# IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management
## 2 Related Works
The experiments adopted a random waypoint model to simulate the movement of the horses in the second scenario. Our evaluation measures included average power consumption, radio duty cycle, and sensor network connectivity levels. The paper proposed a new approach for simulating moving animals using the COOJA simulator. Despite the limitations imposed by the constrained nature of WSNs, extending their lifespan remains a critical challenge. Various techniques, such as power optimization algorithms, low-power communications, and reactive sensor networks, have been proposed to address this issue [34; 35; 36; 37]. However, studies that compare the performance of WSNs in fixed and mobile deployment scenarios in the agriculture sector are limited. Furthermore, the implementation of IoT technologies such as WSNs in the agriculture industry has been slower compared to other domains, indicating a need for further research in this area to promote wider and faster diffusion of IoT in the sector [38; 39; 40; 41; 42]. IoT integration within the agriculture sector has matured, as evidenced by its extension into several commercial applications. There has been a growing interest in understanding the best approaches for integrating IoT solutions within the agricultural sector, which can be broadly categorized into three stages: (1) vertical studies that examine the best practices and technical guidelines for using IoT-enabling technologies such as wireless sensor networks (WSNs); (2) horizontal studies that investigate approaches to bridge gaps among the vertical studies; and (3) business-driven studies that focus on integrating the entire chain of agriculture. This study examined the performance of wireless sensor networks (WSN) in two different agricultural scenarios: olive tree farms with fixed-position sensor nodes and a horse training stable with mobile sensor nodes. Our proposed system builds upon these existing technologies by incorporating machine learning and data analysis techniques to provide farmers with real-time recommendations on crop management practices. By analyzing data from various sources, including remote sensing [43] and ground-based sensors [44], our system can provide farmers with a comprehensive view of their fields and make recommendations tailored to their specific needs. Furthermore, our system aims to be user-friendly and accessible, requiring minimal technical knowledge to operate. By providing easy-to-understand recommendations through a user-friendly interface, we aim to make precision agriculture more accessible to a wider range of farmers. In conclusion, our proposed system builds upon the strengths of existing precision agriculture systems while addressing their limitations, with the ultimate goal of improving efficiency, reducing costs, and increasing crop yields for farmers. The state-of-the-art precision agriculture systems involve the use of advanced technologies such as sensors, GPS, robotics, and data analytics to optimize crop production and reduce costs. Remote sensing and ground-based sensor systems are commonly used to monitor crop health, soil moisture, and other environmental factors. Variable rate application systems use this data to adjust inputs such as fertilizers, pesticides, and water according to the specific needs of different parts of a field. Autonomous agricultural equipment, such as drones and robots, are also being developed and used to automate planting, harvesting, and other tasks. In addition to these technologies, precision agriculture systems are also integrating machine learning and AI algorithms to analyze large volumes of data and make predictions about crop health and yield. This allows farmers to make data-driven decisions and optimize their operations for maximum efficiency and profitability. The research problems in precision agriculture systems primarily involve developing more accurate and reliable sensors, improving data analytics and modeling techniques, and addressing issues related to data privacy and security.