# Intelligent Communication in Wireless Sensor Networks
## Related Work
### Architecture of Wireless Sensor Networks
In addition, the use of the latter, which successively visits all the SNs of a large-scale network, may have the following drawbacks:  
* Conduction at a very long response time, with many source nodes to visit;
* SN that is found in the MA route extracts energy more quickly compared to other sensors;
* When visiting source nodes, the size of MA increases continuously;
* More power consumption by the mobile agent on his return route to the receiver; and
* The increasing amount of information accumulated by the MA during its migration increases its risk of loss due to noise in the wireless medium. The main idea of [PERSON] et al. in [19] is to consider multi-agent itinerary planning (MIP) as an iterative version of single agent itinerary planning (SIP). Another algorithm, genetic algorithm based multi-agents itinerary planning (GA-MIP) is proposed in [20]. GA-MIP first proposes a new two-level coding method to solve the MIP problem. The GA-MIP algorithm considers the MIP problem as a single problem instead of using several steps adopted by the previous algorithms. The proposal of [21]considers MIP problem models as a totally-connected graph (TCG). In the TCG, the vertices are the SN of the network and the weight of an edge is derived from the estimates of the jump between the two edge nodes. The authors indicate that all SNs in a particular sub-tree should be considered as a group. In addition, they have a balancing factor, while calculating the weights in the TCG, to form a minimum (BST). [PERSON] et al. [22] proposed a near-optimal itinerary design (NOID) algorithm to solve the problem of calculating an almost optimal route for an MA. The NOID algorithm was designed based on the rapid adaptation to network modification conditions, the MA route should only include SNs with sufficient energy availability, and the number of AMs depends on the number and size of the network, and the physical location of the SN to visit. In [23], the main idea of SNOID proposed by [PERSON] et al. consists in dividing the area around the base station into concentric areas and start building MA roads with the direction of the interior near Sink. The entirety of SNs in Zone 1 is connected directly to the base station, and these sensors are the starting points of the MA route. The authors in [35] proposed route planning for PAs. Then, any SSIP algorithm can plan the route of each of these agents. The authors in [36] proposed another data collection system in WSN networks with mobile agents and mobile sink for agricultural applications, in order to plan the route; a dynamic route planning approach is used. An energy-efficient route planning for an MMAEMIP algorithm has been proposed in [37] for iteratively partitioning an area to the directional area where the SN are included in the route; the length of the route is controlled by the sector angle. In [38], authors developed a mechanism for planning mobile sensors in time-sensitive WSN applications. To plan these sensors, these researchers applied techniques derived from ant colony optimization and genetic algorithms. The static sensor network is derived in small areas where a mobile sensor is assigned to each region. The MA paradigm has proven to be efficient in terms of energy consumption and run time, but the most difficult constraint with this paradigm is route planning for MA. Many solutions have been proposed to solve this problem, but the best multi-agent route algorithms proposed take a long time, or are too complicated in the network. After analyzing the solutions presented above, one can also deduce that there is still a great deal of work in terms of energy efficiency by paying attention to the relations hip between delivery of the packets and the density of the network. In addition, the solution has to be independent of network deployment.