Introduction: One of the main challenges of Industry 4.0 is ensuring effective communication between different automation systems, such as industrial robots and Programmable Logic Controllers (PLCs). This work presents a validation test procedure for communication between an industrial robot controller and a PLC using a Seamless Message Protocol (SLMP) IoT protocol, addressing the critical need for seamless integration in modern manufacturing environments.

Methods: The proposed solution uses TCP/IP sockets in conjunction with the SLMP protocol, developed by the CC-Link Partner Association, which enables communication between industrial devices using conventional Ethernet networks. The implementation was carried out in the RAPID language, specifically designed for industrial robot programming. Systematic validation tests were conducted to evaluate communication performance, measuring success rate, average response time, and potential communication errors across multiple test cycles.

Results: The experimental validation demonstrated exceptional performance with a 100% success rate and an average response time of 11.3 ms over 100 consecutive test cycles. No communication errors were detected during the entire testing period, indicating robust and reliable data exchange between the robot controller and PLC.

Conclusions: The results confirm the viability and efficiency of the proposed SLMP-based solution for applications in industrial environments that require integration of different automation equipment, providing a reliable foundation for Industry 4.0 implementations

Three-phase induction motors are widely used in industrial systems and are responsible for a significant portion of electricity consumption. Monitoring their operating conditions is essential to support maintenance strategies and reduce unexpected downtime. This work presents the development and implementation of a real-time monitoring system for electrical and mechanical parameters of three-phase induction motors using embedded Internet of Things (IoT) technologies. The proposed system integrates an ESP32 microcontroller with multiple sensors for measuring temperature, vibration, current, and voltage. The collected data are transmitted wirelessly using the MQTT protocol to a monitoring interface built in Python, providing real-time visualization through a graphical dashboard. The firmware was developed using FreeRTOS to manage concurrent tasks for sensor acquisition and data transmission, ensuring synchronization and efficient processing. Prototyping involved two custom-printed circuit boards (PCBs): one for sensor data acquisition and system management and another dedicated to power quality measurement using the ADE7758 chip. Validation tests were conducted by coupling the system to a three-phase induction motor under laboratory conditions. The temperature and vibration sensors recorded readings that varied according to motor behavior, with the vibration sensor capturing signals during both motor startup and steady-state operation. Electrical measurements included current and voltage values obtained through the power quality monitoring circuit, with variations observed in specific intervals due to noise and assembly-related factors.

In emergency scenarios where visibility is compromised, rapid and accurate object detection becomes critical. This study addresses this challenge by proposing an IoT-enabled robotic solution capable of operating in low-visibility environments, with a focus on supporting search and rescue missions through autonomous sensing and real-time data communication. This research presents the development and implementation of an IoT-based sensorized system designed to detect objects in low-visibility environments. The system aims to enhance search and rescue operations by identifying potential human presence in areas with limited access due to smoke, darkness, or hazardous conditions. The platform integrates distance sensors, a thermal camera (AMG8833), a PIR motion sensor, and wireless communication through the Arduino MKR1000 and ESP32-CAM boards. The mobile robot is equipped with obstacle avoidance, person detection, and IoT communication modules, allowing data to be sent to the cloud via ThingSpeak and enabling remote commands through TalkBack. A structured methodology was followed, including technology selection, hardware/software design, and testing under various lighting and opacity conditions. Experimental results showed the effectiveness of the system in identifying obstacles and detecting heat signatures representing human bodies, with optimal performance observed at a 15 cm detection threshold. The system demonstrated robust operation in simulated rescue environments, providing real-time data transmission and remote-control capabilities.

This study investigates Free Space Optical (FSO) communication performance in Algeria's desert environments, where extreme temperatures, low humidity, and dust storms challenge reliability. Through numerical simulations, we analyze atmospheric turbulence effects using the refractive index structure parameter (Cn²), specifically calibrated for El Oued's severe climate conditions.

Our research evaluates critical performance metrics including signal-to-noise ratio (SNR), bit error rate (BER), and scintillation index across different optical wavelengths. Results demonstrate that longer wavelengths (1550 nm) show significantly better turbulence resilience compared to shorter wavelengths (850 nm), providing essential guidance for FSO system design in arid regions. The study reveals a clear correlation between turbulence strength and signal degradation, with BER increasing exponentially under strong turbulence conditions.

