A high-dimensional, multi-transceiver channel state information dataset for enhanced human activity recognition.

Human Activity Recognition (HAR) has emerged as a critical research area due to its extensive applications in various real-world domains. Numerous CSI-based datasets have been established to support the development and evaluation of advanced HAR algorithms. However, existing CSI-based HAR datasets are frequently limited by a dearth of complexity and diversity in the activities represented, hindering the design of robust HAR models. These limitations typically manifest as a narrow focus on a limited range of activities or the exclusion of factors influencing real-world CSI measurements. Consequently, the scarcity of diverse training data can impede the development of efficient HAR systems. To address the limitations of existing datasets, this paper introduces a novel dataset that captures spatial diversity through multiple transceiver orientations over a high dimensional space encompassing a large number of subcarriers. The dataset incorporates a wider range of real-world factors including extensive activity range, a spectrum of human movements (encompassing both micro-and macro-movements), variations in body composition, and diverse environmental conditions (noise and interference). The experiment is performed in a controlled laboratory environment with dimensions of 5 m (width) × 8 m (length) × 3 m (height) to capture CSI measurements for various human activities. Four ESP32-S3-DevKitC-1 devices, configured as transceiver pairs with unique Media Access Control (MAC) addresses, collect CSI data according to the Wi-Fi IEEE 802.11n standard. Mounted on tripods at a height of 1.5 m, the transmitter devices (powered by external power banks) positioned at north and east send multiple Wi-Fi beacons to their respective receivers (connected to laptops via USB for data collection) located at south and west. To capture multi-perspective CSI data, all six participants sequentially performed designated activities while standing in the centre of the tripod arrangement for 5 s per sample. The system collected approximately 300-450 packets per sample for approximately 1200 samples per activity, capturing CSI information across the 166 subcarriers employed in the Wi-Fi IEEE 802.11n standard. By leveraging the richness of this dataset, HAR researchers can develop more robust and generalizable CSI-based HAR models. Compared to traditional HAR approaches, these CSI-based models hold the promise of significantly enhanced accuracy and robustness when deployed in real-world scenarios. This stems from their ability to capture the nuanced dynamics of human movement through the analysis of wireless channel characteristic from different spatial variations (utilizing two-diagonal ESP32 transceivers configuration) with higher degree of dimensionality (166 subcarriers).

A low-cost wireless extension for object detection and data logging for educational robotics using the ESP-NOW protocol.

In recent years, inexpensive and easy to use robotics platforms have been incorporated into middle school, high school, and college educational curricula and competitions all over the world. Students have access to advanced microprocessors and sensor systems that engage, educate, and encourage their creativity. In this study, the capabilities of the widely available VEX Robotics System are extended using the wireless ESP-NOW protocol to allow for real-time data logging and to extend the computational capabilities of the system. Specifically, this study presents an open source system that interfaces a VEX V5 microprocessor, an OpenMV camera, and a computer. Images from OpenMV are sent to a computer where object detection algorithms can be run and instructions sent to the VEX V5 microprocessor while system data and sensor readings are sent from the VEX V5 microprocessor to the computer. System performance was evaluated as a function of distance between transmitter and receiver, data packet round trip timing, and object detection using YoloV8. Three sample applications are detailed including the evaluation of a vision-based object sorting machine, a drivetrain trajectory analysis, and a proportional-integral-derivative (PID) control algorithm tuning experiment. It was concluded that the system is well suited for real time object detection tasks and could play an important role in improving robotics education.

SMART GROW - Low-cost automated hydroponic system for urban farming.

