# A Novel Routing Scheme for Creating Opportunistic Context-Virtual Networks in IoT Scenarios +
## 8 Conclusions and Future Work  
The increase in the number of Internet-connected smart devices and the deployment of the IoT paradigm in the forthcoming 5G era has raised the need for these devices to behave and coordinate \begin{table}
\begin{tabular}{c c c c c c} \hline
**Variable** & **B** & **Std. Error** & **Beta** & **f** & **Sig.** \\ \hline \(d_{\text{prob}}\) & \(-0.025\) & \(0.023\) & \(-0.113\) & \(-1.122\) & \(0.265\) \\ \(\theta\) & \(1.719\) & \(0.141\) & \(0.777\) & \(12.228\) & \(0.000\) \\ \(\tau\) & \(7.168\) & \(1.340\) & \(0.475\) & \(5.348\) & \(0.000\) \\ \(\gamma\) & \(0.023\) & \(0.001\) & \(0.898\) & \(20.164\) & \(0.000\) \\ \(\delta\) & \(-1.363\) & \(0.207\) & \(-0.553\) & \(-6.568\) & \(0.000\) \\ \hline \end{tabular}
\end{table} 
Table 4: OLS regression coefficients with \(N_{g}\) as the independent variable. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Variable** & **B** & **Std. Error** & **Beta** & **f** & **Sig. ** \\ \hline \(d_{\text{prob}}\) & \(1.521\) & \(0.222\) & \(-0.231\) & \(-2.349\) & \(0.021\) \\ \(\theta\) & \(6.794\) & \(1.698\) & \(0.375\) & \(4.002\) & \(0.000\) \\ \(\tau\) & \(3.236\) & \(10.652\) & \(0.031\) & \(0.304\) & \(0.762\) \\ \(\gamma\) & \(-0.047\) & \(0.009\) & \(-0.483 themselves depending on the interests and needs of their users. Therefore, the network has to provide support to this exchange of information in environments where devices have some mobility and the connections among them is intermittent, depending on their concrete location and the used communication interface. This paper proposes the creation of Opportunist Context-Virtual Networks (OCVNs) based on the devices' contextual interests and their capabilities. In OCVNs, IoT devices with similar interests can share their virtual profiles and adapt themselves to the considered context. In particular, four novel routing algorithms, namely SACAR-IR, SACAR-OCVN, SACAR-HC, and SACAR-Hybrid, were proposed to exchange the users' context and interests to the OCVN they belong to at a particular time dynamically. These algorithms allowed us to improve the exchange of contextual information in IoT environments with mobile nodes, without overloading those IoT devices not interested in those data; improving, thus, the behavior of the whole system. The proposed algorithms have been evaluated over two representative scenarios based on an office and a mall with IoT smart devices. These scenarios were chosen because they represent common IoT deployments in which end users are involved, first at a personal level in small areas (such as their home or office) and, second, in medium-sized areas (such as malls, concerts, etc.). Simulation results show that SACAR algorithms outperformed other well-known opportunistic routing algorithms in terms of delivery probability and overhead ratio, while the resource usage of relaying nodes was significantly reduced. Moreover, the multivariate analysis (OLS regression) performed to know the impact of the number of nodes over several performance metrics also reinforced the conclusion that nodes resources were efficiently used by our proposed solution. As future work, we are currently working on applying machine learning techniques in order to use more complex contextual information to perfectly identify those nodes that should belong to a particular OCVN. In addition, since IoT networks are also used in large scenarios with a high number of nodes and large geographical coverage, we are also working on evaluating these algorithms in more complex environments such as agriculture and emergency scenarios in which a higher number of nodes is involved with a larger variability of interests. Finally, cyber-security threats are specially challenging in opportunistic and delay-tolerant networks due to their self-organizing nature and uncertainty in the nodes composing them. These issues are even more challenging in environments in which contextual and sensitive information is shared, such as presented in this paper.