A Cyber-Physical System for Wildfire Detection and Firefighting

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Gerardo Martino	--	2285	2023-07-20 19:49:06	\|
2	layout & references	Sirius Huang	-1 word(s)	2284	2023-07-26 02:38:42	\| \|
3	subheading	Sirius Huang	+ 2 word(s)	2286	2023-07-26 02:44:59	\|

This entry is adapted from the peer-reviewed paper 10.3390/fi15070237

The increasing frequency and severity of forest fires necessitate early detection and rapid response to mitigate their impact. This project aims to design a cyber-physical system for early detection and rapid response to forest fires using advanced technologies. The system incorporates Internet of Things sensors and autonomous unmanned aerial and ground vehicles controlled by the robot operating system. An IoT-based wildfire detection node continuously monitors environmental conditions, enabling early fire detection. Upon fire detection, a UAV autonomously surveys the area to precisely locate the fire and can deploy an extinguishing payload or provide data for decision-making. The UAV communicates the fire's precise location to a collaborative UGV, which autonomously reaches the designated area to support ground-based firefighters. The CPS includes a ground control station with web-based dashboards for real-time monitoring of system parameters and telemetry data from UAVs and UGVs. The real-time fire detection capabilities of the proposed system are demonstrated using simulated forest fire scenarios. The objective is to provide a practical approach using open-source technologies for early detection and extinguishing of forest fires, with potential applications in various industries, surveillance, and precision agriculture.

CPS IoT IoRT UAV UGV ROS PX4 Autopilot wildfire fire detection Node-RED MQTT YOLO

1. Introduction

Forests are crucial ecosystems that provide numerous benefits to the environment and its inhabitants. They filter air and water, provide food and shelter for animals, and play a crucial role in regulating the climate. However, global warming and human error have increased the incidence of forest wildfires, which can cause significant damage to the environment, property, and human health. Once an ignition starts, it is essential to declare it as soon as possible so that it can be rapidly controlled and suppressed. Researchers have focused on providing first responders, firefighters, and decision makers with systems for analyzing the situation at a glance through visual analytics techniques ^[1]^[2]^[3]^[4], sharing data ^[1], or systems that involve active citizenship for early warning ^[5]^[6]. However, wildland firefighters often do not have access to accurate and real-time information about the situation because the location of the fire is remote or hard to reach, and if the fire is not brought under control in time, it can continue to grow, increasing the danger and the suppression costs. Detecting and controlling wildfires is a challenging task, as they can spread rapidly and are difficult to anticipate. Researchers are exploring the use of advanced technologies such as IoT, robotics, and drone technologies ^[7]. The synergy of these technologies converges in a new trend that can be called the Internet of Robotics and Drone Things ^[8]. The interconnection of all these heterogeneous devices and the integration with software components has led to the design and development of a Cyber-Physical System (CPS) for wildfire detection ^[9] and collaborative robots able to navigate autonomously and perform fire attacks on wildland fires before they burn out of control. However, there remains a significant challenge in providing accurate and real-time information to ground control stations and operators overseeing fleets of drones in collaborative environments. In ^[10], the authors introduce a human–machine interface that enables a ground control station to remotely monitor and manage a fleet of drones in a collaborative environment, with the involvement of multiple operators. Collaborative robots, also defined as unmanned systems, have the capability to execute a mission autonomously without human intervention, and commonly they include unmanned aircraft and ground robots ^[11].

2. IoT Forest Fire Systems

In recent years, with global temperature rise, extreme weather, and climate events, especially droughts, wildfires are a natural phenomenon that has increased in frequency and magnitude over the past decade, causing damage to life and property, and impacting climate and air quality ^[12]. The wildfire season has lengthened in many countries due to shifting climate patterns. Studies report that the number of wildfires has declined, thanks to the strict and full implementation of forest fire prevention and management strategies and the philosophy of forest protection and green development in the world; however, climate warming will continue in the world, leading to the more frequent occurrence of dry thunderstorms, increased number of days with forest fire alerts, higher fire risks, and greater challenges in forest fire prevention ^[13]. Therefore, we must strengthen studies on the impacts of climate change on forest fires; improve the monitoring, prediction, and alert system for forest fires; and raise public awareness and knowledge of forest fire prevention ^[14]. To minimize their impact, effective prevention, early warning, and response approaches are needed. Research is ongoing to develop high-accuracy fire detection systems for challenging environments ^[15]. Traditionally, forest fires were mainly detected by human observation from fire lookout towers and involved only primitive tools, such as the Osborne fire Finder ^[16], which is a tool consisting of a card topographic printed on a disc with edge graduated. Unfortunately, these primary techniques are inefficient due to the unreliability of human observation towers and difficult living conditions. Advanced technologies such as satellites, UAV, UGV, ground-based sensor nodes, and camera systems supplement traditional firefighting techniques, focusing on detecting wildfires at early stages, and predicting hot spots by combining all the methods for a robust fire monitoring system ^[17]. Cyber-Physical Systems (CPSs), which integrate computing and control technologies into physical systems aim to contribute to real-time wildfire detection through the Internet of Things (IoT) wireless sensor ^[18] and the use of a collaborative unmanned system ^[11].

