Search Results (97)

Ammonia is a gas primarily produced for use in agriculture, refrigeration systems, chemical manufacturing, and power generation. Despite its benefits, improper management of ammonia poses significant risks to human health and the environment. Consequently, monitoring ammonia is essential for enhancing industrial safety and preventing leaks that can lead to environmental contamination. Given the abundance and diversity of studies on Internet of Things (IoT) systems for gas detection, the main objective of this paper is to systematically review the literature to identify emerging research trends and opportunities. This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, focusing on sensor technologies, microcontrollers, communication technologies, IoT platforms, and applications. The main findings indicate that most studies employed sensors from the MQ family (particularly the MQ-135 and MQ-137), microcontrollers based on the Xtensa architecture (ESP32 and ESP8266) and ARM Cortex-A processors (Raspberry Pi 3B+/4), with Wi-Fi as the predominant communication technology, and Blynk and ThingSpeak as the primary cloud-based IoT platforms. The most frequent applications were agriculture and environmental monitoring. These findings highlight the growing maturity of IoT technologies in ammonia sensing, while also addressing challenges like sensor reliability, energy efficiency, and development of integrated solutions with Artificial Intelligence. Full article

We evaluate tactile-first robotic traversal on the Department of Homeland Security (DHS) figure-8 mobility test using a two-way repeated-measures design across various algorithms (three tactile policies—M1 reactive, M2 terrain-weighted, M3 memory-augmented; a monocular camera baseline, CB-V; a tactile histogram baseline, T-VFH; and an optional tactile-informed replanner, T-D* Lite) and lighting conditions (Indoor, Outdoor, and Dark). The platform is the custom-built Eleven robot—a quadruped integrating a joint-mounted tactile tentacle with a tip force-sensitive resistor (FSR; Walfront 9snmyvxw25, China; 0–10 kg range, ≈0.1 N resolution @ 83 Hz) and a woven Galvorn carbon-nanotube (CNT) yarn for proprioceptive bend sensing. Control and sensing are fully wireless via an ESP32-S3, Arduino Nano 33 BLE, Raspberry Pi 400, and a mini VESC controller. Across 660 trials, the tactile stack maintained ∼21 ms (p50) policy latency and mid-80% success across all lighting conditions, including total darkness. The memory-augmented tactile policy (M3) exhibited consistent robustness relative to the camera baseline (CB-V), trailing by only ≈3–4% in Indoor and ≈13–16% in Outdoor and Dark conditions. Pre-specified, two one-sided tests (TOSTs) confirmed no speed equivalence in any M3↔CB-V comparison. Unlike vision-based approaches, tactile-first traversal is invariant to illumination and texture—an essential capability for navigation in darkness, smoke, or texture-poor, confined environments. Overall, these results show that a tactile-first, memory-augmented control stack achieves lighting-independent traversal on DHS benchmarks while maintaining competitive latency and success, trading modest speed for robustness and sensing independence. Full article

Energy efficiency in shared environments, such as offices and laboratories, is hindered by a lack of individual accountability. Traditional smart metering provides aggregated data but fails to attribute consumption to specific users, limiting the effectiveness of behavioral change initiatives. This paper introduces the “authenticated energy consumption” paradigm, an innovative approach that directly links energy use to an identified user. We present PlugID, a low-cost, open-protocol IoT platform designed and built to implement this paradigm. The PlugID platform comprises a custom smart plug with RFID-based authentication and a secure, cloud-based data analytics backend. The device utilizes an ESP8266 microcontroller, Tasmota firmware, and the MQTT protocol over TLS for secure communication. Seven PlugID units were deployed in a small office environment to demonstrate the system’s feasibility. The main contribution of this work is the design, implementation, and validation of a complete, end-to-end system for authenticated energy monitoring. We argue that by making energy consumption an auditable and attributable event, the PlugID platform provides a powerful new tool to enforce energy policies, foster user awareness, and promote genuine efficiency. Full article

