## 4 Extending Delta Network to Global Solutions: Using Scores to Customize the Coverage  
The previous section exploits solutions to solve the Delta Network considering a specific performance target given by a single tuple \(\{\rho_{1}\),\(\rho_{2}\}\) (i.e., a single point within the curve shown in Figure 1). However, finding feasible solutions (or even, the optimal solution) for one single pair of \(\{\rho_{1}\),\(\rho_{2}\}\) may not guarantee good connectivity for other combinations of \(\{\rho_{1}\),\(\rho_{2}\}\). Thus, now we focus on the main problem of this study: we consider solutions from a global perspective. We are no longer interested in optimizing the network for one single pair \(\{\rho_{1}\),\(\rho_{2}\}\), but we explore strategies for optimizing the Delta Network as a whole. We propose a simple greedy strategy for exploring properties of the Delta Network. As we show, slight changes in the deployment strategy lead to interesting changes in the coverage pattern of the vehicles. Figure 3: Percentage of road network that must be covered in order to achieve the Delta Network for pairs of \(\rho_{1}\) and \(\rho_{2}\) when solving the deployment using _Delta-g_. Recall that \(\rho_{2}\) indicates the share of vehicles that must be connected during \(\rho_{1}\) percent of the trip duration. Results consider the Cologne mobility trace. Source: [PERSON] et al. [53] with kindly permission. Figure 2: Partitioning the road network in a grid-like structure. Source: [29] With kindly permission. Algorithm 1 presents the general greedy heuristic that is used to perform the experiments. It receives as input the trace of vehicles (\(T\)), and the number of available cells to be covered. The set of covered cells is reset. Then, for each uncovered cell, the heuristic covers the cell and re-runs the mobility trace in order to measure the connectivity of vehicles in terms of {\(\rho\)1,\(\rho\)2}. Since no cell is initially covered, the heuristic starts by covering cell [0,0] and evaluates the coverage. Then, it moves the coverage from location [0,0] to [0,1], and recomputes the coverage, and so forth, until evaluating all locations of the grid (lines 3-7). Then, it selects the cell presenting the highest _score_ (score is measured according to the interest of the network designer, and, in this article, it is customized to create three strategies). The cell presenting the highest _score_ is selected permanently for receiving coverage (lines 8-9). Then, the heuristic loops until selecting \(\alpha\) covered cells. The **score computation** (line 5) deserves special attention. In fact, in our opinion, the most interesting feature of this heuristic is the ability to customize the _score_. By customizing the _score_ we have the opportunity to impose specific properties on the vehicular network, such as selecting the kind of trip that we intend to prioritize, and how connectivity is distributed across vehicles. In order to demonstrate how the _score_ computation impacts the outcome of the heuristic, we present three strategies for computing the _score_: (a) Strategy _balanced_; (b) strategy _direct_; and (c) strategy _inverse_. ```
1:\(T,\alpha\); \(\triangleright\) Receives the trace and number of available cells for coverage. 2:covered_cells \(\triangleright\)\(\alpha\) covered cells.