# Building Topic-Driven Virtual IoTs in a Multiple IoTs Scenario
## 5 Topic-Guided Virtual IoTs in a MIoT and Approaches to Constructing Them
### Discussion  
An important issue about the supervised and the unsupervised approaches to address regards their scalability or, better, the possibility to use them in MIoTs comprising thousands or even millions of nodes.  
With regard to this issue, first of all we observe that both approaches aim at deriving virtual IoTs which are, then, exploited by users to perform their desired tasks (such as querying). As a consequence, we can distinguish two moments in the life of a MIoT, namely: _(i)_ the construction of virtual IoTs, which can be performed _offline_, and _(ii)_ their usage, which is generally carried out _online_.  
The first moment is computationally expensive because it involves several network operations in the supervised approach and a clustering activity in the unsupervised one. Clustering's computational cost is intrinsically exponential even if all the corresponding methods adopted in the reality are heuristic and most of them have a linear or a quadratic computational complexity. In any case, as pointed above, this task is performed offline and rarely because it is necessary only when many changes have been made in the MIoT.  
The second moment is certainly less expensive; its cost depends on the size of the involved clusters; in fact, each user activity generally involves one or a few clusters. Concerning this aspect, it is important to verify: _(i)_ if clustering is possible in presence of huge MIoTs, and _(ii)_ how the size of clusters increases against the growth of the MIoT. As for the first point, we observe that, in the past, several algorithms have been specifically conceived to cluster a huge amount of elements [60]. Concerning the second point, instead, first we observe that the size of clusters can be determined by suitably tuning the parameters of the selected clustering algorithm. However, it could be interesting to verify how much the size of clusters increases if we maintain constant all the clustering algorithm parameters and the MIoT size increases. We decided to perform this experiment. It is described in detail in Section 6.6. Here, we evidence the obtained results, i.e., that when the MIoT size highly increases, the cluster size slightly grows, whereas the number of clusters increases very much. This is a positive result for our purposes because the parameter to monitor for investigating the performance obtained during the second moment is just cluster size.  
Another important issue to investigate regards the possible existence of a unique framework handling all the objects of the MIoT and, therefore, in principle, thousands or millions of objects. With regard to this aspect, we evidence that, in the past, several attempts have been successfully performed in this direction (think, for instance, of the SIoT framework proposed in [9; 40]). Clearly, we understand that, in the future, the number of objects possibly belonging to a MIoT is enormously higher than the number of objects available in the past IoT frameworks. However, we point out that: _(i)_ our approach needs to store only the metadata of the involved objects, and these are small; _(ii)_ the real objects can operate in a distributed environment thanks to the new available technologies, such as cloud, edge and fog computing, which can ease the organization and the management of distributed contexts.