# Intelligent Communication in Wireless Sensor Networks
## 8 Results and Analysis  
Thus, to prove the performance of our proposal IIWSN in the WSN, we evaluate it to the LCF and GCF methods, based on two criteria. First, we verify the impact of the number of SN on the energy performance criterion; this parameter is considered the most important criterion. Since energy consumption is the parameter that defines the life of the WSN, we set the number of SN from 05 to 50 in steps of 05, and obtain a set of results for each case. We have shown the results thus obtained in Figure 7, which illustrates the impact of the number of SN on the energy, to obtain sensory data of all the source nodes. \begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameter** & **Values** \\ \hline Network size & 1000 m \(\times\) 1000 m \\ Sensor distribution & Random \\ Radio range & 87 m \\ Debit & 1 Mbps \\ Size of data collected per sensor & variable at 0.5 Ko to 4 Ko \\ Data captured interval & 10 s \\ Simulation time & 1000 s \\ Local processing time & 40 ms \\ Treatment Code Size & 0.4 Ko \\ Fusion Factor (\(\rho\)) & 01 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic simulation parameters. We demonstrate the comparison of the performances of the three approaches in terms of energy in the following figure. Foremost, as we know, when the number of SN is increased, more energy is needed to carry out the data transmission of a communication for the three approaches. We note that our IIWSN approach is always more improved than the GCF approach, not considering the number of SN, and better than the LCF approach even when we increased the number of SNs. We can conclude that the difference between our approach and the other two approaches is becoming more and more important, and this difference increases continuously with the increase in the number of source nodes. At first, there is not a significant difference between IIWSN and LCF, but there is a small difference between our algorithm and GCF. From 20 SNs, our approach minimizes energy consumption by more than 3% at LCF and 12% at GCF. Nevertheless, at 60 SNs, the consumption of our approach minimizes energy consumption by more than 29.5% and 42% than LCF and GCF, respectively. By comparing the result, the explanation of IIWSN technique presents better energy efficiency. Moreover, and in an additional test, we illustrate the comparison of the performances of the three approaches in terms of the duration of a task. For local closest first and global closest first techniques, the duration of the task is equivalent to the average end-to-end reporting delay, from the moment the mobile agent is sent by the sink until this agent returns to the receiver. On the other hand, the duration includes the processing time of the information at the SN to eliminate redundancies. Therefore, since more than a few MA are functioning in parallel, and with several experiments, we calculated the duration of a task of a mobile agent to return to the sink, and the duration of a task is the average time of an agent mobile where his itinerary starts from the sink until it returns to it. However, the duration includes the information processing time at the sensors, added to the co-operating time of the SN that do not have enough energy to transmit the MA with its neighbor sensors, and the longer the time taken by the mobile agent. The results obtained are shown in Figure 8:  
Figure 8 explains the difference of the performance of the three approaches in terms of task duration. First of all, and this is quite normal, when the number of source nodes is increased, added to the time required for mobile agents to perform the tasks of each of the three approaches. By observing the results, we can see with added SNs to visit, the size of a mobile agent grows and a large number of transmissions will be completed. At first, almost both LCF and GCF techniques are similar, other than from 10 SNs, GCF consumes additional time than LCF.