Enriching IoT Modules with Edge AI Functionality to Detect Water Misuse Events in a Decentralized Manner

Abstract

The digital transformation of agriculture is a promising necessity for tackling the increasing nutritional needs of the population on Earth and the degradation of natural resources. Focusing on the “hot” area of natural resource preservation, the recent appearance of more efficient and cheaper microcontrollers, the advances in low-power and long-range radios, and the availability of accompanying software tools are exploited in order to monitor water consumption and to detect and report misuse events, with reduced power and network bandwidth requirements. Quite often, large quantities of water are wasted for a variety of reasons; from broken irrigation pipes to people’s negligence. To tackle this problem, the necessary design and implementation details are highlighted for an experimental water usage reporting system that exhibits Edge Artificial Intelligence (Edge AI) functionality. By combining modern technologies, such as Internet of Things (IoT), Edge Computing (EC) and Machine Learning (ML), the deployment of a compact automated detection mechanism can be easier than before, while the information that has to travel from the edges of the network to the cloud and thus the corresponding energy footprint are drastically reduced. In parallel, characteristic implementation challenges are discussed, and a first set of corresponding evaluation results is presented.

Adding Machine-Learning Functionality to Real Equipment for Water Preservation: An Evaluation Case Study in Higher Education

“…More specifically, students verified the functionality of the two systems by creating water misuse events (Figure 9a) via repositioning the valves of the pumping system (Figure 9b), and monitoring the corresponding results on their mobile phone screens. The recorded results indicated that 9 over 10 predictions were correct, which is in line with the performance of each machine-learning system, as explained in [29,30].…”

“…To that end, this paper takes into consideration the material provided by two studies that use machine learning techniques for developing detection systems in order to address typical irrigation network problems. The first one introduces a water-misuse alert system [29], while the second one utilizes a classification model to detect water pump malfunctions in agricultural premises [30]. This article, except from providing a brief technical overview, is trying to explain how the latter systems can be transformed into effective educational instruments, suitable for serving the priorities of an agricultural engineering laboratory.…”

“…In more detail, the first step of this research, in the direction of the on-device intelligence deployment technique [28], was the development of a classification model executed on a microcontroller attached to a commercial faucet along with a flow sensor so as to determine water-consumption profiles and alert the user about them [29]. To further benefit from this deployment technique, a machine-learning model was developed for classifying and diagnosing possible motor defects in a water pump, using vibration data from an accelerometer to achieve a more comprehensive and precise perspective on the factors influencing the system [30].…”

Technologies to Optimize the Water Consumption in Agriculture: A Systematic Review

“…The designed control was a closed-loop system and was based on data monitored by IoT sensors in the field that evaluated soil moisture and temperature. Furthermore, the authors of [5][6][7] evaluated systems that were aimed at managing irrigation systems by exploiting IoT technologies and machine learning algorithms.…”

“…For example, soil-less crops that can grow vertically indoors close to urban centers have been developed as an effective solution to this challenge. In hydroponic systems, plants are grown out of the soil with their roots in the air, placed inside an inert substrate, or perpetually immersed in a nutrient solution [4] Several researchers have implemented systems with the aim of managing irrigation systems using IoT technologies and monitoring endogenous and exogenous crop factors, and machine learning algorithms were used to optimize the systems obtained [5][6][7].…”