Self-tuning Parameters Of Wireless Sensor Networks RSSI Localization Algorithm

Wireless sensor networks (Sensor Networks Wireless, WSNs) is a large number of micro sensor nodes with communication and computing capabilities, which are connected by a wireless network; For many applications of wireless sensor networks and network services, It is essential to combine the perceived information with the location of the corresponding nodes. For example, in the military, aviation, anti-terrorism, explosion, disaster relief and other scenarios in the application. Therefore, the unknown location of sensor nodes sensing data information is not significant. The measurement accuracy of the distance between the nodes directly affects the accuracy and reliability of the wireless sensor network localization algorithm. In wireless sensor networks, the method of measuring the distance between nodes is mainly TOA(Time of Arrival)、TDOA(Time Difference of Arrival)、RSSI(Received Signal Strength Indicator)、TOF(Time of Light) and so on. In this paper, through the study of the correlation characteristics of the RSSI location algorithm and the experimental data of a large number of simulation data analysis, A method based on RSSI positioning technology and data clustering analysis is proposed. The solution calculation is based on the calibration parameters into the RSSI positioning algorithm, which solves the problem that the positioning accuracy is not high and the error of the single RSSI algorithm is not high. The MATLAB simulation results show that the proposed scheme can effectively reduce the positioning error and improve the positioning accuracy of the RSSI algorithm.In this paper, based on the latest research progress of Wireless Sensor Networks. This paper summarizes the research status of wireless sensor network localization algorithm based on distance measurement,and summarizes the advantages and disadvantages of the algorithm based on ranging and non ranging in wireless sensor networks are analyzed. Secondly, according to the structure and characteristics of Wireless Sensor Networks, this paper focuses on the problems existing in the practical application of the location algorithm of RSSI. and puts forward a through a self correction parameter into the location information of sensor nodes in the network to the calculation to reduce based on the ranging error of RSSI location algorithm and improve the ranging accuracy. Therefore, the use of data mining in a very active research field:cluster analysis produced in the process of the experiment group a large number of experimental data for data analysis, solving the same node location error set optimal solution error parameters, which is defined as self correction parameters and their integration into the positioning algorithm, improve the positioning accuracy of the RSSI localization algorithm based.This paper introduces two kinds of schemes for the simulation data of the experiment: (1) K-means data analysis based on RSSI localization algorithm; (2) the data analysis based on RSSI localization algorithm. The two scheme calculates the error value of the stability, and is defined as the self correction parameter into the RSSI based positioning algorithm, so as to improve the positioning accuracy of RSSI positioning algorithm. Finally, the self correction parameter into localization algorithm in the simulation experiment and and single non fusion tuning parameters of RSSI localization and DV hop non fusion localization algorithm for parameter correction positioning error and precision are compared. The simulation results show that:fusion positioning error correction parameters of single RSSI localization algorithm was significantly less than that under the same conditions without fusion localization algorithm for parameter correction, at the same time, the fusion correction positioning error of parameter localization algorithm has higher and more stable convergence.