This paper presents the concept and development of an autonomous DIY air quality sensor for citizen science. Large civil monitoring projects often rely on air quality calculations based on PM2.5 and PM10 dust readings in combination with some gases and do not cover the full list of air quality indicators. The authors have analyzed existing air quality calculation methodologies and attempted to conceptualize a universal AQI monitoring device for use in citizen science and by volunteers. This device is based on the available ESP32 DevKit v1 platform to which compatible sensors have been selected to monitor AQI indicators such as PM2.5 and PM10 dust particles, ozone, carbon monoxide, nitrogen dioxide, sulfur dioxide, and ammonia. The SD card module was chosen for data storage, the NB-IoT module for data transmission, and a battery pack for autonomy. The housing, sensor design components, and fasteners were also selected. All components are available on the international market. Based on the selected element base, an electrical connection diagram was designed, the device’s design, presented in the form of 3D models, was developed, and the assembly process was described. The cost of the device was also evaluated and compared to the price level of existing DIY devices used in citizen science. Full article

Using systems with machine learning technologies for process automation is a global trend in agriculture. However, implementing this technology comes with challenges, such as the need for a large amount of computing resources under conditions of limited energy consumption and the high cost of hardware for intelligent systems. This article presents the possibility of applying a modern ESP32 microcontroller platform in the agro-industrial sector to create intelligent devices based on the Internet of Things. CNN models are implemented based on the TensorFlow architecture in hardware and software solutions based on the ESP32 microcontroller from Espressif company to classify objects in crop fields. The purpose of this work is to create a hardware–software complex for local energy-efficient classification of images with support for IoT protocols. The results of this research allow for the automatic classification of field surfaces with the presence of “high attention” and optimal growth zones. This article shows that classification accuracy exceeding 87% can be achieved in small, energy-efficient systems, even for low-resolution images, depending on the CNN architecture and its quantization algorithm. The application of such technologies and methods of their optimization for energy-efficient devices, such as ESP32, will allow us to create an Intelligent Internet of Things network. Full article

The integration of Internet of Things (IoT) technologies in agriculture has facilitated real-time environmental monitoring, with low-cost IoT-enabled agrometeorological stations emerging as a valuable tool for climate-smart farming. This systematic review examines low-cost IoT-based weather stations by analyzing their hardware and software components and assessing their potential in comparison to conventional weather stations. It emphasizes their contribution to improving climate resilience, facilitating data-driven decision-making, and expanding access to weather data in resource-constrained environments. The analysis revealed widespread adoption of ESP32 microcontrollers, favored for its affordability and modularity, as well as increasing use of communication protocols like LoRa and Wi-Fi due to their balance of range, power efficiency, and scalability. Sensor integration largely focused on core parameters such as air temperature, relative humidity, soil moisture, and rainfall supporting climate-smart irrigation, disease risk modeling, and microclimate management. Studies highlighted the importance of usability and adaptability through modular hardware and open-source platforms. Additionally, scalability was demonstrated through community-level and multi-station deployments. Despite their promise, challenges persist regarding sensor calibration, data interoperability, and long-term field validation. Future research should explore the integration of edge computing, adaptive analytics, and standardization protocols to further enhance the reliability and functionality of IoT-enabled agrometeorological systems. Full article

