Research On Clustering Routing Protocol For Energy-Harvesting WSN

The rapid development of wireless communication networks and the advent of miniature sensor technology make Wireless Sensor Network(WSN)a new technology,which can collect and transmit information such as temperature,light,sound and video in the monitoring area to observation.In specific applications,sensor nodes are often limited by their battery power.Once the battery energy of the sensor node is exhausted,replacing the battery again will bring a lot of manpower and resource costs.The wireless sensor network(Energy Harvesting WSN,EH-WSN)based on environmental energy harvesting effectively prolongs the network life cycle and has become an important research topic.This dissertation studies the WSN clustering routing protocol suitable for the energy harvesting background,and proves the superiority of the proposed algorithm through simulation analysis.The layout and research work of the dissertation is as follows:(1)An overview of the WSN cluster routing protocol and the characteristics of solar radiation,which is the environmental energy source used in this dissertation.The WSN clustering routing algorithm with energy harvesting characteristics is deeply studied.The conclusion shows that the energy harvesting of WSN can effectively prolong the network life and improve the network performance,the clustering routing protocol can equally distribute the network energy consumption,which is convenient for network maintenance and management.(2)Research on K-Means algorithm based on Genetic Algorithm(GA)optimization.K-Means is a classic unsupervised machine learning algorithm,but it is susceptible to noise and outliers.Genetic algorithm is an adaptive heuristic search algorithm based on natural selection and genetics,which has been widely used in combinatorial optimization problems and search solution spaces.The algorithm aims to minimize the energy consumption of the network to acquire the most suitable number of cluster heads in the network,generate a set of different initial cluster centers of a certain scale,and use the optimal number of cluster heads as the number of clusters in the K-Means algorithm.The fitness function is used to evaluate the clustering effect of the K-Means algorithm under the selection of different initial cluster center points,and the global optimal initial cluster center is iteratively sought through the genetic algorithm.After obtaining the global optimal initial cluster center,the node judges whether it is elected as the cluster head node according to the remaining energy,collected energy and relative position.Data transmission is divided into intra-cluster transmission and inter-cluster transmission.In the cluster,redundant nodes in the cluster are controlled to sleep to reduce data consumption.Outside the cluster,the distance between the sink node and the cluster head node is used as the criterion for single-hop/multi-hop data transmit..Simulation experiments show that the proposed algorithm has improved the cluster head distribution and cluster size control,and consumes less energy compared to other methods under similar network data throughput,demonstrating the effectiveness of the algorithm.(3)LEACH algorithm is a widely used wireless sensor network protocol for efficient data aggregation and communication in energy-limited sensor nodes.However,the disregard of the energy consumption of cluster heads and the single-hop routing method in this algorithm can lead to rapid energy consumption and shorten the network lifetime.To address these issues,a WSN clustering routing algorithm is proposed which is optimized by ant colony optimization(ACO).In the cluster head establishment phase,multiple energy factors are introduced to fully consider the energy factor of cluster head nodes and diminish their energy consumption to a certain degree.During the inter-cluster transmission phase,an enhanced ant colony algorithm is employed to design the communication pathway between cluster head nodes rather than the initial single-hop routing.The simulation outcomes demonstrate that this technique considerably prolongs the lifespan of the network and is still relevant in scenarios involving a large-scale network.