Abstract
With the wide use of mobile devices, spatial crowdsourcing platforms are becoming popular. An important problem of spatial crowdsourcing is assigning a set of spatial tasks tagged with location and time for workers according to their location. In most cases, existing approaches usually take the matching algorithm as a fundamental step to solve this problem which aims to maximize the number of completed tasks. However, in the present of many spatial crowdsourcing platforms, how to assign the tasks at high efficiency and make a relatively fair schedule for multiple workers is a new challenge. In this paper, we study the problem of load balancing based task scheduling for multiple workers. We present fast and effective approximate algorithms for task scheduling problem. With both real and synthetic datasets, we verify the effectiveness of our proposed methods.
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Sun, D., Gao, Y., Yu, D. (2016). Efficient and Load Balancing Strategy for Task Scheduling 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_14
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DOI: https://doi.org/10.1007/978-3-319-47121-1_14
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