Research On Node Localization Algorithm Based On Optimization Theory In Wireless Sensor Networks

Node location technology is an important supporting technology of wireless sensor networks.Through location technology,the location information of events or nodes can be provided to the entire network,and then the data information to be studied in the next step can be collected.The presence or absence of target location and node information determines whether the information collection is meaningful.Sensor network nodes that collect information are usually randomly distributed in a specific environment and coordinate work with each other through self-organization.Since sensor nodes cannot know their location in advance,they need to locate themselves or each other after placement.There are usually two types of localization algorithms in wireless sensor networks.On the one hand,the location method based on ranging mainly uses the actual distance between nodes to estimate the location of the nodes,which requires additional hardware support to obtain angle or distance information.The hardware configuration requirements for the entire sensor network are stricter and the deployment cost is higher,but the location results are more accurate.On the other hand,the non-ranging location method mainly obtain information through the number of hops and hop distances between sensor network nodes,which has low hardware requirements,easy implementation and low cost,but the location error is relatively large.In order to improve the location accuracy of the algorithm without increasing the hardware cost,this thesis introduces the intelligent algorithm based on optimization theory into the field of node location,and uses optimization theory to solve the location problem.The main contents of this thesis are as follows:Aiming at the problem of low location accuracy of the classical DV-Hop algorithm,a DV-Hop location algorithm based on improved Jaya is proposed.Firstly,a correction factor is added to modify the average hop distance,and then the concept of collinearity is proposed to select anchor nodes to reduce location error;Then Tent mapping is introduced to generate the initial population to enhance the diversity of the population and improve the convergence speed;Finally,the objective function is constructed and the unknown node coordinates are obtained by using the improved Jaya optimization algorithm.The simulation results show that compared with the classical DV-Hop algorithm and the improved algorithm,the average location error of the proposed algorithm is reduced by 75.00% and 65.83%,respectively,and the location accuracy is higher.Aiming at the problem of large location error in the RSSI algorithm,a new RSSI trilateral weighted centroid localization algorithm based on standard firefly(MRSSI-SFA)is proposed.Firstly,the RSSI value is optimized by normal distribution to reduce the influence of wireless signal propagation,and then the unknown node position is estimated by trilateral weighted centroid.In order to improve the convergence speed and accuracy of the algorithm,the results obtained from the three-sided weighted centroid of the previous stage are used to constrain the iterative process and the initial range of the population of the SFA algorithm.Finally,the SFA algorithm is used to find the optimal solution iteratively,so as to achieve the goal of jumping out of local optimum and fast convergence.The simulation experiment proves that compared with the traditional RSSI and RSSI-PSO algorithms,the average location error of the MRSSI-SFA algorithm proposed in this thesis is reduced by 66.75%and 53.73%,respectively,which can reduce the influence of noise pulse of transmitter module and signal propagation environment on wireless signal propagation,and effectively reduce the location error.