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.

In 1919, Eddington and Dyson led two famous expeditions to measure the bending of light during a total solar eclipse. The results of this effort led to the first experimental confirmation of Einstein’s General Relativity and contributed to create its unique and enduring fame. Since then, similar experiments have been carried out all around the world, confirming the predictions of the General Relativity. Later, developments in radio interferometry provided a more accurate way to measure the gravitation deflection. We believe that—after more than a century—starlight deflection caused by the Sun’s gravity still represents a simple and intuitive way to introduce high-school students to General Relativity and its effects. To this aim, we gathered measurements taken during eight eclipses spanning from 1919 to 2017, and we created a single dataset of homogeneous values. Together with the whole dataset, this article provides a blueprint for a possible group activity for students, useful to introduce the theory in physics classes with a playful approach.