Research And Application Of Energy-saving Technology For Homogeneous And Heterogeneous Wireless Sensor Networks

Wireless sensor network(WSN)has become a hot research technology because of its ability to cooperate with various sensors to collect and process target information.However,as a network system with limited hardware resources and data transmission as the center,WSN has its own characteristics in terms of cost,computing power and power consumption in homogeneous and heterogeneous network states.If the distribution density and sampling frequency are too high,some nodes will perceive data redundancy.Therefore,how to improve the energy-saving level of each node in homogeneous and heterogeneous WSNs has become the focus of current research.In order to make the energy consumption in WSN reasonably equalized,this paper takes the homogeneous and heterogeneous network states as the background,optimizes the network structure,reduces the overall power consumption and maximizes the life cycle as the goal,carry out research on important energy-saving technologies such as routing protocols and data aggregation technologies in WSN,and mainly complete the following tasks:(1)A homogeneous WSN routing protocol based on hybrid differential evolution and firefly algorithm(RPHDEFA)is designed in the homogeneous WSN: the network is divided into regular hexagon virtual partitions,and then the set of candidate cluster heads is generated based on the residual energy.The hybrid differential evolution and firefly algorithm optimizes the cluster head scheme among the alternative cluster heads,selects the residual energy level,intra-cluster density and inter-cluster position to evaluate the cluster heads,and optimizes the clustering results.Finally,in the data transmission stage,the communication distance and energy are used to evaluate the cluster head.Finally,in the data transmission stage,the relay node election function is established based on the communication distance and energy level,the transmission path is constructed through the combination of single-hop and multi-hop transmission mechanisms.Simulation results show that the algorithm can balance the load of each node in the network,and the network life is prolonged.(2)According to the characteristics of heterogeneous WSN,the clustering mechanism and data aggregation technology of WSN are studied in the state of energy heterogeneity,and a heterogeneous WSN data aggregation algorithm based on extreme learning machine with lion swarm optimization(LSO-ELMDA)is designed.The dual-cluster head clustering mechanism is introduced into the heterogeneous network,and the extreme learning machine model optimized by the lion swarm optimization algorithm is established according to the time correlation of the data for data prediction.By setting an error threshold for prediction,the upload of redundant data is suppressed.Simulation results show that the double cluster head mechanism introduced by the algorithm can alleviate the burden of cluster heads,the prediction model established can effectively suppress the transmission of redundant data,and the network life can be prolonged.(3)Finally,in the context of the precision agriculture,the heterogeneous WSN data aggregation algorithm based on extreme learning machine with lion swarm optimization is applied to the vegetable greenhouse monitoring system.The system realizes the function of real-time collection of various physical quantities in the greenhouse,and can give real-time alarms to abnormal conditions during performance testing.The error between the collected data and the real value is small,which can meet the requirements of low-cost operation and convenient data management of WSN in agricultural scenarios.