# Improving Energy Efficiency in LoRaWAN Networks with Multiple Gateways
## 2 Related Works and Contributions  
In LoRa, a collision occurs when multiple nodes send simultaneous messages using the same SF over the same frequency channel. LoRaWAN is based on the Aloha access scheme, where nodes send traffic on a randomly chosen channel. When employing a large number of nodes, such a simple access protocol will increase the probability of collision drastically and therefore reduce the network throughput. On the one hand, reconfiguring a portion of nodes to use higher SFs can improve the network throughput. On the other hand, this will increase the power consumption at nodes with limited batteries. Thus, improving the energy efficiency in such a network by optimizing the total throughput while minimizing the power consumption of battery-powered nodes at large scales is challenging. Moreover, nodes can save power by reducing the TP to the minimum level that ensures the reliability of communications. Consequently, the energy efficiency can be improved using a suitable algorithm that tunes both SF and TP to keep pace with the pervasive green trend in networking. LoRaWAN introduced the adaptive data rate (ADR), a simple mechanism that aims to dynamically adjust the data rate at every node based on its link budget [5]. However, such an algorithm needs a long convergence time (hours to days). It is suitable only for stationary or slow-moving nodes with stable radio conditions [6]. In addition, it manages the transmission parameters at each node individually without considering the overall network performance. There are many recent studies for resource allocation to enhance the ADR scheme in dense LoRaWAN networks. A comparative study of the proposed solutions was presented in [7; 8; 9]. Some of them employ stochastic geometry, where SF selection is based on the distance between the node and the gateway [6; 10; 11]. However, such algorithms are limited to single-gateway scenarios. In addition, the link budget can differ for nodes the same distance from the gateway, especially in cities. Many other algorithms with different objectives were proposed to optimize LoRaWAN networks by configuring one or more LoRa resources (SF, TP, channel). A summary of recently proposed algorithms is given in Table 1.  
The authors of [12] proposed an SF-selection algorithm that aims to maximize the minimum packet reception probability for all nodes considering energy consumption using a distributed genetic algorithm. An optimization problem for SF selection was devised in [13] in order to maximize the network throughput in multi-gateway scenarios. To achieve the optimal SF distribution, an adaptive algorithm that adjusts the signal-to-noise ratio (SNR) thresholds was developed. A heuristic algorithm for SF assignment named AD MAIORA was presented in [14]. The authors intended to decrease the load that some nodes put on some gateways by selecting a given SF. In multi-gateway scenarios, they iteratively found the best SF for each node. The authors of [15] proposed FADR, an SF-selection and TP-control algorithm for single-gateway deployment, to achieve an equal collision probability among different SFs while avoiding overly high TPs to reduce the energy consumption. An MILP was formulated in [17] to derive the optimal SF and CF settings, considering the network traffic specifications, to reduce collisions and energy consumption in LoRaWAN networks. They evaluated the effectiveness of their approach in a single-gateway scenario. Compared to ADR, they improved the DER by 6% while reducing the network energy consumption. The authors of [18] suggested a modified version of the MAC protocol LoRaWAN called LoRa+ for classes A and B consisting of receiving new channel parameters by nodes just before transmission. They showed that their approach decreased the packet error rate by up to 20% compared to legacy LoRaWAN when the number of nodes was less than 500.