In the past few years, IoT has been gaining significant attention in various fields, including forest fire detection. IoT technologies are very useful in forest management systems because they can provide real-time monitoring of forest conditions, such as temperature, humidity, and wind speed. These data can be used to predict the likelihood of a wildfire, to detect a fire, and to track the progression of an active fire. This kind of technology is also called the Internet of Forest Things (IoFT) ^[19]. The Milesight Company is an example of a company that has developed a technology to detect and prevent forest fires using an IoT system (Figure 1). The system is comprised of:

Figure 1. Architecture of forest fire detection.

IoT nodes: A microcontroller unit connected with carbon dioxide, barometric pressure, temperature, and humidity sensors.
Gateway: A central hub that connects and manages the various sensors and devices, is able to collect data from IoT nodes, transmit it to the cloud, and perform edge computing tasks.
Cloud: A remote server that provides a scalable and flexible platform used to process, and analyze the data collected by the sensors, but also used to store data, provides visualization and reporting tools and enables integration with other systems for enhanced capabilities.

Typically, the IoT nodes used in this type of application employ Low-Power Wide-Area Network (LPWAN) technologies, such as LoRa and Sigfox, to enable long-range communication with a battery life that can last for several years. This allows for coverage of an entire forest at a cost-effective price. Additionally, the use of unmanned systems offers many advantages, including the ability to cover large areas in any weather condition, operate during both day and night with extended flight duration, be quickly recovered and relatively cost-effective compared to other methods, carry various payloads for different missions within a single flight, and efficiently cover larger and specific target areas ^[20]. UAVs equipped with computer vision-based remote sensing systems are becoming a more practical option for forest fire surveillance and detection due to their low cost, safety, mobility, and rapid response characteristics ^[21]. Unmanned Aerial Vehicles (UAVs) based on computer vision fire detection can provide immediate information on the fire spots but are also equipped with a dropping mechanism used to target and shoot the fire by releasing extinguishing balls ^[22]^[23]. Popular real-time object detection systems, such as YOLO can be integrated into UAVs, providing real-time monitoring of large forest areas and detecting fires at an early stage, reducing the risk of the fire spreading ^[24]. Consequently, numerous research studies have been conducted in recent years to develop UAV-based forest fire monitoring and detection applications based on YOLO ^[25]^[26]^[27]. In addition, autonomous ground systems such as the UGV are essential in responding to wildfire emergencies. Equipped with advanced technologies such as computer vision, infrared and depth cameras, and other sensors, UGVs can be used for detecting and extinguishing fires ^[28]^[29]^[30], as well as cooperating with other systems such as UAVs ^[31]. These robots can perform tasks on land without human intervention and access difficult-to-reach areas that may be too hazardous for human firefighters.

Forest fire monitoring and detection applications using Unmanned Aerial Vehicles (UAVs) have received significant attention in recent years, with a particular focus on employing the You Only Look Once (YOLO) algorithm for object detection ^[25]^[26]^[27]. These studies have demonstrated the effectiveness of UAVs in providing aerial surveillance and timely fire detection capabilities. Furthermore, autonomous ground systems such as Unmanned Ground Vehicles (UGVs) have emerged as crucial elements in responding to wildfire emergencies, leveraging advanced technologies such as computer vision, infrared and depth cameras, and other sensors ^[28]^[29]^[30]. UGVs are capable of detecting and extinguishing fires and collaborating with UAVs to access hazardous or hard-to-reach areas that pose risks to human firefighters ^[31]. Object detection algorithms play a critical role in fire detection systems as they enable real-time identification and localization of fire incidents. The You Only Look Once (YOLO) family of object detection algorithms has proven highly effective and efficient in computer vision applications. Notably, YOLOv4 (https://blog.roboflow.com/a-thorough-breakdown-of-yolov4/, accessed on 19 April 2023) and YOLOv5 (https://blog.roboflow.com/yolov5-improvements-and-evaluation/, accessed on 19 April 2023) stand out as prominent versions, each offering distinct advantages for different components as shown in Table 1.

