Study On Swarm Intelligence Based Clustering Routing Algorithm For Wireless Sensor Networks

Wireless Sensor Networks(WSNs)are widely utilized in various fields such as military surveillance,space exploration,and smart homes by self-organizing a large number of nodes that carry their own energy to monitor information in the physical world.Energy conservation has been an essential challenge for WSNs owing to the nodes with constraint capabilities in computation,energy,and storage.Cluster routing algorithms have been verified to save network energy consumption and prolong network life cycle by establishing cluster topology and searching for optimal routing paths.With the purpose of prolonging the network life cycle and balancing the network load,this paper makes full use of the optimization ability of particle swarm and genetic algorithm in swarm intelligence,and integrates with chaos theory,trying to quickly find the optimal solution of clustering and routing,so as to reduce the network energy consumption and maximize the network life cycle.The specific contents include:(1)The background and significance of the study are analyzed,the architecture of WSNs is presented,and existing clustered routing algorithms are reviewed.Moreover,the design principles of clustered routing algorithms are generalized and categorized.Also,based on the analysis of the advantages and disadvantages of the existing algorithms,it is concluded that swarm intelligence is one of the effective mechanisms for the performance enhancement of clustered routing algorithms,which lays a solid foundation for the research in the subsequent chapters.(2)A clustering routing protocol for wireless sensor networks using a chaotic genetic algorithm is presented.The algorithm uses real number encoding and represents cluster head election and route search as a single chromosome.The fitness function is constructed with the objective of minimizing network energy consumption and load balancing.To maintain the diversity of the population,chaotic computation is used for selection,crossover and variation operations to optimize the next generation and find the optimal cluster heads and the best routing paths.In addition,adaptive round time containing energy and load balancing is utilized to maintain the clusters,which reduces the energy consumption due to frequent cluster head replacement.The effectiveness of the algorithm is verified in terms of convergence speed,energy utilization,load balancing,network throughput,and life cycle.(3)A clustering routing protocol for wireless sensor networks using a chaotic particle swarm optimization is presented.In the first place,the optimal number of clusters of the network is calculated with the objective of minimizing the network energy.The node energy,distance and compactness are considered to construct the fitness function,and chaotic particle swarm algorithm with adaptive inertia weights is adopted to select the cluster heads.In the data transmission process,the initial transmission path is constructed considering node distance and energy,and a new fitness function is constructed on the basis of node distance as well as hop count,and the search range is enlarged by chaotic particle swarm optimization to find the optimal routing paths from cluster heads to the base station.At the end,a combination of local and global rotation is employed for cluster heads rotation,thus maintaining the stable state of the cluster structure.The effectiveness of this algorithm is verified in terms of the total energy consumption,the remaining energy of nodes,the moment of node death,the network throughput and life cycle.In this paper,genetic algorithm,particle swarm optimization and chaos theory are integrated and applied to the optimization process of cluster routing algorithm,which helps to balance the network load and prolong the network life cycle.The research of swarm intelligence algorithm not only provides a new solution for clustering routing algorithm,but also provides a solid theoretical support for the practical application of WSNs in the future.