Urban farming has gained popularity in recent years, as more people have become interested in locally grown food and reducing their carbon footprint. Smart hydroponic systems can be an important tool for urban farming as they allow for precise control over plant growth and require less space and resources than traditional farming methods. Urban areas often lack access to land suitable for farming, making hydroponic systems a viable option for growing crops in limited space. Readily available hydroponic systems in the market are costly and not cost effective, thus hydroponic systems are usually only installed in larger scale farming. The challenge here is to connect multiple low-cost sensors to microcontrollers and to any store-bought hydroponic set. This paper describes the development of smart Internet of Things (IoT) hydroponic system integrated with an Android mobile application for small scale urban farming. The new set up of IoT hydroponic set, coined as SMART GROW, is used to monitor and control various aspects of the system based on the basic parameters important in growing a healthy plant. The challenges faced during this build were irregular reading of the analog sensor when connected to a single board microcontroller (ESP32). This issue was resolved. SMART GROW currently is capable of monitoring basic parameters such as pH, EC and water level and can cater to additional sensors for monitoring other parameters if required. SMART GROW can easily be replicated and built at home and customized to the needs of the plant's requirement. SMART GROW is versatile as it can be used to grow a wide variety of plants, including herbs, vegetables, and fruits, and offers several benefits over traditional soil-based growing methods such as automated regulation of the water level.

Low-Power Long-Term Ambulatory Electrocardiography Monitor of Three Leads with Beat-to-Beat Heart Rate Measurement in Real Time.

A low-power long-term ambulatory ECG monitor was developed for the acquisition, storage and processing of three simultaneous leads DI, aVF and V2 with a beat-to-beat heart rate measurement in real time. It provides long-term continuous ECG recordings until 84 h. The monitor uses a QRS complex detection algorithm based on the continuous wavelet transform with splines, which automatically selects the scale for the analysis of ECG records with different sampling frequencies. It includes a lead-off detection to continuously monitor the electrode connections and a real-time system of visual and acoustic alarms to alert users of abnormal conditions in its operation. The monitor presented is based in an ADS1294 analogue front end with four channels, 24-bit analog-to-digital converters and programmable gain amplifiers, a low-power dual-core ESP32 microcontroller, a microSD memory for data storage in a range of 4 GB to 32 GB and a 1.4 in thin-film transistor liquid crystal display (LCD) variant with a resolution of 128 × 128 pixels. It has programmable sampling rates of 250, 500 and 1000 Hz; a bandwidth of 0 Hz to 50% of the selected sampling rate; a CMRR of -105 dB; an input margin of ±2.4 V; a resolution of 286 nV; and a current consumption of 50 mA for an average battery life of 84 h. The ambulatory ECG monitor was evaluated with the commercial data-acquisition system BIOPAC MP36 and its module for ECG LABEL SS2LB, simultaneously comparing the morphologies of two ECG records and obtaining a correlation of 91.78%. For the QRS detection in real time, the implemented algorithm had an error less than 5%. The developed ambulatory ECG monitor can be used for the analysis of the dynamics of the heart rate variability in long-term ECG records and for the development of one's own databases of ECG recordings of normal subjects and patients with cardiovascular and noncardiovascular diseases.

Design of a smart hydroponics monitoring system using an ESP32 microcontroller and the Internet of Things.

This paper presents the design and construction of a hydroponics monitoring system that can collect parameters of hydroponic systems, such as temperature, water limit, pH level, and nutrient levels. The monitoring system was developed using an ESP32 microcontroller and several sensors, including total dissolved solids (TDS), pH, water level, and temperature sensors. The ESP32 microcontroller gathers and processes data from the sensors to automatically activate the water or salt pump and drain the necessary materials into the hydroponic system's plant basin. The user can then view the hydroponic parameters through the Blynk application on a smartphone. The user can also activate the pumps for water, nutrients, or salt using the application's interface on a smartphone, or the ESP32 microcontroller can activate them automatically if the parameter values deviate from the required values. The monitoring hydroponics system and IoT interface were successfully built and implemented. The experiments were compiled, and the data gathered and discussed.•An ESP32 microcontroller with TDS, pH, water level, and temperature sensors was used to build the hydroponic monitoring system.•The ESP32 automatically collects and evaluates sensor data in order to drain water nutrients, or salt into the plant basin of the hydroponic system as necessary.•The user can also check the parameters of the hydroponic system and, if necessary, run the pumps for water, fertilizers, or salt using his smartphone through the Blynk IoT app.

