Optimization of the Deployment of Temperature Nodes Based on Linear Programing in the Internet of Things

作     者：Liang Hu Zhengyu Zhang Feng Wang Kuo Zhao 

作者机构：the School of Computer Science and Technology Jilin University 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2013年第18卷第3期

页      面：250-258页

核心收录：

学科分类：1305[艺术学-设计学（可授艺术学、工学学位）] 080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 13[艺术学] 0809[工学-电子科学与技术（可授工学、理学学位）] 081104[工学-模式识别与智能系统] 08[工学] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 081001[工学-通信与信息系统] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 

基　　金：supported in part by the National High-Tech Research and Development (863) Program of China(No. 2011AA010101) the National Natural Science Foundation of China (Nos. 61103197 and 61073009) the Science and Technology Key Project of Jilin Province(No. 2011ZDGG007) the Youth Foundation of Jilin Province of China (No. 201101035) the Fundamental Research Funds for the Central Universities of China(No. 200903179) 

主　　题：Internet of Things linear programming optimized node deployment energy consumption 

摘      要：The Internet of Things emphasizes the concept of objects connected with each other, which includes all kinds of wireless sensor networks. An important issue is to reduce the energy consumption in the sensor networks since sensor nodes always have energy constraints. Deployment of thousands of wireless sensors in an appropriate pattern will simultaneously satisfy the application requirements and reduce the sensor network energy consumption. This article deployed a number of sensor nodes to record temperature data. The data was then used to predict the temperatures of some of the sensor node using linear programming. The predictions were able to reduce the node sampling rate and to optimize the node deployment to reduce the sensor energy consumption. This method can compensate for the temporarily disabled nodes. The main objective is to design the objective function and determine the constraint condition for the linear programming. The result based on real experiments shows that this method successfully predicts the values of unknown sensor nodes and optimizes the node deployment. The sensor network energy consumption is also reduced by the optimized node deployment.