Research Of WSN Intelligent Routing Algorithm Based On Energy Efficiency Balance

By deploying sensor nodes,wireless sensor network(WSN)can sense the environment information in real time and complete intelligent data collection and processing,which is the key link to connect the perception layer of the Internet of things with the real world.Facing different application scenarios,WSN can adaptively adjust the network structure and dynamically optimize the network performance through routing algorithm,which has the characteristics of flexible deployment and low cost.However,the limited energy of sensor nodes and the randomness of deployment location make the network prone to uneven energy consumption and low communication quality,which affect the stable operation of the network,and lead to the large-scale application of WSN facing great challenges.Aiming at this problem,this paper studies the WSN routing algorithm based on energy efficiency balance.The main research work of this paper is as followsFirstly,the principle of WSN routing algorithm is studied,and the hierarchical topology routing algorithm is deeply studied.The energy-saving idea of routing algorithm is analyzed from three aspects of cluster head node election,node clustering and data transmission.It is found that when the location of cluster head node is uneven,the cluster structure is unreasonable,such as excessive energy consumption of cluster head node,coexistence of maximum cluster and minimum cluster Long network communication distance increases network energy consumption;at the same time,when the energy consumption of nodes in the transmission path between clusters is unbalanced,invalid paths will be generated,resulting in the loss of transmission data,affecting the quality of network service.Then,aiming at the unreasonable cluster structure,a clustering routing algorithm based on elite particle swarm optimization(HGPSO)is proposed.The algorithm adopts a two-layer network topology,which transfers the work of forwarding the information in the cluster to the sharing node,reduces the energy consumption of the cluster head node,and each cluster head node has a unique corresponding sharing node,which can effectively avoid the failure of relay function.A fitness function considering the energy,location and load balance of the nodes is proposed,and the particle swarm intelligence algorithm is used to select the optimal node In order to improve the search performance,the crossover and mutation of particle population are carried out according to the elite idea of genetic algorithm to enhance the diversity of search targets,and the nonlinear adaptive inertia weight related to fitness function is used to help particles escape from local optimum.Finally,in order to reduce the network energy consumption in the process of data transmission,an ant colony optimization multi hop routing algorithm is proposed.Based on the clustering of hgpso algorithm,the shared node is used to transmit data in the way of single hop and multi hop,and the ant colony algorithm is used to plan the optimal transmission path for the network;the state transition probability function considering node energy and location information is designed,and the angle factor related to the shared node and sink location is used to guide the direction of the next hop node to avoid collision In order to protect the nodes with low energy,this paper proposes a new path planning method,which is based on the concept of reinforcement learning agent to update the pheromone globally,so that the path planning can be inspired by the optimal neighbor.