Long-Term or Temporary? Hybrid Worker Recruitment for Mobile Crowd Sensing and Computing

Abstract

This paper explores an interesting worker recruitment challenge where the mobile crowd sensing and computing (MCSC) platform hires workers to complete tasks with varying quality requirements and budget limitations, amidst uncertainties in worker participation and local workloads. We propose an innovative hybrid worker recruitment framework that combines offline and online trading modes. The offline mode enables the platform to overbook long-term workers by pre-signing contracts, thereby managing dynamic service supply. This is modeled as a 0-1 integer linear programming (ILP) problem with probabilistic constraints on service quality and budget. To address the uncertainties that may prevent long-term workers from consistently meeting service quality standards, we also introduce an online temporary worker recruitment scheme as a contingency plan. This scheme ensures seamless service provisioning and is likewise formulated as a 0-1 ILP problem. To tackle these problems with NP-hardness, we develop three algorithms, namely, i) exhaustive searching, ii) unique index-based stochastic searching with risk-aware filter constraint, iii) geometric programming-based successive convex algorithm. These algorithms are implemented in a stagewise manner to achieve optimal or near-optimal solutions. Extensive experiments demonstrate our effectiveness in terms of service quality, time efficiency, etc.