by Umair ul Hassan, Edward Curry
Abstract:
This paper introduces the flag-verify-fix pattern that employs spatial crowdsourcing for city maintenance. The patterns motivates the need for appropriate assignment of dynamically arriving spatial tasks to a pool for workers on the ground. The assignment is aimed at maximizing the coverage of tasks spread over spatial locations; however, the coverage depends of willingness of workers to perform tasks assigned to them. We introduce the maximum coverage assignment problem that formulates two design issues of dynamic assignment. The quantity issue determines the number of worker required for a task and selection issue determines the set of workers. We propose an adaptive algorithm that uses location diversity based on a location-based social network to address the quantity issue and employs Thompson sampling for selecting the workers by learning their willingness. We evaluate the performance of the proposed algorithm in terms of coverage and number of assignments using real world datasets. The results show that our proposed algorithm achieves 30%-50% more coverage than the baseline algorithms, while requiring less workers per task.
Reference:
Umair ul Hassan, Edward Curry, "Flag-Verify-Fix: Adaptive Spatial Crowdsourcing leveraging Location-based Social Networks", In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, New York, NY, USA, pp. 1-4, 2015.
Bibtex Entry:
@inproceedings{UlHassan2015,
abstract = {This paper introduces the flag-verify-fix pattern that employs spatial crowdsourcing for city maintenance. The patterns motivates the need for appropriate assignment of dynamically arriving spatial tasks to a pool for workers on the ground. The assignment is aimed at maximizing the coverage of tasks spread over spatial locations; however, the coverage depends of willingness of workers to perform tasks assigned to them. We introduce the maximum coverage assignment problem that formulates two design issues of dynamic assignment. The quantity issue determines the number of worker required for a task and selection issue determines the set of workers. We propose an adaptive algorithm that uses location diversity based on a location-based social network to address the quantity issue and employs Thompson sampling for selecting the workers by learning their willingness. We evaluate the performance of the proposed algorithm in terms of coverage and number of assignments using real world datasets. The results show that our proposed algorithm achieves 30%-50% more coverage than the baseline algorithms, while requiring less workers per task.},
address = {New York, NY, USA},
author = {ul Hassan, Umair and Curry, Edward},
booktitle = {Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems},
doi = {10.1145/2820783.2820870},
file = {:Users/ed/Library/Application Support/Mendeley Desktop/Downloaded/ul Hassan, Curry - 2015 - Flag-Verify-Fix Adaptive Spatial Crowdsourcing leveraging Location-based Social Networks.pdf:pdf},
isbn = {9781450339674},
month = {nov},
pages = {1--4},
publisher = {ACM},
title = {{Flag-Verify-Fix: Adaptive Spatial Crowdsourcing leveraging Location-based Social Networks}},
url = {http://www.edwardcurry.org/publications/paper_271.pdf},
year = {2015}
}