https://www.insight-centre.org/content/flag-verify-fix-adaptive-spatial-crowdsourcing-leveraging-location-based-social-networks Presented at SIGSPATIAL 2015. 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.

1. Adaptive Spatial Crowdsourcing
leveraging Location-based Social
Networks
Umair ul Hassan, Edward Curry
ACM SIGSPATIAL 2015
November 3-6, 2015 — Seattle, WA, USA

2. Motivation: Flag-Verify-Fix Pattern
Spatial crowdsourcing for
volunteered city maintenance
Flag
General public reports problems in
their neighbourhood
Verify
Volunteer crowd workers verify
reported problem
Fix
Volunteer crowd workers eradicate
the problem

3. Problem: Adaptive Task Assignment
Maximum Coverage Assignment
Broadcast notification to all workers is an
inefficient approach
Quantity Issue
Assign multiple workers to increase
chances of task coverage
How many workers should be assigned?
Selection Issue
Heterogeneous and reluctant workers
Which set of workers should be assigned?

4. Quantity Issue: Location Diversity
• Location-based social
network for location
entropy of a task
• Number of workers

5. Selection Issue: Thompson Sampling
Combinatorial Multi-armed Bandits (CMAB)
Choose workers while learning their willingness over time
Actual Willingness
Estimated Willingness
Ti: Total tasks assigned to so far
Si: Complete tasks so fat

6. Results: DynTS Algorithm
• Thompson sampling based
algorithms have at least
30% more coverage
• Location diversity results
in at least 10% less
assignments