Dynamic Reinforcement Learning based Scheduling for Energy-Efficient Edge-Enabled LoRaWAN

Abstract

Long Range Wide Area Network (LoRaWAN) is suitable for wide area sensor networks due to its low cost, long range, and low energy consumption. A device can transmit without interference if it chooses a unique channel, spread factor, transmission power different than any other transmitting device in network. However, in a dense network, the probability of interference increases because number of devices exceeds the total number of unique choices thus mandating retransmission after collision until successfully transmitted. Eventually, energy consumption of devices increases. In this poster, we present a Deep deterministic policy gradient reinforcement learning-based scheduling algorithm to improve energy efficiency by collision avoidance in a dense LoRaWAN network. We support our proposition with evaluation results for reducing energy consumption.