# Improving Energy Efficiency in LoRaWAN Networks with Multiple Gateways
## 2 Related Works and Contributions
The authors of [16] devise optimization problems to derive the optimal configuration at each node to improve reliability and minimize power consumption in dense LoRaWAN deployment. Since the proposed problems are nonlinear, they were transformed into integer linear programming (ILP) models and solved using IBM CPLEX. A two-stage optimization process was introduced to improve the delivery ratio and minimize the energy consumption in networks with multiple gateways. In the first stage, all nodes are assumed to use the highest TP. They proposed two approaches to assign SF to each node. The first, named OPT-MAX, aims to minimize the maximum probability of collision at each SF, like [20]. The second, named OPT-DELTA, derives the SF for all nodes in a way to balance the probability of collisions in all SFs. Once the SFs for all nodes are assigned, they apply the power-control stage, OPT-TP, to minimize the energy consumption of all nodes. They showed that their proposed solution outperforms the packet delivery ratio (PDR) by up to 8% compared to the state of the art with minimal energy consumption. However, the complexity of ILP is high and it requires a long computation time, which increases exponentially with the number of nodes. The authors of [19] suggested a low-power multi-armed bandit (LP-MAB) mechanism, a centralized adaptive LoRaWAN configuration scheme. They intended to improve the PDR while reducing energy consumption. They used exponential weights for exploration and exploitation (EXP3), as well as the successive elimination (SE) methodology, to combine non-stationary adversarial and stochastic methods. They configured the transmission \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Objective** & **SF Selection** & \
\begin{tabular}{c} **Power** \\ **Control** \\ \end{tabular} &
\begin{tabular}{c} **Multiple** \\ **Gateways** \\ \end{tabular} \\ \hline
[12] & maximize the minimum packet reception probability for all nodes & ✓ & & \\
[13] & maximize network throughput & ✓ & & ✓ \\
[14] & reduce the pressure that some nodes put on some gateways & ✓ & & ✓ \\
[15] & achieve equal collision probability among different SFs & ✓ & ✓ & \\
[16] & improve reliability & ✓ & ✓ & ✓ \\
[17] & reduce collisions and energy consumption & ✓ & & \\
[18] & reduce the packet error rate & ✓ & & ✓ \\
[19] & improve the PDR and reduce energy consumption & ✓ & ✓ & ✓ \\ EE-LoRa & maximize energy efficiency & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of relevant proposed algorithms. parameters of nodes centrally on the network server. The network server tries to learn the optimal set of transmission parameters for each node based on the reward, which is dependent on the receipt of acknowledgment messages. The studies in [12; 15; 17] are limited to single-gateway deployments, whereas the studies in [12; 13; 14; 17; 18] lack a power-control strategy. In contrast to [19], whose method is focused on acknowledgment messages delivered from the network server, we focus our research on class A with unconfirmed transmission mode, where nodes do not request an acknowledgment from the network server after each transmission. Contrary to the work in [14; 16], we do not consider the load of nodes at the gateway level, which requires more complicated calculations and a longer computation time. However, we concentrate on improving the overall network's energy efficiency by extending its lifetime and increasing the throughput with our EE-LoRa algorithm. Moreover, after allocating the SFs and TPs of each node, we implicitly redistribute the load across the various gateways. We summarize the major contributions of our work as follows:  
* We propose a novel algorithm, named EE-LoRa, to improve the energy efficiency of LoRaWAN networks with multiple gateways. We devise an optimization problem that maximizes the energy efficiency of the network, defined as a benefit-cost ratio between the network throughput and the consumed energy.