Firefighting Robot: A Review

Abstract

Firefighting is an inherently dangerous profession, placing human lives at high risk. With advancements in robotics, firefighting robots are becoming viable alternatives for operating in hazardous conditions. This paper explores the design, components, and technologies that power firefighting robots, such as sensors, AI-driven navigation, and suppression systems. It also discusses real-world applications, case studies, the economic and environmental impacts of robotic firefighting solutions, and the challenges in their adoption. The study concludes with insights into future technological advancements and their potential to revolutionize fire management.

Introduction

Firefighting robots are specialized robotic systems designed to operate in life-threatening environments. These machines are developed to either assist human firefighters or take over specific tasks that are too dangerous for humans, such as entering buildings with extreme heat, thick smoke, or hazardous materials. With increasing urbanization, industrial expansion, and climate change exacerbating the risk of wildfires, the need for more efficient firefighting systems has grown exponentially.

Recent innovations in robotics, artificial intelligence (AI), and sensor technologies have enabled firefighting robots to perform complex tasks such as navigating through burning buildings, detecting fire sources, and applying appropriate fire suppression techniques. These robots have the potential to reduce casualties, limit property damage, and improve overall firefighting efficiency. However, several challenges need to be addressed to make these robots fully autonomous and reliable under all conditions[1-20].

Firefighting robots are an innovative advancement in emergency response technology, designed to assist firefighters in their critical tasks during fire emergencies. These autonomous machines are equipped with sensors, cameras, and advanced algorithms that allow them to navigate through hazardous environments, identify fire hotspots, and even extinguish flames. By deploying such robots, fire departments can enhance their operational capabilities, ensuring that human firefighters can focus on rescue operations and other high-priority tasks while minimizing their exposure to danger[21-30].

One of the most significant advantages of firefighting robots is their ability to enter areas that are too dangerous for human responders. For example, in cases of structural fires, the intense heat and toxic smoke can create life-threatening conditions. Robots can be sent in to assess the situation, gather vital information, and deploy water or fire retardants with precision. This not only increases the safety of firefighters but also improves the effectiveness of firefighting efforts, as robots can work tirelessly in high-risk zones where human intervention would be impossible or too risky[31-42].

Additionally, firefighting robots come in various forms, from small drones that can survey and map out fire-affected areas to larger, ground-based units that can directly combat flames. Some robots are designed with advanced firefighting tools, such as powerful water cannons or foam dispensers, capable of tackling different types of fires. As technology continues to evolve, the integration of artificial intelligence and machine learning into these robots allows for real-time decision-making and adaptability to changing fire dynamics, paving the way for smarter and more efficient firefighting solutions[43-56].

The deployment of firefighting robots is still in its early stages, but their potential impact on public safety and emergency management is substantial. By leveraging these technologies, fire departments can not only enhance their response capabilities but also inspire a new era of safety and efficiency in firefighting operations. As research and development in this field progress, we can expect to see even more sophisticated firefighting robots in action, further transforming how we approach fire emergencies and save lives[57-69].

In the face of escalating wildfire threats, urban fires, and hazardous industrial incidents, traditional firefighting methods are increasingly being supplemented and, in some cases, replaced by advanced robotic technology. Firefighting robots are emerging as game-changers in the realm of fire safety, combining cutting-edge engineering with artificial intelligence (AI) to enhance firefighting efforts, reduce human risk, and improve response efficiency.

Firefighters are often placed in perilous situations, facing high temperatures, toxic smoke, and unpredictable environments. The physical and psychological toll on human responders is significant, prompting the exploration of alternative solutions. Firefighting robots can enter hazardous areas where human firefighters cannot operate safely, allowing for immediate assessment and response.

Moreover, with climate change leading to more frequent and intense wildfires, traditional firefighting methods may not suffice. The integration of robotics into firefighting operations provides a proactive and innovative approach to tackling these challenges.

1. How Firefighting Robots Work?

Firefighting robots come in various shapes and sizes, each designed for specific environments and tasks. They can be equipped with high-pressure water hoses, foam dispensers, thermal imaging cameras, and even drone technology for aerial surveillance.

1. Ground-based Robots: These are typically large, all-terrain vehicles designed to carry out firefighting operations on the ground. Equipped with fire suppression systems and sensors, they can navigate challenging terrain, such as uneven ground or areas with debris, allowing them to reach fires that would be too dangerous for human firefighters.
2. Drones: Aerial firefighting drones are equipped with cameras and thermal imaging to provide real-time reconnaissance. They can identify hotspots and monitor fire spread, assisting ground teams in strategic decision-making. Some drones even have the capability to drop fire retardants or water on targeted areas.
3. Robotic Firefighters: These sophisticated machines mimic human capabilities and can operate independently or under human supervision. They can perform a variety of tasks, from extinguishing flames to carrying out search and rescue missions.

