Paper Info
Title
Research On Optimized Online Allocation Of Scope Spatial Crowdsourcing Tasks
Abstract
Task allocation of spatial crowdsourcing tasks is an important branch of crowdsourcing. Spatial crowdsourcing tasks not only require workers to complete a specific task at a specified time, but also require users to go to the designated location to complete the corresponding tasks. In this paper, Scope spatial crowdsourcing task whose work position is a region rather than a location is a kind of spatial crowdsourcing task. Mobile crowdsourced sensing (MCS) is one of the most important platforms to publish spatial crowdsourcing tasks, based on which MCS workers can use smartphones to complete the collect ions of related sensing data. When assigning tasks for scoped crowdsourcing tasks, there is a scope overlap between tasks and one or more tasks due to the association of task scope between tasks, which causes a waste of manpower. The focus of this paper is to study the redundancy of the task scope that occurs when using MCS to collect scoping data in the case of fewer workers and more tasks. Optimizing scope spatial crowdsourcing tasks allocation algorithm (OSSA) can eliminate the redundancy of the task area by integrating and decomposing tasks and achieve the improvement of the assignable number of tasks. In the Windows platform, experiments are made to compare the efficiency of the OSSA algorithm with the greedy algorithm and the two-phasebased global online allocation (TGOA) algorithm to further prove the correctness and feasibility of the algorithm for task scope optimization.
Year
DOI
Venue
2020
10.1142/S0218843020500033
INTERNATIONAL JOURNAL OF COOPERATIVE INFORMATION SYSTEMS
Keywords
DocType
Volume
Spatial crowdsourcing, scope spatial crowdsourcing, task integration and decomposition, task optimization
Journal
29
Issue
ISSN
Citations 
3
0218-8430
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Liping Gao154.49
Kun Dai200.34
Chao Lu3418.60