# SIoT: A New Strategy to Improve the Network Lifetime with an Efficient Search Process
## 5 Simulation and Evaluation
### Simulation Results and Discussion
#### 5.2.2 Service Search Efficiency  
The service search process can be started by incoming requests. When a node generates or receives a request that cannot be executed, it searches among its friends. In the case where the desired service is not available within its friends, then the search proceeds to the next level. Figure 10: Average path length comparison with other strategies. Figure 9: Average local clustering coefficient comparison with other strategies. In our contribution, the service search process uses the sorting factor, which is calculated using the four factors (Section 4.3). We set the weights \(\alpha=0.25\), \(\beta=0.25\), \(\gamma=0.25\) and \(\delta=0.25\) for node capability, group capability, group centrality, and group popularity, respectively, all the factors have the same importance, i.e., we do balance between the rapid search process and an increase of the network lifetime simultaneously. Furthermore, the updating values of the four factors are fixed previously with a specific period. The service search process is performed by sending 10,000 requests in our social network (the case where \(\text{F}_{\text{max}}=15\)), we then compared our search process with two approaches [PERSON] et al. [29] and [PERSON] et al. [30], which are discussed in the related works section of this paper, where the service satisfaction and node similarity are not considered. To facilitate the simulation, we choose randomly a request node and a provider node for each request; therefore, the matching similarity is not considered between the query and nodes. Additionally, we consider the capability as the residual energy. Figure 11 shows the number of hops to achieve the providers according to the number of requests. As illustrated, [PERSON] et al. [29] gives minimal hops than [PERSON] et al. [30] approach, but by a wobbling manner due to the requests that are retransmitted to the friends that have more centrality. However, the number of hops during the first requests is not minimal, but after a number of requests, our approach exploits its previous solutions, so that it converges to minimal hops. Therefore, our approach speeds up the search process by exploiting the obtained solutions. To determine the behavior of the execution time consumed during the service search phase, we have measured the total execution time after each executed request as shown in Figure 12 with a comparison of [PERSON] et al. [29] and [PERSON] et al. [30]. As illustrated, the execution time for our approach is reduced in comparison to the other ones, this indicates that the requests are executed with a short period of time. This result is a consequence of sending requests via the shortest paths (Figure 11) based on factors of centrality and popularity, which make the nodes to exploit the previous solutions locally. Figure 11: Number of hops to find the provider node according to the number of requests. To study the behavior of energy consumption during the search process, according to [39; 40] we consider that the energy for reception, transmission, and execution is 0.001 J, 0.004 J, and 0.001 J, respectively. Moreover, the processing information (by an intermediate node) is 0.0015 J. To simplify the simulation, we calculate the deviation of residual energies between nodes, and it is computed as follows:  
\[\mathrm{e}=\sqrt{\frac{\sum\limits_{i=1}^{N}\left(\mathrm{e_{i}-\overline{e}} \right)^{2}}{N-1}} \tag{12}\]  
where \(\overline{e}\) is the average of all residual energies, \(\mathrm{e_{i}}\) is the residual energy of node i. At first, as shown in Figure 13, the deviation of our approach was high and it is decreased after several numbers of requests. As a result, our mechanism balances between energies during the search process, and therefore leads to an increase in the network lifetime where high number of nodes converge to same residual energies. In contrast, the deviation of consumed energies for [PERSON] et al. [30] is less than [PERSON] et al.