# Temporal Graphs and Temporal Network Characteristics for Bio-Inspired Networks during Optimization
## 8 Conclusions  
We demonstrated how to apply quantitative methods of temporal network analysis to our Voronoi-like algorithms in order to compare and analyze the dynamics of WSN coverage and robustness in the face of environmental noise and obstructions. GA + Voronoi in particular demonstrated robustness in the face of thee various environmental conditions. To summarize, we can provide the following answers to our research questions in Section 1:  
Figure 8: Time -lag correlation heat maps for time-dependent eigenvector centralities from Figures 6 and 7. (selected node pairs shown only—more data in SOM). (**upper**) Voronoi; (**lower**) GA + Voronoi; (both) ND = 0.05 and 10 obstacles. With Voronoi-only, nodes move outwards during their deployment in a regular fashion, and their importance to connectivity and information relaying drops off with this outward movement. GA + Voronoi demonstrates similar behavior; however, nodes are more likely to reestablish their importance later. 2. We see that Voronoi-only in a noise-free environment maintains a small number of long-lasting connections. Adding noise substantially redistributes those connection lengths. By contrast, GA + Voronoi maintains qualitatively similar connection length distributions through a variety of environments, featuring a rapidly changing topology with short, frequent connections between nodes. In all cases, nodes are highly likely to associate only with nodes sharing a similar deployment time
3. We see that noise increases the deviation from regularity, smooths out the EC time trace, and impacts the connection length distribution in the Voronoi-only cases. By contrast, noise has a much smaller impact on the same measures applied to the GA + Voronoi cases. This supports previous work that had indicated Voronoi-only changes substantially in the face of noise, while GA + Voronoi is robust to such environmental changes. 4. Utilizing temporal network characteristics allows us to measure and observe the behavior of the network as a whole and if individual nodes. 5. All this was possible without the explicit use of physical measurements of the nodes' coverage or their environment. These measures also suggest some kind of deeper functional equivalence between Voronoi-only in noisy environments and GA + Voronoi. Such quantification measurements may prove useful in further refining our algorithms, by allowing us to fine-tune communication routing protocols, broadcasting strength, and other implementation details based on the expected behavior [86; 87]. While mapping these theoretical network measures onto applied performance metrics can perhaps be challenging, we do believe there are some clear points to take note of. When discussing regularity, we noted that regularity difference rose in the noisy Voronoi-only cases and GA + Voronoi cases. This irregularity seems to have some correspondence to speed of coverage, and we posit that such highly irregular structures may be crucial in this rapid deployment. By contrast, the lower regularity difference of Voronoi-only likely corresponds to its slower but more consistent nature. This parallels the behavior we see in measuring percent area covered (PAC), with GA + Voronoi rapidly reaching high levels of PAC, while Voronoi-only slowly converges towards an acceptable value. We also discussed EC among nodes in the various cases. By looking at EC, we see that nodes consistently move towards positions of lower importance, especially in the Voronoi-only cases. Fluctuations following this are indicative of a node relocating/reorganizing itself in the network and are much more common in the application of GA + Voronoi. This fluctuating corresponds to the cumulative distance traveled (CDT) measures. Assuming a fluctuating EC value corresponds to a particularly mobile node, we would expect the distance travel to be much higher for cases with fluctuating EC. Indeed, our previous research indicated GA + Voronoi nodes travel significantly longer distances, seemingly matching this explanation.