Abstract
Online task assignment is one of the core research issues of spatio-temporal crowdsourcing technology. The current researches on minimizing travel cost all focus on the scenario of two objectives (task requesters and workers). This paper proposes a two-stage framework (GH) based on Greedy algorithm and Hungarian algorithm for three-objective online task assignment to minimize travel cost. In order to further optimize the efficiency and average travel cost, this paper proposes GH-AT (Adaptive Threshold) algorithm based on GH algorithm, and redesigns the Hungarian algorithm into the sHungarian algorithm. sHungarian algorithm has lower time complexity than Hungarian algorithm. sHungarian algorithm is not only suitable for the problem studied in this paper, but also for all task assignment problems with constraints. Compared with Greedy algorithm, GH-AT algorithm has lower travel cost and higher total utility. In terms of the number of matches, GH-AT is slightly lower than Greedy algorithm. In terms of time cost, GH-AT algorithm is higher than Greedy algorithm, but much lower than GH algorithm.
Highlights
As crowdsourcing technology becomes more and more widely used in daily life, crowdsourcing applications have become more diverse
In order to further improve the efficiency of Greedy Hungrian algorithm (GH) algorithm and reduce the travel cost, this paper designs GH-AT algorithm based on GH algorithm
A large number of experiments are performed on the gMission dataset and synthetic dataset to verify the performance of GH-AT algorithm
Summary
As crowdsourcing technology becomes more and more widely used in daily life, crowdsourcing applications have become more diverse. Spatio-temporal crowdsourcing is developed from traditional crowdsourcing through combining time and space information [1]. There are three core study issues for spatio-temporal crowdsourcing: task assignment [3,4,5,6,7], quality control [8], and privacy protection [9,10,11,12,13,14,15]. Similar to the research of traditional crowdsourcing, incentive mechanism will be one of the important study issues in future [16,17,18,19,20]. More and more researches focused on the combination of crowdsourcing and social networks [21, 22] or Big Data [23]. There are some researches that combine crowdsourcing with blockchain [24, 25]
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