Research On Sensor Network Node Localization Algorithm Based On Improved Wolf Swarm Algorithm

In wireless sensor networks(WSN),the information monitored by sensor nodes,such as wind force,temperature,light intensity,etc.,must have corresponding location information to play their value.Therefore,location information is very important for monitoring activities.The current positioning methods can be divided into two categories: one is range-based,the other is rangefree.In WSN node localization,the most commonly used method is range-free,and DV-hop algorithm is one of the most commonly used algorithms in range-free method,so its position in WSN node localization is self-evident.DV-hop algorithm is widely used in range-free methods because it is easy to implement,low cost,and can meet the requirements of most applications for node location.However,the algorithm has some shortcomings: the average distance per hop of network nodes deviates from the actual distance greatly;the density of sensor nodes also affects the positioning accuracy;in the final positioning stage,the maximum likelihood estimation method may cause the superposition of errors with the increase of the number of equations.In order to solve the above problems,Received Signal Strength Indicator(RSSI)ranging method and Wolf Colony Algorithm(WCA)are introduced into DV-hop algorithm,which reduces the positioning error in the early and late stages of DV hop algorithm.The main research work is as follows:(1)The RSSI ranging method is introduced.RSSI algorithm is used to calculate the distance between anchor node and the unknown nodes with only one hop to anchor node,which can reduce the error of distance calculated from average hop distance to a certain extent.(2)Improved wolf pack algorithm.Compared with other intelligent swarm algorithm,WCA has better solution effect,so this paper discusses a better localization algorithm by introducing WCA into node localization.In order to solve the problem that WCA is easy to fall into local optimum in the early stage and has insufficient accuracy in the later stage,this paper proposes an Improved Wolf Colony Algorithm(IWCA)based on simulated annealing and chaotic mapping,and applies the walking mode of Gray Wolf algorithm(GWO)to the siege stage of wolf swarm algorithm.The simulated annealing and chaotic mapping are introduced into the stage of probe wolves wandering,which makes the probe wolves have the ability to jump out of local optimum.The application of walking mode of GWO makes the WCA have higher calculation accuracy.Simulation results show that IWCA is more robust than other intelligent swarm optimization algorithms,it can avoid local optimization better,and has better calculation accuracy.(3)The improved IWCA algorithm is applied to the node position calculation stage of DV-Hop algorithm,and An improved DV＿Hop algorithm based on improved Wolf Colony Algorithm(IWCADV-Hop)algorithm is proposed.The IWCA algorithm is used to replace the least square method,and the unknown node coordinates are obtained by establishing the fitness function and solving the solution,which avoids the large calculation error caused by the large least squares in the case of too many equations,thus improving the positioning accuracy.Simulation results show that IWCADV-Hop algorithm has higher positioning accuracy,shorter positioning time and lower energy consumption than other localization algorithms.