Throughput of an IEEE 802.11 Wireless Network in the Presence of Wireless Audio Transmission: A Laboratory Analysis

Abstract

Wireless networks, including IEEE 802.11-based or Wi-Fi networks, are inexpensive and easy to install and therefore serve as useful connectivity alternatives in areas lacking wired-network infrastructure. However, IEEE 802.11 networks may not always provide the seamless connectivity and minimal throughput required for Industry 4.0 communications because of their susceptibility to interference from other devices operating in the unlicensed “Industrial, Scientific, and Medical” frequency band. Here we analyzed how a wireless audio transmitter operating on this band influences the throughput of an IEEE 802.11 b/g/n network under laboratory conditions. Wireless audio transmission reduced mean throughput by 85%, rendering the IEEE 802.11 b/g/n network nearly unusable. Our analysis suggests that in order for IEEE 802.11 wireless networks to support Industrial 4.0 applications, attention should be paid to the physical layer as well as the data or upper layers, and critical services should not transmit on the 2.4 GHz band. These findings may contribute to understanding and managing IEEE 802.11 wireless networks in various Industry 4.0 contexts.

Industry 4.0 Solutions Impacts on Critical Infrastructure Safety and Protection–A Systematic Literature Review

“…One study [119] addressed Industry 4.0 and national security issues, while another paper by the same team [120] presented considerations regarding cryptocurrency and national security. Montalban et al [121] described non-orthogonal multiple access (NOMA) in combination with the 802.11n standard, and Forenbacher et al [122] described the results of a laboratory analysis on the use of an IEEE 802.11 wireless network in the presence of wireless audio transmissions. Another study [101] discussed the issues of security and privacy challenges in IIoT, while [123] discussed the issue of securing emergent IoT applications.…”

Machine learning based wireless interference estimation in a robotic force-seeking application

“…The study of the impact, evaluation, and suppression of interference on industrial control systems has been investigated in [12,13] and the references therein. Interference level in an industrial environment can be estimated either using the received signal strength at various nodes of the system or performing spectrum measurements through distributed nodes [14,15]. Although both these approaches can achieve a good estimation result statistically, their real-time performance largely relies on the system's capabilities of transferring data between distributed nodes and processing of data from multiple sources.…”

“…The total cycle time, ∆t ae , is a considered feature where a higher interference level is expected to increase the total cycle time. In the nth cycle, the value of ∆t ae is defined as 15…”