Optimization of LoRa SF Allocation Based on Deep Reinforcement Learning

Abstract

LoRa is an IoT communication technology that realizes ultra-long-distance transmission through spread spectrum modulation. However, its ultra-long-distance transmission also sacrifices the corresponding rate, and data conflicts are prone to occur when the number of nodes is large. In this article, we investigate various types of data collisions in LoRa wireless work, most of which are affected by Spreading Factor (SF) assignment. At present, the distribution of the SF for LoRa in the industry is mostly based on Min-airtime and Min-distance. In the case of a large number of nodes, the data collision between nodes will increase sharply. This paper proposes a SF redistribution scheme under limited network resources, in order to improve the terminal capacity of the LoRa gateway. First, the problem of minimizing the data collision rate without expanding gateway or network resources is presented. Specifically, the reallocation of SF with increasing number of terminals is studied. Finally, considering the randomness of the data sent by the terminal, SF redistribution schemes based on deep reinforcement learning (DRL) are developed. The simulation results show that the collision rate of the proposed SF redistribution scheme is nearly 30% lower than Min-airtime and Min-distance, and its total energy consumption is close to Min-distance. Therefore, the proposed SF redistribution scheme can effectively improve the gateway capacity of LoRa wireless network.

A Comprehensive Survey of Data-Driven Solutions for LoRaWAN: Challenges & Future Directions

“…The proposed Artificial Neural Network (ANN) estimates optimal SFs for EDs, reducing collisions and optimizing energy consumption. Similarly, in [48], a DRL approach was developed to minimize collision rates in LoRa dense networks. Furthermore, few researchers introduced novel approaches to specific challenges in the LoRaWAN standard.…”

“…Packet Delivery Ratio, Total Energy Consumption [48] Minimizing the collision rate without any additional resource or infrastructure.…”

A Scalable and Energy-Efficient LoRaWAN-Based Geofencing System for Remote Monitoring of Vulnerable Communities

“…Existing studies focusing on optimizing routing protocols in MANETs and LoRa networks have emphasized the significance of reducing overhead and enhancing network efficiency [11], [12]. The ECHO protocol [8] is designed to deliver messages across LoRa mesh network nodes efficiently.…”

LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning

“…Their proposed multi-agent DRL ADR mechanism showed improved energy consumption compared to the traditional ADR. Another approach based on DRL for optimizing the SF allocation is studied to improve the GW capacity of the LoRa network [ 173 ]. The proposed approach utilizes a DQN to learn the optimal SF assignment policy for a given network state.…”

“…LoRaSim is a valuable framework for understanding the behavior and performance of LoRaWAN. It can be used to design and optimize LoRaWAN networks to ensure reliable and efficient communication, as utilized in [ 29 , 30 , 70 , 141 , 173 , 181 , 182 , 183 , 184 , 185 , 186 , 187 , 188 , 189 , 190 , 191 , 192 , 193 , 194 , 195 , 196 ].…”