Research on Task Assignment to Minimize Travel Cost for Spatio-temporal Crowdsourcing

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-objective (task requesters and workers). This paper proposes a Greedy algorithm and Hungarian algorithm-based algorithm (GH) for online task assignment to minimize travel cost for three objectives. In order to further optimize algorithm efficiency and average travel cost, this paper proposes GHAT (Adaptive Threshold) algorithm based on GH algorithm, and improves Hungarian algorithm to propose 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, GHAT is slightly lower than GH algorithm. In terms of time cost, GH-AT algorithm is higher than Greedy algorithm, but much lower than GH algorithm.