# Temporal Graphs and Temporal Network Characteristics for Bio-Inspired Networks during Optimization
## 3 Overview of the BISON and GA-BISON Approaches  
We previously reported our work using a Voronoi-based algorithm for the autonomous self-deployment of a WSN into two-dimensional bounded target spaces of unknown geometry and topology in [4; 5]. The proposed method assumed entering the target space from predefined inlet and triggering the sequential controlled and optimized release of vehicle or drone carried WSN nodes, which autonomously spread and connect throughout the space to rapidly form a blanket coverage network ready for delivering a variety of sensing, monitoring or communication services. The sensor nodes autonomously move toward their range-dependent, partially observable Voronoi cells' centers, as shown in Figure 1, maintaining a stable collision-free flow designed to rapidly explore and cover the whole target space at the minimum possible time, using as few nodes as possible and draining as little energy as possible, all without any prior knowledge about the geometry of the space and the obstacles in it [60]. Extensive sets of simulated deployment experiments demonstrate convergence to the stable near-full coverage network, achieved at a fraction of a deployment cost and time, compared to competitive models reported in the literature, which allow to consider BISON as a strong new approach to AI-flavored, blanket-coverage WSN deployment achieved virtually without any human intervention. In an attempt to further improve, simplify and generalize Voronoi-only deployment, the core BISON algorithm is merged with the localized Genetic Algorithm (GA) applied to push the trade-off between the pace of space exploration and the energy expense further towards faster deployments, especially when faced with complex obstruction structures. The GA with Voronoi approach has an influence on the discovery process of the next best possible local position of nodes in the network [61]. The opportunity of evaluating several candidate solutions instead of Voronoi centroid allowed sensor nodes to discover better locations, hence enhancing the network performance at faster rates [51; 53; 62]. GA allowed us to control the randomization of the candidate solutions to best fit our optimization problem, by tuning the location and the rate of the generated solutions in every iteration. \begin{table}
\begin{tabular}{p{12.3 pt} p{12.3 pt} p{12.3 pt} p{12.3 pt}} \hline \hline  & **Voronoi + ACO**[27; 36; 37; 46] & **Voronoi + GA**[11; 28; 31; 32; 38; 43; 45; 50; 51; 52; 53; 54; 55; 56; 57] & **Voronoi + PSO**[29; 30; 39; 40; 42; 44; 58; 59] \\ \hline
**Method** & Voronoi tessellations determine _every_ possible paths in the entire network while ACO can then be used to identify the _shortest_ path. & Voronoi tessellations detect coverage holes and GA generates favorable node locations to reduce energy consumption and maximize network lifetime. & Voronoi tessellations are used to detect coverage holes, PSO generates virtual points (Voronoi vertices). \\ \hline
**Process** & Assign weight values to the Voronoi edges to guide the search. & Change the node distribution and add extra mobile nodes. & Change either a node’s location and velocity and/or the node’s sensing range. \\ \hline
**Impact** & Impacts node distribution, weights and the evaluation function. & Influences the objective function as well as coverage holes (and GA parameters). & Affects virtual points, node-location and -velocity and best known local/global solution. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A brief overview over the prior work combining Voronoi tessellations with other approaches. We identified three popular nature-inspired heuristics that are favored in the literature: _Ant Colony Optimization_ (**ACO**), _Genetic Algorithms_ (**GA**) or _Particle Swarm Optimization_ (**PSO**). In the above table, we distinguish between _how the two approaches are applied_ (the **Method**), _what this does/how this works_ (the **Process**) and _the effect(s) this has_ (the **Impact**).