Research On RSSI Based Node Location Algorithm In Wireless Sensor Networks

With the in-depth development of wireless sensor network(WSN)research,its characteristics and advantages are increasingly significant,and its applications are more and more extensive.Node positioning technology is the core technology of the entire WSN,which determines whether the entire network can work normally and whether the monitoring data is accurate.The node position needs to be obtained through positioning technology.Existing positioning algorithms can be divided into two categories based on whether to measure distance: ranging algorithms and non ranging algorithms.Among them,the received signal strength indicator(RSSI)positioning algorithm in non ranging algorithms is widely used,because it does not require hardware equipment and has advantages such as low power consumption and low cost.How to reduce positioning errors through the improvement and optimization of RSSI node positioning algorithms is one of the hotspots in node positioning technology research.This article focuses on the issues of low positioning accuracy and susceptibility to environmental impact in RSSI algorithms.The main research content is as follows:In response to the low accuracy of RSSI localization,a chimpanzee optimized RSSI centroid node localization algorithm(SRSSI-Ch OA)is proposed.This algorithm first filters the RSSI value with a velocity constant to improve the anti-interference and stability of ranging.Then,the optimized received signal strength value is calculated using the RSSI distance formula to calculate the distance,and the reciprocal distance is used as a weight factor for centroid localization.Secondly,to correct the centroid coordinates,The fitness function is established according to the obtained position information,and the Chimp Optimization Algorithm(Ch OA)is introduced for iterative optimization.The final simulation experiment shows that the proposed algorithm reduces the normalized average positioning error by30.78%,24.42%,and 8.3%,respectively,compared to RSSI centroid,Gaussian model RSSI centroid average,and RSSI PSO algorithm.In order to further improve the accuracy of RSSI positioning,a sparrow search optimized BP neural network based RSSI node positioning algorithm(SSA-LM-BP)is proposed to address the environmental impact of RSSI positioning.This algorithm uses a fixed width sliding window to optimize the signal strength value to reduce the impact of signal fluctuations on measurement,and then utilizes the powerful function approximation ability of BP neural network to locate,For the blindness in selecting the initial parameters of the BP neural network,Sparrow Search Algorithm(SSA)is introduced to find the initial weights and thresholds.Secondly,Levenberg Marquardt(LM)algorithm is used to train the neural network by constantly modifying the weights and thresholds with second derivative to improve the convergence speed of the neural network.Finally,simulation experiments show that the proposed algorithm is compared with BP algorithm,and SA-BP algorithm,and traditional RSSI algorithm,The normalized average positioning error decreased by 69.18%,38%,and25.93%,respectively.