Jul 2026· Journal of Trends in Computer Science and Smart Technology· Vol 8, pp. 538-557· 0 citations· 24 references
Abstract
The challenges faced by Unmanned Aerial Vehicles (UAVs) used in precision agriculture are growing. Operational risks arise from harsh conditions, such as abnormal flight behaviors, extended missions, and environmental circumstances. Current flight controllers primarily focus on navigation and stabilization, with only limited ability to monitor real-time safety. This study presents a low-cost, real-time safety monitoring platform for agricultural UAVs using an ESP32 microcontroller integrated with an MPU6050 inertial measurement unit (IMU) and a DHT11 temperature sensor. The system continuously measures roll, pitch, and on-board temperature, sending telemetry data to a cloud-based IoT platform for visualization and alerts. An empirically derived, threshold-based anomaly detection plan, based on over 100 normal flight data points, more than 20 Pixhawk crash log analyses, and observations of climatic conditions from 28 Indian states, allows for timely detection of abnormal operating conditions. The monitoring module has a sampling rate of 30-40 Hz and uses persistence-based detection logic to ensure that sustained anomalies are present over a 125-165 ms timespan. It was experimentally verified that roll and pitch estimates based on an atan2-based approach have a mean absolute error of 7.62° and 13.52°, respectively, compared to reference flight controller measurements. Temperature validation shows that the DHT11 sensor's temperature readings do not exceed the ±4.6°C deviation limit relative to the reference readings. These findings reveal that the suggested framework offers a scalable, cost-effective, and easily implemented solution for safety monitoring and early detection of abnormal flight conditions in agricultural UAV delivery.
Many lives, buildings, crops, and public safety are at risk during earthquakes, floods, and other severe weather occurrences. Unfortunately, continuous monitoring in distant and catastropheprone locations is frequently not possible with conventional catastrophe monitoring systems due to their centralization, high costs, and restricted geographic coverage. A Distributed Internet with Things (IoT) sensor infrastructure for smart emergency monitoring & risk analysis utilizing an ESP-based controller is presented in this research to overcome these shortcomings. Diverse sensor nodes placed in strategic areas throughout the world would gather data on vibrations and the surrounding environment in real time under the proposed system. The gyroscopes, temperature sensors, and precipitation analyzers that come standard on every node allow for the detection of unusual ground vibrations, environmental monitoring, and tracking of precipitation. A central Internet of Things (IoT) server is used for monitoring, analysis, & risk assessment after the ESP controller processes the acquired sensor data locally. The technology detects out-of-the-ordinary occurrences and sends out warnings to users in the event of possible catastrophes based on established thresholds and trends in sensor data. To help with decision-making and being ready, the system also shows environmental conditions & risk levels graphically via a web-based dashboard. Scalability, affordability, and applicability to either remote or high-risk regions, thereby supporting early warning, distributed monitoring, and improved disaster management.
M. Pranitha, Vinukonda Keerthana· International Journal of AI...· 0 citations
Industrial machinery operating in manufacturing and process environments is frequently subjected to adverse operating conditions such as excessive temperature rise, abnormal current consumption, and mechanical vibrations, which may lead to performance degradation, unexpected failures, production losses, and safety hazards. To address these challenges, this paper presents the design and implementation of an Internet of Things (IoT)-enabled real-time machine health monitoring and protection system based on the ESP32 microcontroller platform. The proposed system integrates a DHT11 sensor for temperature and humidity monitoring, an ACS712 Hall-effect sensor for current measurement, and an MPU9250 inertial measurement unit (IMU) for vibration analysis. Sensor data are continuously acquired, processed, and transmitted through Wi-Fi to a cloud-based Firebase Realtime Database, enabling remote access and centralized monitoring. A responsive web dashboard hosted on GitHub Pages provides real-time visualization of machine operating parameters, status indicators, and fault notifications. To enhance operational safety and equipment reliability, threshold-based fault detection algorithms are implemented to identify abnormal operating conditions. When predefined critical limits are exceeded, the ESP32 automatically initiates protective actions by disconnecting the machine through a relay module, activating a visual alarm, and updating the fault status on the cloud platform. The dashboard additionally supports bidirectional communication, allowing authorized operators to remotely restart the machine, while a local push-button interface enables manual system recovery. Furthermore, the developed platform incorporates a browser-based logging mechanism that records timestamped sensor measurements, machine status transitions, fault events, and downloadable CSV trend data for maintenance analysis and performance evaluation. Experimental validation demonstrates reliable real-time monitoring with a data refresh interval of approximately 3 s, accurate threshold-based fault detection, dependable cloud connectivity, and effective remote supervisory control. The proposed solution offers a low-cost, scalable, and practical framework for predictive maintenance and industrial equipment condition monitoring in smart manufacturing environments.
Sudharshana, Kratika V Ulman, Kishan K Kulal et al.· 2026 International Conferenc...· 0 citations
Workers in the oil and gas industry, construction, and mining are routinely exposed to life-threatening hazards that existing safety systems are too slow and too limited to address. Traditional safety approaches rely on manual reporting and passive physical protection, leaving critical gaps in real-time detection and emergency response. This paper presents the design, development, and evaluation of an Internet of Things (IoT) powered smart safety helmet, named the BEYOND HELMET, built specifically for oil and gas field workers in Nigeria.