This paper introduces a new quadruped spider robot platform specializing in environmental reconnaissance and mapping. The robot measures 180 mm × 180 mm × 95 mm and weighs 385 g, including the battery, providing a compact yet capable platform for reconnaissance missions. The robot consists of an ESP32 microcontroller and eight servos that are disposed in a biomimetic layout to achieve the biological gait of an arachnid. One of the major design revolutions is in the power distribution network (PDN) of the robot, in which two DC-DC buck converters (LM2596M) are used to isolate the power domains of the computation and the mechanical subsystems, thereby enhancing reliability and the lifespan of the robot. The theoretical analysis demonstrates that this dual-domain architecture reduces computational-domain voltage fluctuations by 85.9% compared to single-converter designs, with a measured voltage stability improving from 0.87 V to 0.12 V under servo load spikes. Its proprietary Bluetooth protocol allows for both the sending and receiving of controls and environmental data with fewer than 120 ms of latency at up to 12 m of distance. The robot’s mapping system employs a novel motion-compensated probabilistic algorithm that integrates ultrasonic sensor data with IMU-based motion estimation using recursive Bayesian updates. The occupancy grid uses 5 cm × 5 cm cells with confidence tracking, where each cell’s probability is updated using recursive Bayesian inference with confidence weighting to guide data fusion. Experimental verification in different environments indicates that the mapping accuracy (92.7% to ground-truth measurements) and stable pattern of the sensor reading remain, even when measuring the complex gait transition. Long-range field tests conducted over 100 m traversals in challenging outdoor environments with slopes of up to 15° and obstacle densities of 0.3 objects/m² demonstrate sustained performance, with 89.2% mapping accuracy. The energy saving of the robot was an 86.4% operating-time improvement over the single-regulator designs. This work contributes to the championing of low-cost, high-performance robotic platforms for reconnaissance tasks, especially in search and rescue, the exploration of hazardous environments, and educational robotics. Full article

The rapid expansion of the Internet of Things (IoT) demands scalable, efficient, and user-friendly gateway solutions that seamlessly connect resource-constrained edge devices to cloud services. Low-cost, widely available microcontrollers, such as the ESP32 and its ecosystem peers, offer integrated Wi-Fi/Bluetooth connectivity, low power consumption, and a mature developer toolchain at a bill of materials cost of only a few dollars. For smart-home deployments where budgets, energy consumption, and maintainability are critical, these characteristics make MCU-class gateways a pragmatic alternative to single-board computers, enabling always-on local control with minimal overhead. This paper presents the design and implementation of an embedded IoT gateway powered by the ESP32 microcontroller. By using lightweight communication protocols such as Message Queuing Telemetry Transport (MQTT) and REST APIs, the proposed architecture supports local control, distributed intelligence, and secure on-site data storage, all while minimizing dependence on cloud infrastructure. A real-world deployment in an educational building demonstrates the gateway’s capability to monitor energy consumption, execute control commands, and provide an intuitive web-based dashboard with minimal resource overhead. Experimental results confirm that the solution offers strong performance, with RAM usage ranging between 3.6% and 6.8% of available memory (approximately 8.92 KB to 16.9 KB). The initial loading of the single-page application (SPA) results in a temporary RAM spike to 52.4%, which later stabilizes at 50.8%. These findings highlight the ESP32’s ability to serve as a functional IoT gateway with minimal resource demands. Areas for future optimization include improved device discovery mechanisms and enhanced resource management to prolong device longevity. Overall, the gateway represents a cost-effective and vendor-agnostic platform for building resilient and scalable IoT ecosystems. Full article

The escalating concerns regarding environmental pollution, particularly industrial water pollution, have necessitated the development of advanced monitoring systems to ensure water resource safety and sustainability. This paper presents an innovative IoT-based Industrial Water Pollution Evaluation System that integrates sensor networks, communication technologies, and data analytics to provide real-time, comprehensive water quality assessment in industrial environments. Our system employs an ESP32 microcontroller connected to four critical sensors (pH, temperature, turbidity, and TDS) to monitor water quality parameters continuously. The collected data is transmitted to a cloud platform and visualized through a dedicated Android application, enabling proactive pollution control measures. Experimental results demonstrate the system’s effectiveness in detecting various pollution scenarios with high accuracy. This solution addresses the limitations of traditional monitoring methods by offering cost-effective, real-time monitoring capabilities that support sustainable water management practices in industrial settings. Full article

