Combining Proximity Estimation With Visible Symbol Assignment to Simplify Line-of-Sight Connections in Mobile Industrial Human-Machine Interaction

Abstract

The mobile industrial human-machine interaction plays an important role in the industrial internet of things, since the engineers can use a mobile device to interact with machines that greatly improves the efficiency and safety. Nevertheless, connecting to a specific machine becomes a non-trivial problem due to the massive machines in the network that make the connection list too long to identify the target machine. Some solutions such as QR code scanning and proximity estimation have been proposed to solve this problem. However, they have limited performance in scalability and accuracy correspondingly, and thus cannot satisfy the requirements in most applications. Observing the fact that the engineers generally interact with the machines in their line-of-sight, we propose the LightCon scheme which adopts proximity estimation to estimate the machines in the line-of-sight, and controls the display module of machines to show different visible symbols (colors or numbers). To connect with a specific machine, the engineers just need to select the corresponding symbol on the mobile device. Therefore, they do not have to remember the trivial address of each machine. Furthermore, the symbol assignment algorithm is designed to reduce the complexity of manual symbol selection, and its performance is analyzed theoretically. The performance of LightCon is evaluated in the testbed, and the experimental results prove that LightCon is a promising solution to simplify line-of-sight connections with low complexity.

Keywords

• Line-of-sight connection
• proximity estimation
• visible symbols
• mobile industrial human machine interaction
• industrial Internet of Things