The system integrates an ESP32 microcontroller, an MPU-6050 inertial measurement unit for fall detection, an MQ-7 gas sensor for carbon monoxide monitoring, a DHT22 temperature and humidity sensor, a SEN-11574 pulse rate sensor for heart rate monitoring, a NEO-6M GPS module for precise location tracking, a SIM800L GSM module for SMS-based emergency alerts, an ESP32-CAM camera for visual confirmation, and a 16x2 LCD display for local status output. All sensor data are processed onboard and transmitted in real time to supervisors through an IoT monitoring platform structured in JavaScript Object Notation (JSON) format.
Testing results indicate that the system achieves fall detection accuracy of approximately 95%, carbon monoxide hazard detection accuracy of approximately 90%, and abnormal heart rate detection accuracy of approximately 92%. Emergency alerts are dispatched in under 10 seconds compared to the 10 to 20 minutes typical of manual reporting systems, representing a response time reduction of 70 to 85%. The prototype was assembled at a total cost of approximately 43,400 Nigerian Naira, making it highly affordable and scalable for large industrial deployments. The results demonstrate that the BEYOND HELMET offers a comprehensive, cost-effective, and proactive safety solution for workers in hazardous environments.
O. S. Ogboro, Kehinde O. Adegboye, Chi-ife D. Ileka et al.· SPE Nigeria Annual Internati...· 0 citations
Purpose – Crude Palm Oil (CPO) distribution using tanker trucks faces challenges including delayed reporting, limited visibility, and potential cargo losses due to leakage or human error. This study develops an IoT-based dashboard for real-time monitoring and threshold-triggered leakage alerting during CPO distribution.
Methods – A prototype was developed and tested in a controlled laboratory-scale environment. The system integrated an ESP32 microcontroller with a YF-S201 flow sensor, HC-SR04 ultrasonic level sensor, and NEO-6M GPS module. Sensor data were transmitted via MQTT, visualized on a web dashboard, and stored in a Supabase cloud database for historical tracking and operational review.
Findings – Testing showed average error rates of 2.48% for the flow sensor and 2.67% for the ultrasonic level sensor. The GPS module captured location data across all test points. The system supported four simulated distribution phases: Loading, Transportation, Unloading, and Completed. During Transportation, a leakage alert was generated when outward flow was detected, confirming that the rule-based alert mechanism operated according to predefined logic.
Research Implications – The system offers a prototype framework for improving transparency, traceability, and operational monitoring in CPO logistics. However, field validation using industrial-grade sensors and full-scale tanker truck deployment is required.
Originality – This study integrates flow monitoring, tank level measurement, GPS tracking, cloud storage, and rule-based leakage alerting in a single IoT dashboard for CPO tank truck distribution.
Abud Jabidi, Andi Prayogi, Muhammad Akbar Syahbana Pane· Journal of Digital Technolog...· 0 citations
Floods pose a significant hazard in India, causing severe damage to life,
property, and the economy. Existing flood monitoring systems often suffer from delayed response,
limited coverage, and high costs. The objective of this study is to design and implement a low-cost,
real-time IoT-based smart flood monitoring and early warning system that can improve prediction
accuracy and provide timely alerts to minimize flood impacts
The proposed system integrates multiple sensors-ultrasonic for water level, water flow
sensors, and DHT22 for temperature and humidity-with an Arduino Uno microcontroller. Data is
transmitted to the ThingSpeak cloud platform using the ESP8266 Wi-Fi module and visualized via
the ThingView mobile application. A GSM module sends SMS alerts to authorities and residents
when threshold conditions are detected. The system was simulated using Proteus Professional to
verify performance, and individual modules were tested for accuracy and responsiveness.
The proposed system overcomes limitations of traditional flood monitoring approaches
by enabling automated, continuous, and low-cost sensing with cloud-based data access. Multisensor integration reduces false alarms compared to single-parameter systems. While the Wi-Fi +
GSM approach provides effective coverage for urban and semi-urban areas, rural deployments may
require extended-range communication protocols. Security enhancements and machine learning integration are recommended for predictive analytics and robust performance in div
This IoT-based flood monitoring and early warning system provides a scalable, affordable, and effective solution for real-time flood risk management. By integrating multiple environmental parameters, cloud storage, and multi-channel alerts, it significantly improves upon existing methods. The architecture offers a strong foundation for future enhancements, including AIdriven prediction models and secure data transmission protocols, to further strengthen disaster preparedness and response.
N. Benni, S. S, A. G. et al.· International Journal of Sen...· 0 citations
Fire hazards pose a significant threat to human safety and environmental sustainability, particularly in densely populated and infrastructure-critical regions. This paper presents the design and experimental evaluation of a low-cost autonomous fire detection and suppression system based on a multi-sensor fusion approach, aimed at smart and sustainable environments. The proposed system integrates flame, smoke (MQ-2), temperature (DHT22), and ultrasonic sensors to enable reliable fire detection, environmental monitoring, and autonomous navigation. A decision-based sensor fusion algorithm is employed to minimize false alarms and improve detection robustness under varying conditions. The system is capable of autonomously locating fire sources and performing targeted suppression using a servo-controlled water nozzle. Experimental validation conducted across multiple indoor scenarios demonstrates a detection accuracy of 91.2%, an average response time of 2.4 s, and a 30% reduction in false alarms compared to single-sensor methods. The proposed solution offers a cost-effective and scalable approach for early-stage fire response and can be extended to IoT-enabled smart safety systems for sustainable infrastructure.
Deep Singh, Archisman Ghosh, Disha Biswas et al.· 2026 7th International Confe...· 0 citations
