Research On Node Location Algorithm Based On RSSI In Wireless Sensor Network

Under the wave of the rapid development of Internet of Things technology and artificial intelligence,WSNs(Wireless Sensor Networks)have been better developed and become one of the important directions of academic research.WSNs have the characteristics of mutual information transmission,which is a self-organizing distributed network,and the positioning algorithms involved have been deeply studied.Under the premise that a small number of sensor nodes are known,relying on the positioning algorithm to calculate the sensor node coordinates of unknown locations can effectively meet the user's requirements and research needs.This paper mainly describes the current background and current status of wireless sensor networks,and introduces the composition and the key technologies of network and the commonly used positioning algorithms,and the RSSI(Receive Signal Strength Indication)is further studied.A weighted centroid localization algorithm based on RSSI integral improvement,An improved RSSI weighted centroid localization algorithm based on adaptive reference values and An improved localization algorithm based on RSSI adaptive parameters and two-norm are proposed.By comparing the simulation results,it is verified that the three improved algorithms have higher positioning accuracy than the traditional algorithms.The main research contents of this paper are as follows:(1)Firstly,the Gaussian distribution model filtered by data optimization is proposed,which improves the reliability of measurement data,establishes multiple sets of signal propagation models,and uses the least squares method to fit the signal strength values lost at unit distance and under the topology network.The path loss parameter value,the definite integral is introduced in the weighting formula,and the fitted value is used as the parameter in the weighting factor.Compared with the traditional RSSI weighted localization algorithm,the optimization accuracy of the positioning accuracy reaches 35.7%.(2)Secondly,in the transmission process of wireless signals in the network,as the signal loss capability changes due to the change of the propagation distance,the positioning result may have nonlinear error.A signal strength measurement is proposed by setting a signal strength.The threshold value is used to update the method of measuring the value of each beacon node in the network structure relative to the unknown node,to correct the influence degree of the nearest anchor node and other anchor nodes to be tested,and to optimize the primary-secondary relationship between the nodes.The method not only reduces the computational complexity of the algorithm,but also effectively reduces the positioning error.In the simulation range of 10m*10m,compared with the traditional centroid localization algorithm,the average positioning accuracy of the method is improved by 2.13 m,and the optimization rate of its positioning is also 17.9%.(3)Finally,an improved algorithm for adaptive parameter fusion two norms is proposed.In the first stage,the parameter threshold is used to update the signal strength measurement value,and in the second stage,the measured signal strength values are used as vectors,the weight of the positioning algorithm is corrected,and the degree of mutual influence between the nodes is redefined.In the simulation range of 10m*10m,its average positioning error is 1.675 m higher than the traditional centroid localization algorithm,and the optimization degree is 52.8%.