Proper management of water resources in agriculture is of utmost importance for sustainable productivity, especially under the current context of climate change. However, many smart agriculture systems, including for managing irrigation, involve costly, complex tools for most farmers, especially small/medium-scale producers, despite the availability of user-friendly and community-accessible tools supported by well-established providers (e.g., Google). Hence, this paper proposes an irrigation management system integrating low-cost Internet of Things (IoT) sensors with community-accessible cloud-based data management tools. Specifically, it resorts to sensors managed by an ESP32 development board to monitor several agroclimatic parameters and employs Google Sheets for data handling, visualization, and decision support, assisting operators in carrying out proper irrigation procedures. To ensure reproducibility for both digital experts but mainly non-technical professionals, a comprehensive set of guidelines is provided for the assembly and configuration of the proposed irrigation management system, aiming to promote a democratized dissemination of key technical knowledge within a do-it-yourself (DIY) paradigm. As part of this contribution, a market survey identified numerous e-commerce platforms that offer the required components at competitive prices, enabling the system to be affordably replicated. Furthermore, an irrigation management prototype was tested in a real production environment, consisting of a 2.4-hectare yellow kiwi orchard managed by an association of producers from July to September 2021. Significant resource reductions were achieved by using low-cost IoT devices for data acquisition and the capabilities of accessible online tools like Google Sheets. Specifically, for this study, irrigation periods were reduced by 62.50% without causing water deficits detrimental to the crops’ development. Full article

This paper presents a comprehensive study of an ESP32 microcontroller-based self-balancing mobile robot system designed in conjunction with an Android app for Bluetooth control. The robot employs an MPU6050 accelerometer/gyroscope to execute dynamic equilibrium control for robotic balance. This study explores the design of a system composed of an ESP32-based dual-platform architecture. The firmware for the ESP32 executes real-time motor control and sensor processing, while the Android application provides the user interface, data visualization, and command transmission. The system achieves stable operation with tilt angle variations of ±2.5° ($\sigma =0.8°$, n = 50 trials) during normal operation with a PID controller tuned to KP = 6.0, KI = 0.1, and KD = 1.5. In experimental tests, control latency was measured at 38–72 ms (mean = 55 ms, $\sigma =12$ ms) over distances of 1–10 m with a robust Bluetooth connection. Extended operational tests indicated the reliability of both autonomous obstacle avoidance mode and manual control exceeding 95%. Key contributions include gyro drift compensation using a progressive calibration scheme, intelligent battery management for operational efficiency, and a dual-mode control interface to facilitate seamless transition between manual and autonomous operation. Processing of real-time telemetry on the Android application allows visualization of important parameters like tilt angle, motor speeds, and sensor readings. This work contributes to a cost-effective mobile robotics platform (total cost: USD 127) through the provision of detailed design specifications, implementation strategies, and performance characteristics. Full article

Understanding the combined gravi-phototropic behavior of plants is essential for space agriculture. Existing single-axis clinostats and gel-based grow media provide limited simulation fidelity. This study developed a Cloud-enabled triple-axis clinostat with built-in automated aeroponic and artificial photosynthetic lighting systems for Earth-based simulation under Martian gravity ranging from 0.35 to 0.4 g. Finite element analysis validated the stability and reliability of the acrylic and stainless steel rotating platform based on stress, strain, and thermal simulation tests. Arduino UNO microcontrollers were used to acquire and process sensor data to activate clinorotation and controlled environment systems. An Arduino ESP32 transmits grow chamber temperature, humidity, moisture, light intensity, and gravity sensor data to ThingSpeak and the Create IoT online platform for seamless monitoring and storage of enviro-physical data. The developed system can generate 0.252–0.460 g that suits the target Martian gravity. The combined gravi-phototropic tests confirmed that maize seedlings exposed to partial gravity and grown using the aeroponic approach have a shoot system growth driven by light availability (395–400 μmol/m²/s) across the partial gravity extremes. Root elongation is more responsive to gravity increase under higher partial gravity (0.375–0.4 g) even with low light availability. The developed soilless clinostat technology offers a scalable tool for simulating other high-value crops aside from maize. Full article