Rapid IoT Prototyping: A Visual Programming Tool and Hardware Solutions for LoRa-Based Devices.

LoRa technology has gained popularity as one of the most widely used standards for device interconnection due to its ability to cover long distances and energy efficiency, making it a suitable choice for various Internet of Things (IoT) monitoring and control applications. In this sense, this work presents the development of a visual support tool for creating IoT devices with LoRa and LoRaWAN connectivity. This work significantly advances the state of the art in LoRa technology by introducing a novel visual support tool tailored for creating IoT devices with LoRa and LoRaWAN connectivity. By simplifying the development process and offering compatibility with multiple hardware solutions, this research not only facilitates the integration of LoRaWAN technology within educational settings but also paves the way for rapid prototyping of IoT nodes. The incorporation of block programming for LoRa and LoRaWAN using the Arduinoblocks framework as a graphical environment enhances the capabilities of the tool, positioning it as a comprehensive solution for efficient firmware generation. In addition to the visual tool for firmware generation, multiple compatible hardware solutions enable easy, economical, and stable development, offering a comprehensive hardware and software solution. The hardware proposal is based on an ESP32 microcontroller, known for its power and low cost, in conjunction with an RFM9x module that is based on SX127x LoRa transceivers. Finally, three successfully tested use cases and a discussion are presented.

Design and Implementation of ESP32-Based IoT Devices.

The Internet of Things (IoT) has become a transformative technology with great potential in various sectors, including home automation, industrial control, environmental monitoring, agriculture, wearables, health monitoring, and others. The growing presence of IoT devices stimulates schools and academic institutions to integrate IoT into the educational process, since IoT skills are in demand in the labor market. This paper presents educational IoT tools and technologies that simplify the design, implementation, and testing of IoT applications. The article presents the introductory IoT course that students perform initially and then presents some of the projects that they develop and implement on their own later in the project.

Development a Low-Cost Wireless Smart Meter with Power Quality Measurement for Smart Grid Applications.

Developing a low-cost wireless energy meter with power quality measurements for smart grid applications represents a significant advance in efficient and accurate electric energy monitoring. In increasingly complex and interconnected electric systems, this device will be essential for a wide range of applications, such as smart grids, by introducing a real-time energy monitoring system. In light of this, smart meters can offer greater opportunities for sustainable and efficient energy use and improve the utilization of energy sources, especially those that are nonrenewable. According to the 2020 International Energy Agency (IEA) report, nonrenewable energy sources represent 65% of the global supply chain. The smart meter developed in this work is based on the ESP32 microcontroller and easily accessible components since it includes a user-friendly development platform that offers a cost-effective solution while ensuring reliable performance. The main objective of developing the smart meters was to enhance the software and simplify the hardware. Unlike traditional meters that calculate electrical parameters by means of complex circuits in hardware, this project performed the calculations directly on the microcontroller. This procedure reduced the complexity of the hardware by simplifying the meter design. Owing to the high-performance processing capability of the microcontroller, efficient and accurate calculations of electrical parameters could be achieved without the need for additional circuits. This software-driven approach with simplified hardware led to benefits, such as reduced production costs, lower energy consumption, and a meter with improved accuracy, as well as updates on flexibility. Furthermore, the integrated wireless connectivity in the microcontroller enables the collected data to be transmitted to remote monitoring systems for later analysis. The innovative feature of this smart meter lies in the fact that it has readily available components, along with the ESP32 chip, which results in a low-cost smart meter with performance that is comparable to other meters available on the market. Moreover, it is has the capacity to incorporate IoT and artificial intelligence applications. The developed smart meter is cost effective and energy efficient, and offers benefits with regard to flexibility, and thus represents an innovative, efficient, and versatile solution for smart grid applications.

