# IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management
## 4 Experiments and Simulation
### Experiment Mobility Model for Both Scenarios  
In the first set of experiments conducted at the olive tree farm, experiments deployed a wireless sensor network consisting of 36 static nodes, each representing a tree equipped with a sensor board. The nodes were distributed on a grid and strategically placed within different squared areas of 25 m \(\times\) 25 m, 50 m \(\times\) 50 m, 75 m \(\times\) 75 m, and 100 m \(\times\) 100 m. The sink node, which serves as the central point for data collection and transmission, was located at the center of the simulation area. This configuration allowed us to evaluate the network performance and assess the impact of node density and area coverage on the overall system performance. The density and coverage of nodes in a wireless sensor network (WSN) can have a significant impact on the overall system performance. A higher node density results in a more extensive coverage area, which can improve the accuracy of data collection and increase the chances of successful data transmission. However, a higher node density also increases the number of nodes that need to be maintained and managed, which can increase the complexity of the system and reduce overall performance. Additionally, a higher node density can lead to increased interference between nodes, which can negatively impact the system's communication performance. On the other hand, a lower node density can result in lower coverage areas, which can reduce the accuracy of data collection and decrease the chances of successful data transmission. Therefore, the optimal node density and coverage will depend on the specific requirements of the application and must be carefully considered during the design and deployment of the WSN. In the second set of experiments, the focus is on mobile horse farms and the impact of node density and area coverage on the overall system performance. The experiments are conducted using the same grid-based areas as in the first set, with dimensions of 25 m \(\times\) 25 m, 50 m \(\times\) 50 m, 75 m \(\times\) 75 m, and 100 m \(\times\) 100 m. However, in this scenario, 36 nodes representing sensor boards mounted on horses are used, and the nodes are made \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **Data Type** & **Size of Data in Bits** \\ \hline Speed rate & Float & 32 \\ Air flow sensor & Float & 32 \\ Body temperature sensor & Double & 64 \\ Heart rate sensor & Float & 32 \\ Indicate colic & Boolean & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sensors Platforms and Sensors Data Parameters. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of nodes used & 31 \\ Type of Routing & IPv6 \\ Standard used & 6 lowPan IEEE 802.15.4 \\ Scenario area & (25 \(\times\) 25), (50 \(\times\) 50), (75 \(\times\) 75), and (100 \(\times\) 100) \\ Type of transport protocol & UDP \\ Transmission range and interface range & 20, 40 \\ Transmission ratio and receiver ratio & 80, 100 \\ Mobility model & Random waypoint \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sensors Platforms and Simulation Settings. to move based on the random waypoint model (RWP) [58]. The sink node remains static and is located at the center of the simulation area. The random waypoint model (RWP) is a commonly used mobility model in wireless sensor networks (WSN) and mobile ad hoc networks (MANET) simulations. The RWP model simulates a scenario where nodes move randomly within a defined area, with each node having a destination point chosen randomly and a random speed between a defined minimum and maximum value. The node then moves towards the destination point at the chosen speed, pauses for a random amount of time upon reaching the destination, and then chooses a new destination point. This model is used to simulate a variety of mobile scenarios, such as vehicular networks or animal tracking, and allows researchers to study the impact of mobility on network performance, such as connectivity, throughput, and delay.