1. Advantages of Using Firefighting Robots:

1. Enhanced Safety: The primary advantage of firefighting robots is the increased safety they provide to human firefighters. By deploying robots in high-risk situations, the likelihood of injury or fatalities among personnel decreases significantly.
2. Improved Efficiency: Firefighting robots can work continuously without the need for rest, significantly improving response times. Their ability to operate in harsh conditions—whether it’s extreme heat or thick smoke—means that they can engage with fires more quickly and effectively.
3. Data Collection: Many firefighting robots are equipped with advanced sensors and cameras that can collect data in real-time. This data can be used for post-incident analysis, improving future firefighting strategies and enhancing training programs.
4. Cost-Effectiveness: While the initial investment in robotic technology can be substantial, the long-term savings from reduced property damage and lower healthcare costs for injured firefighters can be significant.

1. Challenges and Future Prospects

Despite the numerous advantages, the deployment of firefighting robots does not come without challenges. Initial costs, maintenance, and the need for specialized training for operators are key considerations. Additionally, integrating robotic systems into existing firefighting frameworks requires careful planning and coordination.

The future of firefighting robots looks promising, with ongoing advancements in AI and machine learning. As these technologies evolve, robots will become more autonomous and capable of making real-time decisions. Researchers are also exploring the use of collaborative systems where multiple robots can communicate and work together to manage fires more effectively.

1. DESIGN OF FIREFIGHTING ROBOTS

1. Robotic Structure and Mobility

The structure of firefighting robots must be robust and capable of enduring extreme environmental conditions, including intense heat, fire, smoke, and unstable surfaces. Depending on the environment, different designs of firefighting robots are deployed. For example, robots intended for outdoor or forest firefighting have larger, more rugged frames to traverse uneven terrain, whereas those designed for indoor fires are more compact for maneuverability in tight spaces.

In urban firefighting, tracked systems are favored for their ability to maintain stability across uneven or debris-laden surfaces(2) demonstrated the effectiveness of using caterpillar tracks, which provide better traction and stability compared to traditional wheels. Spider-legged robots are another emerging design for firefighting, offering enhanced mobility in navigating staircases, rubble, or other vertical obstacles(3).

Robust designs also incorporate heat-resistant materials such as aluminum alloys, steel, or advanced composites that prevent warping or melting at high temperatures. This allows the robot to maintain structural integrity even when exposed to extreme heat.

1.  Sensors and Navigation Systems

Sensors are critical for the effective operation of firefighting robots, providing real-time information about the environment, fire conditions, and obstacles. The main types of sensors used include:Infrared (IR) Sensors: IR sensors detect heat signatures and provide thermal images of the environment, helping the robot identify fire sources. These sensors are crucial for operating in smoke-filled environments where visibility is low. For example, in wildfire suppression, IR cameras are used to map out areas of intense heat, guiding the robot to critical locations (4).

Ultrasonic and LIDAR Sensors: These sensors help the robot detect obstacles in its path. Ultrasonic sensors use sound waves to measure distances, whereas LIDAR (LightDetection and Ranging) sensors create a 3D map of the environment by measuring the time taken for laser beams to reflect off objects. This is particularly useful in urban firefighting, where navigating through narrow, cluttered corridors is common (5).

Gas Sensors: Robots often carry gas sensors to detect hazardous gases such as carbon monoxide (CO), methane, or volatile organic compounds (VOCs). These gases can pose significant risks to humans during firefighting operations, and early detection helps mitigate these risks.

Humidity and Temperature Sensors: These sensors allow the robot to assess environmental conditions in real-time, alerting operators to extreme heat or the risk of steam formation when water is applied to a fire.

GPS and SLAM: GPS (Global Positioning System) is essential for outdoor firefighting robots, particularly in tracking their movement over large areas. For indoor environments where GPS signals may be obstructed, SLAM (Simultaneous Localization and Mapping) algorithms are used to help the robot understand and map its surroundings in real-time (6).

1. Fire Suppression Systems

Firefighting robots employ a variety of fire suppression mechanisms tailored to the specific type of fire they are combating. Water is the most commonly used suppressant, but other methods are often necessary, depending on the fire's nature.Water Cannons: Many firefighting robots are equipped with high-pressure water cannons capable of dousing flames from a distance. Robots like TAF20, widely used in Europe, can shoot water or foam over long distances, making them suitable for combating large-scale industrial fires or wildfires (7).

Foam Extinguishers: For fires involving oil, flammable liquids, or electrical equipment, foam-based suppression systems are preferred. Foam suppressants work by smothering the fire and cutting off the oxygen supply needed for combustion (8).Chemical Suppression: Some firefighting robots carry dry chemical extinguishers to deal with Class D fires, which involve combustible metals like magnesium or potassium. These are commonly used in industrial or laboratory settings.

