# Routing with Renewable Energy Management in Wireless Sensor Networks
## 3 REBORN-Renewable Energy-Based Routing Algorithm
Finally, it is possible to observe that a relevant amount of packets to be forwarded by the GAF-EH algorithm do not reach the sink. This behavior was already expected, since the algorithm does not have energy economy controls. Figure 8: Energy consumption over time. Figure 9: Number of dead nodes over time. ### Sent Packets

In the present results, we evaluated the number of packets sent during the simulations. As can be seen in Figure 11, the GAF-EH algorithm presents significant variations in the sending rates. This is due to the natural energy depletion of the nodes and consequently the process of harvesting energy from the nodes. As expected, this algorithm sends a large number of packets, although it has already been shown to be infinitely related to the lifetime (activity). However, the REBORN-BAT algorithm sends smaller quantities of packets, even though both the number of dead nodes and the delivery rate were found to be unsatisfactory. The smallest number of packets observed among the evaluated algorithms is that of REBORN-SI. This is covered by managing the amount of energy already stored despite having its focus on power consumption while sending packets. Regarding REBORN-SI, REBORN-both sends a larger number of packets although both have similar delivery rates. Based on these evaluations, it is possible to state that REBORN-both can send larger quantities of packets to the sink and still maintain high rates of delivery. The relation between cost and benefit of using the REBORN-both algorithm can be adjusted through the parameter K, as observed in Equation (7). Figure 12 shows details of the simulation performed between 24 and 48 h.

Figure 11: Number of sent packets over 3 days. Figure 10: Delivery rate of sent packets. ### Impact of the Network Density

The impact of the network density was evaluated by increasing this metric from 0.01 to 0.09 nodes/m\({}^{2}\). As shown in Figure 12(a), despite the high density, the GAF-EH still kept its residual energy low, when compared to the other algorithms. On the other hand, it is possible to observe in Figure 12(b) that the delivery rate for all solutions increases when the density of nodes increases. ### The Impact of Scalability

Finally, the scalability was evaluated by increasing the network size from 225 to 961 nodes with a constant density of 0.03 nodes/m\({}^{2}\). The grid in which the sensors were implanted was resized according to the number of sensor nodes. As can be seen from Figure 13(a), energy consumption tends to increase as the number of nodes in the grid increases. In this sense, as the density value holds the same, more cells are generated in the grid. Because of the increase in the number of cells in the grid, the number of packets to be forwarded also increases, according to the position of the nodes in relation to the sink, that is, the closer the node is to the sink, the more packets it will go forward. In this way, a greater amount of energy will be spent in the referral process. Finally, despite the observed energy cost, REBORN-both shows scalable because it presents a satisfactory delivery rate even when the number of cells in the grid increases. Figure 12: Number of sent packets over 1 day. Figure 13: Impact of density on (**a**) energy consumption and (**b**) delivery rate. ## 5 Conclusions

In this work, we proposed a new routing protocol, called REBORN (Renewable Energy-Based Routing), that takes advantage of both the residual battery energy and the available renewable energy to allow energy savings in the wake/sleep schedules of the nodes and also on the management of the number of data packets sent to the sink node. Our proposed approach uses a grid-based opportunistic geographic routing to forward data packets between cells toward the sink node. Extensive simulations were carried out in several scenarios to evaluate the performance of the proposed solution. We also compared our solution to a variation of the GAF algorithm that uses energy harvesting.