TLS Protocol Analysis Using IoTST-An IoT Benchmark Based on Scheduler Traces.

The Internet of Things (IoT) envisions billions of everyday objects sharing information. As new devices, applications and communication protocols are proposed for the IoT context, their evaluation, comparison, tuning and optimization become crucial and raise the need for a proper benchmark. While edge computing aims to provide network efficiency by distributed computing, this article moves towards sensor nodes in order to explore efficiency in the local processing performed by IoT devices. We present IoTST, a benchmark based on per-processor synchronized stack traces with the isolation and precise determination of the introduced overhead. It produces comparable detailed results and assists in determining the configuration that has the best processing operating point so that energy efficiency can also be considered. On benchmarking applications which involve network communication, the results can be influenced by the constant changes that occur in the state of the network. In order to circumvent such problems, different considerations or assumptions were used in the generalization experiments and the comparison to similar studies. To present IoTST usage on a real problem, we implemented it on a commercial off the-shelf (COTS) device and benchmarked a communication protocol, producing comparable results that are unaffected by the current network state. We evaluated different Transport-Layer Security (TLS) 1.3 handshake cipher suites at different frequencies and with various numbers of cores. Among other results, we could determine that the selection of a specific suite (Curve25519 and RSA) can improve the computation latency by up to four times over the worst suite candidate (P-256 and ECDSA), while both providing the same security level (128 bits).

Internet of Things (IoT) for Soil Moisture Tensiometer Automation.

Monitoring of water retention behavior in soils is an essential process to schedule irrigation. To this end, soil moisture tensiometers usually equipped with mechanical manometers provide an easy and cost-effective monitoring of tension in unsaturated soils. Yet, periodic manual monitoring of many devices is a tedious task hindering the full exploitation of soil moisture tensiometers. This research develops and lab validates a low cost IoT soil moisture tensiometer. The IoT-prototype is capable of measuring tension up to -80 Kpa with R2 = 0.99 as compared to the same tensiometer equipped with a mechanical manometer. It uses an ESP32 MCU, BMP180 barometric sensor and an SD card module to upload the measured points to a cloud service platform and establishes an online soil water potential curve. Moreover, it stores the reading on a micro-SD card as txt file. Being relatively cheap (76 USD) the prototype allows for more extensive measurements and, thus, for several potential applications such as soil water matric potential mapping, precision irrigation, and smart irrigation scheduling. In terms of energy, the prototype is totally autonomous, using a 2400 mAh Li-ion battery and a solar panel for charging, knowing that it uses deep sleep feature and sends three data points to the cloud each 6 h.

Internet of Things (IoT) based automated sanitizer dispenser and COVID-19 statistics reporter in a post-pandemic world.

Purpose: Coronavirus is among the deadliest viruses of the 21st century. There is still a Coronavirus epidemic that affects most countries worldwide today. To prevent future outbreaks and protect public health, it is essential to invest in research and innovation on vaccines, treatments, diagnostic tests, public health infrastructure, and emergency response planning. Additionally, we need to work on mitigation strategies and take a comprehensive and multidisciplinary approach to prevent and fight against the virus. Methods: For the purpose of preventing the spread of microbial organisms, it is essential to take advantage of automatic sanitizer dispensers by deploying them in public places. This is one of the most feasible and effective ways to ensure that people have easy access to hand sanitizer and can reduce the spread of germs. Results: The proposed solution is a contactless sanitizer dispenser with an integrated temperature monitoring system, as well as an alert system for users who exhibit the symptom of infection. Moreover, the proposed solution has added advantage of interfacing with an electronic door so that we can easily implement it at the entrance of a public building/public transportation. This dispenser will also collect data that can be used to identify a symptomatic user and alert the appropriate authorities for safe quarantine. In addition, it is also used to monitor usage metrics, record user entries, and conduct statistical surveys using the ThinkSpeak platform. Conclusions: The proposed model could be a feasible solution to prevent the entry of infected persons and asymptomatic carriers indoors. This can be achieved by implementing automated temperature screening before allowing entry into the building. This can help identify individuals who are potentially infected with the virus and prevent them from entering the premises and potentially spreading the disease to others. Overall, the proposed model is a comprehensive and practical solution that can help to prevent the entry of infected persons and asymptomatic carriers indoors and help to keep the public safe.

