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.

翻译：本文研究了一个新颖的混合工人招募问题，其中移动群智感知与计算（MCSC）平台在工人参与及其本地工作负载存在不确定性的情况下，雇佣工人为具有不同质量要求和预算约束的MCSC任务提供服务。我们提出了一种包含离线与在线交易模式的混合工人招募框架。前者通过预先签署合同使平台能够超额预订长期工人（服务）以应对动态服务供给，该问题被建模为带有服务质量与预算概率约束的0-1整数线性规划（ILP）。此外，考虑到现有不确定性可能导致长期工人无法满足每个任务的服务质量要求，我们通过在线临时工人招募方案（作为备份计划B）对方法进行补充，以支持MCSC任务的无缝服务供应，该方案同样构成一个0-1 ILP问题。为了解决这些已被证明为NP-hard的问题，我们开发了三种算法，即：i）穷举搜索算法，ii）基于唯一索引的、带有风险感知过滤约束的随机搜索算法，以及iii）基于几何规划的逐次凸优化算法，这些算法能够实现最优或次优解。实验结果表明了我们在服务质量、时间效率等方面的有效性。