The bundled task assignment problem in mobile crowdsensing: a lagrangean relaxation-based solution approach

Abstract

This paper studies the Bundled Task Assignment Problem in Mobile Crowdsensing (BTAMC), a significant extension of the traditional Task Assignment Problem in Mobile Crowdsensing (TAMC). Unlike TAMC, BTAMC introduces a more realistic scenario where requesters present bundles of two tasks to the platform, giving the platform the flexibility to accept both tasks, accept one, or reject both. This added complexity reflects the multifaceted nature of task assignment in mobile crowdsensing. To address the challenges inherent in BTAMC, we examine two pricing strategies—discount and premium pricing—available to platform operators for pricing task bundles. Additionally, we delve into the critical issue of task quality, emphasizing the quality of workers assigned to each task. This is achieved by ensuring that the overall quality of workers assigned to each task consistently meets a predefined quality threshold, which, in turn, offers a more favorable outcome for all task requesters. The paper presents an integer programming formulation for the BTAMC. This formulation serves as the foundation for a Lagrangean-based solution approach, which has proven to be remarkably effective. Notably, it provides near-optimal solutions even for instances considerably larger than those traditionally encountered in the literature. These contributions offer valuable insights for platform operators and stakeholders in the mobile crowdsensing domain, presenting opportunities to augment profitability and enhance system performance.