Long-Term or Temporary? Hybrid Worker Recruitment for Mobile Crowd Sensing and Computing
arxiv(2022)
Abstract
This paper investigates a novel hybrid worker recruitment problem where the
mobile crowd sensing and computing (MCSC) platform employs workers to serve
MCSC tasks with diverse quality requirements and budget constraints, under
uncertainties in workers' participation and their local workloads.We propose a
hybrid worker recruitment framework consisting of offline and online trading
modes. The former enables the platform to overbook long-term workers (services)
to cope with dynamic service supply via signing contracts in advance, which is
formulated as 0-1 integer linear programming (ILP) with probabilistic
constraints of service quality and budget.Besides, motivated by the existing
uncertainties which may render long-term workers fail to meet the service
quality requirement of each task, we augment our methodology with an online
temporary worker recruitment scheme as a backup Plan B to support seamless
service provisioning for MCSC tasks, which also represents a 0-1 ILP problem.
To tackle these problems which are proved to be NP-hard, 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, which achieve the optimal or sub-optimal
solutions. Experimental results demonstrate our effectiveness in terms of
service quality, time efficiency, etc.
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