Journal Article

Intelligent Sensors for Real-Time Decision-Making

Coito, Tiago, Figueiredo, João, Martins, Miguel S. E., Firme, Bernardo, Sousa, João M. C., Vieira, Susana M.

2021

Overview

The simultaneous integration of information from sensors with business data and how to acquire valuable information can be challenging. This paper proposes the simultaneous integration of information from sensors and business data. The proposal is supported by an industrial implementation, which integrates intelligent sensors and real-time decision-making, using a combination of PLC and PC Platforms in a three-level architecture: cloud-fog-edge. Automatic identification intelligent sensors are used to improve the decision-making of a dynamic scheduling tool. The proposed platform is applied to an industrial use-case in analytical Quality Control (QC) laboratories. The regulatory complexity, the personalized production, and traceability requirements make QC laboratories an interesting use case. We use intelligent sensors for automatic identification to improve the decision-making of a dynamic scheduling tool. Results show how the integration of intelligent sensors can improve the online scheduling of tasks. Estimations from system processing times decreased by over 30%. The proposed solution can be extended to other applications such as predictive maintenance, chemical industry, and other industries where scheduling and rescheduling are critical factors for the production.

• Publisher: MDPI AG
• Subject: Artificial intelligence / automatic identification / Chemical industry / Computer aided scheduling / Connectivity / Customization / Data mining / Decision making / Decision support systems / fog computing / Humidity / Industrial applications / Interoperability / Laboratories / Machine learning / Predictive maintenance / Programmable logic controllers / Quality control / Radio frequency identification / Real time / real-time systems / Scheduling / Sensors / smart sensors / Task scheduling / Wireless communications