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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