Research On WSN Routing Algorithm Based On Improved K-Means Clustering And Gray Wolf Optimization

A wireless sensor network is a network formed by a large number of sensor nodes through wireless communication according to a certain networking method.The node is responsible for collecting information about the monitored object,and then the collected data is finally transmitted to the user through the sink node.Due to the low cost,wide coverage and strong resistance to destruction,wireless sensor networks are widely used in various aspects such as national defense,medical treatment,and home furnishing.Nodes in the network are sometimes damaged or the battery energy is used up during use,and it is impractical for environmental conditions to maintain artificially.Therefore,the design of the routing algorithm plays a very important role at this time.Many routing algorithms have good scalability and stability.Aiming at the shortcomings of existing clustering routing protocols,this paper uses improved clustering algorithm and gray wolf optimization algorithm to improve and design the routing algorithm,which can prolong the service life of the network and balance the energy consumption of each node.The main The research content is as follows:Aiming at the shortcoming of unsatisfactory clustering effect in k-means clustering algorithm,an improved k-means clustering algorithm based on spatial distribution density is proposed.The algorithm takes the distance standard deviation of the node as the node density value,and selects the initial cluster center according to the density value;after the clustering is completed,the isolated points are added to the corresponding clusters according to the principle of minimum distance.The experimental results can be seen that the improved The algorithm is more accurate.Aiming at the problem that gray wolf optimization algorithm is easy to fall into the optimal in the later stage,the convergence parameters are optimized.The convergence parameter gradually decreases according to the linear law during the operation of the algorithm,which is inconsistent with the search process of the algorithm.Therefore,a calculation formula for the convergence factor that is more consistent with the search process is proposed.It can be seen in the simulation experiment that the improved algorithm Convergence and stability have been improved to a certain extent.Aiming at the problem of unreasonable cluster head selection and cluster distribution in the existing clustering routing algorithm,a WSN routing algorithm(KGRA algorithm)based on improved k-means clustering and gray wolf optimization is proposed.The algorithm adopts improved k-means clustering and gray wolf optimization.The means clustering algorithm is clustered,and the competition radius is introduced to optimize the cluster structure;then the improved gray wolf optimization algorithm is used to select the cluster head;finally the node uses a single-hop or multi-hop method to send data to the sink node.Simulation experiments show that the KGRA algorithm effectively increases the lifetime of the network,reduces the energy consumption rate of nodes,and balances the network load.