Table 1. Comparison of YOLOv4 and YOLOv5.

Feature	YOLOv4	YOLOv5
Architecture	Utilizes a complex architecture with a CSPDarknet53 backbone, which includes multiple convolutional layers and skip connections.	Introduces a simplified architecture that focuses on efficiency and speed. Replaces the CSPDarknet53 backbone with a smaller and more streamlined CSPNet-based architecture.
Model Size and Speed	Larger model size, requiring more computational resources. Provides slightly slower inference times compared to YOLOv3.	Smaller model size, achieving faster inference times without compromising much on accuracy.
Performance	Aims to improve object detection performance by introducing various techniques, such as PANet, SAM, and PAN. Achieves state-of-the-art accuracy on popular object detection benchmarks.	Aims to improve object detection performance by introducing various techniques, such as PANet, SAM, and PAN. Achieves state-of-the-art accuracy on popular object detection benchmarks.
Training Approach	Adopts a traditional single-stage object detection training approach, utilizing anchor boxes and predefined anchor scales. Employs the focal loss function to address class imbalance issues.	Introduces a new approach called “Generalized Anchors,” which adapts anchor sizes during training. Incorporates a focal loss function and implements a novel loss function called “CIoU loss” to improve localization accuracy.
Availability	Open-source implementation widely used and supported by the computer vision community. Available as a publicly accessible codebase.	Open-source implementation is gaining popularity due to improved speed and efficiency. Codebase is publicly available, allowing researchers and developers to utilize and contribute to its development.

Research Gap

Recently, the Internet of Things (IoT) has gained significant popularity as a rapid and effective technique for detecting fires, particularly in the context of forest fire prevention. The IoT forest fire system is designed to facilitate continuous monitoring and detection of forest fires, operating 24/7. This system relies on the strategic deployment of sensors throughout the forest, enabling comprehensive coverage. To establish seamless communication among these sensors, a low-power wide-area network protocol, such as LoRa, is commonly implemented. Such protocols possess the capability to cover expansive forest areas. Leveraging the data collected by these sensors, combined with advanced machine learning algorithms, early-stage fire detection becomes achievable ^[32]^[33]. However, one limitation of IoT is its incapability to directly extinguish fires. In order to overcome this limitation, researchers are investigating the integration of IoT with the robotic ecosystem, which holds promise for forest fire response. In fact, a recent study introduced the IoT-UAV architecture ^[34], which entails the synergistic collaboration between IoT and Unmanned Aerial Vehicles (UAVs). In this system, the IoT network detects the fire and transmits an approximate location to a UAV, enabling it to locate the precise fire location and perform fire suppression. Utilizing UAVs in forest fire fighting offers several advantages. First, UAVs have the ability to access difficult-to-reach areas, facilitating intervention in challenging terrain. Second, they exhibit a faster response time for deploying fire extinguishing measures. Third, UAVs provide real-time aerial reconnaissance, enabling effective planning of fire suppression strategies. Moreover, their overhead view allows for precise identification of the fire’s point of ignition. UAVs can also utilize remote extinguishing systems, reducing risks to human operators. They are capable of dropping water or fire retardant to delay the fire’s spread. However, UAVs have certain limitations when it comes to fire suppression. These limitations include limited payload capacity for carrying water or fire retardant, short flight duration, restricted battery life, and difficulties in navigating smoke-filled environments. To overcome these limitations, the collaboration between UAVs and Unmanned Ground Vehicles (UGVs) has gained attention in the field of firefighting forest management ^[31]. UGVs offer unique advantages that can be leveraged in fire suppression efforts. They excel in navigating rough terrain and reaching remote areas that may be inaccessible for human-operated vehicles. UGVs demonstrate robustness in collision scenarios and can operate effectively in adverse weather conditions and low visibility. Equipped with powerful pumps and larger water tanks, UGVs can hold more water and fire retardant than UAVs, increasing the likelihood of successful fire suppression. Furthermore, UGVs can remain on-site for longer durations, monitoring the fire and preventing re-ignition. They can be outfitted with cameras and sensors to collect valuable data on the fire and the surrounding environment, contributing to future fire management planning. Additionally, UGVs can be remotely operated, minimizing risks to human operators. When combined with UAVs, which provide aerial surveillance and real-time data, coordinated operations between UAVs and UGVs enhance the overall efficiency of the firefighting system.

