A Wiener-based RSSI localization algorithm exploiting modulation diversity in LoRa networks

Modern wireless sensor networks (WSNs) for Internet of Things (IoT) applications require low-complexity algorithms for positioning, due to the large number of nodes with low power consumption. Thus, simple received signal strength indicator (RSSI) based ranging techniques represent an attractive option for low power systems such as LoRa ones. However, interactions of the RSSI with a real-world environment are difficult to predict and often lead to significant errors in the localization process. Based on this, a novel algorithm is proposed to improve the RSSI ranging output by a Wiener-based method. According to the free-space path loss model, the distance is expressed as an exponential function of the collected RSSI measures, considering, during the training and calibration phase, the channel model information, i.e., the received power at 1 m and the loss exponent. The presented algorithm minimizes the distance logarithm error, instead of interpolating the free-space path loss model as in common solutions, resulting in a more precise ranging and positioning. Moreover, we also study the possibility to suitably combine the diversity information coming from the LoRa physical layer modes, corresponding to different data rates and bandwidths. The performance of the proposed algorithm is evaluated by using both simulated and real experimental data sets, proving the effectiveness of the presented solution compared to existing RSSI-based methods.