TY - GEN
T1 - Task assignment method in spatial crowdsourcing based on graph search
AU - Meng, Fanchao
AU - Zhang, Shuo
AU - Zheng, Xuanchi
AU - Sun, Shanxin
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - In the research on the urban crowdsourcing distribution service scheduling, one of the keys is the spatial crowdsourcing task assignment. Aiming at the current spatial crowdsourcing lacking of the consideration for the actual road network, a graph-based crowdsourcing task optimization assignment model and its optimization algorithm are proposed, in order to obtain the maximum number of courier assigned tasks at a time. The optimal assigned task and delivery path of the courier are obtained under the condition of satisfying the goal and constraints. In this study, at first the actual geographical location is mapped into a map structure, considering the crowdsourcing task starting location, task target location and the courier location. Then, three graph-based spatial task assignment methods are implemented: the first is to realize the realistic grab strategy, location service-based grab strategy (LSGS); the second is crowdsourcing task assignment algorithm based on the task location (TATL); the third is crowdsourcing task assignment algorithm based on ant colony planning (TAAP). Finally, through the comparison of multiple sets of experiments, the proposed TATL and TAAP algorithms can obtain the distribution path and improve the task assignment efficiency.
AB - In the research on the urban crowdsourcing distribution service scheduling, one of the keys is the spatial crowdsourcing task assignment. Aiming at the current spatial crowdsourcing lacking of the consideration for the actual road network, a graph-based crowdsourcing task optimization assignment model and its optimization algorithm are proposed, in order to obtain the maximum number of courier assigned tasks at a time. The optimal assigned task and delivery path of the courier are obtained under the condition of satisfying the goal and constraints. In this study, at first the actual geographical location is mapped into a map structure, considering the crowdsourcing task starting location, task target location and the courier location. Then, three graph-based spatial task assignment methods are implemented: the first is to realize the realistic grab strategy, location service-based grab strategy (LSGS); the second is crowdsourcing task assignment algorithm based on the task location (TATL); the third is crowdsourcing task assignment algorithm based on ant colony planning (TAAP). Finally, through the comparison of multiple sets of experiments, the proposed TATL and TAAP algorithms can obtain the distribution path and improve the task assignment efficiency.
KW - Graph structure
KW - Optimal solution algorithms
KW - Path planning
KW - Spatial crowdsourcing
KW - Task assignment
UR - https://www.scopus.com/pages/publications/85073563327
U2 - 10.1109/HPCC/SmartCity/DSS.2019.00368
DO - 10.1109/HPCC/SmartCity/DSS.2019.00368
M3 - 会议稿件
AN - SCOPUS:85073563327
T3 - Proceedings - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019
SP - 2623
EP - 2629
BT - Proceedings - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019
A2 - Xiao, Zheng
A2 - Yang, Laurence T.
A2 - Balaji, Pavan
A2 - Li, Tao
A2 - Li, Keqin
A2 - Zomaya, Albert
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019
Y2 - 10 August 2019 through 12 August 2019
ER -