Harnessing Context for Budget-Limited Crowdsensing with Massive Uncertain Workers

TL;DR

This paper introduces a context-aware algorithm for selecting workers in crowdsensing tasks, effectively managing uncertainty and resource constraints to maximize sensing revenue under a limited budget.

Contribution

The paper proposes the CAWS algorithm that leverages context information to efficiently select workers in crowdsensing with uncertain abilities and limited resources.

Findings

CAWS outperforms baseline methods in maximizing sensing revenue.

Theoretical analysis confirms the algorithm's effectiveness under constraints.

Extensive experiments validate the practical benefits of CAWS.

Abstract

Crowdsensing is an emerging paradigm of ubiquitous sensing, through which a crowd of workers are recruited to perform sensing tasks collaboratively. Although it has stimulated many applications, an open fundamental problem is how to select among a massive number of workers to perform a given sensing task under a limited budget. Nevertheless, due to the proliferation of smart devices equipped with various sensors, it is very difficult to profile the workers in terms of sensing ability. Although the uncertainties of the workers can be addressed by standard Combinatorial Multi-Armed Bandit (CMAB) framework through a trade-off between exploration and exploitation, we do not have sufficient allowance to directly explore and exploit the workers under the limited budget. Furthermore, since the sensor devices usually have quite limited resources, the workers may have bounded capabilities to perform the sensing task for only few times, which further restricts our opportunities to learn the uncertainty. To address the above issues, we propose a Context-Aware Worker Selection (CAWS) algorithm in this paper. By leveraging the correlation between the context information of the workers and their sensing abilities, CAWS aims at maximizing the expected total sensing revenue efficiently with both budget constraint and capacity constraints respected, even when the number of the uncertain workers is massive. The efficacy of CAWS can be verified by rigorous theoretical analysis and extensive experiments.