Research on task assignment to minimize travel cost for spatio-temporal crowdsourcing

EURASIP Journal on Wireless Communications and Networking

Tingwei Pan¹  Hongbin Dong¹  Qingxian Pan²  Zhaolong Gao³ 

[1] College of Computer Science and Technology, Harbin Engineering University, 150000, Harbin, China;College of Computer Science and Technology, Harbin Engineering University, 150000, Harbin, China;College of Computer and Control Engineering, Yantai University, 264000, Yantai, China;Wenjing College, Yantai University, 264000, Yantai, China;

关键词: Spatio-temporal crowdsourcing;    Online task assignment;    Travel cost;    Hungarian algorithm;   

DOI  :  10.1186/s13638-021-01909-3

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY