# Intelligent Communication in Wireless Sensor Networks
## Related Work
### Architecture of Wireless Sensor Networks  
As mentioned earlier, the main problem in wireless sensor networks is energy. Indeed, as the energy of the SNs is not rechargeable, they must remain operational as long as possible, and it is, therefore, necessary to save the maximum energy consumed by these nodes. One of the characteristics of sensor networks is the ability to reduce the amount of data flowing through the network to conserve energy by merging data through particular sensors in the network. This is called data aggregation. Aggregation does not only require the transmission of data, but also imposes constraints on the network architecture. The idea is to merge data from multiple SNs by eliminating redundancies and reducing the number of transmissions as much as possible to save energy consumed. In what follows, we present a set of previous works conducted within the same framework. The traditional client/server (C/S) model is one of the most energy-efficient proposals for managing multi-sensor data fusion in the WSN. Several studies [4; 8; 9; 10; 11] have been made to optimize the architecture of this model. In this architecture, when information from the environment is detected by a sensor, the node sends it as raw data through the other sensors to perform processing. Unnecessary or redundant information is not eliminated by the transmission of raw information, which requires costly construction in terms of energy. Some works have proposed compression/decompression (C/D) algorithms for reducing the capacity of information transmitted by sensors. To compress the data in a WSN, the authors of [5; 6] proposed a data correlation algorithm. In this search, only one sensor is chosen to send raw data to the receiver while the other nodes send only encoded data. After reception, the data is decoded through the correlations between the compressed and uncompressed data. That is to say, it is difficult to find an uncomplicated coding algorithm appropriate for a SN where a minimum of processing energy is consumed. Authors in [3; 5; 7] proposed a data fusion (DF) of a maximum number of sensors. When data is sent by one node to the receiver, the intermediate sensors merge their data with that of the first sensor. Thus, this information is merged into one message instead of several, which saves energy. However, intermediate sensors do not always contain useful information to send, and do not eliminate redundant or irrelevant information. In addition, the authors have not studied the importance of the scalability of this type of network. Moreover, it has been proved in [2; 3; 4; 9] that the cluster is a fundamental technique in the WSN. Its purpose is to reduce the processing of the aggregation of the necessary data at the sensor node and to move the load on the sink. The clustering algorithm uses a hierarchical classification and sensors are organized into groups (clusters), which elect a cluster node as a leader. The latter collects the aggregated information from the sensors of its group and transmits them to the sink. In this route, energy consumption is reduced significantly. Unfortunately, the authors do not consider the complexity and the amount of energy required to make such grouped sensor arrays. In addition to [8; 9], the authors of [12; 13; 14; 15] also proposed a structured tree and cluster strategy based on data aggregation for the WSN. However, according to [16; 17], structured approaches are not really practical for dynamic scenarios because of the excessive communication costs and the centralization of WSN structure management. An ant colony algorithm for data aggregation in the WSN has been proposed by the authors of [8]. All possible routes from the SN to the receiver will be explored by each ant. The accumulated pheromone builds the data aggregation tree. However, one must take into consideration the great dependence of the construction of the appropriate tree of a large part of the deployment of the nodes, which is generally random and consumes a large amount of energy.