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

• 作 者: Liang Hu Zhengyu Zhang Feng Wang Kuo Zhao
• 机 构: the School of Computer Science and Technology, Jilin University,
• 出 处: Tsinghua Science and Technology
• 发表日期: 2013年03期
• 关键词: Internet of Things linear programming optimized node deployment energy consumpti

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.

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 networks since sensor nodes have energy constraints. Deployment of thousands of wireless sensors in an appropriate pattern will next satisfy the application requirements and reduce the sensor network energy consumption. This 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. 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 met hod 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.