## 2 Related Work
### Architectures for Large-Scale Wireless Sensor Networks Deployments  
The application stakes of LS-WSNs are huge [12; 14; 15]. However, the real challenge for these networks is to manage to operate for several years without any major external intervention. In these types of networks, it is impossible to organize the deployed sensors according to a centralized architecture (too costly in energy) or to preserve a _flat_ communication structure because several issues are even more critical with the transition to scale [1; 23]. Among other things, the size of the routing table per sensor must be reduced, - the number of (re-)transmissions, - the bandwidth occupation, - the power consumption per node. Therefore, the introduction of a hierarchy in LS-WSNs is an option that is increasingly being considered in order to maximize the lifetime of the sensors and thus the lifetime of the network. This results in the creation of a virtual logical topology over the physical topology of the network. The logical topologies of LS-WSN proposed in the literature are numerous and take into account the physical architecture of the network. In this study, we focus on the topologies based on clustering [24; 25]. Depending on the sensor deployment mode (deterministic or random), the cluster-head election process, the cluster size, and the network operating model, several clustering approaches have been proposed in recent years. In [26], a reference study on WSN architectures where the authors propose a taxonomy of logical topologies of WSN illustrated by Figure 2. It is composed of an architecture with: a \"Single-tier flat\"  
Figure 1: Field of services offered in a smart city. capture level, a \"Single-tier clustered\" capture level and finally several \"multi-tier flat\" capture levels in a cluster. The choice of an architecture is most often dictated by the nature of the application to be deployed but also by the nature of the sensors to be used. Moreover, the single capture level architecture is the most widely used in many works [27; 28; 29]. It is usually a network of homogeneous sensors with distributed job processing and centralized storage. Each sensor in the network performs all the tasks required by the application (e.g., capturing temperature, acceleration, motion, etc.). A variant of this architecture is the single-sensor architecture but the data collecting sensors are grouped in a cluster. There are heterogeneous sensors with processing and storage that are centralized at a dedicated central node, as shown in Figure 2.  
The architecture with multiple sensor levels consists of heterogeneous sensors with the processing and storage of data distributed across different levels. In such an architecture, tasks are distributed between the different sensors. It is therefore not useful for a sensor to embark on all the desired functionalities of the network (the nodes are classified according to their capacities: processing, capture, storage, nodes constrained and less constrained in energy, etc.). For example, for such a network, sensors with limited energy resources will be able to perform simple tasks such as temperature capture, motion detection, low-resolution image capture, etc.). On the other hand, sensors with large energy and computational resources will handle more complex tasks, such as high-resolution cameras that can be woken up at the supervisor's request for recognition and localization. In order to achieve the common monitoring objective, the different sensors must cooperate (according to a suitable communication protocol) to reach the well. The major disadvantage of the architectures described above is that they shorten the lifetime of the network. Numerous researches [17; 28; 29; 30] have shown that these architectures pose a problem of the rapid depletion of the energy resources of the sensors located in the vicinity of the well. The energy consumption of the network is not uniform throughout the network.