# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 6 Evaluation
### Experimental Results
#### 6.3.1 NPA Experimental Results  
For the execution example in Section 6.2.1, we also present some basic experimental results.  
In Figure 9a, we present the average throughout the network. We observe that as the network load (i.e., sources' data rate) increases, there is a point when the data rate is at 100 pkts/s (i.e., 100-48 bytes/s = 38.4 K bits/s) at which the DAIPaS algorithm starts failing to find alternative paths in the existing topology and the network experiences congestion. At higher network loads (above 150 pkts/s), the network actually disconnects due to congestion (several energy-depleted nodes). This is the point in the simulation when the MobileCC algorithms are initiated.  
Figure 8: Scenarios of returning back. (**a**) Returning Scenario (3A), (**b**) Replacement Scenario (3B).  
The breaking point, when essentially no packets reach the sink, is at a source rate of 300 pkts/s (115.2 Kpps). It is interesting to note that this rate, which is roughly half of the nominal link rate, matches the theoretical results on network capacity found in [32].  
Our results show that, when engaged, both Direct MobileCC and Dynamic MobileCC can relieve the network from the congestion occurrence and maintain the packet transmissions at a high level. The Direct MobileCC algorithm manages to recover from congestion and recover to a received packet ratio of 94%. This is just 3% less than the original 97% achieved with no congestion.  
It is worth mentioning that Direct MobileCC delivers more packets than Dynamic MobileCC. This was expected since Direct MobileCC creates new disjoint paths of mobile nodes to the sink. In this case, any new appearance of congestion hotspots through this path is avoided. On the other hand, the Dynamic MobileCC algorithm places just the required number of nodes in specific points of the network, targeting the creation of new paths and routing traffic through nodes that were not initially accessible. In such a case, congestion may re-appear, especially in cases where some of these nodes are already in use by other flows.  
In Figure 8(b), we present the total source-to-sink delay in the network. In this plot, we notice that both algorithms have a total source-to-sink delay that increases as a function of the source data rate. This is normal due to the fact that collisions exist in the network, and until the network stabilizes with the help of the mobile nodes, many packets are either being re-sent or sometimes are even dropped. As mentioned before, Dynamic MobileCC places only mobile nodes in positions where paths are created from existing nodes in the network, so the delay is higher in comparison to Direct MobileCC, which creates a new path with mobile nodes.  
In Figure 8(c), we present the total energy consumed, measured in \(mJ\), during the operation of the network. In this plot, we observe that both Direct MobileCC and Dynamic MobileCC have a stable increment based on the total packets injected in the network. In comparison, Direct MobileCC has higher energy consumption than Dynamic MobileCC. That was expected, as Direct MobileCC injects more mobile nodes in the network by creating new alternative paths consisting of only mobile nodes.