Topology Structure Design Of Wireless Sensor Network In Forest Environment Monitoring

As an important part of natural resources,forests have important social and natural values.The use of modern high-tech technology to monitor the forest environment is conducive to the rational management and macro regulation of forest resources,and promotes the maximization of forest value.Wireless sensor network is a self-organization network formed by a large number of wireless sensor nodes,which has the advantages of low cost,low power and multi-function.Wireless sensor network technology can be applied to forest environment monitoring,but the limited energy of wireless sensor network also restricts its development.Therefore,the energy-saving forest environment monitoring wireless sensor network has become a research hotspot.Refer to the characteristics and requirements of the actual forest environment,this paper establishes a wireless sensor network model based on forest environment monitoring,and adopts the cluster head positioning,clustering and routing to explore the energy efficient of wireless sensor network clustered topology.Differential evolution algorithm is a population optimization algorithm,with strong robustness,adaptability,easy expansion,etc.In the cluster head positioning scheme,this paper simulates and analyzes the control parameters of the differential evolution algorithm and the effect of mutation strategy on the optimization performance,and solves the reasonable control parameter values and mutation strategy.In order to further improve the performance and considering the influence of control parameters on the performance of the algorithm,this paper presents a differential evolution clustering scheme based on simulated annealing parameter adaptive algorithm and a differential evolution routing scheme based on simulated annealing parameter adaptive algorithm with chaos.The superiority of the proposed improved algorithm is proved,which prolongs the life of wireless sensor network based on forest environment monitoring.