Optimization Of The Deployment Of Temperature Nodes Based On Linear Programing In The Internet Of Things

The Internet of Things(IoT) emphasizes on the concept that objects connectedwith each other, which means everything is connected to Internet. Internet is thecentral network of IoT which is combined of different kinds of heterogeneousnetworks. In Wireless Sensor Networks(WSN), energy consumption, computing andstorage resources is extremely limited. One of the most important issues is how tosave the energy consumption of WSN. To satisfy the large-scale of IoT, we need tofind out an appropriate way to deploy thousands of sensor nodes. In this paper, wedeployed ten temperature sensor nodes to do monitoring. The temperature data areused to predict the temperature of some node with linear programming. In this case,we proposed an optimized way of the node deployment of temperature sensor nodes.Based on this experiment, we present the architecture of information gatheringplatform system in IoT, providing a integral vision of the whole information gatheringplatform system in IoT. This architecture summarizes different kinds of sensorequipment, allows heterogeneous networks access, processes the sensor informationinto unified data flow by an middleware and provides the unified data flow to theupper application of IoT. In addition, the architecture uses distributed NoSQLdatabase to provide data storage services.In the result of our experiment, we successfully optimized the deployment oftemperature sensor nodes and achieve the goal of saving energy consumption andsolving the problem of node failure and data abnormal. Based on the realexperimental data, this paper proved that our optimization of node deployment issuccessful. According to the result, we can reduce the node sampling rate and copewith the situation of node failure and data abnormal. In this way, we can save the energy consumption in IoT.