1. Control Systems and Autonomy

The control systems of firefighting robots have evolved significantly in recent years. Many robots are now semi-autonomous, allowing human operators to direct the robot remotely while it autonomously performs navigation and fire suppression. This hybrid control model ensures human oversight while allowing the robot to make micro-decisions such as obstacle avoidance or optimizing fire suppression techniques.

Artificial Intelligence (AI) plays a key role in enhancing the autonomy of these systems. AI algorithms analyze data from the robot's sensors and adjust its behavior in real-time. For example, deep learning techniques are used to teach the robot how to identify different types of fires or predict fire spread patterns. AI-based algorithms such as reinforcement learning also help robots adapt to unpredictable environments, improving their decision-making abilities over time (9).

Fully autonomous robots, although less common, are becoming a growing area of research. Such robots are designed to make independent decisions based on environmental conditions (10) discuss how AI-based robots can analyze fire intensity, direction, and fuel sources to determine the most effective approach to extinguishing a fire.

1. CASE STUDIES

1. TAF20 Firefighting Robot

One of the most widely known firefighting robots is the TAF20, designed and developed by Emi controls in Europe. The robot, known for its powerful water jet system, has been deployed in several large-scale industrial and tunnel fires. In 2018, it played a crucial role in fighting the Notre-Dame cathedral fire in Paris. The TAF20's water cannon could shoot water from a distance of 80 meters, allowing firefighters to combat the fire without entering the dangerously hot structure (11).

1. Colossus Robot

The Colossus robot, also used in the Notre-Dame fire, is a semi-autonomous robot that operates in highly hazardous environments. It is equipped with caterpillar tracks for mobility, an onboard camera system, and an array of fire suppression tools. The robot is operated remotely by firefighters, who use its camera feed to navigate through the fire. Colossus was able to enter the interior of the cathedral, helping contain the blaze by delivering water directly to the flames while its onboard thermal camera provided critical information about fire hotspots (12).

1. ECONOMIC AND ENVIRONMENTAL IMPACT

Firefighting robots have the potential to reduce firefighting costs by minimizing the need for human intervention in highly dangerous or labor-intensive operations. Deploying a robot to fight fires in environments such as oil refineries, chemical plants, or tunnels reduces the need for costly protective gear, medical expenses from firefighter injuries, and property damage. Additionally, robots can operate continuously for longer periods than human firefighters, increasing the efficiency of fire suppression efforts.

From an environmental perspective, firefighting robots reduce the environmental impact by preventing the uncontrolled spread of fires. Forest fires, for example, cause massive carbon emissions and destroy ecosystems. Early detection and swift response by firefighting robots can help minimize the scale of such disasters, reducing the overall carbon footprint and preserving biodiversity (13).

1. Environmental Factors

Environmental factors, such as high heat, dense smoke, and the presence of hazardous materials, are among the biggest challenges in developing firefighting robots. Robots need to be built to withstand these extreme conditions without losing functionality. High temperatures can damage electronic components or distort mechanical structures, while smoke can interfere with sensors, limiting visibility and communication.

1. Communication and Connectivity

Communication between operators and robots can be disrupted in environments with intense electromagnetic interference or fire-related obstacles. In tunnels or deep urban environments, radio frequency (RF) signals may be obstructed, leading to the loss of control. Advances in wireless communication technologies such as 5G may help solve these issues by providing faster, more reliable data transfer between robots and human operators (14).

1. ETHICAL CONSIDERATIONS IN THE USE OF FIREFIGHTING ROBOTS

The deployment of firefighting robots raises several ethical considerations that must be addressed as technology evolves.

1. Accountability and Responsibility

As firefighting robots take on more autonomous roles, questions arise about accountability in the event of failures or accidents. If a robot fails to extinguish a fire or causes unintended damage, who is responsible? This issue necessitates clear guidelines and regulations to determine liability, ensuring that accountability remains with human operators and developers.

1. Human Interaction and Trust

Public trust in robotic systems is crucial for their successful integration into firefighting operations. As robots take on more significant roles, firefighters and the public must believe in their reliability and effectiveness. Training and education programs can help foster trust by demonstrating the robots' capabilities and the safety measures in place.

Moreover, human-robot interaction (HRI) plays a critical role in ensuring that robots effectively complement human firefighters. Developing intuitive interfaces that allow operators to easily control and communicate with robots is essential for maintaining a collaborative approach to firefighting (12).

1. Job Displacement Concerns

The rise of firefighting robots has raised concerns about job displacement among human firefighters. While robots can enhance safety and efficiency, it is essential to consider the human element in firefighting. The goal should be to develop robots that assist and augment human capabilities rather than replace them. This requires a shift in training and workforce development, ensuring that firefighters are equipped to work alongside robots and utilize their capabilities effectively (13).

