Design and Empirical Validation of a LoRaWAN IoT Smart Irrigation System

[Abstract] In some parts of the world, climate change has led to periods of drought that require managing efficiently the scarce water and energy resources. This paper proposes an IoT smart irrigation system specifically designed for urban areas where remote IoT devices have no direct access to the Internet or to the electrical grid, and where wireless communications are difficult due to the existence of long distances and multiple obstacles. To tackle such issues, this paper proposes a LoRaWAN-based architecture that provides long distance and communications with reduced power consumption. Specifically, the proposed system consists of IoT nodes that collect sensor data and send them to local fog computing nodes or to a remote cloud, which determine an irrigation schedule that considers factors such as the weather forecast or the moist detected by nearby nodes. It is essential to deploy the IoT nodes in locations within the provided coverage range and that guarantee good speed rates and reduced energy consumption. Due to this reason, this paper describes the use of an in-house 3D-ray launching radio-planning tool to determine the best locations for IoT nodes on a real medium-scale scenario (a university campus) that was modeled with precision, including obstacles such as buildings, vegetation, or vehicles. The obtained simulation results were compared with empirical measurements to assess the operating conditions and the radio planning tool accuracy. Thus, it is possible to optimize the wireless network topology and the overall performance of the network in terms of coverage, cost, and energy consumption.This work was funded by Xunta de Galicia (ED431C 2016-045, ED431G/01) and Ministerio de Ciencia, Innovación y Universidades, Gobierno de España (MCIU/AEI/FEDER,UE), Project RTI2018-095499-B-C31Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED431G/0

A 3-D indoor analysis of path loss modeling using kriging techniques

This study proposes a novel measurement-based method to predict and model three-dimensional (3-D) path loss in indoor scenarios, which first regresses 28 GHz measurements via median path loss modeling and then includes ordinary Kriging to interpolate shadowing. The performance of this method is evaluated by investigating the spatial structure that follows shadowing through the semivariogram, covariance function, and correlogram as variography tools. It is shown that semivariogram outperforms the other statistics to describe shadowing spatial continuity in path loss modeling in terms of the mean absolute error.This work was supported by the National Council of Science and Technology CONACYT, through the student scholarship number 746015, under Project RTI2018-095499-B-C31, funded by the Ministerio de Ciencia, Innovación y Universidades, Gobierno de España (MCIU/AEI/FEDER, UE)