Indoor localization and tracking based on received signal strength in wireless sensor networks

Following the success of Global Positioning System (GPS) in outdoor localization, the attention has shifted to indoor localization and its applications. Due to the very different wave propagation effects indoors, outdoor localization techniques are unable to obtain accurate results indoors. This project explores the feasibility and limitations of several localization techniques based on received signal strength in an indoor environment with the use of Wireless Sensor Network (WSN). The three received signal strength based localization techniques implemented are trilateration, neural network and minimum root mean squared error (RMSE) estimation. The experiments are conducted in a static indoor environment with and without stationary obstacles. The experimental results show that minimum RMSE estimation produces the most accurate result for both scenarios among the three localization techniques with the loss of some precision.Bachelor of Engineerin