1. FUTURE DIRECTIONS FOR FIREFIGHTING ROBOTICS

1. Research and Development Initiatives

Continued research and development efforts are crucial for advancing firefighting robotics. Investment in innovative technologies, such as AI, robotics, and sensor integration, will pave the way for new firefighting solutions. Collaborative efforts between academia, industry, and government organizations can accelerate advancements and facilitate the practical implementation of firefighting robots.

B. Integration with Smart City Infrastructure

As urban areas increasingly adopt smart city technologies, there is a significant opportunity to integrate firefighting robots into broader urban safety and emergency response systems. Smart city infrastructure can provide real-time data on environmental conditions, building layouts, and population density, enabling robots to respond more effectively to fires and other emergencies (9).

C. Environmental Sustainability

Future firefighting robots should prioritize environmental sustainability. The development of eco-friendly fire suppression agents and systems that minimize environmental impact will be essential as society becomes more aware of climate change and ecological preservation. Furthermore, robots can be designed to assist in controlled burns and other fire management practices that promote ecosystem health (15).

Conclusion

Firefighting robots represent a transformative advancement in fire management, enhancing safety, efficiency, and effectiveness in combating fires. With the integration of advanced technologies, ongoing research, and a focus on ethical considerations, these robots have the potential to revolutionize the firefighting landscape. As the challenges and barriers to their adoption are addressed, the future of firefighting robotics looks promising, paving the way for a safer and more efficient approach to fire References. As the global community continues to grapple with the increasing threat of wildfires and other fire-related emergencies, the role of firefighting robots is becoming more prominent. These innovative machines are not just a supplement to traditional firefighting methods; they represent a paradigm shift in how we approach fire safety and disaster response. By harnessing the power of technology, we can enhance our firefighting capabilities, protect lives, and safeguard our communities against the devastating impacts of fire.

