4.8 Article

LoRa Performance in Industrial Environments: Analysis of Different ADR Algorithms

Journal

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 19, Issue 10, Pages 10501-10511

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2023.3240696

Keywords

Adaptive data rate (ADR); energy consumption; industrial applications; packet delivery ratio (PDR); propagation model

Ask authors/readers for more resources

This article compares the performance of LoRaWAN with six different ADR algorithms in industrial environments. The research shows that algorithms that decrease the link budget in poor channel conditions have better performance. Overestimating the channel behavior decreases packet delivery.
Long-range wide-area network (LoRaWAN) technology can be a good candidate for industrial applications in the Internet of Things and adaptive data rate (ADR) algorithms help to improve LoRaWAN performance. These algorithms compute a link budget to define the data rate and the transmission power. However, the performance of these algorithms must be evaluated in industrial environments that pose challenging conditions to wireless communications due to obstacles, interference, and movement. Therefore, this article compares the performance of LoRaWAN with six different ADR algorithms using a realistic propagation model from an industrial environment. This research uses the framework for LoRa (FLoRa) environment in objective modular network testbed in C++ (Omnet++) to compare the packet delivery ratio and energy consumption with different gateway locations and data traffic. Results show that the algorithms that decrease the link budget in poor channel conditions have better performance in industrial scenarios. Furthermore, overestimating the channel behavior decreases packet delivery.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.8
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available