# Cyber Physical Systems Dependability Using CPS-IOT Monitoring
## 6 Conclusions  
This paper addressed the issue of the dependability of CPS by using mathematical epidemiology to model the CPS-IoT monitoring process and proposed a new reconfiguration protocol that uses network partitioning through clustering and nodes migration between clusters to restore the safety of CPS-IoT subsystems. A CPS-IoT monitoring model was proposed as a key component of a CPS orchestration system that uses a closed loop to affect inbound surveillance of the physical space and outbound actuation (based on epidemic curves visualised and analysed in the cyber space of the CPS). The model employs state transitions to identify the combination of parameters best suited for both sensitive and non-sensitive applications. Numerical analysis of the proposed model revealed that: (i) The transmission between diffusion sets could indeed be used to quantify energy usage in the CPS-IoT subsystem by showing that an increase in interference could cause nodes to move across states. (ii) By adjusting the migration rates based on the networking needs, high availability and/or fault tolerance/resilience, could be achieved. (iii) With predictable migration time, the proposed model could mitigate the destructive effect of interference on the network, especially when deployed in an autonomous mode. Finally, the experimental results of the proposed interference-aware clustering protocol with node migration revealed the relevance of the partitioning of the network in terms of energy consumption and interference as well as the beneficial impact of balancing nodes across the CPS-IoT network.  
Amending CTPs algorithms to track the diffusion sets occupancy in order to move the path selection towards a given application specific behaviour is an avenue for future work. Achieving Quality of Service (QoS) in the CPS-IoT subsystem through service differentiation as done in [43] can also be addressed as extensions to this work. Finally, network reconfiguration through parameters optimisation could also be another possible direction for further research work. The extension of the work done in [44; 45] with the techniques proposed in this paper when viewing teams of drones as cyber physical systems is another avenue for future work.  
Conceptualisation, A.B.; methodology, A.B.; software, O.A. and H.M.; validation, O.A. and A.B.; formal analysis, A.B.; investigation, O.A.; data curation, O.A. and H.M.; writing--original draft preparation, A.B.; writing--review and editing, O.A.; visualisation, H.M.; supervision, A.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Configuration 4**} & \multicolumn{3}{c}{**Configuration 5**} \\ \hline \multirow{3}{*}{Sink4} & Highest & Average & Highest & Average \\  & 6.6\% & 3.9\% & 6.48\% & 3.88\% \\  & 6.3\% & 3.79\% & 6.55\% & 3.92\% \\  & Variance & 0.045. & 0.0162 & 0.00245 & 0.0008 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Node migration: Energy consumption (86-nodes).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Configuration 4**} & \multicolumn{3}{c}{**Configuration 5**} \\ \hline \multirow{3}{*}{Sink5} & 0 & 1 & 2 & 3 & 0 & 1 & 2 & 3 \\  & 25 & 12 & 8 & 4 & 22 & 10 & 7 & 4 \\  & 19 & 9 & 6 & 3 & 20 & 11 & 8 & 4 \\  & 18 & 4.5 & 2 & 0.5 & 1.414 & 0.707 & 0.5 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Node migration: Node interference (86-nodes).