Research On WSN Heuristic Clustering Routing Protocol Based On Fuzzy Intelligent Computin

Wireless sensor networks are composed of many miniature sensor nodes that can sense and collect environmental information.With the development of low-power digital circuits and wireless communication technologies,it is widely used in military facilities,ecological environment monitoring,and tracking of moving targets.Sensor nodes are tiny in size and generally powered by limited energy batteries and the nodes are usually deployed in large numbers in complex environments.So replacing the nodes’ batteries is difficult.Therefore,designing efficient and energy-efficient routing protocols is one of the current research hotspots in WSN.In this work,we mainly focus on the division of clusters,selection of cluster head and routing path of hierarchical cluster routing algorithm to optimize the division of clusters,cluster head selection and routing path establishment process of the network.Finally,we can achieve a reasonable division of clusters,select the optimal cluster head and routing path,balance the energy consumption of nodes,save network energy,delay the node death time point,and extend the network life cycle.Firstly,aiming at the problem of unbalanced clustering division in the clustering process of classical clustering routing algorithm,a Kmeans clustering optimization algorithm based on genetic algorithm is proposed.The algorithm uses the efficient K-means clustering algorithm in the clustering stage,and introduces the Genetic Algorithm to perform the clustering optimization again for solving the problem that K-means is sensitive to the initial clustering center and easy to fall into the local optimum.Compared with the simulation results of cluster division of LEACH algorithm,the cluster division and cluster head distribution of monitoring area obtained by genetic K-means algorithm are more uniform and reasonable.Secondly,to address the problem of strong randomness and singularity for the method in cluster head selection,a genetic K-means clustering routing algorithm based on fuzzy control algorithm is proposed on the basis of the optimized clustering model.The algorithm uses the clustering model to obtain uniformly distributed clusters in the cluster formation stage,and makes a fuzzy control algorithm based on the relative distance,relative residual energy and relative density of nodes to calculate the rank probability of each node to be elected as the cluster head,which the optimal cluster head of each cluster is selected.Finally,the effect of optimal clustering division and optimal cluster head selection is achieved.The algorithm simulation test of node energy consumption and network life cycle is carried out by establishing single-hop routing between clusters.The algorithm can effectively delay the node death time point to 1200 rounds,and the energy consumption rate is reduced by 34.92 % compared with LEACH.Finally,a multi-hop routing algorithm with a hybrid intelligent algorithm is proposed to solve the problem that the single-hop communication method is difficult to be applied to the transmission of many nodes in a long distance.The algorithm uses gravitational search algorithm to improve the fitness function and pheromone update strategy of classical ant colony algorithm in the route establishment stage.Then,it introduces angle search factor to accelerate the search efficiency and eventually seeks the global optimal transmission routing path.Finally,LEACH,LEACH＿C,TSILEACH and ACO algorithms are compared and simulated.After increasing the monitoring range to 400 meters,the algorithm can effectively extend the network life cycle to about 1300 rounds,and the node death time points are delayed by 750,700,700 and 300 rounds respectively.