## 4 Simulation and Results Discussion
The time for the sink not to be reachable by the alive sensor nodes of the CRPLEOGALSPI is compared with the CRPLOGARL for increasing sensor nodes' initial energy as shown in Figure 7. The network lifetime increases with the increase in the initial sensor node energy. This is because the lifetime of a sensor node is proportional to the residual energy of the sensor node. Figure 6: Number of alive sensors with Round of Data Transmission. Figure 7: Network lifetime with Initial Node Energy. The proposed CRPLEOGALSPI has an improved network lifetime performance of 18.04% when compared to the CRPLOGARL with increasing sensor node residual energy. This is because the CRPLEOGALSPI utilizes LSPI, which is more data efficient and converges faster to the RT, which balances the energy consumption among the sensor nodes and minimizes the network energy consumption as shown in Figure 8. This is against the CRPLOGARL that utilizes the baseline Q-learning that takes larger episodes to converge to the RT that optimizes the network lifetime due to the many RTs. The network energy consumption for both protocols increases with the increase of the initial energy of the sensor nodes. This is because the more the initial sensor nodes' energy, the more rounds of data transmission it takes for the sensor nodes to deplete their energy. Subsequently, the CRPLEOGALSPI has a reduced energy consumption of 58.96% when compared to CRPLOGARL for increasing the initial energy of the sensor nodes. This is because CRPLEOGALSPI optimizes the network lifetime and the network energy consumption using LSPI. This is against the CRPLOGARL, which optimizes only the network lifetime using Q-learning. This also implies that CRPLEOGALSPI has a reduced \(CO_{2}\) footprint when compared to CRPLOGARL for increased initial sensor node energy. This is because \(CO_{2}\) footprint is directly proportional to the network energy consumption. However, the improved performance in the network lifetime and network energy consumption of the proposed protocol comes with an increased computation time of about ten times when compared to CRPLOGARL for an increased initial sensor node energy, as shown in Figure 9. This is because CRPLEOGALSPI requires the collection of samples to learn the optimal path each time the network is rebuilt because of the death of sensor node(s). Figure 8: Network Energy Consumption with Initial Node Energy. Figure 9: Computation time with Initial Node Energy. The time for the sink not to be reachable by alive sensor nodes of the CRPLEOGALSPI is compared with the CRPLOGARL for increasing sensor nodes' packet generation rate as shown in Figure 5:0 The network lifetime decreases with the increased packet generation rate of the sensor nodes. This is because the network lifetime of a sensor node is inversely proportional to the number of packets transmitted by the sensor node. The proposed CRPLEOGALSPI has an improved network lifetime performance of 14.91% when compared to CRPLOGARL with an increasing packet generation rate of the sensor nodes. This is because CRPLEOGALSPI utilizes LSPI, which is more data efficient and converges faster to the RT, which balances the energy consumption among the sensor nodes and minimizes the network energy consumption, as shown in Figure 5:1 This is against the CRPLOGARL that utilizes the baseline Q-learning that takes larger episodes to converge to the RT that optimizes the network lifetime due to the many RTs. The network energy consumption for both protocols decreases with the increasing packet generation rate of the sensor nodes. This is because the more the number of data packets generated and transmitted by the sensor nodes, the fewer rounds of data transmission it takes for the sensor nodes to deplete their energy. Subsequently, the CRPLEOGALSPI has a reduced energy consumption of 56.36% when compared to the CRPLOGARL for increasing the packet generation rate of the sensor nodes. This is because the CRPLEOGALSPI optimizes the network lifetime and the network energy consumption using LSPI.