# Intelligent Communication in Wireless Sensor Networks
## Related Work
### Architecture of Wireless Sensor Networks
However, the Euclidean distance calculated from a base station may not be applicable in the WSN because the communication range of a node is limited. In [11; 18; 19; 20; 21; 22], which relates to route planning research, authors rely solely on geographic information. As a result, a variation in the size of the data is provided because of the irregular distribution of SN groups. However, this could lead to an imbalance of data between the sensors. The balancing factor of the data is compromised between the energy cost and the duration of the task. As an increasing amount of data is collected by the sensor in its memory, the information size increases, which, therefore, takes more time and consumes more energy to return to the receiver. Thus, the balance between the mobile agents at the information size level optimizes the duration of the data collection task on the network and decreases the percentage of data loss. The main idea of authors' proposal in [23] is the inclusion of the sensor, which minimizes the total cost. Indeed, it distributes in parallel a number of mobile agents that sequentially visit the sensor nodes grouped into tree structures. The purpose of this proposal is to reduce the overall energy expenditure and response time, but its shortcoming is the increase in the size of the WSN, where more branches will be created, which would significantly degrade performance due to interference. Tree-based design directions are illustrated in [24] in which the authors propose a heuristic algorithm that improves the one proposed in [21] and determines the appropriate number of mobile agents to minimize the total cost of aggregation, as well as creates inexpensive routes for all agents and selects the nearest node to form the binary tree. It generates small routes for the MA, but energy consumption is doubled in reverse routes and interference between very large numbers of branches. Researchers [25; 26; 27; 28; 29; 30] realized the artificial intelligence community with SNs using the multi-agent system for adaptation to distribute and complex wireless sensor networks. In our previous work [3; 4; 18; 31], we proposed a communication approach using MAS in a WSN plan. Thus, with this solution, we have solved the problem of density by partitioning the network into clusters to some extent. We still have, in [25], proposed a scheme to aggregate a maximum of information. In this work, we have integrated in each SN an agent, in order to process the information and enable each sensor to cooperate with its neighbors. The authors of [11; 12; 13; 14; 15; 16; 31; 32; 33; 34] proposed mobile agent technology in the WSN for data collection. In these proposals, the management agent is defined as a message containing an application code, a SN list predefined by the receiver and an empty field to place the collected data. It is able to move between all the sensors of the network by moving the captured data in a single message instead of having each sensor bringing this raw information to the receiver separately. The use of a MA allows for more efficient data aggregation on C/S. In these proposals, the management agent visits the SN of the network and progressively merges the sensory data before returning to the receiver. The disadvantage of this type of solution is the difficulty of creating the SN list. Another limitation is the definition of the regions to be treated by the AM, but, as a solution to optimize the route of the MA, the authors [16] have proposed two algorithms of heuristic routes. The Local Closest First algorithm (LCF), the AM starts its route from one sensor and searches for the next destination with the shortest distance to its location. In the Global Closest First algorithm (GCF), the mobile agent begins its route from a sensor and selects the destination closest to the center of the surveillance zone. The use of a single mobile agent can lead to a very inefficient design due to limited bandwidth and the density of the wireless sensor network.