Abstract
Spatial crowdsourcing is an activity consisting in outsourcing spatial tasks to a community of online, yet on-ground and mobile, workers. Presently an increasing number of spatial crowdsourcing applications emerges due to the related technologies tends to maturity. Distinct from traditional crowdsourcing dualistic entities, task and worker, a special kind of applications imports the third one of skill. Consequently, a novel assignment problem called multiple skills assignment problem (MSAP) is generated which extends the entity relationship from 2 to 3 dimensions. Inspired by group strategy we first propose a lightweight algorithm GMA that could achieve approximate optimal solution quickly. However, GMA exists a defect of ignoring that workers with multiple skills can decrease total travel distance significantly. Thus we propose a revised algorithm RGMA to cut down distance cost. With synthetic datasets, we empirically and comparatively evaluate the performance of the baseline and two proposed algorithms.
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Liu, J., Zhu, H., Chen, X. (2016). Complicated-Skills-Based Task Assignment in Spatial Crowdsourcing. In: Song, S., Tong, Y. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9998. Springer, Cham. https://doi.org/10.1007/978-3-319-47121-1_18
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DOI: https://doi.org/10.1007/978-3-319-47121-1_18
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