# Performance Evaluation of LoRa Communications in Harsh Industrial Environments
###### Abstract  
LoRa technology is being integrated into industrial applications as part of Industry 4.0 owing to its longer range and low power consumption. However, noise, interference, and the fading effect all have a negative impact on LoRa performance in an industrial environment, necessitating solutions to ensure reliable communication. This paper evaluates and compares LoRa's performance in terms of packet error rate (PER) with and without forward error correction (FEC) in an industrial environment. The impact of integrating an infinite impulse response (IIR) or finite impulse response (FIR) filter into the LoRa architecture is also evaluated. Simulations are carried out in MATLAB at 868 MHz with a bandwidth of 125 kHz and two spreading factors of 7 and 12. Many-to-one and one-to-many communication modes are considered, as are line of sight (LOS) and non-line of Sight (NLOS) conditions. Simulation results show that, compared to an environment with additive white Gaussian noise (AWGN), LoRa technology suffers a significant degradation of its PER performance in industrial environments. Nevertheless, the use of forward error correction (FEC) contributes positively to offsetting this decline. Depending on the configuration and architecture examined, the gain in signal-to-noise ratio (SNR) using a 4/8 coding ratio ranges from 7 dB to 11 dB. Integrating IIR or FIR filters also boosts performance, with additional SNR gains ranging from 2 dB to 6 dB, depending on the simulation parameters. LoRa; industrial environment; industrial noise; interference; fading; FEC; IIR filter; FIR filter; LOS; NLOS +
Footnote †: journal: Sensor and Actuator Networks  
as the presence of metallic equipment, make it difficult to transmit data over long or medium distances [14; 15; 16; 17]. This leads to insufficient bandwidth and throughput to reach the sensors [18]. It is used in machine monitoring, asset tracking, energy management, and environmental monitoring [8; 9; 10; 11]. However, the quality of data transmission via LoRa can be affected by environmental constraints [13]. Multiple obstacles, high temperatures, humidity, excessive dust, and particles, as well as the presence of metallic equipment, make it difficult to transmit data over long or medium distances [14; 15; 16; 17]. This leads to insufficient bandwidth and throughput to reach the sensors [18]. Indeed, the implementation of wireless communications in industrial environments can be complex due to several factors [14; 19; 20; 21; 22]. On the one hand, interference resulting from signal reflection, echoes, and multipath attenuation causes alterations in transmissions. These interferences are caused by obstacles or reflective surfaces, resulting in multipath propagation. In addition, the presence of other wireless devices operating in the same frequency range also contributes to this interference, impacting overall communication performance. On the other hand, electromagnetic emissions from various industrial sources, such as heavy machinery, powerful generators, lasers, etc., generate high noise levels that disrupt wireless communications. This makes communication between transmitters and receivers difficult, resulting in data loss. Atmospheric conditions, such as temperature and humidity, present a further challenge to wireless communications by altering signal propagation. Extremely high or low temperatures affect receiver sensitivity and transmitter stability, resulting in reduced range and signal quality. The use of LoRa as a wireless technology is limited by these factors in many industrial scenarios, although LoRa holds great promise for the industry. As a result, evaluating the performance of LoRa technology in these environments is of vital importance for improving the reliability of data sent and optimizing communication networks for critical industrial applications. In this work, we explore and highlight the potential and challenges of LoRa technology within industrial environments, relying on rigorous MATLAB simulations.