## 4 Simulation Analysis
### Energy Consumption and Energy Tax  
In addition to data broadcast overhead and PDR, we also analyzed the total energy consumption of the network as the simulation time progresses. The total energy consumption consists of transmission,  
Figure 8: Average packet delivery ration versus the network sizes. Figure 9: Average packet delivery ration versus the transmission range. -reception and idle energy consumption of the network. This total consumed energy value is used in the normalized energy consumption that we discussed previously. This analysis shows the real behavior of the routing scheme proposed for the network. Figure 10 depicts the total network energy consumption for the fixed transmission range and different network sizes for the complete simulation course. It is evident from the figure that the total energy consumption of the network is directly proportional to the network size. However, the proposed EDOVE consumes less energy than the WDFAD-DBR. Next, the total energy consumption of the network for the fixed network size and different transmission ranges throughout the simulation course is shown in Figure 11. It shows that there is not that much impact of variation of the \(t_{r}\) on the energy consumption of the EDOVE. However, it is noticeable from the same graph that WDFAD-DBR consumes slightly less energy for the \(t_{r}=600\) m than the \(t_{r}=1000\) m. In addition to that, it is also apparent from the figure that after 350 s the energy consumption become flat, which shows that there is no more data forwarding in the network. The main reason behind this occurrence is due to the fact that there has been a void layer or most of the relaying nodes have been depleted and no more data could be forwarded towards the sink(s). Energy consumption of the WDFAD-DBR saturates earlier than the EDOVE, which shows that most of the relaying nodes in WDFAD-DBR run out of the battery power earlier. In the context of this paper, we call this theory as the operation time that is discussed later in this paper. The operational time is the duration through which data packet can be forwarded from the source node towards the sink(s). Figure 11: Total network energy consumption during simulation duration for the fixed number of nodes (120 nodes) and different transmission ranges in the network. Figure 10: Total network energy consumption during the simulation duration for fixed transmission range of 800 m and varying number of nodes in the network. In addition to the network energy consumption analysis, we also compared the proposed EDOVE with WDFAD-DBR in terms of the _energy tax_, \(E_{Tax}\). WDFAD-DBR computes energy tax as the average energy consumed per successfully received data packet by the sink(s) and per network node. \[E_{Tax}=\frac{E_{Total}}{P_{Success}\times Nodes} \tag{7}\]  
where, \(P_{Success}\) is the total number of successfully received packets at sink node(s). In case of multiple sinks, \(P_{Success}\) is the distinct data packets that are received by multiple nodes. It is believed the WDFAD-DBR authors that energy tax parameter is more meaningful than total energy consumption. Hence, we compared the performance of EDOVE with the WDFAD-DBR in terms of energy tax for varying network sizes and transmission ranges, which is discussed below. The energy tax analysis of the EDOVE for different network sizes and transmission ranges are shown Figures 12 and 13, respectively. It shows that the energy tax of the EDOVE is far less than the WDFAD-DBR for different network sizes. The main reason for this behavior is that WDFAD-DBR consumes more energy and has very less delivery ratio. In result, it has very high energy tax compared to the EDOVE scheme. However, it is worth noting that the energy tax trend for both the schemes slightly increases as the network size increases. It is only because the volume of energy consumed due to large copies of data are disseminated is very high and number of generated data packets by the source node are same for any network size.