Research On Anycast Routing Algorithm For WSN Based On Improved GWO Algorithm

Wireless sensor network(WSN)is a network composed of a large number of sensor nodes deployed in the monitoring area interconnected by wireless communication for collecting monitoring data and finally transmitting it to the user.Wireless sensor nodes are easy to deploy and WSN have a wide range of monitoring,so that WSN are widely used in military,medical,and environmental monitoring.Wireless sensors are often used to monitor areas with harsh environments,so a good and stable routing algorithm is particularly important.At present,many routing algorithms have achieved good results,but there are still some shortcomings in path evaluation and network balance.Anycast has a broad application prospect in balancing network load,and has achieved certain results in routing algorithms,scalability,and security.Aiming at the shortcomings of the current wireless sensor network routing algorithms,this thesis combined with anycast to improve and design the routing algorithms,which can not only extend the network lifetime,balance the network load,but also have stability and scalability.The main content of this thesis is as follows:To improve the path evaluation model of existing routing algorithms,this paper proposes a WSN Anycast routing algorithmbased on energy and hop optimization(AREO).The AREO algorithm uses area division to reduce the problems of path crossing and transmission collisions.It uses the remaining energy of the nodes and the number of path hops to design a path weight model to comprehensively evaluate the path.It transmit data packets with different weights in different paths by anycast to balance network load.Simulation experiments show that the AREO algorithm can effectively extend the network lifetime and balance the network load.And when some nodes die,the algorithm is still relatively stable.It also has good performance and scalability in different communication radii or different sending node ratios.Aiming at the complex problem of finding the optimal weight adjustment parameter in the single case in the AREO algorithm,this thesis introduces the gray wolf optimization algorithm(GWO),improves the convergence factor and coefficient vector,and proposes an improved gray wolf optimization algorithm(IGWO).Simulation experiments show that the IGWO algorithm has faster convergence effect and higher optimization accuracy even when the population is small.For the application of the AREO algorithm in a single environment,this thesis combines the IGWO and the AREO algorithm,and proposes an optimization algorithm(IGAREO)for AREO with improved gray wolf optimization which is convenient to quickly find the optimal weight adjustment parameters.Simulation experiments show that IGAREO can shorten the search time and improve the accuracy to make AREO play its best performance.The IGAREO algorithm determines better weight adjustment parameters,resulting in a longer network lifetime and better network balance.