## 5 Performance Evaluation of EMOR
### Results
#### 5.2.3 Energy Consumption  
In order to evaluate the performance of energy consumption, we have designed two scenarios using our simulation parameters. In the first scenario, we consider that the source (sensor) node is forwarding its sensing data to the mobile sink, so we evaluate the energy consumption after contemplating this consideration. In the second scenario, we evaluate the total energy consumption against the simulation time. Figure 11 depicts that energy consumed during the sensing data forwarding by a sensor node to mobile sink changes by network density. We have assumed that the maximum distance between the sensor node and the mobile sink can be \(3R\)/4 just for the sake of this experiment. We can observe from the Figure 11 that energy consumption increases when the number of nodes increases in the network or when network density increases. The energy consumption by the POFA scheme is more obvious than in any other scheme including our proposed scheme. The energy consumption in POFA can be explained in a way that when network density increases, the number of hops used for data delivery between the sensor node and mobile sink increases too, so the compilation of energy consumed by every hop results in the overall increase of energy consumption. Furthermore, the energy consumed by the proposed scheme is slightly higher than DCBONC and ExOR schemes. The reason is when a node sleeps in the neighborhood of a source node due to asynchronous working-sleeping cycle strategy, which was used as a data forwarding node towards mobile sink in the past, several processes like optimal path calculation, optimal path selection for real-time/non-real-time data and multipath opportunistic routing protocol are repeated for neighbors which are awake, thus resulting in a slight increase in energy consumption but still far better than POFA scheme. The energy consumption for non-real-time EMOR is slightly higher than real-time EMOR as we select the second best hop for our path connectivity in non-real-time EMOR in which it becomes more probable that the PDR for the second best hop path will be slightly less, and energy consumption will be slightly more than that of real-time EMOR. Figure 12 shows the increase in number of hops when the network density is increased. It is evident that the increase in number of hops in the POFA scheme are higher than in any other scheme. The number of hops for non-real-time EMOR are more than that of real-time EMOR because of the fact that non-real-time path selection is based on the second best hop decision in which the second-to-highest priority is given to the non-real-time path. The probability of a node having a certain status transition is higher in the non-real-time path than in the real-time path (depending on the TF parameter). Moreover, the immediate nodes present near the mobile sink are already loaded as the traffic convergence nodes, so in order to avoid them, we have to select alternative paths (resulting in increased hops), which  
Figure 11: Energy consumption against network density, with long radio range of mobile sink \(R_{l}\) as 100 m.  
means that as the network density increases, the number of hops for non-real-time path selection are increased more than the real-time path. In the second scenario, we conducted the total energy consumption against the network density simulation. It is quite obvious from Figure 13 that total energy consumed increases as the simulation time increases. Here, we have considered only the total energy consumed during the data collection phase by the mobile sink and energy consumed during the sensing data delivery by source node, regardless of the condition that the sensing data is successfully delivered to the mobile sink or not. In comparison to proposed scheme, ExOR consumes 84% more energy, POFA consumes 307% more energy and RPRDC consumes 107% more energy, whereas in the case of DCBONC, the energy consumption of the proposed scheme EMOR is slightly higher (i.e., 18% more).