Framework for the Integration of Transmission Optimization Components into LoRaWAN Stack

Abstract

The Internet of things has grown in recent years, and new applications are now emerging, many requiring long-range coverage and low energy consumption while operating on low data rates. These requirements have driven the emergence of new technologies, like low power wide area networks. LoRa wide area network is one of these technologies that operate on the unlicensed frequency band and is extremely customizable. More specifically, its parameters can be set to increase the quality of packet transmission, by increasing the time on air, at the expense of bandwidth. This also leads to an increase in power consumption. Therefore, in order to increase the data rate and save energy, optimization procedures, which seek to dynamically adjust the airtime, should be used. The goal of this chapter is to architect an optimization agnostic framework for ChirpStack, which is an open-source LoRa wide area network server stack, for the incorporation of any optimizer, e.g., learning agent, aiming to adapt LoRa transmission parameters to the current network scenario. The framework includes a Handler that waits for frame information from the devices, filters relevant data and places it in a broker, and includes a subscriber that gets optimization procedure results from a subscribed topic at the broker and converts them to an acceptable downlink format.

This work is supported by Fundação para a ciência e Tecnologia within CEOT (Center for Electronic, Optoelectronic and Telecommunications) and the UID/MULTI/00631/2020 project.