## 6 Conclusions  
To address the low reliability of the RPL protocol in mobile IoT applications, we have proposed SDMob. In this proposed architecture, an edge device collaborates with the distributed nodes to provide seamless handoff for the mobile nodes. However, new challenges arise, such as delivering real-time link quality reports to the controller as well as the downward packets to install root-initiated routes. SDMob addresses these challenges by employing a lightweight controller that tunes its operation for a constrained environment. Having the controller deployed, the computation-intensive tasks can be offloaded to the controller to benefit from the global view and resources available at the centralized controller.  
The results show that SDMob significantly improves RPL and outperforms state-of-the-art ARMOR with close to 100 percent PDR, with reasonable and adjustable overhead in scenarios with a varying number of mobile nodes. Given the requirements of the applications, SDMob by design aims at reliably maintaining the connectivity of a limited number of mobile nodes since with increasing mobile nodes the control traffic increases as well. The solutions in the literature that aim at a higher number of mobile nodes usually make a compromise by less reliable communication for mobile nodes. Through analytical evaluations, we analyzed the behavior of the system when exposed to increasing path loss variance in the radio environment, and velocity of the mobile node which was also verified by simulations. We extended the simulation results to networks with varying densities of neighbors, the distance of the mobile node from the controller (in terms of the number of hops), and mobility patterns.  
For future work, we consider employing redundant controllers to avoid a single point of failure in the control layer, although, in the event of a failure in the controller, RPL resumes its normal operation. Another interesting direction is applying machine learning methods to either localize the mobile node and predict its link quality in time, or automatically optimize different system parameters such as beacon interval and congestion and buffer timers.  
Conceptualization, I.R. and H.F.; methodology, I.R., M.A., and H.F.; software, I.R. and S.P.S.; analytical evaluation, I.R.; writing--original draft preparation, I.R.; writing--review and editing, H.F., M.A., S.P.S., M.V., A.B., and M.B.; visualization, I.R. and S.P.S.; project administration, H.F.; funding acquisition, H.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Swedish Foundation for Strategic Research via the FiC project, by the Swedish Research Council (Vetenskapsradet) through the MobiFog starting grant, Vinnova by the GREENER project, the ECSEL Joint Undertaking (JU) projects InSecTT, and DAIS under grant agreement No.876038 and No.101007273.  
[]**Institutional Review Board Statement:** Ethical review and approval were not applicable for this study.  
[]**Informed Consent Statement:** Not applicable.  
Figure 18: Average localization error as a function of (**a**) MN’s velocity, and (**b**) path loss variance.  
Data Availability Statement: Not applicable.  
The authors would like to thank [PERSON] for sharing his implementation of ARMOR.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.