Furthermore, we examine climate change impacts on FSO viability, as projections indicate increasing extreme heat, droughts, and dust storms in Algeria. These changes are expected to intensify atmospheric turbulence, potentially reducing FSO reliability by 15-20% in coming decades. Our analysis shows that adaptive wavelength selection and power adjustment strategies can mitigate these effects, maintaining acceptable performance levels even under worsening conditions.

This work provides both theoretical and practical contributions to FSO deployment in challenging environments. The Algerian case study offers valuable insights for similar desert regions worldwide, addressing the critical need for robust communication infrastructure in climate-vulnerable areas. Our findings support the development of more resilient FSO systems capable of withstanding the combined challenges of existing harsh conditions and future climate changes, while maintaining high-bandwidth performance essential for modern communication networks.

Introduction: The integration of legacy machines into modern Industry 4.0 environments remains a significant challenge due to the lack of embedded communication capabilities. Many industrial assets operate without the ability to provide real-time electrical data, limiting their inclusion in digitalized ecosystems.
Methods: This study proposes a monitoring strategy based on the Reference Architectural Model for Industry 4.0 (RAMI 4.0) framework to enable the acquisition and historical recording of electrical parameters from non-connected machines. An IoT-based solution was implemented using an ESP32 microcontroller and an ADE9000 energy metering integrated circuit to measure key electrical parameters, including voltage, current, active power, and energy consumption. Data were transmitted through wireless communication and structured according to Asset Administration Shell (AAS) principles for interoperability and digital representation.
Results: The implementation resulted in a functional digital twin capable of providing real-time monitoring of electrical parameters and storing historical data. The system successfully integrated a previously isolated asset into a connected industrial environment, ensuring compliance with Industry 4.0 standards.
Conclusions: This work demonstrates a standardized and scalable strategy for monitoring and digitizing legacy equipment. The approach enables efficient data collection, historical analysis, and enhanced decision-making, contributing to predictive maintenance and energy optimization. It offers a practical solution for bridging the gap between outdated industrial assets and modern digital ecosystems.

Water scarcity is a major challenge in agriculture, particularly in regions reliant on irrigation for crop growth, such as deserts. Traditional irrigation methods, which often follow fixed schedules and manual observations, result in inefficient water application. Soil moisture sensors could offer a solution by providing real-time data on soil moisture levels, enabling more precise irrigation scheduling based on actual demands rather than estimations. This study evaluates the impact of integrating soil moisture sensors into irrigation systems to enhance water efficiency and agricultural productivity. The field study was conducted in the Thal Desert (Punjab), Pakistan, where water management is critical due to scarcity. The study compares the two irrigation methods—traditional and sensor-based—over 10 months in multiple crop fields. Weekly soil moisture data were collected at various depths, alongside measurements of water applied, irrigation frequency, crop yield, and soil health indicators. Statistical data were analyzed using ANOVA to assess the effectiveness of both irrigation methods. The results show that sensor-based irrigation method led to a 20-30% reduction in water consumption compared to traditional methods, while simultaneously enhancing crop yield by 15-25%. These findings demonstrate that soil moisture sensors, even actuating once a week, significantly improve irrigation efficiency, reducing water consumption, and boosting crop productivity. These results make the soil sensor-based irrigation systems a promising solution for water management in water-scarce regions.

Introduction: Elderly people sometimes experience fall accidents since some of them cannot recognize their own stability in walking. Thus, fall prevention systems that measure and inform unstable walking have been developed. However, many previous systems required multiple sensors. The purpose of this study was to develop and test a gait recognition system using only a single inertial sensor for daily fall prevention. Methods: The proposed method recognizes an unstable gait by machine learning with trunk inertial data (three-axis acceleration and angular velocity) on the lower back and body measurement values (height and weight). The proposed method was trained and tested by the North American Congress on Biomechanics (NACOB) multi-surface walking dataset published by Jlassi et al. The trunk inertial data and body measurement values of 134 people in the NACOB public dataset were used in this study. The proposed method recognized two gait patterns on flat (stable) and bumpy (unstable) roads. Machine learning was implemented using the k-nearest neighbor algorithm (k=1). Training and testing were conducted via 5-fold cross validation. The accuracy and confusion matrix of gait recognition were evaluated. Results: The results showed that the proposed method could recognize stable and unstable gait patterns with greater than 80% accuracy. This accuracy was comparable to previous gait recognition. Conclusions: The results indicate the possibility that the proposed method can be used for daily gait recognition systems using a single inertial sensor. Acknowledgements: This study was supported by JSPS KAKENHI (Grant Number: 25K16012).

