Mobile crowd sensing and computing (MCSC) enables heterogeneous users (workers) to contribute real-time sensed, generated, and pre-processed data from their mobile devices to the MCSC platform, for intelligent service provisioning. This paper investigates a novel hybrid worker recruitment problem where the MCSC platform employs workers to serve MCSC tasks with diverse quality requirements and budget constraints, while considering 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 related to 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, and iii) geometric programming-based successive convex algorithm, which achieve the optimal (with high computational complexity) or sub-optimal (with low complexity) solutions. Experimental results demonstrate the effectiveness of our proposed hybrid worker recruitment mechanism in terms of service quality, time efficiency, etc.
翻译:移动人群感测和计算(MCSC)使不同用户(工人)能够提供实时感测、生成和预处理的数据,用于智能服务提供。本文调查了一个新的混合工人征聘问题,即MCSC平台雇用工人为MCSC任务服务,其质量要求和预算限制各不相同,同时考虑到工人参与及其当地工作量的不确定性。我们提议了一个混合工人征聘框架,由离线和在线交易模式组成。前者使平台能够超编长期工人(服务),通过提前签署合同处理动态服务供应,合同是0-1整级线性编程(ILP),具有与服务质量和预算有关的概率限制。此外,由于现有的不确定性,可能使长期工人无法达到每项任务的服务质量要求,我们用在线临时工人征聘计划作为后备计划B,支持为MCSC任务提供无缝服务,这也代表一个0-1 ILP问题。为了解决这些问题,事实证明,这是以0-1直线性线性编程(ILP)编程(ILP)为主,我们用三种不同程度的计算方法,即搜索成本(ILP-CA)的精细算方法,我们用最精确的计算方法,我们三个的计算方法,我们用最精确的计算方法,我们用最难的计算方法,我们用最精确的计算方法,我们用最精确的计算方法,我们用最精确的计算方法,我们用最精确的计算方法,用最精确的计算法的计算法的计算方法,我们的方法,我们用最精确的计算方法,我们研究的计算。