Sensor for Real-Time Glucose Measurement in Aqueous Media based on Nanomaterials Incorporating an Artificial Neural Network Algorithm on a System-On-Chip / Sensor para Medición de Glucosa en Tiempo Real para Medios Acuosos basado en Nanomateriales Incorporando un Algoritmo de Red Neuronal Artificial en un Sistema en Chip

Abstract The aim of this paper is to present the development of a real-time measurement system for glucose in aqueous media. The proposed system incorporates two lines of research: i) design, synthesis, and implementation of a non-enzymatic electrochemical sensor of Multi-Walled Carbon Nanotubes with Copper nanoparticles (MWCNT-Cu) and ii) design and implementation of a machine learning algorithm based on an Artificial Neural Network Multilayer Perceptron (ANN-MLP), which is embedded in an ESP32 SoC (System on Chip). From the current data that is extracted in real-time during the oxidation-reduction process to which an aqueous medium is subjected, it feeds the algorithm embedded in the ESP32 SoC to estimate the glucose value. The experimental results show that the nanostructured sensor improves the resolution in the amperometric response by identifying an ideal place for data collection. For its part, the incorporation of the algorithm based on an ANN embedded in a SoC provides a level of 97.8 % accuracy in the measurements. It is concluded that incorporating machine learning algorithms embedded in low-cost SoC in complex experimental processes improves data manipulation, increases the reliability of results, and adds portability.

Resumen El objetivo de este artículo es presentar el desarrollo de un sistema de medición en tiempo real de glucosa en medios acuosos. El sistema que se implementa incorpora dos lineas de investigación: i) diseño, síntesis e implementación de un sensor electroquímico no enzimático de Nanotubos de Carbono de Pared Múltiple con nanopartículas de Cobre (NTCPM-Cu) y ii) diseño e implementación de un algoritmo de aprendizaje automático basado en una Red Neuronal Perceptrón Multicapa (RN-PM), embebido en un ESP32 SoC (Sistema en Chip). Un dato de corriente que se extrae en tiempo real durante el proceso de oxidación-reducción a la que se somete un medio acuoso, alimenta el algoritmo embebido en el ESP32 para estimar el valor de glucosa. De los resultados experimentales se demuestra que el sensor nanoestructurado mejora la resolución en la respuesta amperométrica al identificar un lugar ideal para la toma de datos. Por su parte, la incorporación del algoritmo basado en una RN embebido en SoC otorga un nivel de 97.8 % de exactitud en la mediciones. Se concluye que incorporar algoritmos de aprendizaje automático embebidos en SoC de bajo costo en procesos experimentales complejos, mejora la manipulación de datos, incrementa la confiabilidad en resultados y adiciona portabilidad.

Gait Trajectory Prediction on an Embedded Microcontroller Using Deep Learning.

