Research On The Key Technology Of Biological Innovation Laboratory Based On WSN

With the popularity of Internet of Things technology,there have been such as the future classroom,intelligent lectures and other innovative management and teaching models in the campus.This innovative models combine the classroom education with the wireless sensor networks technology to make the teaching method more colorful and improve the students' interest in learning and the efficiency of the teachers.However,the energy of the sensor node in the model is limited and can not be supplemented.How to effectively reduce the energy consumption of the nodes and make full use of the resources in the system is the key point to prolong the operation time of the network.This paper designs a biological innovation laboratory system based on wireless sensor network,through the deployment of a large number of sensor nodes to monitor and real-time monitoring the biological growth environment to enable students to be more intuitive understanding of the biological growth process and environmental parameters change,and to achieve the intelligent management of biological innovation laboratory.At the same time,aiming at the problem of energy consumption of sensor nodes in the system,the data fusion algorithm based on the deep learning and the ant colony optimization multi-path routing algorithm based on the hierarchical model are designed respectively from the two aspects of data fusion and route transmission.(1)Data fusion algorithm,the algorithm applies the deep learning technology to the field of data fusion of sensor networks.Firstly,clustering the nodes in the monitoring area,and then the feature extraction and classification modules are used to extract and classify the nodes in each cluster.Finally,according to the feature classification result,the cluster head fuses the data collected by the nodes and sends them to the cluster fusion point for routing and forwarding.(2)Multi-path routing algorithm,aiming at the shortcomings of traditional ant colony algorithm,a hierarchical model is designed to improve the link initialization pheromone,heuristic value link and link pheromone update strategy.Finally,considering the residual energy and network transmission delay and packet loss rate of the sensor node and path correlation factors,the data is transmitted by selecting the multiple reachable paths with the largest target value in turn by the custom objective function.The experimental results show that the data fusion algorithm based on deep learning can effectively compress the redundant data collected by the sensor,reduce the amount of data in the network and improve the correct rate of the data received by the base station.And comparing with other routing protocols,the ant colony optimization multi-path routing algorithm based on hierarchical model has obvious advantages in node average energy consumption,transmission delay and data packet loss rate.It ensures that the environmental parameters collected in the biological innovation laboratory system are transmitted to the local server in real time and accurately.