Mobile crowdsensing (MCS) has become a popular paradigm for data collection in urban environments. In MCS systems, a crowd supplies sensing information for monitoring phenomena through mobile devices. Depending on the degree of involvement of users, MCS systems can be participatory, opportunistic or hybrid, which combines strengths of above approaches. Typically, a large number of participants is required to make a sensing campaign successful which makes impractical to build and deploy large testbeds to assess the performance of MCS phases like data collection, user recruitment, and evaluating the quality of information. Simulations offer a valid alternative. In this paper, we focus on hybrid MCS and extend CrowdSenSim 2.0 in order to support such systems. Specifically, we propose an algorithm for efficient re-route users that would offer opportunistic contribution towards the location of sensitive MCS tasks that require participatory-type of sensing contribution. We implement such design in CrowdSenSim 2.0, which by itself extends the original CrowdSenSim by featuring a stateful approach to support algorithms where the chronological order of events matters, extensions of the architectural modules, including an additional system to model urban environments, code refactoring, and parallel execution of algorithms.

移动人群感知（MCS）已成为城市环境中数据收集的流行范例。在MCS系统中，人群通过移动设备提供传感信息以监视现象。根据用户的参与程度，MCS系统可以是参与式，机会式或混合式的，结合了上述方法的优势。通常，需要大量的参与者才能成功进行感知运动，这对于构建和部署大型测试平台来评估MCS阶段的性能（例如数据收集，用户招募和评估信息质量）不切实际。模拟提供了有效的替代方法。在本文中，我们将重点放在混合MCS上，并扩展CrowdSenSim 2.0以支持此类系统。特别，我们提出了一种用于有效重新路由用户的算法，该算法将为需要参与型感应贡献的敏感MCS任务的位置提供机会贡献。我们在CrowdSenSim 2.0中实现了这样的设计，该设计本身通过以有状态的方法来支持原始算法的扩展CrowdSenSim，以支持事件按时间顺序重要的算法，体系结构模块的扩展，包括用于建模城市环境的附加系统，代码重构和并行执行算法。