Jul 2026· International Scientific Journal of Engineering and Management· Vol 05, pp. 1-9· 0 citations
TL;DR
The design and development of an IoT-Based Firefighting Robot that integrates autonomous fire detection and suppression capabilities with remote control via Bluetooth communication is presented, offering enhanced flexibility and safety in firefighting operations.
Abstract
ABSTRACT
Firefighting is an inherently hazardous occupation, with numerous incidents each year resulting in injuries and fatalities among fire service personnel. To mitigate these risks and enhance operational efficiency, this paper presents the design and development of an IoT-Based Firefighting Robot that integrates autonomous fire detection and suppression capabilities with remote control via Bluetooth communication.
The system leverages Internet of Things (IoT) technologies for real-time monitoring and control, enabling the robot to navigate hazardous environments and extinguish fires autonomously or under manual operator control when required. Equipped with flame sensors for fire detection, a fire extinguishing mechanism, and Bluetooth-based communication, the robot can be operated from a safe distance using an Android application, allowing precise intervention in critical situations.
This hybrid approach combines the strengths of autonomous decisionmaking with human supervision, offering enhanced flexibility and safety in firefighting operations. The system is particularly suited for industrial settings and environments where the risk of accidental fires is high, enabling rapid response while minimizing human exposure to danger. The proposed solution demonstrates the effectiveness of IoT-enabled robotics in improving fire safety, offering a scalable and reliable approach to safeguarding lives, property, and critical infrastructure.
Keywords: DC motor, Flame sensors, Firefighting, Pump, Robot.
The increasing complexity of vehicle electronics and conventional fuel systems have elevated the risks of gas leaks and fires. In addition, high-density batteries in Electric Vehicles (EVs) raises the risk of fire. Vehicle fires represent a significant safety hazard, causing hundreds of deaths and billions in property damage annually. One of the main dangers related with firefighting operations is the harmful ambience resulted from combusting materials. The four main hazards related to these cases are smoke, the oxygen insufficient atmosphere, high temperatures, and toxic ambience. Hence, fire is a real danger and fire safety is a critical issue in vehicle’s industry. This paper proposes a real-time intelligent vehicle gas and fire detection and suppression system. The system uses Arduino Microcontroller based-system that integrates multi-sensor data fusion capable of detecting gas, smoke, over-temperature and combustion. The system uses alarm signaling devices, solenoid valve, fire-extinguishing equipment, and feeders for the fire-extinguishing substance. This comprehensive vehicle safety system provides multi-layered protection against gas leaks, fires, and overheating and automatic response without driver intervention. Hence, it is capable of detecting and suppressing fire. In addition, the system is interfaced to GSM network to enable wireless communication through SMS.
Adnan M. Al-Smadi, H. J. Badarneh, G. Sulieman· The eurasia proceedings of s...· 0 citations
: Firefighting robots are, at the time being, confronted with serious setbacks, environmental flexibility that prevents them to negotiate hazardous conditions; limitations in sensor technologies that make it hard to detect the fire accurately amidst smoke and extreme heat; high development cost-a factor impeding its full-scale adoption by fire departments. These challenges are enlarged by the lack of synchronization between human firefighters and robotic systems during emergencies, as well as by the incompatible findings about the efficiency of sensors in the detection of fire. The proposed model comprises a new mechanism of fire suppression using sodium acetate, which is usually very effective in extinguishing fire with a minimum use of water. Advanced communication structures allow the robot to report the authorities via an SMS message, so any updates in case of crisis events will be timely. Low power consumption is emphasized in this model to operate for extended periods in difficult environments without depending on outside sources of power. The mixture of many sensors -flame sensors allowing real-time detection of fire, and an IR sensor to trace the heat source-very powerfully increases the decision-making by the robot. Additionally, GPS technology delivers autonomy in movement, hence the ability of the robot to reach sites in emergencies efficiently. The firefighting robot model proposed is meant to enhance firefighting strategies toward better safety and effectiveness in firefighting missions. This paper addresses the existing challenges in the integration of advanced technologies into one model, which will provide better collaboration between human firefighters and robotic systems for more efficient and reliable emergency services in dangerous situations.
