## 6 Analysis and Results
### Libellium Connector: Implementation and Observations
#### 6.2.1 ASCII Encoding
Thanks to the introduction of the connector concept [82], the proposed architecture presents itself as a solution to the problem of _interoperability_ among IoT devices. This solution offers several advantages, making it easy to implement and well-suited for the precision agriculture sector. First of all, the resources required by a connector are minimal: the operations performeddo not require substantial computational power, and the memory usage is very limited. A connector can therefore be easily implemented on an embedded device with an Internet connection, such as a Raspberry Pi, making it economically efficient [106]. Each connector connected to the network can be considered a module of the system, making it adaptable to various needs. The network will only host the necessary modules, reducing non-essential functionalities that could add complexity and incompatibility to the system. Additionally, if a connector fails or its software needs updating to comply with new guidelines from manufacturers, it is sufficient to address the single module by replacing or modifying it without the need to reconfigure the entire system. RQ5 aims to identify the most suitable open-source software to implement the proposed architecture, focusing on solutions that align with the identified requirements and enhance system scalability and ease of deployment. Based on the analysis presented in Section 4.5, the FROST-Server (FRAunhofer Opensource SensorThings-Server) [96] emerges as the most suitable open-source solution for implementing an interoperable architecture in precision agriculture. This is primarily due to its comprehensive implementation of the OGC SensorThings API, which includes all necessary specifications and extensions for effective data management and interoperability. FROST-Server's ability to function as a Docker container enhances its deployment flexibility, making it easier for users to configure and manage the server in various environments. Additionally, the use of open-source software reduces architecture implementation costs, which are often a barrier to the widespread adoption of IoT in precision agriculture [10]. Moreover, the interoperability of the proposed system makes the architecture suitable for both local and large-scale contexts, facilitating the scalability of the solution. Each connector is an independent unit and can be added or removed from the network without compromising its integrity. This way, it is possible to adapt the system's size to the needs of the context without interruptions in the data flow. The combination of the connector device and the chosen standard makes the approach to the system simple and intuitive. The end user can focus on analysing the collected data without needing a deep understanding of the architecture. The development of third-party applications to interface with the data collection server requires only knowledge of the standard's specifications, which have been specifically chosen for their intuitiveness to make the experience for end users as seamless as possible. ### Future Work

In this work, the development and analysis of the software for connector devices has focused on specific types of sensors produced by Libellium and Digital Matter. However, the architecture is designed to be flexible, allowing for the addition of other sensors and devices from different manufacturers. By incorporating their respective technical specifications, we can easily integrate additional functionalities into the system. Each implementation of a new type of connector can be developed as needed and added to the system once completed, thanks to the modular nature of the architecture. Furthermore, as demonstrated in the literature, the integration of machine-learning algorithms for pattern recognition in incoming data represents an opportunity to improve the scalability and interoperability of the system [107; 108].