References

Ginige, A.; Paolino, L.; Romano, M.; Sebillo, M.; Tortora, G.; Vitiello, G. Information Sharing Among Disaster Responders - An Interactive Spreadsheet-Based Collaboration Approach. Comput. Support. Coop. Work 2014, 23, 547–583.
Battistoni, P.; Romano, M.; Sebillo, M.; Vitiello, G. Monitoring Urban Happiness through Interactive Chorems. Sustainability 2023, 15, 8733.
Romano, M. Urban Happiness Planning Through Interactive Chorems. In Proceedings of the International Symposium: New Metropolitan Perspectives, Reggio Calabria, Italy, 24–26 May 2022; Springer: Berlin/Heidelberg, Germany, 2022; pp. 2822–2832.
Battistoni, P.; Grimaldi, M.; Romano, M.; Sebillo, M.; Vitiello, G. Interactive maps of chorems explaining urban contexts to align smart community’s actors. In Proceedings of the International Conference on Computational Science and Its Applications, Cagliari, Italy, 13–16 September 2021; Springer: Berlin/Heidelberg, Germany, 2021; pp. 549–564.
Romano, M.; Díaz, P.; Aedo, I. Gamification-less: May gamification really foster civic participation? A controlled field experiment. J. Ambient. Intell. Humaniz. Comput. 2022, 13, 4451–4465.
Romano, M.; Díaz, P.; Aedo, I. Emergency management and smart cities: Civic engagement through gamification. In Proceedings of the Information Systems for Crisis Response and Management in Mediterranean Countries: Third International Conference, ISCRAM-med 2016, Madrid, Spain, 26–28 October 2016; Proceedings 3. Springer: Berlin/Heidelberg, Germany, 2016; pp. 3–14.
Wang, H.; Liu, J.; Han, J. RS-CPS: A distributed architecture of robotic surveillance cyber-physical system in the nature environment. In Proceedings of the 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Shenyang, China, 8–12 June 2015.
Singh, K.K.; Nayyar, A.; Tanwar, S.; Abouhawwash, M. Emergence of Cyber Physical System and IoT in Smart Automation and Robotics-Computer Engineering in Automation; Advances in Science, Technology & Innovation; SpringerLink: Berlin/Heidelberg, Germany, 2021.
Goyal, N.; Sharma, S.; Rana, A.K.; Tripathi, S.L. Internet of Things Robotic and Drone Technology; CRC Press: Boca Raton, FL, USA; Taylor & Francis Group: Abingdon, UK, 2022.
Gregorio, M.D.; Romano, M.; Sebillo, M.; Vitiello, G.; Vozella, A. Improving Human Ground Control Performance in Unmanned Aerial Systems. Future Internet 2021, 13, 133.
Gandhi, A.; Garg, S.; Parkhiya, P.; Zhu, Z. UAV - UGV Collaborative Robots for Fire-Fighting. Available online: https://mrsdprojects.ri.cmu.edu/2018teamh/wp-content/uploads/sites/38/2019/05/TeamH_CDRR.pdf (accessed on 1 May 2023).
San-Miguel-Ayanz, J.; Durrant, T.; Boca, R.; Maianti, P.; Liberta‘, G.; Artes Vivancos, T.; Jacome Felix Oom, D.; Branco, A.; De Rigo, D.; Ferrari, D.; et al. Forest Fires in Europe, Middle East and North Africa 2021. In JRC Technical Report—European Commission; Publications Office of the European Union: Luxembourg, 2021.
Grant, C.; Hamins, A.; Bryner, N.; Jones, A.; Koepke, G. Research Roadmap for Smart Fire Fighting; National Institute of Standards and Technology: Gaithersburg, MA, USA, 2015.
Ai, W.; Wu, Y.; Wang, C.; Liang, L. Impacts of Climate Change on Forest Fires. In Annual Report on Actions to Address Climate Change (2019); Springer: Singapore, 2023; pp. 195–209.
Barmpoutis, P.; Papaioannou, P.; Dimitropoulos, K.; Grammalidis, N. A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing. Sensors 2020, 20, 6442.
Kresek, R. History of the Osborne Firefinder. Available online: http://www.nysforestrangers.com/archives/osborne%20firefinder%20by%20kresek.pdf (accessed on 27 May 2023).