References

[1] Hassanein, Ahmed, Mohanad Elhawary, Nour Jaber, and Mohammed El-Abd. \"An autonomous firefighting robot.\" In 2015 International Conference on Advanced Robotics (ICAR), pp. 530-535. IEEE, 2015. [2] Mittal, Shiva, Manish Kumar Rana, Mayank Bhardwaj, Meenakshi Mataray, and Shubham Mittal. \"CeaseFire: the fire fighting robot.\" In 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), pp. 1143-1146. IEEE, 2018. [3] Chien, Ting L., H. Guo, Kuo L. Su, and Sheng V. Shiau. \"Develop a multiple interface based fire fighting robot.\" In 2007 IEEE International Conference on Mechatronics, pp. 1-6. IEEE, 2007. [4] Su, Kuo L. \"Automatic fire detection system using adaptive fusion algorithm for fire fighting robot.\" In 2006 IEEE International Conference on Systems, Man and Cybernetics, vol. 2, pp. 966-971. IEEE, 2006. [5] Liu, Pengcheng, Hongnian Yu, Shuang Cang, and Luige Vladareanu. \"Robot-assisted smart firefighting and interdisciplinary perspectives.\" In 2016 22nd international conference on automation and computing (ICAC), pp. 395-401. IEEE, 2016. [6] Kim, Jong-Hwan, Brian Keller, and Brian Y. Lattimer. \"Sensor fusion based seek-and-find fire algorithm for intelligent firefighting robot.\" In 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 1482-1486. IEEE, 2013. [7] Kanwar, Megha, and L. Agilandeeswari. \"IOT based firefighting robot.\" In 2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), pp. 718-723. IEEE, 2018. [8] Maddukuri, Satya Veera Pavan Kumar, Uday Kishan Renduchintala, Aravinthan Visvakumar, Chengzong Pang, and Sravan Kumar Mittapally. \"A low-cost sensor based autonomous and semi-autonomous fire-fighting squad robot.\" In 2016 Sixth International Symposium on Embedded Computing and System Design (ISED), pp. 279-283. IEEE, 2016. [9] Bhosale, Swaroopa S., Y. N. Bhosale, Uma M. Chavan, and Sachin A. Malvekar. \"Power quality improvement by using UPQC: A review.\" In 2018 International conference on control, power, communication and computing technologies (ICCPCCT), pp. 375-380. IEEE, 2018.. [10] Kim, Jeongwan, J. Eric Dietz, and Eric T. Matson. \"Modeling of a multi-robot energy saving system to increase operating time of a firefighting robot.\" In 2016 IEEE symposium on technologies for homeland security (HST), pp. 1-6. IEEE, 2016. [11] Kapse, Mrunal M., Nilofar R. Patel, Shruti K. Narayankar, Sachin A. Malvekar, and Kazi Kutubuddin Sayyad Liyakat. \"Smart grid technology.\" International Journal of Information Technology and Computer Engineering 2, no. 6 (2022): 10-17. [12] Kosasih, Kristi, E. Merry Sartika, M. Jimmy Hasugian, and Muliady Muliady. \"The intelligent fire fighting tank robot.\" Maranatha Electrical Engineering Journal 1, no. 1 (2010): 147532. [13] Malvekar, Sachin A., C. L. Bhattar, and Viraj B. Savakhande. \"Non-Isolated High Voltage Gain DC-DC Converters using Inductors for DC Microgrid.\" In 2018 International Conference on Control, Power, Communication and Computing Technologies (ICCPCCT), pp. 455-459. IEEE, 2018.. [14] Naghsh, Amir M., Jeremi Gancet, Andry Tanoto, and Chris Roast. \"Analysis and design of human-robot swarm interaction in firefighting.\" In RO-MAN 2008-The 17th IEEE International Symposium on Robot and Human Interactive Communication, pp. 255-260. IEEE, 2008. [15] Rakib, Tawfiqur, and MA Rashid Sarkar. \"Design and fabrication of an autonomous fire fighting robot with multisensor fire detection using PID controller.\" In 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), pp. 909-914. IEEE, 2016. [16] Vaibhav L. Jadhav, Arjun P. Shinde, (2024). Detection of Fire in the Environment via a Robot Based Fire Fighting System Using Sensors, International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), Volume 4, Issue 4, pp. 410 – 418. [17] Kazi Kutubuddin Sayyad Liyakat (2024). Nanotechnology in Medical Applications: A Study. Nano Trends: A Journal of Nanotechnology and Its Applications. 2024; 26(2): 1–11p. [18] Kazi Kutubuddin Sayyad Liyakat. (2024). Nanotechnology in BattleField: A Study. Journal of Nanoscience, Nanoengineering & Applications. 2024; 14(2): 18–30p. [19] Sultananbanu Sayyad Liyakat Kazi, (2024). Polymer Applications in Energy Generation and Storage: A Forward Path. Journal of Nanoscience, Nanoengineering & Applications. 2024; 14(2): 31–39p. [20] Kazi Kutubuddin Sayyad Liyakat, (2024). Review of Biopolymers in Agriculture Application: An Eco-Friendly Alternative. International Journal of Composite and Constituent Materials. 