Achieving a normal gait trajectory for an amputee's active prosthesis is challenging due to its kinematic complexity. Accordingly, lower limb gait trajectory kinematics and gait phase segmentation are essential parameters in controlling an active prosthesis. Recently, the most practiced algorithm in gait trajectory generation is the neural network. Deploying such a complex Artificial Neural Network (ANN) algorithm on an embedded system requires performing the calculations on an external computational device; however, this approach lacks mobility and reliability. In this paper, more simple and reliable ANNs are investigated to be deployed on a single low-cost Microcontroller (MC) and hence provide system mobility. Two neural network configurations were studied: Multi-Layered Perceptron (MLP) and Convolutional Neural Network (CNN); the models were trained on shank and foot IMU data. The data were collected from four subjects and tested on a fifth to predict the trajectory of 200 ms ahead. The prediction was made for two cases: with and without providing the current phase of the gait. Then, the models were deployed on a low-cost microcontroller (ESP32). It was found that with fewer data (excluding the current gait phase), CNN achieved a better correlation coefficient of 0.973 when compared to 0.945 for MLP; when including the current phase, both network configurations achieved better correlation coefficients of nearly 0.98. However, when comparing the execution time required for the prediction on the intended MC, MLP was much faster than CNN, with an execution time of 2.4 ms and 142 ms, respectively. In summary, it was found that when training data are scarce, CNN is more efficient within the acceptable execution time, while MLP achieves relative accuracy with low execution time with enough data.

Wireless Temperature, Relative Humidity and Occupancy Monitoring System for Investigating Overheating in Buildings.

With the climate change we are experiencing today, the number and intensity of heatwaves are increasing dramatically, significantly impacting our buildings' overheating. The majority of the prefabricated concrete panel buildings in Hungary are considered outdated from an energy point of view. These buildings may be at greater risk from extreme weather events. To examine this, long-term monitoring measurements are needed. Therefore, we developed a unique, reliable, and cost-effective wireless monitoring system, which can track in real time the indoor air quality data (temperature, relative humidity, CO2) of the investigated apartment building, as well as users' habits, such as resident presence, window opening, and blind movement. The data were used to analyse and quantify the summer overheating of the dwelling and user habits. The measurements showed that the average temperature in all rooms was above 26 °C, and there were several occasions when the temperature exceeded 30 °C. Overheating in apartment buildings in summer is a significant problem that needs to be addressed. Further investigation of ventilation habits will help develop favourable ventilation strategies, and using these measurements in dynamic simulations will also help improve the models' validity for further studies.

An open source 16-channel fluidics system for automating sequential fluorescent in situ hybridization (FISH)-based imaging.

Fluorescent in situ hybridization (FISH) can provide spatial information about DNA/RNA targets in fixed cells and tissues. However, the workflows of multiplexed FISH-based imaging that use sequential rounds of hybridization quickly become laborious as the number of rounds increases because of liquid handling demands. Here, we present an open-source and low-cost fluidics system that is purpose built for automating the workflows of sequential FISH-based imaging. Our system features a fluidics module with 16 addressable channels in which flow is positive pressure-driven and switched on/off by solenoid valves in order to transfer FISH reagents to the sample. Our system also includes a controller with a main printed circuit board that can control up to 120 solenoid valves and allows users to control the fluidics module via serial communication. We demonstrate the automatic and robust fluid exchange with this system by targeting the alpha satellite repeat in HeLa cell with 14 rounds of sequential hybridization and imaging. We anticipate that this simple and flexible system will be of utility to researchers performing multiplexed in situ assays in a range of experimental systems.

TactCube: An Intelligent Device to 'converse' with Smart Environments.

Ambient Intelligence is a vision of daily life in which intelligent devices interact with humans to make their lives easier, and the technology is invisible. Artificial Intelligence (AI) governs this smart environment and must interact with humans to best meet their needs and demands. Although voice assistants are very popular and efficient as conversational AI, under some conditions they cannot be used. Therefore, this work proposed a complementary tactile and tangible interface to converse with AI, creating new Tactile Signs. A prototype of TactCube, a wireless cube-shaped device that can interact with AI using only the tactile sense, is presented. The hypothesis is that TactCube can be manipulated with one hand and generate a sequence of numbers that can be interpreted as a new tactile language by a neural network solution. The paper describes the initial research made to define how these sequences can be generated and demonstrates how TactCube is able to do it.

A low-cost 3D-printable differential scanning fluorometer for protein and RNA melting experiments.

