# An Energy-Balanced Geographic Routing Algorithm for Mobile Ad Hoc Networks
## 4 Simulation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Scene size & 1000 m \(\times\) 1000 m & Mobile model & Random Way Point \\ Node Velocity & 0–50 m/s & Node number of network & 40 \\ Initial node energy & 1000 J & Packet rate & 1–10 packets/s \\ MAC protocal & IEEE 802.11 & Data rate & 1 Mbps \\ Communication range & 250 m & Packet size & 512 Bytes \\ Simulation time & 200 s & HELLO packet size & 15 Bytes \\ Max waiting time of HELLO packet & 12 s & Update cycle of HELLO packet & 3.5–4.5 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parametersthe packet delivery rate. The long-lived link of the road carries out data transmission, which improves the packet delivery rate. Figure 10 shows the variation of death time of the first node in the network with the movement speed of the node under different routing algorithms. It can be seen from the figure that as the node moves faster, the death time of the first node in the network also increases. The first node of GPSR has the lowest dead time. This is because when the node moves at a lower speed, the greedy forwarding selects the node closest to the destination node as the next hop for data transmission. Only when the node energy on the link is exhausted, which causes the current node to leave the network, are other  
Figure 8: Average end-to-end delay versus the velocity of a mobile node. Figure 9: Packet delivery rates versus the velocity of a mobile node. nodes selected for data transmission. The IEMACO curve is higher than the other three curves. This is because the routing of IEMACO is based on the topology and the node status of the whole network. In terms of energy consumption, not only the next-hop node but also the energy state of all nodes on the path to the destination node are considered, and so the performance is better in terms of energy balance. With the increase of node movement speed, the EM-GPSR algorithm proposed in this paper can guarantee link stability and predict the residual lifetime of the node according to the node energy consumption rate in the next-hop selection. The balance of energy consumption is achieved. Figure 11 shows the number of dead nodes at different node velocities under different routing algorithms. As can be seen from Figure 11, as the node moves faster, the number of node deaths also increases. The number of node deaths in GPSR is significantly higher than that of SU-GPSR and EM-GPSR. This is because the algorithm does not consider node energy. When the current node cannot provide service because it has exhausted its energy, it will continue to select the node closest to the destination node as the next hop to continue transmission until the end of energy consumption. The number of node deaths in EM-GPSR proposed in this paper is small. This is because the concept of the remaining lifetime of nodes is proposed in this paper. On the basis of ensuring the stability of data transmission, the EM-GPSR algorithm tries to select nodes with more remaining energy to ensure the balance of energy consumption. Figure 12 shows the average end-to-end delay varying with node-sending speed for different routing algorithms. It can be seen that with the increase of network load, the average end-to-end delay in the network is on the rise, which is due to the limited data processing capacity and bandwidth of the nodes. The end-to-end delay of GPSR grows fastest because the link breaks often occur when the node is moving constantly, which makes the node need to choose the next hop again, and so the re-transmission increases the end-to-end delay. The SU-GPSR partly considers the mobility of nodes, that is, distinguishing between mobile and static nodes, so the delay is relatively low. The proposed EM-GPSR algorithm takes the mobility of nodes fully into account, and takes it as the primary condition for selecting the next hop.