2024; 10(1): 50–62p. [21] Kazi Kutubuddin Sayyad Liyakat (2024). Railway Health-Monitoring Using KSK Approach: Decision-Making Using AIIoT Approach in Railways, Journal of Controller and Converters, 9(3), 1-10. Available at: https://matjournals.net/engineering/index.php/JCC/article/view/1047 [22] K K Sayyad Liyakat. (2024). Impact of Nanotechnology on Battlefield Welfare: A Study. International Journal of Nanobiotechnology. 2024; 10(2): 19– 32p. [23] Sultanabanu Sayyad Liyakat, (2024q). Nanotechnology in Healthcare Applications: A Study. International Journal of Nanobiotechnology. 2024; 10(2): 48–58p. [24] Kazi Kutubuddin Sayyad Liyakat (2024). A Study on AI-driven IoT (AIIoT) based Decision Making: KSK Approach in Robot for Medical Applications, Recent Trends in Semiconductor and Sensor Technology, 1(3), 1-17. Available at: https://matjournals.net/engineering/index.php/RTSST/article/view/1044 [25] Kazi Kutubuddin Sayyad Liyakat (2024). Wireless Train Collision Avoidance System, Advance Research in Communication Engineering and its Innovations, 1(3), 16-25 [26] Kazi Kutubuddin Sayyad Liyakat. (2024). Internet of Battlefield Things: An IoBT-inspired Battlefield of Tomorrow. Journal of Telecommunication, Switching Systems and Networks. 2024; 11(3): 11–19p. [27] Sunil B. Mishra (2024d). AI-Driven-IoT (AIIoT)-Based Decision Making in Manufacturing Processes in Mechanical Engineering, Journal of Mechanical Robotics, 9(2), 27-38. [28] Sunil B. Mishra (2024e). AI-Driven-IoT (AIIoT) Based Decision-Making in Molten Metal Processing, Journal of Industrial Mechanics, 9(2), 45-56. [29] Kazi Kutubuddin Sayyad Liyakat, Impact of Nanotechnology on Battlefield Welfare: A Study. International journal of Nanobiotechnology. 2024; 10(02): 19-32p. [30] Kazi Sultanabanu Sayyad Liyakat and Kazi Kutubuddin Sayyad Liyakat, Nanosensors in Agriculture Field: A Study. International Journal of Applied Nanotechnology. 2024; 10(02): 12-22p. Available from:https://journalspub.com/publication/ijan-v10i02-11625/ [31] Kazi Kutubuddin Sayyad Liyakat, Nanotechnology in Space Study. International Journal of Applied Nanotechnology. 2024; 10(02): 39-46p. Available from:https://journalspub.com/publication/ijan-v10i02-11616/ [32] Dr. Kazi Kutubuddin Sayyad Liyakat. (2024). KSK Approach to Smart Agriculture: Utilizing AI-Driven Internet of Things (AI IoT). Journal of Microcontroller Engineering and Applications. 2024; 11(03):21-32. [33] Liyakat, K.K.S. (2024). Machine Learning Approach Using Artificial Neural Networks to Detect Malicious Nodes in IoT Networks. In: Udgata, S.K., Sethi, S., Gao, XZ. (eds) Intelligent Systems. ICMIB 2023. Lecture Notes in Networks and Systems, vol 728. Springer, Singapore.https://doi.org/10.1007/978-981-99-3932-9_12 available at: https://link.springer.com/chapter/10.1007/978-981-99-3932-9_12 [34] M Pradeepa, et al. (2022). Student Health Detection using a Machine Learning Approach and IoT, 2022 IEEE 2nd Mysore sub section International Conference (MysuruCon), 2022. Available at: https://ieeexplore.ieee.org/document/9972445 [35] K. K. S. Liyakat. (2023).Detecting Malicious Nodes in IoT Networks Using Machine Learning and Artificial Neural Networks, 2023 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, 2023, pp. 1-5, doi:10.1109/ESCI56872.2023.10099544. Available at: https://ieeexplore.ieee.org/document/10099544/ [36] K. Kasat, N. Shaikh, V. K. Rayabharapu, M. Nayak. (2023). Implementation and Recognition of Waste Management System with Mobility Solution in Smart Cities using Internet of Things, 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India, 2023, pp. 1661-1665, doi: 10.1109/ICAISS58487.2023.10250690 . Available at: https://ieeexplore.ieee.org/document/10250690/ [37] Liyakat, K.K.S. (2023). Machine Learning Approach Using Artificial Neural Networks to Detect Malicious Nodes in IoT Networks. In: Shukla, P.K., Mittal, H., Engelbrecht, A. (eds) Computer Vision and Robotics. CVR 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-4577-1_3 [38] Kazi, K. (2024a). AI-Driven IoT (AIIoT) in Healthcare Monitoring. In T. Nguyen & N. Vo (Eds.), Using Traditional Design Methods to Enhance AI-Driven Decision Making (pp. 77-101). IGI Global. https://doi.org/10.4018/979-8-3693-0639-0.ch003 available at: https://www.igi-global.com/chapter/ai-driven-iot-aiiot-in-healthcare-monitoring/336693 [39] Kazi, K. (2024b). Modelling and Simulation of Electric Vehicle for Performance Analysis: BEV and HEV Electrical Vehicle Implementation Using Simulink for E-Mobility Ecosystems. In L. D., N. Nagpal, N. Kassarwani, V. Varthanan G., & P. Siano (Eds.), E-Mobility in Electrical Energy Systems for Sustainability (pp. 295-320). IGI Global.https://doi.org/10.4018/979-8-3693-2611-4.ch014 Available at: https://www.igi-global.com/gateway/chapter/full-text-pdf/341172 [40] Kazi, K. S. (2024a). Computer-Aided Diagnosis in Ophthalmology: A Technical Review of Deep Learning Applications. In M. Garcia & R. de Almeida (Eds.), Transformative Approaches to Patient Literacy and Healthcare Innovation (pp. 112-135). IGI Global. https://doi.org/10.4018/979-8-3693-3661-8.ch006 Available at: https://www.igi-global.com/chapter/computer-aided-diagnosis-in-ophthalmology/342823 [41] Prashant K Magadum (2024). Machine Learning for Predicting Wind Turbine Output Power in Wind Energy Conversion Systems, Grenze International Journal of Engineering and Technology, Jan Issue, Vol 10, Issue 1, pp. 2074-2080. Grenze ID: 01.GIJET.10.1.4_1Available at:https://thegrenze.com/index.php?display=page&view=journalabstract&absid=2514&id=8 [42] Priya Mangesh Nerkar, BhagyarekhaUjjwalganeshDhaware. (2023). Predictive Data Analytics Framework Based on Heart Healthcare System (HHS) Using Machine Learning, Journal of Advanced Zoology, 2023, Volume 44, Special Issue -2, Page 3673:3686. Available at: https://jazindia.com/index.php/jaz/article/view/1695 [43] P. Neeraja, R. G. Kumar, M. S. Kumar, K. K. S. Liyakat and M. S. Vani. (2024), DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring. 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT), Greater Noida, India, 2024, pp. 589-594, doi: 10.1109/IC2PCT60090.2024.10486714. Available at: https://ieeexplore.ieee.org/document/10486714 [44] Kazi Kutubuddin Sayyad Liyakat, (2024). Explainable AI in Healthcare. In: Explainable Artificial Intelligence in healthcare System, editors: A. Anitha Kamaraj, Debi Prasanna Acharjya. ISBN: 979-8-89113-598-7. DOI: https://doi.org/10.52305/GOMR8163 [45] Liyakat Kazi, K. S. (2024). ChatGPT: An Automated Teacher\'s Guide to Learning. In R. Bansal, A. Chakir, A. Hafaz Ngah, F. Rabby, & A. Jain (Eds.), AI Algorithms and ChatGPT for Student Engagement in Online Learning (pp. 1-20). IGI Global. https://doi.org/10.4018/979-8-3693-4268-8.ch001 [46] Veena, M. Sridevi, K. K. S. Liyakat, B. Saha, S. R. Reddy and N. Shirisha,(2023). HEECCNB: An Efficient IoT-Cloud Architecture for Secure Patient Data Transmission and Accurate Disease Prediction in Healthcare Systems, 2023 Seventh International Conference on Image Information Processing (ICIIP), Solan, India, 2023, pp. 407-410, doi: 10.1109/ICIIP61524.2023.10537627. Available at: https://ieeexplore.ieee.org/document/10537627 [47] K. Rajendra Prasad, Santoshachandra Rao Karanam (2024). AI in public-private partnership for IT infrastructure development, Journal of High Technology Management Research, Volume 35, Issue 1, May 2024, 100496. https://doi.org/10.1016/j.hitech.2024.100496 [48] Kazi, K. S. (2024b). IoT Driven by Machine Learning (MLIoT) for the Retail Apparel Sector. In T. Tarnanidis, E. Papachristou, M. Karypidis, & V. Ismyrlis (Eds.), Driving Green Marketing in Fashion and Retail (pp. 63-81). IGI Global. https://doi.org/10.4018/979-8-3693-3049-4.ch004 [49] Kutubuddin Kazi, (2024a). Machine Learning (ML)-Based Braille Lippi Characters and Numbers Detection and Announcement System for Blind Children in Learning, In Gamze Sart (Eds.), Social Reflections of Human-Computer Interaction in Education, Management, and Economics, IGI Global. https://doi.org/10.4018/979-8-3693-3033-3.ch002 [50] Kazi, K. S. (2024). Artificial Intelligence (AI)-Driven IoT (AIIoT)-Based Agriculture Automation. In S. Satapathy & K. Muduli (Eds.), Advanced Computational Methods for Agri-Business Sustainability (pp. 72-94). IGI Global. https://doi.org/10.4018/979-8-3693-3583-3.ch005 [51] Kazi Kutubuddin, (2024c). Vehicle Health Monitoring System (VHMS) by Employing IoT and Sensors, Grenze International Journal of Engineering and Technology, Vol 10, Issue 2, pp- 5367-5374.Grenze ID: 01.GIJET.10.2.429. Available at: https://thegrenze.com/index.php?display=page&view=journalabstract&absid=3371&id=8 [52] Kazi Kutubuddin, (2024d). A Novel Approach on ML based Palmistry, Grenze International Journal of Engineering and Technology, Vol 10, Issue 2, pp- 5186-5193. Grenze ID: 01.GIJET.10.2.393. Available at: https://thegrenze.com/index.php?display=page&view=journalabstract&absid=3344&id=8 [53] Kazi Kutubuddin, (2024e). IoT based Boiler Health Monitoring for Sugar Industries, Grenze International Journal of Engineering and Technology, Vol 10, Issue 2, pp. 5178 -5185. Grenze ID: 01.GIJET.10.2.392. Available at: https://thegrenze.com/index.php?display=page&view=journalabstract&absid=3343&id=8 [54] Kazi, K. S. (2024). Machine Learning-Based Pomegranate Disease Detection and Treatment. In M. Zia Ul Haq & I. Ali (Eds.), Revolutionizing