The recent remarkable advancement of smart devices is enabling a higher-level flexibility of mobile sensing. Along with the rapid development of mobile devices and applications, a challenging issue is becoming more serious than ever before. A large number of mobility-based services have brought heavy workloads to mobile devices. Resource outsourcing via resource allocations is a type of method to mitigate local workloads. However, most current solutions are restricted by two issues, namely, the variety of inputs and the contradiction between optimal outputs and latency. In this article, we utilize the mechanism of Reinforcement Learning (RL) and propose a novel approach, named Smart Reinforcement Learning-based Resource Allocation (SRL-RA), to achieve optimal allocation through a self-learning process.

中文翻译：

---

移动传感中基于强化学习的以内容为中心的服务


智能设备最近的显着进步使得移动传感具有更高水平的灵活性。随着移动设备和应用程序的快速发展，一个具有挑战性的问题变得比以往任何时候都更加严重。大量基于移动的业务给移动设备带来了繁重的工作负载。通过资源分配的资源外包是减轻本地工作负载的一种方法。然而，当前大多数解决方案都受到两个问题的限制，即输入的多样性以及最优输出与延迟之间的矛盾。在本文中，我们利用强化学习（RL）机制，提出了一种名为基于智能强化学习的资源分配（SRL-RA）的新方法，通过自学习过程实现最优分配。