# IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management
## 1 Introduction  
The integration of the Internet of Things (IoT) within the agriculture sector has advanced significantly, as evidenced by its incorporation into various commercial applications such as monitoring weather conditions, soil moisture, temperature, fertility, and crop growth, as well as weed and pest detection, animal intrusion, irrigation control, and supply chain and food waste management. By deploying IoT sensors, farmers can reduce human effort and increase operational efficiency in monitoring their farms. Furthermore, sensorsuse communication channels to transmit the obtained status information into consolidated, scalable data repositories [1]. The application of data-processing algorithms on collected data can create opportunities for the development of further innovations and data-driven services, such as decision-making applications that can be combined with IoT actuators to act on physical objects (e.g., turning on a water pump) with minimal or no farmer intervention. However, sensor integration into industrial-scale frameworks requires a large volume of constrained sensor devices, resulting in a self-contained network known as a wireless sensor network (WSN) [2; 3; 4; 5; 6; 7]. Despite the existence of these technologies and services, they are not always deployed. In WSNs, four constrained elements are used to organize the internal structure of any sensor device: (1) sensing element (such as a horse heartbeat signal sensor); (2) limited computation power (e.g., main memory and central processing unit (CPU)); (3) short-distance, limited-bandwidth radio transceiver; and (4) limited battery power. These constraints make it challenging to integrate such a sensor network into the agriculture sector, in terms of meeting the scalability and performance requirements of the harsh environments of agricultural farms [8; 9; 10]. In this study, the 6 LowPAN and Routing protocol for low-power and lossy networks (RPL) were considered in assessing the performance of the WSN. The evaluation results of the RPL protocol in the two farming scenarios were modeled and simulated using COOJA, a realistic WSN simulator. The first scenario represents an olive tree farm with an integrated WSN that uses fixed-position sensor nodes and settings, while the other scenario represents a horse training stable, with each horse equipped with a sensor node representing the WSN in a mobile case. This approach helps ensure the ability to capture the impact of mobility in both scenarios and provides a realistic comparison of networking performance indices by sharing the simulation settings for both fixed and mobile nodes, with the main difference being node mobility. This paper characterized different aspects of the performance requirements in the two farming scenarios by comparing: (1) average power consumption; (2) average radio duty cycle; and (3) connectivity degrees of the sensor network graph. A new approach was proposed to model and simulate moving animals within the COOJA simulator, adopting the random waypoint model (RWP) to represent horse movements. The aims of this study are twofold: to assess the performance scalability of WSN in the agriculture sector and to provide a classification framework for studying IoT in the agriculture sector. Several performance metrics, such as network graph connectivity and power consumption, were evaluated. Furthermore, the stationary configuration and mobility covered both animal and plant farms. The main contributions of this study can be summarized as follows: (1) introducing performance assessment metrics for stationary and mobile scenarios in 6 LowPAN networks; (2) introducing a novel holistic IoT ecosystem suitable for the precision agriculture domain that satisfies the requirements for both studied scenarios; (3) introducing a new classification approach of IoT in agriculture based on several factors.