Pest Management for Sustainable Agriculture (pp. 469-498). IGI Global. https://doi.org/10.4018/979-8-3693-3061-6.ch019 [55] Liyakat. (2025). IoT Technologies for the Intelligent Dairy Industry: A New Challenge. In S. Thandekkattu& N. Vajjhala (Eds.), Designing Sustainable Internet of Things Solutions for Smart Industries (pp. 321-350). IGI Global. https://doi.org/10.4018/979-8-3693-5498-8.ch012 [56] Liyakat, K. K. (2025). Heart Health Monitoring Using IoT and Machine Learning Methods. In A. Shaik (Ed.), AI-Powered Advances in Pharmacology (pp. 257-282). IGI Global. https://doi.org/10.4018/979-8-3693-3212-2.ch010 [57] Sayyad. (2025f). AI-Powered-IoT (AIIoT)-Based Decision-Making System for BP Patient\'s Healthcare Monitoring: KSK Approach for BP Patient Healthcare Monitoring. In S. Aouadni& I. Aouadni (Eds.), Recent Theories and Applications for Multi-Criteria Decision-Making (pp. 205-238). IGI Global.https://doi.org/10.4018/979-8-3693-6502-1.ch008 [58] Kazi, K. S. (2025c). AI-Driven-IoT (AIIoT)-Based Decision Making in Drones for Climate Change: KSK Approach. In S. Aouadni& I. Aouadni (Eds.), Recent Theories and Applications for Multi-Criteria Decision-Making (pp. 311-340). IGI Global. https://doi.org/10.4018/979-8-3693-6502-1.ch011 [59] Liyakat. (2025d). AI-Driven-IoT(AIIoT)-Based Decision Making in Kidney Diseases Patient Healthcare Monitoring: KSK Approach for Kidney Monitoring. In L. Özgür Polat & O. Polat (Eds.), AI-Driven Innovation in Healthcare Data Analytics (pp. 277-306). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-7277-7.ch009 [60] Mahant, M. A. (2025). Machine Learning-Driven Internet of Things (MLIoT)-Based Healthcare Monitoring System. In N. Wickramasinghe (Ed.), Digitalization and the Transformation of the Healthcare Sector (pp. 205-236). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-9641-4.ch007 [61] Priya Nerkar and Kazi Sultanabanu, (2024). IoT-Based Skin Health Monitoring System, International Journal of Biology, Pharmacy and Allied Sciences (IJBPAS). 2024, 13(11): 5937-5950. https://doi.org/10.31032/IJBPAS/2024/13.11.8488 [62] Sayyad (2025e). AI-Powered IoT (AI IoT) for Decision-Making in Smart Agriculture: KSK Approach for Smart Agriculture. In S. Hai-Jew (Ed.), Enhancing Automated Decision-Making Through AI (pp. 67-96). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-6230-3.ch003 [63] Sayyad (2025f). KK Approach to Increase Resilience in Internet of Things: A T-Cell Security Concept. In D. Darwish & K. Charan (Eds.), Analyzing Privacy and Security Difficulties in Social Media: New Challenges and Solutions (pp. 87-120). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-9491-5.ch005 [64] Kazi, K. S. (2025). Machine Learning-Driven Internet of Medical Things (ML-IoMT)-Based Healthcare Monitoring System. In B. Soufiene & C. Chakraborty (Eds.), Responsible AI for Digital Health and Medical Analytics (pp. 49-86). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-6294-5.ch003 [65] Kazi Kutubuddin, (2024c). Vehicle Health Monitoring System (VHMS) by Employing IoT and Sensors, Grenze International Journal of Engineering and Technology,Vol 10, Issue 2, pp- 5367-5374. Available at: https://thegrenze.com/index.php?display=page&view=journalabstract&absid=3371&id=8 [66] Kazi Kutubuddin, (2024e). A Novel Approach on ML based Palmistry, Grenze International Journal of Engineering and Technology,Vol 10, Issue 2, pp- 5186-5193. Available at: https://thegrenze.com/index.php?display=page&view=journalabstract&absid=3344&id=8 [67] Kazi Kutubuddin, (2024e).IoT based Boiler Health Monitoring for Sugar Industries,Grenze International Journal of Engineering and Technology,Vol 10, Issue 2, pp. 5178 -5185. Available at: https://thegrenze.com/index.php?display=page&view=journalabstract&absid=3343&id=8 [68] Prashant K Magadum (2024). Machine Learning for Predicting Wind Turbine Output Power in Wind Energy Conversion Systems, Grenze International Journal of Engineering and Technology, Jan Issue, Vol 10, Issue 1, pp. 2074-2080. Grenze ID: 01.GIJET.10.1.4_1 Available at: https://thegrenze.com/index.php?display=page&view=journalabstract&absid=2514&id=8 [69] ALTAF O. MULANI, ARTI VASANT BANG, GANESH B. BIRAJADAR, AMAR B. DESHMUKH, HEMLATA MAKARAND JADHAV, (2024). IOT BASED AIR, WATER, AND SOIL MONITORING SYSTEM FOR POMEGRANATE FARMING, ANNALS OF AGRI-BIO RESEARCH. 29 (2): 71-86, 2024.

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Copyright © 2025 Miss. Rutuja Parshetti, Miss. Shruti Burbure, Miss. Shrutika Chavan, Miss. Vijayalaxmi Tamshetti, Miss. Pradnya Jagjap, S. A. Malvekar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.