# Urban Free-Space Optical Network Optimization
## 5 Discussion and Conclusions  
This paper presents a new method for the urban FSO network optimization problem. The method uses the Genetic Algorithm (GA) approach to find a connected graph among the network components (which represent buildings in the city). Until recently, FSO technology was not commonly used as a backbone urban network, mainly due to the vulnerability of the laser communication technology to extreme weather conditions. Yet, recent improvements in FSO technology has pushed the expected availability (\"up-time\") of FSO networks from [99; 99; 99;  
(from 2-3, 9's to 4-5, 9's) [4; 37] allowing the FSO technology to reach service standards required by the urban infrastructure. Thus, the use of FSO in cost-effective 5G networks is expected to increase [37]. The suggested algorithm can be used for both the initial optimal design of an FSO network and for upgrading (adapting) existing FSO network to changes in the network demands and weather conditions. The suggested GA method is designed to create a solution for a variety of problems regarding the minimization of the facilities and infrastructures in urban FSO networks. The algorithm's advantage is considered in two main factors: run time and the \"anytime\" feature. As our experiments show, the GA method has no run-time advantage on relatively small (200 vertices) graphs. However, as the graphs increase in size, our method gains a significant advantage over other methods for calculating the Steiner graph. In addition, after correctly setting the algorithm parameters, it appears that the GA can find high-quality solutions. This advantage can be seen in both the random graphs and the real-world graphs examined. The algorithm is shown to have improved performance on visibility graphs representing real cases of urban information (with relatively high density). Based on these results, and because the algorithm is generic for several problems other than the Steiner problem, we can determine that the algorithm proposed here is suitable for finding relatively low-cost networks on urban FSO graphs. We also observed that the suggested algorithm for finding a connected graph could be generalized to the generic multi-source case. Consider the case of a large city with several internet gateways (a set \(S\) of sources). The multi-source problem is defined as follows. Given a graph \(G=<R,B,E>\) and a set \(S\subset B\) (representing the sources), find a subgraph \(G^{\prime}\subset G\), in which each vertex in \(B\setminus S\) is connected to one of the vertices in \(S\). **Observation 3**. : _Let \(\hat{G}\) be the graph \(G\) with additional edges that connect all the sources. Applying the GA on \(\hat{G}\) will imply a valid (multi-source) solution over \(G\). Figure 29 demonstrates a GA solution on a multi-source graph._  
For future work, we plan to examine whether the suggested methods can be expanded to dynamic networks with adaptive weights (costs) on the edges. Another possible generalization of the model is to construct robust two-connectivity solution. **Author Contributions:** This research was equally contributed by all three authors: [PERSON], [PERSON]-M., and T.G. Conceptualization, B.B.-M. and R.M. ; methodology, T.G. ; software, R.M. ; validation, B.B.-M., T.G. and R.M. ; formal analysis, T.G. ; data curation, R.M. ; writing--original draft preparation, [PERSON]M., T.G., and [PERSON]; writing--review and editing, T.G. ; visualization, R.M. ; All authors have read and agreed to the published version of the manuscript. **Funding:** This work was partially supported by the Ariel Cyber Innovation Center in conjunction with the Israel National Cyber Directorate in the Prime Minister's Office. **Conflicts of Interest:** The authors declare no conflicts of interest. Figure 29: Multi-source graph example. Graph **(a)** is a full red–blue visibility graph comprised of two subgraphs with a single connecting edge (with weight 0). Graph **(b)** is the GA result for graph (a).