Mohapatra, A.; Trinh, T. Early Wildfire Detection Technologies in Practice—A Review. Sustainability 2022, 14, 12270.
Sanislav, T.; Mois, G.; Folea, S.; Miclea, L.; Gambardella, G.; Prinetto, P. A cloud-based Cyber-Physical System for environmental monitoring. In Proceedings of the Mediterranean Conference on Embedded Computing (MECO), Budva, Montenegro, 15–19 June 2014; pp. 6–9.
Sahal, R.; Alsamhi, S.H.; Breslin, J.G.; Ali, M.I. Industry 4.0 towards Forestry 4.0: Fire detection use case. Sensors 2021, 21, 694.
Partheepan, S.; Sanati, F.; Hassan, J. Autonomous Unmanned Aerial Vehicles in Bushfire Management: Challenges and Opportunities. Drones 2023, 7, 47.
Esfahlani, S.; Cirstea, S.; Sanaei, A.; Cirstea, M. Fire detection of Unmanned Aerial Vehicle in a Mixed Reality-based System. In Proceedings of the IECON 2018—44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, USA, 21–23 October 2018; pp. 2757–2762.
Zadeh, N.R.N.; Abdulwakil, A.H.; Amar, M.J.R.; Durante, B.; Santos, C.V.N.R. Fire-fighting UAV with shooting mechanism of fire extinguishing ball for smart city. Indones. J. Electr. Eng. Comput. Sci. 2021, 22, 1320–1326.
Jayapandian, N. Cloud Enabled Smart Firefighting Drone Using Internet of Things. In Proceedings of the International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 27–29 November 2019; pp. 1079–1083.
Xu, R.; Lin, H.; Lu, K.; Cao, L.; Liu, Y. A forest fire detection system based on ensemble learning. Forests 2021, 12, 217.
Zhao, L.; Zhi, L.; Zhao, C.; Zheng, W. Fire-YOLO: A Small Target Object Detection Method for Fire Inspection. Sustainability 2022, 14, 4930.
Nori, R.; Farhan, R.; Abed, S. Indoor and Outdoor Fire Localization Using YOLO Algorithm. J. Phys. Conf. Ser. 2021, 2114, 012067.
Bahhar, C.; Ksibi, A.; Ayadi, M.; Jamjoom, M.M.; Ullah, Z.; Soufiene, B.O.; Sakli, H. Wildfire and Smoke Detection Using Staged YOLO Model and Ensemble CNN. Electronics 2023, 12, 228.
Fonollosa, J.; Solórzano, A.; Marco, S. Chemical Sensor Systems and Associated Algorithms for Fire Detection: A Review. Sensors 2018, 18, 553.
Gaur, A.; Singh, A.; Kumar, A.; Kulkarni, K.; Lala, S.; Kapoor, K.; Srivastava, V.; Kumar, A.; Mukhopadhyay, S. Fire Sensing Technologies: A Review. IEEE Sens. J. 2019, 19, 3191–3202.
Liu, P.; Yu, H.; Cang, S.; Vladareanu, L. Robot-assisted smart firefighting and interdisciplinary perspectives. In Proceedings of the 22nd International Conference on Automation and Computing (ICAC), Colchester, UK, 7–8 September 2016; pp. 395–401.
Ghamry, K.A.; Kamel, M.A.; Zhang, Y. Cooperative forest monitoring and fire detection using a team of UAVs-UGVs. In Proceedings of the 2016 International Conference on Unmanned Aircraft Systems (ICUAS), Arlington, VA, USA, 7–10 June 2016; IEEE: Piscataway, NJ, USA, 2016; pp. 1206–1211.
Benzekri, W.; Moussati, A.E.; Moussaoui, O.; Berrajaa, M. Early Forest Fire Detection System using Wireless Sensor Network and Deep Learning. Int. J. Adv. Comput. Sci. Appl. 2020, 11, 496.
Avazov, K.; Hyun, A.E.; Sami, A.A.; Khaitov, A.; Abdusalomov, A.B.; Cho, Y.I. Forest Fire Detection and Notification Method Based on AI and IoT Approaches. Future Internet 2023, 15, 61.
Bushnaq, O.M.; Chaaban, A.; Al-Naffouri, T.Y. The role of UAV-IoT networks in future wildfire detection. IEEE Internet Things J. 2021, 8, 16984–16999.

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Robotics

Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Pietro Battistoni

Andrea Antonio Cantone

Gerardo Martino

Valerio Passamano

Marco Romano

Monica Sebillo

Giuliana Vitiello

View Times: 481

Update Date: 26 Jul 2023

Table of Contents

Video Upload Options

Confirm

1. Introduction

2. IoT Forest Fire Systems

Research Gap

References