Design, Implementation, and Empirical Validation of an IoT Smart Irrigation System for Fog Computing Applications Based on LoRa and LoRaWAN Sensor Nodes

Abstract

Climate change is driving new solutions to manage water more efficiently. Such solutions involve the development of smart irrigation systems where Internet of Things (IoT) nodes are deployed throughout large areas. In addition, in the mentioned areas, wireless communications can be difficult due to the presence of obstacles and metallic objects that block electromagnetic wave propagation totally or partially. This article details the development of a smart irrigation system able to cover large urban areas thanks to the use of Low-Power Wide-Area Network (LPWAN) sensor nodes based on LoRa and LoRaWAN. IoT nodes collect soil temperature/moisture and air temperature data, and control water supply autonomously, either by making use of fog computing gateways or by relying on remote commands sent from a cloud. Since the selection of IoT node and gateway locations is essential to have good connectivity and to reduce energy consumption, this article uses an in-house 3D-ray launching radio-planning tool to determine the best locations in real scenarios. Specifically, this paper provides details on the modeling of a university campus, which includes elements like buildings, roads, green areas, or vehicles. In such a scenario, simulations and empirical measurements were performed for two different testbeds: a LoRaWAN testbed that operates at 868 MHz and a testbed based on LoRa with 433 MHz transceivers. All the measurements agree with the simulation results, showing the impact of shadowing effects and material features (e.g., permittivity, conductivity) in the electromagnetic propagation of near-ground and underground LoRaWAN communications. Higher RF power levels are observed for 433 MHz due to the higher transmitted power level and the lower radio propagation losses, and even in the worst gateway location, the received power level is higher than the sensitivity threshold (−148 dBm). Regarding water consumption, the provided estimations indicate that the proposed smart irrigation system is able to reduce roughly 23% of the amount of used water just by considering weather forecasts. The obtained results provide useful guidelines for future smart irrigation developers and show the radio planning tool accuracy, which allows for optimizing the sensor network topology and the overall performance of the network in terms of coverage, cost, and energy consumption.

Deployment Strategies of Soil Monitoring WSN for Precision Agriculture Irrigation Scheduling in Rural Areas

“…The authors stress the importance of performing preliminary tests before designing and deploying the network. A smart irrigation system for urban areas that utilized LoRa was presented by Iván Froiz-Míguez et al [ 32 ]. Air temperature and soil moisture and temperature were monitored to determine water requirements.…”

Six-Gear Roadmap towards the Smart Factory

“…Network infrastructure scaling can be achieved by enhancing the factory floor communication, network capability and performance by using emerging wireless communication technologies such as 5G for rapid and reliable communication, Wi-Fi, LTE, ZigBee, LoRa and LoRaWAN for cost effective and rapid deployment. Wireless Sensor Networks (WSNs) systems that deploy wireless communication technologies and IoT connectivity architecture have been widely applied in various applications such as in smart cities [96], in agriculture and environment monitoring [97][98][99], in transportation [100] and in food manufacturing [101]. These applications can be transferred and widely adapted in manufacturing to enable further data-driven smart manufacturing applications.…”

Sustainable Irrigation System for Farming Supported by Machine Learning and Real-Time Sensor Data

“…In [35], soil moisture sensors and evapotranspiration prediction were used to create a better irrigation scheduling, achieving a 39% reduction in water consumption. In [36] presents a solution capable of reducing 23% of water in irrigation using LoRa nodes that gather information about air temperature and humidity and soil moisture, that were analysed using fog computing. The Smart&Green framework, presented in [33], recommends the optimal irrigation management-based field configuration and soil moisture data, using computational models to reduce the water used in irrigation in 56%.…”