Abstract
Geospatial tagging (geotagging) is an emerging and very promising application that can help users find a wide variety of location-specific information, and thereby facilitate the development of advanced location-based services. Conventional geotagging systems share some limitations, such as the use of a two-phase operating model and the tendency to tag popular objects with simple contexts. To address these problems, a number of geotagging systems based on the concept of ‘Games with a Purpose’ (GWAP) have been developed recently. In this study, we use analysis to investigate these new systems. Based on our analysis results, we design three metrics to evaluate the system performance, and develop five task assignment algorithms for GWAP-based systems. Using a comprehensive set of simulations under both synthetic and realistic mobility scenarios, we find that the Least-Throughput-First Assignment algorithm (LTFA) is the most effective approach because it can achieve competitive system utility, while its computational complexity remains moderate. We also find that, to improve the system utility, it is better to assign as many tasks as possible in each round. However, because players may feel annoyed if too many tasks are assigned at the same time, it is recommended that multiple tasks be assigned one by one in each round in order to achieve higher system utility.
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Notes
The Sigmoid Function, which starts with a small value and accelerates over time to approach a maximum, is widely used for modeling natural processes and complex system learning curves [16].
Information about bus routes in Taipei City is available at http://www.e-bus.taipei.gov.tw/english/en_index_6_1.html.
The parameters (α, the grid size, and the number of LOIs) are set to the values based on the scenarios and heuristics in this study, and they are tunable to match various scenarios of interest.
Note that a solver may accept a task but fail to provide any solutions. We regard the case, for the sake of generality, as equivalent to that the solver takes an infinite time to complete a task.
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Acknowledgements
We wish to thank the editors and anonymous reviewers for their insightful comments and suggestions. This study is based on research supported by the National Science Council of Taiwan under NSC Grants: NSC 98-2221-E-001-014-MY3 and NSC 99-2631-S-003-002.
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A preliminary version of this study was published in the IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom’09), Washington D.C., USA, 2009 [12]. In this extended version paper, we have refined our analysis in modeling GWAP-based geospatial tagging systems, re-evaluated the five task assignment algorithms based on the new analytical model, and included a more comprehensive set of evaluations with different numbers of tasks per assignment and different buffer sizes per LOI. Moreover, we have updated the literature review of this study, and incorporated all the comments/suggestions of the conference attendees. Hence, this manuscript is a much more thorough and authoritative presentation of our study on GWAP-based geospatial tagging systems.
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Chen, LJ., Syu, YS., Chen, HC. et al. The Design and Evaluation of Task Assignment Algorithms for GWAP-based Geospatial Tagging Systems. Mobile Netw Appl 17, 395–414 (2012). https://doi.org/10.1007/s11036-011-0314-6
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DOI: https://doi.org/10.1007/s11036-011-0314-6