# Temporal Graphs and Temporal Network Characteristics for Bio-Inspired Networks during Optimization
## 2 Review of Prior Work Using Voronoi Tessellations for WSN Deployment  
As mentioned above, the BISON algorithm was merged with a localized Genetic Algorithm (GA) to facilitate faster deployment of the WSN nodes. While GA ended up being our preferred choice, this is by no means a foregone conclusion, as other approaches have also been successfully used to achieve similar goals. While we refer the reader to [4, 5] for the in depth discussion of the various benefits and disadvantages as well as the motivation for our choices, we will briefly review the most prominent approaches found in the literature. In the literature, approaches to WSN deployment and optimization are often based on heuristics. For example, Ant Colony Optimization (**ACO**) [27; 28] has been shown to increase a network's lifetime by reducing the individual nodes' energy consumption, a feat they achieve by optimizing the routing paths between the individual nodes in an established WSN. Particle Swarm Optimization (**PSO**) [29; 30; 31] and Genetic Algorithm (**GA**) [32; 33] can reduce the energy consumption during node deployment by suggesting superior node positions. This can reduce the number of steps required for the deployment as well as reduce the energy consumption of the WSN. Voronoi-based approaches [34; 35; 36] are commonly used to maximize the nodes' coverage by detecting existing coverage holes. We summarized a comparison between these three approaches in Table 1, but first we would like to briefly elaborate how each of them can be used to complement or augment Voronoi tessellations:  
* **Ant Colony Optimization (ACO):** often used in combination with Voronoi tessellations, it is used to optimize the routing of communication between nodes within the network. Characteristically, ACO continuously explores and optimizes the connection paths in a WSN [37; 38]. * **Particle Swarm Optimization (PSO):** merging PSO with Voronoi tessellations is a common approach to maximize the coverage and prolong the lifetime of the WSN [39; 40; 41; 42; 43]. For example, the authors of [41; 42] use PSO to determine the next position of a sensor nodes, while Voronoi tessellations are generated to detect the coverage holes and evaluate the fitness function. Such results normally rely on either a centralized node to perform the majority of the computational work (such as the gathering of information about all nodes' locations, performing calculations, and suggesting new locations/new sensing ranges for each sensor node) or assume that each node has prior knowledge about its location and that of all other nodes. The application goal then determines whether the objective function focuses on either minimizing the energy consumption or instead on minimizing the gaps in coverage. * **Genetic Algorithms (GA):** GAs have also been widely used in combination with Voronoi tessellations, often in the context of WSN deployment and to address the problem of node re-allocation. Results indicate WSN coverage and network lifetime can be enhanced this way [11; 44; 45; 46; 47; 48]. Note that these approaches rely heavily on the GA using a developed objective function; the Voronoi tessellations exclusively detect coverage holes. The latter requires prior knowledge about the nodes' locations; GA candidate solutions are restricted to inside their respective Voronoi regions. The reduction of coverage gaps can be achieved by moving WSN nodes away from/toward their neighbors [11; 45] with new nodes being added to locations as specified by the GA [44]. Both GA and PSO can be used to reduce the energy consumption in a WSN and have been shown to extend the network's lifetime by improved routing and informed node movements. As GA only generate new candidate solutions inside a node's coverage and on the basis of locally available information [49] this seems an advantageous choice, given that our use case is a network of physically independent drone borne nodes.