Loading [MathJax]/extensions/tex2jax.js
Spatial Task Assignment Based on Information Gain in Crowdsourcing | IEEE Journals & Magazine | IEEE Xplore

Spatial Task Assignment Based on Information Gain in Crowdsourcing

Publisher: IEEE

Abstract:

Spatial crowdsourcing provides workers for performing cooperative tasks considering their locations, and is drawing much attention with the rapid development of mobile In...View more

Abstract:

Spatial crowdsourcing provides workers for performing cooperative tasks considering their locations, and is drawing much attention with the rapid development of mobile Internet. The key techniques in spatial crowdsourcing include worker-mobitlity-based task matching for more information gain and efficient cooperation among coworkers. In this paper, we first propose information gain based maximum task matching problem, where each spatial task needs to be performed before its expiration time and workers are moving dynamically. We then prove it is a NP-hard problem. Next, we propose two approximation algorithms: greedy and extremum algorithms. In order to improve the time efficiency and the task assignment accuracy, we further propose an optimization approach. Subsequently, for complex spatial tasks, we propose a feedback-based cooperation mechanism, model the worker affinity and the matching degree between a task and a group of coworkers, and design a feedback-based assignment algorithm with group affinity. We conducted extensive experiments on both real-world and synthetic datasets. The results demonstrate that our approach outperforms related schemes.
Published in: IEEE Transactions on Network Science and Engineering ( Volume: 7, Issue: 1, 01 Jan.-March 2020)
Page(s): 139 - 152
Date of Publication: 16 January 2019

ISSN Information:

Publisher: IEEE

Funding Agency:


1 Introduction

SPATIAL crowdsourcing is such a platform through which requesters can outsource spatial tasks to a set of workers and the workers must physically move to the specified locations in order to perform those tasks. Currently, spatial crowdsourcing is emerging and drawing much attention from both industry and academia because mobile devices can easily collect and transmit multi-modal data, such as taking photos and tracking locations, for spatial applications.

References

References is not available for this document.