A. A, D. P., Radhika et al.· Proceedings of the 1st Inter...· 0 citations
Fire incidents are among the most common disasters worldwide and frequently result in significant economic losses, property damage, environmental degradation, and loss of human life. In Indonesia, the number of fire incidents increased in 2023, highlighting the need for more effective fire prevention and early detection mechanisms. Many conventional fire detection systems are limited in their ability to provide real-time monitoring and rapid notification, which can delay emergency response efforts and increase the severity of fire-related losses. Therefore, the development of intelligent and automated fire early warning systems has become increasingly important. This research aims to design and implement an indoor fire early warning system by integrating Internet of Things (IoT) technology with fuzzy logic techniques. The proposed system utilizes a flame sensor to detect the presence of fire and an MQ-2 sensor to monitor smoke concentration levels. An ESP32 microcontroller functions as the central processing unit, collecting sensor data, processing information, and transmitting results to users. To improve decision-making accuracy under uncertain environmental conditions, the Tsukamoto fuzzy logic method is employed to determine the level of fire risk based on sensor inputs. The system is equipped with a real-time notification feature that automatically sends warning alerts through the Telegram application whenever potential fire hazards are detected. In addition, sensor readings and fire status information can be monitored remotely through a web-based platform. Experimental results demonstrate that the proposed system effectively detects fire and smoke conditions while providing timely alerts. The integration of IoT and fuzzy logic enables rapid response and supports proactive fire management, thereby reducing potential losses and enhancing indoor safety.
Farhan Assidiqi, I. Sp, Muhammad Fikri· International journal of sci...· 0 citations
- Fire incidents in industrial, laboratory and restricted environments can expose firefighters to extreme temperature, toxic gases, explosions and structural hazards. This work presents the design and implementation of a compact autonomous fire-fighting robotic vehicle based on an Arduino Uno controller. The prototype integrates infrared flame sensors, geared DC motors, a motor driver, a servo-controlled water nozzle, a water pump, a lithium-ion battery and a microcontroller-based decision algorithm. The robot continuously monitors the fire-sensor outputs, determines the direction of the detected flame, moves toward the source and activates a water-spraying mechanism. The nozzle is swept by a servo motor to increase the effective coverage of the extinguishing stream. The original prototype dissertation demonstrates the complete hardware, software and control sequence, including forward, backward, left, right and stop functions. The present paper reformulates that work as a research article and identifies the experimental parameters that should be reported for publication-quality validation. The approach is intended as a low-cost platform for initial fire response in controlled indoor environments, rather than as a replacement for certified industrial firefighting equipment.
N. Sriram· Iconic research and engineer...· 0 citations
The project demonstrates an early fire-detection and protection system for electric vehicle (EV) batteries using temperature sensors, current sensors, and IoT technology. Real-time monitoring through IoT allows the battery status to be viewed remotely, enabling quick decision-making in emergency situations. Automatic safety actions, such as activating the cooling system or buzzer alert, help prevent the fire from starting or spreading. Continuous data logging supports analysis of battery behavior and helps improve EV safety and performance.The system provides a low-cost and efficient solution, making it suitable for student projects and real-world applications. Overall, the project enhances the safety, reliability, and intelligence of EV battery systems, reducing the risk of fire accidents.
Aarushe Kavanashree N, Aishwarya D R, S. N et al.· International Journal of Cre...· 0 citations
Robots that are used in dangerous places, including chemical plants, mines and disaster-stricken areas, must work reliably and keep the human operator away from the danger. This study proposes and tests a system based on Internet of Things (IoT) that allows remote monitoring and control of robots in hazardous environments, complemented by a machine learning (ML) algorithm to automatically classify the hazard. The proposed system combined the environmental sensors (battery level, motor current, and vibration) with the environmental sensors (temperature, humidity, gas concentration, and smoke) and an edge-computing/microcontroller unit with an IoT communication module and a remote monitoring-and-control interface installed on a mobile robotic platform. A supervised machine learning (ML) model was trained, to classify the sensed environment into three safety classes: Safe, Caution and Hazardous with 4 candidate algorithms (Random Forest, Support Vector Machine (SVM), Decision Tree, Artificial Neural Network (ANN)) evaluated on accuracy, precision, recall, F1 score and inference time with 3000 simulated sensor data. The SVM classifier had the highest overall accuracy (90.0%) and F1-score (.890) compared to the Random Forest (89.5% accuracy) and ANN (88.9% accuracy) classifiers, with the Decision Tree also providing the fastest inference time (0.09 ms) at a relatively small cost in accuracy (86.5%). Overall, experimental tests conducted at the system level revealed that the end-to-end communication latency was highly meaningful with hazard intensity (p < 0.001; F = 335.1), ranging from a mean of 119 ms for the lowest hazard intensity to 210 ms for the highest hazard intensity, and that command-execution success rate and real-time monitoring accuracy decreased moderately as hazard intensity increased (p < 0.001; F = 205.5 and 72.9, respectively), but remained above 83% and 92% for the highest hazard intensity, respectively. These results have shown that the combination of IoT communication, edge sensing, and the classification of hazards by machine learning can realize low-latency and reliable monitoring and control of robots in hazardous environments and also show that there is a measurable performance trade-off when increasing the intensity of the environment hazards.
Hamna Anis, Zahoor Ahmed, Waseema Batool et al.· International journal of com...· 2 citations
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