## 4 Performance Evaluation
### Simulation
In this context, the lack of a technique to smooth the variation of LQI values leads to a greater switching of routes in LABILE, and thus reduces the data delivery. For these reasons, although LABILE has a mechanism to evaluate end-to-end link quality, its performance was below of the REL protocol. On the other hand, AODV selects routes that are only based on minimum hop count, which reduces its overall reliability. Figure 9: Packet delivery ratio in simulation experiments. (**a**) 20 Nodes, (**b**) 40 Nodes, (**c**) 60 Nodes, (**d**) 80 Nodes, (**e**) 100 Nodes, (**f**) 200 Nodes. The analysis showed that results with 100 nodes, _i.e._, those with high density, had a PDR difference that reached 12% when compared with AODV, and 9.2% when compared with LABILE. Thus, if the results of the high PDR and low latency provided by REL are combined, we can observe high degrees of efficiency and reliability in its route selection and load balancing scheme. These results confirm that REL achieves a level of QoS that is appropriate for ubiquitous computing and IoT applications. The values of the PDR for systems with 200 nodes show that REL keeps its superior performance even in dense topologies, where the PDR is around 94.5%. For the same scenarios, AODV and LABILE results were 80.8% and 83.3%, respectively. As mentioned earlier, despite recent technological advances in embedded systems, the energy constraint remains a critical factor in WSNs/IoT, given the fact that sensor nodes have limited energy resources. Furthermore, an external energy supply is usually unavailable and the replacement of batteries is not feasible for large-scale WSNs. In this context, it is essential to have an integrated routing and load balancing scheme that can extend the network lifetime without affecting its reliability. Figure 10 shows the percentage of nodes that remained alive during the simulation. Two metrics were employed to evaluate the energy-efficiency: the entire lifetime of the network and the saturation time. The network lifetime was measured as the time spent by sensor nodes until the point when only 1% of the network nodes remains alive. The saturation time is similar to the network lifetime, although it starts from the moment of the first node death. The saturation time is useful in assessing the capacity of the routing protocols to dynamically adapt to new topologies resulting from the death of the nodes. The lack of energy management in existing protocols, such as AODV and LABILE, means that their results can be similar in most of the experiments. The experiments outlined in Figure 10 show differences between the percentage of nodes that were alive during the saturation period for these protocols, although the difference in the network lifetime was a maximum of 1 minute. The reason for this is that LABILE is a proposal that extends the AODV protocol, but does not improved energy-efficiency features. AODV and LABILE do not provide mechanisms that can take advantage of the large number of network nodes with respect to energy. As a result, in the scenario with 60, 80 and 100 nodes (Figure 10(c-e)) these protocols do not exceed a lifetime of 42 min. Furthermore, the saturation time also remains stable. When the protocol REL is used, the saturation time in these three scenarios with more nodes also increased by up to 8 min. The saturation time reached 32 min for scenarios of 60 and 80 nodes, while the scenario with 100 nodes reached a maximum of 36 min. Since the beginning of the saturation period occurs at the moment of the death of the first node, it is also included in the assessment of the energy-efficiency of a protocol. The maximum time spent until the first death, when the protocols AODV and LABILE were used, was 12 min. For the REL protocol, the minimum value was 18 min in the scenario with 20 nodes, and 22 min in topologies with more nodes. The beginning of saturation occurs earlier in the scenario with 200 nodes compared with the scenario with 100.