This study investigates the electrical and thermal behavior of an inductively coupled plasma (ICP) torch operating at atmospheric pressure. A comprehensive numerical model is developed under the assumption of local thermodynamic equilibrium (LTE), enabling the analysis of temperature and current density distributions within the plasma region. The temperature field is primarily governed by ionization phenomena and Joule heating, both of which contribute significantly to the overall energy balance. Simultaneously, the current density is influenced by the spatial variation of the plasma's electrical properties, particularly its electrical conductivity. The model also accounts for the magnetic field induced by the alternating current in the induction coils. This self-generated magnetic field plays a critical role in plasma confinement and influences both the flow dynamics and the spatial distribution of electrical energy deposition. By coupling electromagnetic, thermal, and fluid flow equations, the simulation provides detailed insights into the physical mechanisms responsible for plasma stabilization and energy transfer. The main objective of this work is to deepen the understanding of the complex interactions between electromagnetic and thermal fields in ICP torches. Such understanding is essential for improving torch design and performance in various industrial processes, including materials processing, waste treatment, and surface modification. The results obtained can guide the optimization of operating parameters to achieve better energy efficiency, uniform temperature profiles, and improved plasma stability.

With the growing demand for high-energy and high-power lithium-ion batteries in electric vehicles and stationary energy storage systems, large-format pouch cells have emerged as a preferred solution due to their high energy density, lightweight construction, and design flexibility. However, their large physical dimensions and the use of localized tab-based current collection introduce significant challenges, particularly in achieving uniform current and potential distribution across the electrodes. These spatial in homogeneities can result in uneven electrode utilization, localized heat accumulation, and accelerated degradation of cell components. This study presents a comprehensive three-dimensional multiphysics simulation of a large-format lithium-ion pouch cell using COMSOL Multiphysics®. The model incorporates realistic geometry, material properties, and electrochemical parameters to investigate the influence of tab placement and collector design under different charging regimes. Fast charging at 4C is shown to induce notable ohmic voltage drops in the current collectors, approximately 5 mV in copper and 9 mV in aluminum, leading to strongly asymmetric lithium intercalation. Current density is initially concentrated near the tabs but progressively shifts toward central regions due to local saturation effects. These findings underline the critical role of cell architecture in dictating performance and reliability. Optimizing tab configuration, increasing collector conductivity, and improving electrode layout are shown to significantly enhance current uniformity, minimize thermal hotspots, and extend battery life. The results demonstrate that 3D multiphysics modeling is a powerful tool for diagnosing internal imbalances and guiding the design of next-generation lithium-ion batteries with improved durability and efficiency.

Wireless sensor systems, today widely adopted within the Internet of Things (IoT) paradigm, can collect and share large amount of data for different applications, but they are also vulnerable to cyberattacks. The impact of cyberattacks on the systems’ confidentiality, integrity and availability can be mitigated by the adoption of authentication procedures and cryptographic algorithms. Authentication passwords and cryptographic keys may be stored in a non-volatile memory, which may be easily tampered. Alternately, Physical Unclonable Functions (PUFs) can be adopted. They generate a chip’s unique fingerprint, by exploiting the randomness of process parameters’ variations occurring during chip fabrication, thus constituting a more secure alternate to the adoption of non-volatile memories for password storage. PUF reliability is of primary importance to guarantee system availability. In this paper, the reliability of a SRAM-based PUF implemented by a standard 32nm CMOS technology is investigated, as a function of different operating conditions, such as noise, power supply voltage and temperature, also considering different values of transistor conduction threshold voltages. The achieved results will show that transistor conduction threshold voltage tolerance and noise are the operating conditions that mostly affect PUF reliability, while the impact of temperature variations is lower, and the impact of power supply variations is negligible.