Research On Multicast Energy-saving Routing Algorithm In Wireless Sensor Networks

With the rapid development of wireless technology,energy saving becomes one of the focus issues of Wireless Sensor Network(WSN)research.The WSN node is small in size and can be flexibly deployed in a complex and changeable environment,so as to detect the environment of the neighborhood and disseminate information.In addition,the transmission of information is inseparable from the guarantee of network quality of service(QOS).This article mainly discusses the WSN multicast QOS energy-saving routing algorithm in a multi-obstacle environment.The environment modeling is carried out through the MAKLINK link graph,and the improved ant colony algorithm is used to optimize the initial path obtained by the improved Dijkstra algorithm.The main research contents of this article are as follows:1.An improvement is proposed for the greedy problem of Dijkstra algorithm which is traversing all nodes.The locations of the starting node and the destination node are used to clarify the search direction that divides the network into two parts.On this basis,the constraints of QOS are used to further reduce the number of search nodes,to avoid a large number of invalid calculations,and then to reduce the running time of the algorithm.2.The ant colony algorithm has some defaults like local optimization solution and slower convergence speed.This research proposed to modify the heuristic function and transition probability of the ant colony algorithm by using the ant search direction,search direction angle and environmental factors.For the pheromone evaporation coefficient,an adaptive concentration update method is introduced.The simulation results proved the effectiveness of the introduced strategies and improve the existing problems in some content.3.Aiming at the problem of network energy saving,a node transmission threshold mechanism is proposed by introducing one or more new nodes.This skill can reduce the network energy consumption.Finally,the effectiveness of the improved algorithm is simulated.The simulation results show that the improved algorithm has some advantages in reducing the time used for optimization,optimizing the deployment position of nodes,and saving network energy consumption and so on.