Mobile User Localization Based on Wireless Sensor Network Signals in Smart Cities

Mobile user localization techniques in the smart city by wireless sensor networks (WSNs) can be achieved based on the received signal strength indication (RSSI). In order to examine the ability of WSNs to localize the position of a static and moving handset based on its RF transmission, we simulated the propagation of radio waves for unattended transceiver nodes in an urban environment by using ray tracing. In this work we investigate the performance of two types of localization and tracking algorithms for a mobile user (handset), weighted mean and extended Kalman filter (EKF), with static and dynamic prediction models. The performance of the proposed algorithms is investigated for several maneuvering trajectories, in a simulated urban environment with different building densities. The results show that the EKF algorithm with a dynamic prediction model presents the lowest position estimation error.