# Digital Twins, Virtual Devices, and Augmentations for Self-Organising Cyber-Physical Collectives +
## 5 Evaluation
### Experiment Results and Discussion
Figure 8 shows that the count of generated virtual devices \(N_{v}\) grows sub-linearly with time, and approximately linearly with shorter ranges. Deploying more explores leads to more virtual devices generated more quickly in the initial phase of the simulation, and then to a flatter generation curve, as most of the map gets covered. Finer strategies to deploy devices may present further trade-offs, especially in case virtual devices can be removed dynamically. Finally, we remark that the _augmented CDT approach_ can be useful whenever there is the need to affect the behaviour of collective and self-organising systems. Example scenarios might include swarm robotics [13], WSNs [38] and crowd computing [21], as well as large-scale CPS ecosystems. For instance, a CDT can be developed to represent and control the state and motion of a fleet of drones; at this point, virtual devices may be spawned to _dynamically_ regulate the overall distribution and direction of the fleet, using techniques like those presented in [13; 38]. The idea is that such a soft control technique can help when (i) we cannot change the behaviour of the components of the CPS, (ii) to avoid the specification of an overly complex behaviour (e.g., whereby decisions are based on several non-local situations or to previsions about future events), or (iii) to apply perturbations or simulate conditions that do not exist in the real world (e.g., to test how the CPT reacts to crowding situations). In other words, the approach provides an _additional_ mechanism for self-organisation design (steering). Whether it is the most suitable approach depends on the problem at hand, but some general observations can be made. Indeed, the approach appears most applicable when the application depends on spatial structures (e.g., to propagate information, to move in space, or to make decisions based on spatial arrangements). More generally, it is a matter of decentralisation and partial observability: in our and most approaches to self-organisation engineering, the behaviour of individuals is organised in terms of local environment information (e.g., sensed data) and messages (e.g., neighbour data); augmentations through virtual devices, then, allow changes in the local context of the individual devices, hence affecting the overall emergence of global results. The main issue, which may affect applicability, lies in the ability to determine reasonable local contexts for the virtual devices (or, similarly, defining their ad-hoc interactive behaviour). Figure 8: System cost in computational resources, using the count of virtual devices \(N_{v}\) as a proxy metric, for 50 explorers (**left column**) and 500 explorers (**right column**), and increasing logical communication range \(R\) (**top** to **bottom**). The system cost grows more than linearly with shorter \(R_{v}\) values. Considering the performance seen in Figures 6 and 7, it is clear that the value of \(N_{v}\) needs to be selected carefully, as values too small do not improve performance substantially, but increase the computation cost considerably. Figure 9: System communication cost, measured as the sum of messages received per round on all real devices \(\sum_{j\in H}M_{j}\), for 50 explorers (**left column**) and 500 explorers (**right column**), and increasing logical communication range \(R\) (**top to bottom**). Similarly to the results depicted in Figure 8, the cost grows more than linearly with \(R_{\beta}R\) The cost is lower for intermediate values of \(R\), suggesting that a trade-off in the logical communication range could be found between performance and use of network resources.