Differential scanning fluorimetry (DSF) is a widely used biophysical technique with applications to drug discovery and protein biochemistry. DSF experiments are commonly performed in commercial real-time polymerase chain reaction (qPCR) thermal cyclers or nanoDSF instruments. Here, we report the construction, validation, and example applications of an open-source DSF system for 176 €, which, in addition to protein-DSF experiments, also proved to be a versatile biophysical instrument for less conventional RNA-DSF experiments. Using 3D-printed parts made of polyoxymethylene, we were able to fabricate a thermostable machine chassis for protein-melting experiments. The combination of blue high-power LEDs as the light source and stage light foil as filter components was proven to be a reliable and affordable alternative to conventional optics equipment for the detection of SYPRO Orange or Sybr Gold fluorescence. The ESP32 microcontroller is the core piece of this openDSF instrument, while the in-built I2S interface was found to be a powerful analog-to-digital converter for fast acquisition of fluorescence and temperature data. Airflow heating and inline temperature control by thermistors enabled high-accuracy temperature management in PCR tubes (±0.1 °C) allowing us to perform high-resolution thermal shift assays (TSA) from exemplary biological applications.

Maasi: A 3D printed spin coater with touchscreen.

Spin coaters are widely used to apply thin films of a material uniformly over a flat substrate. Despite the simplicity of this technique the entry price for such machines might be prohibitive, ranging from few hundreds to thousands of Euros. Here we present Maasi, an affordable alternative that is easy to build and has all functional key features to be used in a wide range of applications. Our design has a price of less than hundred Euros and an assembly time of only two hours. One of the key design principles was to use only 3D printed parts in combination with affordable Commercial Off-The-Shelf (COTS) components [1]. Reducing the complexity we use an electronic speed controller (ESC) with telemetry, to eliminate the need for a rotor position sensor [2]. A touchscreen further improves its usability, thus setting a perfect startpoint for the design of other affordable lab tools. The Maasi project includes different 3D printable substrate holders allowing treatment of formats up to 80 mm in diameter. We furthermore validate the Maasi spin coater by measuring its speed accuracy and performance for coating polydimethylsiloxane (PDMS) on glass coverslips for mechanobiological assays.

Implementation of an Embedded System into the Internet of Robotic Things.

The article describes the use of embedded systems in the Industrial Internet of Things and its benefits for industrial robots. For this purpose, the article presents a case study, which deals with an embedded system using an advanced microcontroller designed to be placed directly on the robot. The proposed system is being used to collect information about industrial robot parameters that impact its behavior and its long-term condition. The device measures the robot's surroundings parameters and its vibrations while working. Besides that, it also has an enormous potential to collect other parameters such as air pollution or humidity. The collected data are stored on the cloud platform and processed and analysed. The embedded system proposed in this article is conceived to be small and mobile, as it is a wireless system that can be easily applied to any industrial robot.

Deployment Strategies of Soil Monitoring WSN for Precision Agriculture Irrigation Scheduling in Rural Areas.

Deploying wireless sensor networks (WSN) in rural environments such as agricultural fields may present some challenges that affect the communication between the nodes due to the vegetation. These challenges must be addressed when implementing precision agriculture (PA) systems that monitor the fields and estimate irrigation requirements with the gathered data. In this paper, different WSN deployment configurations for a soil monitoring PA system are studied to identify the effects of the rural environment on the signal and to identify the key aspects to consider when designing a PA wireless network. The PA system is described, providing the architecture, the node design, and the algorithm that determines the irrigation requirements. The testbed includes different types of vegetation and on-ground, near-ground, and above-ground ESP32 Wi-Fi node placements. The results of the testbed show high variability in densely vegetated areas. These results are analyzed to determine the theoretical maximum coverage for acceptable signal quality for each of the studied configurations. The best coverage was obtained for the near-ground deployment. Lastly, the aspects of the rural environment and the deployment that affect the signal such as node height, crop type, foliage density, or the form of irrigation are discussed.