# 3D Void Handling Geographic P2P-RPL for Indoor Multi-Hop IR-UWB Networks
## 4 Performance Evaluation
### Greedy Forwarding and Void Handling Point-to-Point RPL in 3D Indoor Environments
#### 4.2.1 125 Node Grid Deployment in 75 m \(\times\) 75 m \(\times\) 75 m Network Configuration
2.2 98 Node Grid Deployment with Void Area in 75 m \(\times\) 75 m \(\times\) 75 m Network Configuration  
Figure 19 shows the network node deployment of 98 nodes with a large void area depicted as the cuboid where no node resides inside. This deployment is different from the previous 125 nodes grid deployment in that 27 nodes that are not near the floor, walls and ceiling are not available for route discovery. This special simulation case with the big void cuboid is presented to demonstrate how well the proposed GVA-P2P-RPL recovers from the local minima by locally broadcasting P2P-DIO messages when the local minimum is encountered in real-life 3D environments. Figure 20 shows the result of the route discovery success ratio. P2P-RPL, PF-P2P-RPL, ER-RPL, GVA-P2P-RPL, and greedy forwarding have about 99.7%, 98.0%, 98.8%, 99.4%, and 79.4% of the success ratio, respectively. As the void cuboid is inserted into the network deployment, the local minima occur more often, and thus the greedy forwarding case achieved 20.0% lower route discovery success ratio compared to GVA-P2P-RPL. Even with the big void cuboid, GVA-P2P-RPL achieved route discovery success ratio almost as high as P2P-RPL. Figure 21 shows the results of the number of P2P-DIO messages sent and the number of P2P-DIO messages received. As shown, the routing overhead result of GVA-P2P-RPL still outperforms that of P2P-RPL, PF-P2P-RPL and ER-RPL in the presence of the void cuboid. GVA-P2P-RPL reduces about 90.5%, 75.4% and 83.6% of the number of DIOs sent, compared to P2P-RPL, PF-P2P-RPL and ER-RPL, respectively, and about 97.5%, 93.6% and 95.7% of the number of DIOs received, compared to P2P-RPL, PF-P2P-RPL and ER-RPL, respectively. Figure 18: Route discovery time (ms). Figure 22 shows the results of the energy consumption and hop count in the presence of the void cuboid. The energy consumption results of GVA-P2P-RPL are also greatly reduced to be the same as the routing overhead results. GVA-P2P-RPL reduces about 94.9%, 87.0% and 91.3% of the energy consumption compared to P2P-RPL, PF-P2P-RPL  
Figure 21: Number of DIOs sent and number of DIOs received. Figure 19: 75 m \(\times\) 75 m \(\times\) 75 m 98 nodes grid deployment with void. Figure 20: Route discovery success ratio. and ER-RPL, respectively. The average hop count of GVA-P2P-RPL is about 0.335 hops longer than that of P2P-RPL, about 0.268 hops longer than that of PF-P2P-RPL and about 0.301 hops longer than that of ER-RPL. Since GVA-P2P-RPL may construct longer routes when void situations are encountered, GVA-P2P-RPL produced a slightly higher hop count value than the previous deployment of 125 nodes without void. Figure 23 shows the result of the route discovery time. The average route discovery time of GVA-P2P-RPL with the adaptive trickle timer (ATT) is about 40.1% less than that of P2P-RPL, about 42.0% less than that of PF-P2P-RPL, about 41.1% less than that of ER-RPL, and about 53.7% less than that of GVA-P2P-RPL with the standard trickle algorithm. The insertion of the big void cuboid did not lead to degradation of the route discovery time compared to the previous deployment of 125 nodes without a void area.