English title

Wireless sensor networks consist of a set of sensor nodes that communicate with each other via radio waves. In many applications, obtaining sensor data is crucial for determining the target source's location. Various localization algorithms have been developed based on the characteristics of the received signal. In this paper, a localization algorithm based on received signal strength (RSS) using a log-normal model is proposed. In this model, the path loss exponent is considered a random variable with a uniform distribution. To enhance localization accuracy, a cost function is defined, and the steepest descent method is employed to minimize the error. The proposed algorithm is simulated in different scenarios with four, eight, and twelve antennas. The results demonstrate that the proposed method accurately estimates the source position while maintaining low computational complexity. Moreover, increasing the number of antennas improves localization accuracy. Simulation results show that under the condition of using 8 sensors and a signal-to-noise ratio (SNR) of 10 dB, the root mean square error (RMSE) is reduced by 50% compared to the closest competing method, namely the semidefinite programming (SDP) approach. Furthermore, by increasing the number of sensors from 8 to 12 (with the same SNR), the RMSE decreases from 0.25 meters to 0.20 meters, indicating an improvement of approximately 20% in localization accuracy.