## 6 Tool Support  
The DISCO Deterministic Network Calculator (DiscoDNC) [20; 21] offers tool support for network calculus in general, as well as specialized SensorNC optimizations. For both, the DiscoDNC builds upon an implementation of piecewise linear curves and the network calculus operations. A recent overview of its core architecture can be found in [24].  
The DiscoDNC implements the three analysis methods on the path of the flow of interest discussed in Section 3.4, TFA, SFA and PMOO analysis, as well as some more [25; 26]. Additionally, the DiscoDNC provides methods to bound interfering flows' descriptions when they enter the flow of interest's path. Alternatives either implement the pay bursts only once principle (Section 2) or the PMOO principle. They either aggregate the interfering flows or execute a per-flow computation during the computation [27], and all alternatives can be augmented with an additional output burstiness restriction [28]. Thus, the DiscoDNC comprehensively covers a wide range of alternative analyses proceeding from generic network calculus.  
For SensorNC, only a subset of these alternatives is relevant. For example, neither TFA nor SFA can outperform PMOO on the flow of interest's path. On the other hand, SensorNC offers specific solutions exploiting the oftentimes used token-bucket arrival curves and rate-latency service curves and the network's sink tree topology. For both of these specialized settings, the DiscoDNC implements separate code paths, as well. In particular, efficient computations of convolution and deconvolution of token-bucket arrival curves and rate-latency service curves are provided alongside the SensorNC concatenation theorem presented in Section 5. These specialized code paths are also suitable to demonstrate the reduced computational demand of SensorNC compared to generic network calculus [22]. Figure 10 shows such a comparison:  
In Figure 10a, every flow's delay bound in a homogeneous sink tree with 100 sensor nodes is computed. Arrival curves are set to \(\alpha=\gamma_{1,1}\); service curves are \(\beta=\beta_{75,1}\); and the sink tree is randomly created with a maximum of five child nodes per sensor, as well as a maximum sink tree depth of 20. Whereas an alternative approach to achieve flow locality for self-modeling results in considerably worse delay bounds, the generic network calculus approach without flow locality and the SensorNC approach presented in the previous section yield the same delay bounds.  
For a second set of results, we randomly created 40 sink trees of sizes up to 1000 sensor nodes. Each flow in each sink tree network was analyzed with generic network calculus (no flow locality) and SensorNC. The reduction of computation times achieved by SensorNC is shown in Figure 10b. Whereas the reduction is negligible in very small sink trees, it first rapidly increases and stays at a level of about five-times, although slowly decreasing with the WSN's size. Thus, the available tool support provided by the DiscoDNC shows that SensorNC can achieve the same accuracy as generic network calculus, yet at a fraction of the computational cost; a crucial improvement for deployment in self-modeling WSNs.