# A Proposal for IoT Dynamic Routes Selection Based on Contextual Information
## 4 Performance Assessment and Results
### Results Analysis  
The results obtained show that the application that require high priority regarding lifetime and delay used, among the functions available in the DATABASE, the DQCA-OF4 function for the route selection process. This function was selected because it can forecast, in its structure, the metrics energy consumed and number of hops, allowing for the routes selection with lower energy consumed and a lower number of hops. Figure 4 shows the performance of the evaluated objective functions. The function DQCA-OF4 achieved higher remaining energy (approximately 170 joules), at the end of the simulation, when compared to the DQCA-OF4(FL), OF-FL, ERAOF, MRHOF and OF0 objective functions which achieved consumption rates of 136 joules, 130 joules, 107 joules, 99 joules and 76 joules, respectively. The function DQCA-OF4(FL) obtained lower performance when compared to the DQCA-OF4 as a function of the energy consumed with the complexity of the fuzzy system processing. In Figure 5, from 525 s to the end of the experiment, the DQCA-OF4(FL) function obtained a smaller delay when compared to the DQCA-OF4, OF-FL, ERAOF, MRHOF and OF0 objective functions. This occurred because the DQCA-OF4 (FL) function uses a fuzzy-based route classifier, which is not the case for the objective functions DQCA-OF4, OF-FL, ERAOF, MRHOF and OF0. Figure 5 also shows that there was no variation in results between 0 and 525 s of simulation. This is justified because the network, in this interval, is in the process of stabilization, occurring variation of the delay only after 525 s of simulation. Figure 4: Energy consumption for the network nodes. For applications that require reliability in the delivery of data, Figure 6 shows that DQCA-OF1(FL) function achieved the best performance, with a delivery ratio of 94% when compared to the DQCA-OF1, OF-FL, ERAOF, MRHOF and OF0 objective functions, which achieved delivery ratios of 89%, 70%, 67.98%, 65.62% and 50.4%, respectively. This was because the expected Transmission Count (ETX) and Hop Number (NH) metrics; in this case, both with high priority, used the fuzzy-based route classifier. For reliability, between all the DQCA-OF applied to the classifier of routes based on fuzzy system, only the DQCA-OF1 (FL) was the more efficient when compared with the others. That is why only the DQCA-OF1 (FL) was considered for this analysis. The ETX union with _NH_, represented by DQCA-OF1 (FL), demonstrated to be relevant, since the application requiring reliability requires low ETX. In addition, the presence of the number of hops in this function allowed to increase the percentage of reliability, because the smaller the number of hops the greater the probability of delivery of the messages. Figure 7 shows that, for applications that require priority in the quality of the delivery of data, the DQCA-OF2(FL) with EC = High achieved the best performance in the number of transmissions/retransmissions represented by ETX. The DQCA-OF2(FL) with _EC_ = High got ETX = 31 when compared to the DQCA-OF2 (EC = High) functions, which achieved ETX = 46, DQCA-OF2 (_EC_ = Low) with ETX = 96, OF-FL with ETX = 115, ERAOF with ETX = 125, MRHOF with ETX = 140 and OF0 with ETX = 180. This difference was achieved because the application required a high level of priority in the Energy consumed (_EC_) metric combined with the use of route classifier based on fuzzy system. Figure 5: Message delivering delay for the sink. Figure 6: Delivery rate for the sent messages. For the QoS, between all the DQCA-OF applied to the classifier of routes based on fuzzy system, only the DQCA-OF2 (FL) with Ec = high (high priority for energy consumed), was the more efficient when compared with the others. That is why only the DQCA-OF2 (FL) was considered for this analysis. The ETX junction with EC, represented by DQCA-OF2 (FL), proved to be important, since the application requiring QoS requires also low ETX. In addition, the presence of the metric energy consumed in this function, allows the increase of the network lifetime.