial/temporal diversities of spatial tasks are maximized. We prove that the RDB-SC problem is NP-hard and intractable. Thus, we propose three effective approximation approaches, including greedy, sampling, and divide-and-conquer algorithms. In order to improve the efficiency, we also design an effective cost-model-based index, which can dynamically maintain moving workers and spatial tasks with low cost, and efficiently facilitate the retrieval of RDB-SC answers. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches over both real and synthetic data sets.

• The paper addresses the NP-hard Reliable Diversity-Based Spatial Crowdsourcing (RDB-SC) problem for moving workers, aiming to maximize both task reliability and spatial/temporal diversity under time constraints.
• To solve RDB-SC, the authors propose and evaluate three approximation algorithms: greedy, sampling, and a divide-and-conquer approach, with sampling and divide-and-conquer showing performance close to ground truth.
• An efficient cost-model-based grid index, RDB-SC-Grid, is introduced for dynamic management and optimization of task-worker assignments, supporting scalability in real-world dynamic environments.

Overview of Reliable Diversity-Based Spatial Crowdsourcing by Moving Workers

The paper presents a detailed investigation into the problem of Reliable Diversity-Based Spatial Crowdsourcing (RDB-SC), in which spatial tasks are assigned to moving workers. This problem is characterized by time constraints on tasks and dynamic movement directions of workers. The authors aim to maximize both the reliability of task completion and the spatial/temporal diversity of the finished tasks. The RDB-SC problem is demonstrated to be NP-hard, necessitating the introduction of approximation algorithms to effectively address it.

Problem Definition and Challenges

The central challenge of RDB-SC lies in dynamically assigning workers to tasks such that the reliability and the spatial/temporal diversities of the tasks are maximized. The task reliability is defined in terms of the confidence that at least one assigned worker can complete a task, which is modeled probabilistically. Similarly, spatial and temporal diversities are quantified to ensure diversity in task completion. The RDB-SC problem bears computational challenges due to its NP-hard nature and the large solution space, making exact solutions impractical for real-world applications with dynamic changes in tasks and worker availability.

Approximation Algorithms

To tackle the RDB-SC problem, the paper proposes three different approaches: greedy, sampling, and a divide-and-conquer strategy.

1. Greedy Algorithm: This algorithm iteratively assigns workers to tasks, selecting the pair that maximizes the increase in reliability and diversity during each step. Though efficient, its performance can fall short in situations with larger search spaces, indicated by its relatively lower diversity compared to other methods.
2. Sampling Method: By leveraging a random sampling technique, multiple sample assignments are generated to approximate a high-quality solution. This approach is advantageous in providing better diversity by probabilistically exploring more of the solution space.
3. Divide-and-Conquer Method: The problem space is partitioned into smaller subproblems, which are then solved independently using the sampling approach. This method is particularly effective for large-scale instances due to its reduced computational complexity compared to a direct approach.

Indexing and Maintenance

For efficient retrieval and updating of task-worker assignments, a cost-model-based grid index, termed RDB-SC-Grid, is introduced. It dynamically manages workers and tasks, optimizing for both construction and retrieval under real-world conditions with frequently changing worker positions and task requirements.

Experimental Evaluation

The algorithms were rigorously tested on synthetic and real-world data from spatial crowdsourcing applications. Results demonstrated that the sampling and divide-and-conquer approaches achieved reliability and diversity metrics close to the ground truth while maintaining computational efficiency. The experiments also highlighted the scalability and practical applicability of the proposed solutions to large and dynamic environments.

Implications and Future Directions

The RDB-SC framework has significant implications for enhancing spatial crowdsourcing systems' robustness and effectiveness, addressing real-world applications such as urban sensing, dynamic monitoring, and virtual tourism. Future work could explore more refined models for capturing worker availability and task constraints, and extending the proposed methods to accommodate deviations in task priorities or additional diversity dimensions.

Conclusion

This paper contributes to the field of spatial crowdsourcing by addressing the complex problem of simultaneously maximizing reliability and diversity, innovatively introducing approximation strategies, and practical solutions aligned with real-world needs. As mobile devices and sensing technologies continue to advance, the findings from this research hold promising potential for future enhancements and applications in the domain of dynamic spatial data processing.