论文标题
基于时空范围信息模型的有效传感器调度策略
Efficient Sensor Scheduling Strategy Based on Spatio-temporal Scope Information Model
论文作者
论文摘要
在本文中，提出了基于物联网中传感器节点的时空相关性（IoT），提出了一个时空范围信息模型（SSIM），以量化传感器数据的范围有价值的信息，这些信息随时间和时间衰减，以指导系统在感应区域中有效决策。考虑了一个简单的传感器监视系统，其中包含三个传感器节点，并为优化问题提出了两种最佳调度决策机制，即最佳和长期最佳决策机制。对于单步机制，理论上分析了调度结果，并获得了一些调度结果之间节点布局的数值界限，这与仿真结果一致。对于长期机制，使用Q学习算法获得具有不同节点布局的调度结果。通过使用相对湿度数据集进行实验来验证这两种机制的性能，并讨论了两种机制的性能差异。另外，总结了模型的局限性。
In this paper, based on the spatio-temporal correlation of sensor nodes in the Internet of Things (IoT), a Spatio-temporal Scope information model (SSIM) is proposed to quantify the scope valuable information of sensor data, which decays with space and time, to guide the system for efficient decision making in the sensed region. A simple sensor monitoring system containing three sensor nodes is considered, and two optimal scheduling decision mechanisms, single-step optimal and long-term optimal decision mechanisms, are proposed for the optimization problem. For the single-step mechanism, the scheduling results are analyzed theoretically, and approximate numerical bounds on the node layout between some of the scheduling results are obtained, consistent with the simulation results. For the long-term mechanism, the scheduling results with different node layouts are obtained using the Q-learning algorithm. The performance of the two mechanisms is verified by conducting experiments using the relative humidity dataset, and the differences in performance of the two mechanisms are discussed; in addition, the limitations of the model are summarized.
