# Robust Downlink Mechanism for Industrial Internet of Things Using LoRaWAN Networks
## 5 Discussion and Conclusions  
The implementation of interference sources and the proposed DROB mechanism lead to relevant conclusions. On one hand, simulations often fail at some point to consider relevant parameters that are probably only reproducible in real life testbeds. Even using channel and noise models, real applications can introduce unexpected elements hard to consider during simulations. Therefore, apart from trying to improve the simulator with these external interference sources, the mechanism provided is of interest because it addresses cases where channel conditions may change frequently and impact greatly on the network operation.  
From a superficial analysis of the results, two main choices can seem optimal: selecting a fixed DR5, which allows sending more packets and uses less energy, or selecting DR3 to ensure the best PDR results, at the cost of sending fewer messages due to duty cycle restrictions. A relevant comment here is that the tested configuration was selected to be very challenging for class B operations, as the GW tried to send one packet per node per beacon cycle (128 s), and this was the cause of the quantity of buffer discard events. In a real application, the GW would probably need to send much less frequently, reducing greatly the impact on buffer events. In any case, it is a good test of possible situations such as bursty traffic, when the GW needs to send large quantities of data (OTA firmware updates, configurations, etc.)  
The main goal of the proposed mechanism was to adapt to variable conditions, which implies that it worked on the higher DR when possible, but used lower DRs when the channel presents too much interference. Therefore, the results achieved are expected, as the performance of DROB was located between the limits DR5 and DR3. The results when focusing on periods of high frequency interference and noise are promising. This also implies that it can be expected for DROB to succeed more in other scenarios--for instance, in more densely populated or larger areas and distances (note that for the initial analysis with total control of environment variables, the proposed scenario was uniformly distributed). This depends on the conditions of the scenario and the network deployed, so it is key that DROB achieves stable and adaptive results and that it can be used for a wide range of scenarios. By using this type of reactive and autonomous mechanism, the need for deployment pre-planning tools is reduced.  
Therefore, the next steps for refining this mechanism include applying it to a wider range of conditions and scenarios, taking into consideration scalability and application requirements for reducing GW buffer events, and optimizing the duty cycle and scheduling.  
Figure 15: Energy consumed by the whole network during 24 h.  
**Author Contributions:** Methodology, J.S.-B. and V.S.-P.; Software, D.T.-F.; Validation, J.S.-B., V.S.-P. and D.T.-F.; Formal analysis, D.T.-F.; Writing--original draft preparation, D.T.-F.; Writing--review and editing, D.T.-F., [PERSON]-B. and [PERSON]-[PERSON]; Supervision, [PERSON] and [PERSON]-[PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research leading to these results received funding from the Horizon 2020 Program of the European Commission under Grant Agreement No. 825631 \"Zero Defect Manufacturing Platform (ZDMP)\". It was also partially supported by the MCyU (Spanish Ministry of Science and Universities) under the project ATLAS (PGC2018-094151-B-I00).  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to GDPR compliance.  
**Conflicts of Interest:** The authors declare no conflict of interest.