Most people utilize their time indoors, either at home or in the workplace. However, certain human interventions badly affect the indoor atmosphere, causing potential health problems for occupants. This study aims to propose an air monitoring device integrated with an air purifier that monitors the pollutants affecting the indoor environment and automatically turns on/off the air purifier based on the pollution level. In the system, MQ7, MQ2, DHT11, and GP2Y1010AU0F sensors are integrated with ESP32 to detect indoor air pollutants, e.g., carbon monoxide (CO), methane (CH₄), temperature, humidity, and PM2.5. Data were collected for 30 days by mounting a proposed device in different indoor locations, including a poorly ventilated average living room, an indoor kitchen, and a crowded office space. The emission of carbon monoxide (CO) and methane (CH₄) was at 29.4 ppm and 10.9 ppm, PM2.5 was detected as 3 µg/m³, and the temperature and humidity were at 23 °C and 28%, respectively. Utilizing the Wi-Fi ability of ESP32, the data were transferred to the ThingSpeak IoT platform for the live tracking and analysis of the indoor atmosphere. Observing the measured data, the proposed system’s accuracy was calculated by comparing the results against a known standard device, which was estimated to be 95%. To protect the designed system, a protective case was also designed. Full article

This study aims to develop an integrated wireless monitoring system named MANTiS-32, which leverages an open-source platform to enable autonomous modular operation, high-speed large-volume data transmission via Wi-Fi, and the integration of multiple complex sensors. The MANTiS-32 system is composed of ESP32-based MANTiS-32 hubs connected to eight MPU-6050 sensors each via RS485. Four MANTiS-32 hubs transmit data to a main PC through an access point (AP), making the system suitable for real-time monitoring of modal information necessary for structural performance evaluation. The fundamental performance of the developed MANTiS-32 system was validated to demonstrate its effectiveness. The evaluation included assessments of acceleration and frequency response measurement performance, wireless communication capabilities, and real-time data acquisition between the MANTiS-32 hub and the eight connected MPU-6050 sensors. To assess the feasibility of using MANTiS-32 for performance monitoring, a flexible model cable-stayed bridge, representing a mid- to long-span bridge, was designed. The system’s ability to perform real-time monitoring of the dynamic characteristics of the bridge model was confirmed. A total of 26 MPU-6050 sensors were distributed across four MANTiS-32 hubs, and real-time data acquisition was successfully achieved through an AP (ipTIME A3004T) without any bottleneck or synchronization issues between the hubs. Vibration data collected from the model bridge were analyzed in real time to extract dynamic characteristics, such as natural frequencies, mode shapes, and damping ratios. The extracted dynamic characteristics showed a measurement error of less than approximately 1.6%, validating the high-precision performance of the MANTiS-32 wireless monitoring system for real-time structural performance evaluation. Full article

Given the rising importance of cost-effective solutions in battery research, this study employs an accessible testing approach using low-cost, sensor-equipped platforms that enable broader research and educational applications. It presents a comparative evaluation of lithium-ion battery degradation under two charging strategies: static charging (constant current at 1.2 A) and dynamic charging (stepped current from 400 mA to 800 mA) over 200 charge–discharge cycles. A custom-built, low-cost test platform based on an ESP32 microcontroller was developed to provide real-time monitoring of voltage, current, temperature, and internal resistance, with automated control and cloud-based data logging. The results indicate that static charging provides greater voltage stability and a lower increase in internal resistance (9.3%) compared to dynamic charging (30.17%), suggesting reduced electrochemical stress. Discharge time decreased for both strategies, by 6.25% under static charging and 18.46% under dynamic charging, highlighting capacity fade and aging effects. Internal resistance emerged as a reliable indicator of degradation, closely correlating with reduced runtime. These findings underscore the importance of selecting charging profiles based on specific application needs, as dynamic charging, while offering potential thermal benefits, may accelerate battery aging. Furthermore, the low-cost testing platform proved effective for long-term evaluation and degradation analysis, offering an accessible alternative to commercial battery cyclers. The insights gained contribute to the development of adaptive battery management systems that optimize performance, lifespan, and safety in electric vehicle applications. Full article