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Long Range (LoRa) wireless technology, characterized by low power consumptionand a long communication range, is regarded as one of the enabling technologiesfor the Industrial Internet of Things (IIoT). However, as the network scaleincreases, the energy efficiency (EE) of LoRa networks decreases sharply due tosevere packet collisions. To address this issue, it is essential toappropriately assign transmission parameters such as the spreading factor andtransmission power for each end device (ED). However, due to the sporadictraffic and low duty cycle of LoRa networks, evaluating the system EEperformance under different parameter settings is time-consuming. Therefore, wefirst formulate an analytical model to calculate the system EE. On this basis,we propose a transmission parameter allocation algorithm based on multiagentreinforcement learning (MALoRa) with the aim of maximizing the system EE ofLoRa networks. Notably, MALoRa employs an attention mechanism to guide each EDto better learn how much ''attention'' should be given to the parameterassignments for relevant EDs when seeking to improve the system EE. Simulationresults demonstrate that MALoRa significantly improves the system EE comparedwith baseline algorithms with an acceptable degradation in packet delivery rate(PDR).

Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks