Smart and Efficient IoT-Based Irrigation System Design: Utilizing a Hybrid Agent-Based and System Dynamics Approach

• URL: http://arxiv.org/abs/2502.18298v1
• Date: Tue, 25 Feb 2025 15:34:38 GMT
• Title: Smart and Efficient IoT-Based Irrigation System Design: Utilizing a Hybrid Agent-Based and System Dynamics Approach
• Authors: Taha Ahmadi Pargo, Mohsen Akbarpour Shirazi, Dawud Fadai,
• Abstract summary: There is a shortage of available water resources for irrigation in arid and semi-arid countries.<n>It is possible to utilize modern technologies to control irrigation and reduce water loss.<n>Despite the possibility of using the IoT in irrigation control systems, there are complexities in designing such systems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Regarding problems like reduced precipitation and an increase in population, water resource scarcity has become one of the most critical problems in modern-day societies, as a consequence, there is a shortage of available water resources for irrigation in arid and semi-arid countries. On the other hand, it is possible to utilize modern technologies to control irrigation and reduce water loss. One of these technologies is the Internet of Things (IoT). Despite the possibility of using the IoT in irrigation control systems, there are complexities in designing such systems. Considering this issue, it is possible to use agent-oriented software engineering (AOSE) methodologies to design complex cyber-physical systems such as IoT-based systems. In this research, a smart irrigation system is designed based on Prometheus AOSE methodology, to reduce water loss by maintaining soil moisture in a suitable interval. The designed system comprises sensors, a central agent, and irrigation nodes. These agents follow defined rules to maintain soil moisture at a desired level cooperatively. For system simulation, a hybrid agent-based and system dynamics model was designed. In this hybrid model, soil moisture dynamics were modeled based on the system dynamics approach. The proposed model, was implemented in AnyLogic computer simulation software. Utilizing the simulation model, irrigation rules were examined. The system's functionality in automatic irrigation mode was tested based on a 256-run, fractional factorial design, and the effects of important factors such as soil properties on total irrigated water and total operation time were analyzed. Based on the tests, the system consistently irrigated nearly optimal water amounts in all tests. Moreover, the results were also used to minimize the system's energy consumption by reducing the system's operational time.

Related papers

• AI-Enhanced Automatic Design of Efficient Underwater Gliders [60.45821679800442]
  Building an automated design framework is challenging due to the complexities of representing glider shapes and the high computational costs associated with modeling complex solid-fluid interactions. We introduce an AI-enhanced automated computational framework designed to overcome these limitations by enabling the creation of underwater robots with non-trivial hull shapes. Our approach involves an algorithm that co-optimizes both shape and control signals, utilizing a reduced-order geometry representation and a differentiable neural-network-based fluid surrogate model.
  arXiv  Detail & Related papers  (2025-04-30T23:55:44Z)
• Hierarchical Conditional Multi-Task Learning for Streamflow Modeling [24.040194424037065]
  Hierarchical Conditional Multi-Task Learning (HCMTL) is a hierarchical approach that jointly models soil water and snowpack processes based on causal connections to streamflow. HCMTL's superior performance across hundreds of drainage basins over extended periods shows that integrating domain-specific causal knowledge into deep learning enhances both prediction accuracy and interpretability.
  arXiv  Detail & Related papers  (2024-10-18T03:14:57Z)
• Towards an Autonomous Surface Vehicle Prototype for Artificial Intelligence Applications of Water Quality Monitoring [68.41400824104953]
  This paper presents a vehicle prototype that addresses the use of Artificial Intelligence algorithms and enhanced sensing techniques for water quality monitoring. The vehicle is fully equipped with high-quality sensors to measure water quality parameters and water depth. By means of a stereo-camera, it also can detect and locate macro-plastics in real environments.
  arXiv  Detail & Related papers  (2024-10-08T10:35:32Z)
• SLIDE: A machine-learning based method for forced dynamic response estimation of multibody systems [42.87502453001109]
  SLiding-window Initially-truncated Dynamic-response Estimator (SLIDE) Deep learning-based method designed to estimate output sequences of mechanical or multibody systems. Results demonstrate significant speedups from the simulation up to several millions, exceeding real-time performance substantially.
  arXiv  Detail & Related papers  (2024-09-26T20:34:07Z)
• TRG-Net: An Interpretable and Controllable Rain Generator [61.2760968459789]
  This study proposes a novel deep learning based rain generator, which fully takes the physical generation mechanism underlying rains into consideration. Its significance lies in that the generator not only elaborately design essential elements of the rain to simulate expected rains, but also finely adapt to complicated and diverse practical rainy images. Our unpaired generation experiments demonstrate that the rain generated by the proposed rain generator is not only of higher quality, but also more effective for deraining and downstream tasks.
  arXiv  Detail & Related papers  (2024-03-15T03:27:39Z)
• Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
  Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems. This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
  arXiv  Detail & Related papers  (2023-08-14T16:48:57Z)
• AquaFeL-PSO: A Monitoring System for Water Resources using Autonomous Surface Vehicles based on Multimodal PSO and Federated Learning [0.0]
  The preservation, monitoring, and control of water resources has been a major challenge in recent decades. This paper proposes a water monitoring system using autonomous surface vehicles, equipped with water quality sensors.
  arXiv  Detail & Related papers  (2022-11-28T10:56:12Z)
• IoT based Smart Water Quality Prediction for Biofloc Aquaculture [1.820324411024166]
  Biofloc technology in aquaculture transforms the manual into an advanced system that allows the reuse of unused feed by converting them into microbial protein. The article presented a system that collects data using sensors, analyzes them using a machine learning model, generates decisions with the help of Artificial Intelligence (AI) and sends notifications to the user.
  arXiv  Detail & Related papers  (2022-07-27T03:00:48Z)
• Underwater Acoustic Networks for Security Risk Assessment in Public Drinking Water Reservoirs [5.227907960942717]
  We implement an innovative AI-based approach to detect, classify and localize underwater events. We discuss the challenges of installing and using the hydrophone network in a water reservoir.
  arXiv  Detail & Related papers  (2021-07-29T14:02:51Z)
• Autonomous Optimization of Fluid Systems at Varying Length Scales [55.41644538483948]
  We propose a computer vision-driven Bayesian optimization framework to discover the precise hardware conditions that generate uniform droplets. This framework is validated on two fluid systems, at the micrometer and millimeter length scales, using microfluidic and inkjet systems.
  arXiv  Detail & Related papers  (2021-05-28T02:08:03Z)
• Learning Dependencies in Distributed Cloud Applications to Identify and Localize Anomalies [58.88325379746632]
  We present Arvalus and its variant D-Arvalus, a neural graph transformation method that models system components as nodes and their dependencies as edges to improve the identification and localization of anomalies. Given a series of metric, our method predicts the most likely system state - either normal or an anomaly class - and performs localization when an anomaly is detected. The evaluation shows the generally good prediction performance of Arvalus and reveals the advantage of D-Arvalus which incorporates information about system component dependencies.
  arXiv  Detail & Related papers  (2021-03-09T06:34:05Z)
• IoT and Neural Network-Based Water Pumping Control System For Smart Irrigation [0.0]
  This article aims at saving the wasted water in the process of irrigation using the Internet of Things (IoT) based on a set of sensors and Multi-Layer Perceptron (MLP) neural network. The developed system handles the sensor data using the Arduino board to control the water pump automatically.
  arXiv  Detail & Related papers  (2020-05-08T16:51:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.