# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 6 Evaluation
### Experimental Results
#### 6.3.2 eNPA Experimental Results  
For the scenarios presented in Section 6.2.2, we also present some basic results. Each scenario described is executed for both search methods, the optimistic method (see Algorithm 8), referred to as _OM_, and the allocation method (see Algorithm 9), referred to as _ALM_. The plots of throughput and delay start at 600 pkts/s loads in the network. This happens because this is the point where the different scenarios execute their algorithm, and difference can be shown. From the beginning of the simulation until this point, the  
Figure 9: Evaluation graphs of the NPA algorithm. (**a**) Average Throughput, (**b**) Source to Sink Delay, (**c**) Total Energy Consumed. results given are the same, which is normal, as the algorithm of all scenarios until then run the same commands. In Figure 9(a), we observe that based on the methods used to find an alternative node when requested from the mobile node, the results of the _allocation method_ are slightly better than the optimal method. This is normal due to the nature of the two methods since the allocation method is more strategic and makes use of all information in the neighbor table, accepting an allocation request guarantees that the mobile node will not be further required. On the other hand, the optimal method does not require the node to search for an alternative node by examining exhaustive their neighboring node in order to make its decision. This decision does not always comply with the whole network information. In both methods, it is shown that the worst scenario is the one where the mobile node needs a replacement. This is normal and accepted because the replacement of the node will incur a delay in the routing process until the process is accomplished. The best scenario is the one where the node becomes idle and at some point will need charging and then will return to its initial position. The scenario where the mobile node is reused has definitely better results than the one where no reuse is performed. This is normal because the mobile node needs more time to move from the near sink position in comparison with the node that is already in the network. Figure 9(b) shows the total source to sink delay in the network. It is noticeable that both methods have an increased delay during the simulation. This is normal due to the fact of the collisions that exist in the network where many packets are retransmitted or sometimes dropped. The scenarios with the replacement of the mobile node has the most delay, which is expected due to the fact that more information is injected into the network to accomplish the replacement. The scenarios in which mobile nodes are reused have the lowest results, which is normal due to the fact that the relocation of the mobile node saves time, and fewer packets are re-sent or dropped. In Figure 9(c), we observe that both methods act similarly in response to energy consumption. However, it is noticeable that in all scenarios, the allocation method has slightly more energy consumption than the optimal method, which is acceptable due to the nature of the algorithm sending more information messages in the network. It is shown that after 150 s is the time where each scenario has an individual execution with a different outcome. The scenario with the most consumed energy is the one where no reuse is employed. This can be explained due to the fact that the mobile node inserted into the network will operate until the end, and new congestion issues will be handled by another mobile node. The scenario with the least consumed energy is the one where the mobile node is not required anymore and returns after its battery is exhausted to its initial/charging location. This scenario operates at some point with one less node in the network, which justifies the results. In the scenario of reusing the mobile node already placed in the network, the results are acceptable, where its result is between the no reuse  
Figure 9: Evaluation graphs of eNPA algorithm.