# Robust Downlink Mechanism for Industrial Internet of Things Using LoRaWAN Networks
## 2 Related Work  
Simulations must be made as close as possible to real world conditions so results can be extrapolated correctly. Related work and studies fail to consider special noise or interference sources that may appear in these harsh environments (machines, motors, etc.), other than regular baseline channel noise. The study presented in [7] shows Nakagamimodels can fairly model baseline channel fading in LoRa due to the environment. Another interesting case study, with practical measurements, can be found in [8]. The work to model individual interference sources was inspired by [9] and [10]. This article analyses the impact of this type of interference by modelling and simulating noise sources in the same scenario as the network.  
A good starting point for ADR mechanisms, how they work, and interesting proposals in the scientific community is the review of ADR-related work and the state of the art that can be found in [11], where authors present recent solutions and challenges. In other recent relevant work, [12] presents a new adaptive strategy for smart metering application focusing on uplink, and [13] introduces a new MAC protocol called FCA-LoRa, based on LoRaWAN, which addresses the downlink stage, but relies on multiple GWs to achieve results. The authors of [14] analyse the possibility of optimizing performance by modifying the SF used, but instead of using the ADR mechanism, the gradient projection method was chosen. Numerical results are obtained for a number of nodes from 0 to 10,000 with the use of SF7, SF8, and SF9 and compared to theoretical limits. This case shows that the length of the packet can be a consideration in optimizing performance but lacks downlink traffic analysis.  
Regarding the scope of simulators, [15; 16] show simulations performed with ns3 for LoRaWAN technology. The authors of [15] analyse an indoor industrial scenario with LoRaWAN class A devices. Simulations have been performed with a number of nodes ranging from 10 to 1000 within a radius of 200 m. These simulations compared the probability of success with constant spreading factors (SFs). On the other hand, battery consumption has also been compared with other technologies such as IEEE 802.15.4. The authors of [16] studied LoRaWAN class B devices. In this article, devices were distributed within a radius of 6100 m. Two simulations were performed to study downlink performance, one with 1% and the other with 10% duty cycle channel limitation. However, this work does not consider the impact of enabling ADR, as DR was kept static during the simulations in both uplink and downlink.  
Other simulation tool options can also be found in the literature. A LoRa-MAB simulator is analysed in [17]. The performance of LoRaWAN class A during uplink was evaluated with 100 nodes, with the possibility of selecting an SF parameter from 7 to 12. Nodes were distributed in a radius of 4.5 km. The duty cycle limitation was 1%. In this case, the algorithm used improved the performance of LoRaWAN regarding the value of successful packages and energy consumption. However, the connection times were excessively long.  
In [18], the FADR algorithm with a LoRaSim simulator is presented. Simulations were performed for a number of nodes from 100 to 4000 within a radius of 100 to 3200 m. This mechanism controlled the transmission power but could not completely eliminate all collisions. In [19], the mechanism of ADR is analysed using the LoRaWANSim simulator. The chosen duty cycle was 1% with a radius of 670 m. The results show network behaviour for a different number of nodes. This work did not analyse class B device operation.  
Some initial tests and simulations with ns3 can be found in [20], but while this study addressed ADR for downlink, it lacked a realistic scenario model to validate different channel condition changes, and the ADR implementation itself could be improved with changes to the key statistics for triggering operations.