Abstract

Long-Range Wide-Area Network (LoRaWAN) enables flexible long-range service communications with low power consumption which is suitable for many IoT applications. The densification of LoRaWAN, which is needed to meet a wide range of IoT networking requirements, poses further challenges. For instance, the deployment of gateways and IoT devices are widely deployed in urban areas, which leads to interference caused by concurrent transmissions on the same channel. In this context, it is crucial to understand aspects such as the coexistence of IoT devices and applications, resource allocation, Media Access Control (MAC) layer, network planning, and mobility support, that directly affect LoRaWAN’s performance.We present a systematic review of state-of-the-art works for LoRaWAN optimization solutions for IoT networking operations. We focus on five aspects that directly affect the performance of LoRaWAN. These specific aspects are directly associated with the challenges of densification of LoRaWAN. Based on the literature analysis, we present a taxonomy covering five aspects related to LoRaWAN optimizations for efficient IoT networks. Finally, we identify key research challenges and open issues in LoRaWAN optimizations for IoT networking operations that must be further studied in the future.

Document Type

Article

Publication Date

5-11-2021

Notes/Citation Information

Published in IEEE Access, v. 9.

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.

Digital Object Identifier (DOI)

https://doi.org/10.1109/ACCESS.2021.3079095

Funding Information

This work was supported in part by the Research and Development project entitled ‘‘IoT-cloud de medição de energia centralizada voltado a rede CEA’’ under Grant 001/2017, in part by the national funding from the Fundação para a Ciência e a Tecnologia (FCT) under Project UID/EEA/500008/2019, in part by the MAYA project (MCTIC/CGI/FAPESP) under Grant 2020/05155-6, in part by the Brazilian National Council for Research and Development (CNPq) under Grant 309335/2017-5, and in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES) under Grant 001.

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