Budget-aware Online Task Assignment in Spatial Crowdsourcing
Jia-Xu Liu, Ke Xu

TL;DR
This paper introduces a novel online task assignment problem in spatial crowdsourcing with budget constraints, proposing algorithms that adaptively improve matching efficiency in dynamic environments.
Contribution
It formally defines the BOA problem and proposes two algorithms, Greedy-RT and Greedy-OT, for more effective online task assignment under budget constraints.
Findings
Greedy-RT performs well in adversary models.
Greedy-OT significantly improves matching size.
Algorithms are validated on real and synthetic datasets.
Abstract
The prevalence of mobile internet techniques stimulates the emergence of various spatial crowdsourcing applications. Certain of the applications serve for requesters, budget providers, who submit a batch of tasks and a fixed budget to platform with the desire to search suitable workers to complete the tasks in maximum quantity. Platform lays stress on optimizing assignment strategies on seeking less budget-consumed worker-task pairs to meet requesters' demands. Existing research on the task assignment with budget constraint mostly focuses on static offline scenarios, where the spatiotemporal information of all workers and tasks is known in advance. However, workers usually appear dynamically on real spatial crowdsourcing platforms, where existing solutions can hardly handle it. In this paper, we formally define a novel problem Budget-aware Online task Assignment(BOA) in spatial…
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Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Auction Theory and Applications · Optimization and Search Problems
