Study On Clustering Optimized Topology Control Algorithms For Wireless Sensor Networks

Wireless sensor network(Wireless sensor networks,WSNs)are composed of a large number of sensor nodes to achieve data collection in the target monitoring area.Because it is usually deployed in harsh environment,balancing the energy consumption of nodes and prolonging the network life cycle is an important goal of network design,and clustering topology control is one of the effective methods to save energy and prolong the network life.At present,it has become a research hotspot in the field of Wireless Sensor Networks.Traditional clustering topology control algorithms usually have some problems,such as difficult of determining the optimal number of clusters,uneven clustering,and unreasonable cluster head(Cluster Head,CH)elections.This paper proposes a variety of intelligent optimization algorithms to solve the above problems.The specific research content as follows:First of all,to solve the problem that the optimal number of clusters in traditional clustering is difficult to determine,a new clustering topology control algorithm FCME(An Energy constrained Fuzzy C-means clustering algorithm based on Wireless Sensor Networks)is proposed.The best cluster number is determined according to the compactness,overlap degree and the degree of separation between clusters,and the target function of energy constraint is designed to select the optimal cluster head(cluster head,CH)so as to construct the cluster topology structure with effective energy.When data transmission,each cluster member communicates with cluster head,and cluster head communicates directly with base station.The simulation results show that FCM-E algorithm can obtain more reasonable cluster head distribution,more balanced energy consumption and longer network life cycle compared with reach algorithm.Secondly,in view of the computational complexity of determining the optimal number of clusters in the FCM-E algorithm,an energy aware clustering topology algorithm based on improved AP and GA algorithm(EAPGA)is proposed.The AP algorithm is used to determine the optimal number of clusters and cluster.Firstly,it uses the AP algorithm to determine the optimal number of clusters and clusters.The remaining energy of the node and the distance between the node and the base station are used as the measure of similarity,and the remaining energy of the node and the centrality of the node are considered to calculate the attractiveness.Then,the optimized cluster topology is obtained by iteratively updating the attractiveness and belongingness.Finally,in order to save the communication energy consumption between the cluster head and the base station,an improved genetic algorithm is used to search for the optimal next-hop relay cluster head until the base station.The algorithm is simulated in terms of energy consumption and life cycle.The results show that compared with LEACH,LEACH-C and APSA algorithms,EAPGA algorithm can effectively balance the energy consumption between nodes and extend the life cycle of wireless sensor networks.Finally,in view of the problem that the bias parameter in the EAPGA algorithm is fixed,it will easily lead to the poor dynamic adaptability of the optimal number of clusters.A firefly-optimized wireless sensor network clustering topology control algorithm TCFA(clustering Topology Control for Wireless Sensor Networks based on Firefly Algorithm optimization)is proposed.The firefly algorithm is used to calculate the brightness to get the initial bias parameter value of AP algorithm,and then the fitness function is constructed based on the average distance and average energy consumption deviation in the cluster to iteratively solve the optimal bias parameter value.Based on the optimal bias parameter,the AP algorithm is run to get the optimal cluster topology.Finally,in order to save the energy of inter cluster communication and cyclic clustering,the optimized chaotic Gray Wolf algorithm is used to search the optimal next hop relay cluster head,and the cluster maintenance is adaptive on demand.The simulation results show that compared with EAPGA algorithm,it can obtain more stable network topology.In this paper,fuzzy C-means,Affinity Propagation,Firefly and other intelligent algorithms are used to optimize and construct the topology of wireless sensor networks,so as to reduce the overall energy consumption of the network and effectively extend the network life cycle.It provides a reference for the application of swarm intelligence algorithm in the field of wireless sensor network topology control,and provides theoretical support for the practical application of wireless sensor network.