Performance evaluation of localization techniques in wireless sensor networks using RSSI and LQI

Low-cost precise localization is crucial for wireless sensor networks. RSSI based localization is cost effective when compared to TOA, AOA, TDOA, ultrasonic and acoustic localization as it does not require any extra hardware, power or bandwidth. The radio of sensor nodes provides information about both the RSSI and LQI of a received radio signal. Localization error can be decreased by simultaneously observing both RSSI and LQI. We propose two novel techniques for localizing a target node using RSSI+LQI. They are Recursive Bayesian-RSSI-LQI (RB-RSSI-LQI) and Maximum a posteriori-RSSI-LQI (MAP-RSSI-LQI). A comparison between these techniques is done with the existing Mean-RSSI technique. We show that MAP-RSSI-LQI gives the best results in terms of localization error and computational complexity. The root mean square error of the RB-RSSI-LQI is 53.35% less than Mean-RSSI in case of stationary target node. The root mean square error of MAP-RSSI-LQI is 52.25% and 58.88% less than Mean-RSSI in case of stationary and mobile target nodes. A combination of simulation and experimental evaluation is used to develop and validate the proposed techniques.