The IoT era observes the increasing demand for data to support various applications and services. The Mobile Crowdsensing (MCS) system then emerged. By utilizing the hybrid intelligence of humans and sensors, it is significantly beneficial to keep collecting high-quality sensing data for all kinds of IoT applications, such as environmental monitoring, intelligent healthcare services, and traffic management. However, the service quality of MCS systems relies on a dedicated designed task allocation framework, which needs to consider the participant resource bottleneck and system utility at the same time. Recent studies tend to use a different solution to solve the two challenges. The incentive mechanism is for resolving the participant shortage problem, and task assignment methods are studied to find the best match of participants and system utility goal of MCS. Thus, existing task allocation frameworks fail to consider the participant’s expectations deeply. We propose a semiopportunistic concept-based solution to overcome this issue. Similar to the “shared mobility” concept, our proposed task allocation framework can offer the participants routing advice without disturbing their original travel plan. The participant can accomplish the sensing request on his route. We further consider the system constraints to determine a subgroup of participants that can obtain the utility optimization goal. Specifically, we use the Graph Attention Network (GAT) to produce the target sensing area’s virtual representation and provide the participant with a payoff-maximized route. Such a method makes our solution adapt to most of MCS scenarios’ conditions instead of using fixed system settings. Then, a reinforcement learning- (RL-) based task assignment is adopted, which can help the MCS system towards better performance improvements while support different utility functions. The simulation results on various conditions demonstrate the superior performance of the proposed solution.

中文翻译：

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具有深度学习的移动人群感知的半机会任务分配框架

物联网时代见证了对数据的不断增长的需求，以支持各种应用程序和服务。随后出现了移动人群感知（MCS）系统。通过利用人与传感器的混合智能，为各种IoT应用（例如环境监控，智能医疗服务和交通管理）持续收集高质量的传感数据将非常有益。但是，MCS系统的服务质量依赖于专门设计的任务分配框架，该框架需要同时考虑参与者资源瓶颈和系统实用程序。最近的研究倾向于使用不同的解决方案来解决这两个挑战。激励机制是为了解决参与者短缺的问题，研究了任务分配方法，以找到参与者的最佳匹配和MCS的系统效用目标。因此，现有的任务分配框架无法深入考虑参与者的期望。我们提出了一种基于半机会主义概念的解决方案来克服此问题。与“共享出行”概念相似，我们提出的任务分配框架可以为参与者提供路线建议，而不会干扰他们的原始旅行计划。参与者可以在其路线上完成感知请求。我们进一步考虑系统约束以确定可以获得效用优化目标的参与者子组。具体来说，我们使用图注意力网络（GAT）生成目标感知区域的虚拟表示，并为参与者提供收益最大化的路线。这种方法使我们的解决方案能够适应大多数MCS场景的条件，而无需使用固定的系统设置。然后，采用基于强化学习（RL-）的任务分配，这可以帮助MCS系统更好地提高性能，同时支持不同的实用程序功能。在各种条件下的仿真结果证明了所提出解决方案的优越性能。