Many industrial sectors still lack automation resources to optimize their production processes, aiming to make manufacturing leaner and offer better working conditions to operators. Without these improvements, workers can suffer physical and even psychological damage from the ergonomic risks of the activities performed. Thus, the aim of this paper is to present the ergonomic evaluation of packaging tapes workstation before and after the implementation of an automatic packaging machine, called Guzzetti. In the Guzzetti context, the paper shows the implementation of an electrical system based on controlling a mechanical device powered by servomotors and controlled by a PLC is necessary. For ergonomic evaluation, the paper presents the application of three methods: Suzanne Rodger, Strain Index, called Moore and Garg and REBA (Rapid Entire Body Assessment). With the results collection, was possible to obtain improvements in ergonomic risks that changed from the intermediate level to low level in all methods.

Monitoring vast and remote areas like forests using Wireless Sensor Networks (WSNs) presents significant challenges, such as limited energy resources and signal attenuation over long distances due to natural obstacles. Traditional solutions often require extensive infrastructure, which is impractical in such environments. To address these limitations, we introduce the “FloatingBlue” architecture. This architecture, known for its superior energy efficiency, combines Bluetooth Low Energy (BLE) technology with Delay Tolerant Networks (DTN) and data mules. It leverages BLE’s low power consumption for energy-efficient sensor broadcasts while utilizing DTN-enabled data mules to collect data from dispersed sensors without constant network connectivity. Deployed in a remote agricultural area in the Amazon region, “FloatingBlue” demonstrated significant improvements in energy efficiency and communication range, with a high Packet Delivery Ratio (PDR). The developed BLE beacon sensor achieved state-of-the-art energy consumption levels, using only 2.25 µJ in sleep mode and 11.8 µJ in transmission mode. Our results highlight “FloatingBlue” as a robust, low-power solution for remote monitoring in challenging environments, offering an energy-efficient and scalable alternative to traditional WSN approaches.

Indoor positioning systems have been driven by the increased use of mobile devices and the need for precise location or orientation in various sectors. Bluetooth Low Energy (BLE) 5.1 stands out for spatial orientation tasks due to its low energy consumption and the availability of resources for angle of arrival (AoA) determination through IQ quadrature samples. The processing of these samples is crucial for AoA estimation. However, the literature does not yet provide machine learning models, specifically deep learning models, to estimate the angle of arrival based on these samples. In this context, this work proposes the use of deep learning techniques for AoA estimation through a regression model applied to Bluetooth-based indoor orientation systems. We present the entire data collection scenario and the necessary procedures for sample and proposed model validation. Using the Mean Absolute Error (MAE) performance metric, we observed an error of 1.38° in estimating the angle of 135°, and overall, the proposed model achieved an MAE of 1.87°.