## 2 Control Architectures for WSN: A State of the Art
### Synthesis and Research Issues  
The control techniques in WSN exposed in this literature review enable us to identify four distinct types of network architecture (A, B, C, and D), as shown in Figure 7. This classification is based on the location of the monitoring or observation level (green circles), the position of the control or decision-making level (red circles), and the direction of transfer of control (red arrow) and observation (green arrow) packets. Architectures A and B are distributed in nature, as follows: in architecture A [12], each node monitors and reconfigures its parameters independently, without interacting with other nodes, while architecture B [14] is based on collective monitoring and decision-making, with each node assessing its own energy state as well as that of its neighbors. On the other hand, architectures C and D are centralized, as follows: architecture C [19; 20] operates with a system in which nodes monitor the entire network and send their data to a higher-level node, responsible for decision-making, while architecture D [13; 21] centralizes both monitoring and decision-making in a single control point.  
Figure 6: ADACOR architecture [24].  
Distributed control architecture involves distributing control and management functions across several nodes. This approach offers great adaptability to local changes and reduces communication costs. However, it can present challenges in terms of overall coordination, particularly in applications requiring synchronized actions or a centralized view of the network. In contrast, centralized control architecture relies on a central management entity making all decisions for the entire network. It simplifies overall management and coordination, providing a consistent overview and synchronized actions. However, it is more vulnerable to failures of the central control point, so it may be less adaptable to changes and cause load imbalances between nodes. The fundamental challenge lies in designing hybrid architecture control that balances the advantages of distributed and centralized approaches. This architecture combines local decision-making with centralized global optimization. Each node makes decisions based on its immediate conditions, enabling rapid, relevant reactions to local situations. At the same time, central management analyzes the entire network for global optimization, balancing the load and making efficient use of resources. This combination ensures high performance, with local flexibility and global efficiency, making the network resilient and adaptable to change. As a result, hybrid architectures are the key to better eco-efficient networks and able to reduce energy consumption.  
The objective of this research work is thus to propose a WSN-adapted HCA, based on POLLUX, the latest published to our knowledge. This architecture should allow us to test different levels of hybridization, from the fully hierarchical to the distributed ones, to compare their respective performances. To embrace the SDWSN paradigm, control and data transmission functions must be separated, and the HCA may be located remotely, which imposes to specify the coordination mechanism between the control and the data plane, i.e., in our case, the HCA situated remotely and the different physical wireless nodes). For the development of this architecture, a model-based engineering approach will be used to obtain formalized UML models.