Politicians and the general public are concerned about climate change, water scarcity, and the constant reduction in agricultural land. Water reserves are scarce in many regions in the world, negatively affecting agricultural productivity, which makes it a necessity to introduce sustainable water resource management. Nowadays, there is a number of commercial IoT systems for irrigation scheduling, helping farmers to manage and save water. However, these systems focus on using the available fresh water sources, without being able to manage alternative water sources. In this study, an Arduino-based low-cost IoT system for automated irrigation scheduling is developed and implemented, which can provide measurements of water parameters with high precision using low-cost sensors. The system used weather station data combined with the FAO56 model for computing the water requirements for various crops, and it was capable of handling and monitoring different water streams by supervising their quality and quantity. The developed IoT system was tested in several field trials, to evaluate its capabilities and functionalities, including the sensors’ accuracy, its autonomous controlling and operation, and its power consumption. The results of this study show that the system worked efficiently on the management and monitoring of different types of water sources (rainwater, groundwater, seawater, and wastewater) and on automating the irrigation scheduling. In addition, it was proved that the system is can be used for long periods of time without any power source, making it ideal for using it on annual crops.

Wireless sensor networks (WSN) have found wide applications in many fields (such as agriculture) over last few years, and research interest is constantly increasing. However, power supply to the sensor nodes remains an issue to be resolved. Batteries are usually used to power the sensor nodes, but they have a limited lifetime, so solar energy harvesters are a good alternative solution. This study provides a comparative analysis between battery and solar energy harvesters for sensor nodes used for soil water monitoring. Experimental results show that small-sized solar panels with low-power energy harvester circuits and rechargeable batteries distinctly outperform secondary batteries in outdoor and continuous-operation applications. The power level of the energy storage device of sensor node 1, which was powered by a small PV panel, remained constantly close to 90% for all days. The power of the other three nodes, which were powered by a rechargeable battery, was initially at 100% of the charge and gradually started to reduce. Sensor node 1 performed a total of 1288 activations during the experimental period, while sensor nodes 2 and 4 behaved satisfactorily and performed a total of 781 and 803 activations, respectively. In contrast, sensor node 3 did not exhibit the same behavior throughout the experiments.

The present study aims to design a low-cost smart AI-powered node to serve as a flood Early Warning System with a complete solution. The node is designed to predict forthcoming flood events by capturing and combining critical data related to such phenomena. Such data are the water levels at rivers or other water discharge basins, rainfall, soil moisture, and material displacement at river slopes. The node will autonomously monitor the above quantities at a high frequency rate and selectively upload them to a server only when verified conditions for a forthcoming flood will exist. These conditions will be evaluated by the local ML model. This will allow each node to reliably predict flood events and issue local and remote alarms. The combination of several nodes at an area of interest will form a robust and reliable Early Warning System.

Among all natural disasters, river floods are becoming increasingly frequent. They present high risk and their impact can be fairly destructive and of strong economic, health, and social importance. Key tools to avoid their catastrophic results are the Early Warning Systems (EWS). An EWS usually monitors various physical quantities through a specific hardware, and produce data which after certain processing can detect and estimate the level of the risk.In the current work we present the concept, the design, the application, and some preliminary data regarding a low cost imaging node, part of an EWS aimed for river floods. This EWS consists of various sensing nodes which are mainly equipped with water presence detectors, water level meters, water temperature sensors, along with the necessary networking capability. The novelty of this new node design is that it utilizes a VGA resolution camera which captures still images of a view of interest. The latter can be for example an implementation prone to defects in case of flood, such as a river basin level road crossing, or a bridge. The images can also provide constant monitoring of the river basin state, i.e. to detect the presence of any unwanted objects (waste or other natural & artificial bring materials). Through image processing the images can even provide some coarse data, i.e. water level measurements by utilizing vertical stripped rods within the field of view of the camera.The ability to have a camera usually counteracts the IoT characteristics of an electronic device. Nevertheless, in this design the IoT character of the node was not constrained. The nodes have extended power autonomy (several months via Li-Ion battery, optionally solar rechargeable), present a small size, each node is network independent using GSM and LoRaWAN technology. The data usage is minimized by uploading only 2 QVGA images per day in normal operation (can be increased to a maximum of 48 VGA images per day, if required). In case of risk detection the node also supports the actuation of a local warning sign.

River floods occupy a respectable percentage among all natural disasters, are presenting high risk, and usually cause great damage. Important tools in managing and preventing river floods are the Early Warning Systems (EWS), which are usually consisted both by a hardware infrastructure (sensors, communication network) and a relevant software infrastructure (data logging, signal processing, modeling, risk detection).In the current work we are presenting preliminary results from a novel, low-cost and low-power hardware system, part of a EWS aimed for river floods. The system consists of multiple sensing nodes, each to be strategically positioned at certain points along the route of river Evros, Greece. Each sensing node will bear a low-cost and high-quality ultrasonic water level sensor, along with an embedded microcomputer to control its functionality. An additional novelty of the proposed work is the design and utilization of a private low-power wide-area wireless network (LPWAN), taking advantage of IoT technologies and especially the LoRaWAN implementation. This way, the proposed system will have even lower power demands, together with greater expandability by allowing many nodes to be simultaneously connected and measuring, and having the ability to utilize crowd-sensing techniques. The power supply is battery based and autonomously recharged with the aid of small solar panel. Each node will measure the water level of the river, and upload the data to a cloud server at variable time intervals, depending on the actual water level variation and the system’s power consumption optimization.Future upgrades of the system will involve extra sensors, allowing the nodes to measure water quality parameters i.e. suspended solids, pH, etc. Although of secondary importance, these parameters might prove to be important in the development of the risk detection and alarm issuing algorithms.