基于三维温度场快速重建的传感器节点优化部署研究

Optimal Deployment Algorithm for Sensors Based on 3D Temperature Field Reconstruction

物联网背景下,工业场景的温度场可视化可以反映设备的使用状况,有利于提高工业设备的工作效率、节约能源以及降低事故发生率。在工业三维温度场可视化研究中,为提高温度场重建精度、降低工业成本,优化部署传感器节点成为重要解决途径。由此,提出了一种基于模拟退火算法(SA)和Kriging算法的优化算法(SA-Kriging算法),在保证三维温度场重建精度的前提下,用最少数目和最优位置的传感器节点快速重建三维温度场。MATLAB模拟实验显示:相比于均匀布置传感器节点,采用该算法部署传感器节点获得的平均重建均方根误差降低4.42%。结果表明SA-Kriging算法对于传感器节点的数目与位置皆可实现优化,极大提高了三维温度场重建的准确性。

In the context of the Internet of Things,the visualization of the temperature field in an industrial scene can reflect the use of equipment,which is conducive to improving the working efficiency of industrial equipment,saving energy and reducing the incidence of accidents.In order to improve the accuracy of temperature field reconstruction and reduce industrial costs,optimizing the deployment of sensors has become an important solution in the research of industrial 3 D temperature field visualization.Therefore,an optimization algorithm is proposed based on simulated annealing(SA)algorithm and Kriging algorithm,which can quickly reconstruct the 3 D temperature field with the minimum number and optimal position of sensors under the premise of ensuring the accuracy of 3 D temperature field reconstruction.MATLAB simulation experiments show that the average reconstruction root mean square error obtained by deploying sensors using this algorithm(SA-Kriging algorithm)is reduced by 4.42%compared with the method of evenly arranging sensors.The results show that the SA-Kriging algorithm can optimize the number and location of sensors,which greatly improves the accuracy of 3 D temperature field reconstruction.