METHODS AND HARDWARE AND SOFTWARE FOR INFORMATION PROCESSING IN INTELLIGENT FOREST FIRE MONITORING SYSTEMS BASED ON UAV SWARMS
DOI:
https://doi.org/10.26906/SUNZ.2024.4.123Keywords:
forest fires, monitoring, intelligent systems, unmanned aerial vehicles, UAV swarms, hybrid architectures, artificial intelligenceAbstract
Artificial intelligence methods for forest fire monitoring systems based on UAV swarms were investigated. The methods used both for individual UAVs and for different architectures using a UAV swarm are considered. AI integration has been shown to enhance UAV capabilities for early fire detection, real-time monitoring and decision-making. The study found that while the information collected by individual UAVs is valuable, there are limitations that can be overcome by using a UAV swarm that augments intelligent computing capabilities. Different architectures – centralized, distributed and hybrid – have been proven to provide unique advantages in different fire monitoring scenarios. The study emphasized the importance of choosing a rational architecture based on specific monitoring tasks. Each architecture has certain limitations, but the proposed solutions are marked by improvements in efficiency, reliability and scalability. The final configuration consists of a combination of UAVs and AI tools designed to maximize the effectiveness of fire monitoring. The development of the potential of swarm intelligence, predictive analytics and adaptive task allocation is identified as an important direction for future research. It is noted that these results are important for the development of new management systems for monitoring, preventing, limiting and determining the consequences of forest fires based on UAV swarms.Downloads
References
Cunningham Calum X., Williamson Grant J., Bowman David M.J.S. (2024). Increasing frequency and intensity of the most extreme wildfires on Earth. Nat Ecol Evol. 8. PP. 1420–1425. 10.1038/s41559-024-02452-2.
Sadowska Beata, Zimon Grzegorz, Stępnicka Nina. (2021). Forest Fires and Losses Caused by Fires – An Economic Approach. WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT. 17. PP. 181-191. 10.37394/232015.2021.17.18.
Francesco Carta, Zidda Chiara, Putzu Martina, Loru Daniele, Anedda Matteo, Giusto Daniele. (2023). Advancements in Forest Fire Prevention: A Comprehensive Survey. Sensors. 23. 6635. 10.3390/s23146635.
Omar Madiah Binti, Ibrahim Rosdiazli, Bingi Kishore, Haikal Wan Mohd Nadzri Wan Muhammad, Faqih Mochammad. (2023). Real-Time UAV System Integration for Fire Detection and Classification. IEEE International Conference on Computing. PP. 237-241. 10.1109/ICOCO59262.2023.10397888.
Jia Xuanbo, Wang Yike, Chen Taiming. (2023). Forest Fire Detection and Recognition Using YOLOv8 Algorithms from UAVs Images. IEEE 5th International Conference on Power, Intelligent Computing and Systems. PP. 646-651. 10.1109/ICPICS58376.2023.10235675.
Xie Feifei, Huang Zhiqing. (2023). Aerial Forest Fire Detection based on Transfer Learning and Improved Faster RCNN.IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence. PP. 1132-1136. 10.1109/ICIBA56860.2023.10165603.
Avola Danilo, Cinque Luigi, Di Mambro Angelo, Diko Anxhelo, Fagioli Alessio, Foresti Gian Luca, Marini Marco Raoul, Mecca Alessio, Pannone Daniele. (2022). Low-Altitude Aerial Video Surveillance via One-Class SVM Anomaly Detection from Textural Features in UAV Images. Information. 13. 2. 10.3390/info13010002.
Yang Lei, Zhang Yizong, Li Shaobo, Zhang Ansi , Liao Zihao, Li Chuanjiang0. (2023). UAV Flight Data Anomaly Detection Based on Parameter Selection and Multiple Regression. Global Reliability and Prognostics and Health Management Conference. PP. 1-6. 10.1109/PHM-Hangzhou58797.2023.10482575.
Umunnakwe Amarachi, Davis Katherine. (2023). An Optimization of UAV-Based Remote Monitoring for Improving Wildfire Response in Power Systems. IEEE Open Access Journal of Power and Energy. 10. PP. 678-688.10.1109/OAJPE.2023.3337760.
Habiba Ummey, Jahan Roshan. (2023). Path Planning for UAV “Drones” Using SARSA: Enhancing Efficiency and Performance. International Conference on Integration of Computational Intelligent System. PP. 1-6. 10.1109/ICICIS56802.2023.10430246.
Hu Qiao, Zhang Ligang, Drahota Jeff, Woldt Wayne, Varner Dana, Bishop Andy, LaGrange Ted, Neale Christopher M. U., Tang Zhenghong. (2024). Combining Multi-View UAV Photogrammetry, Thermal Imaging, and Computer Vision Can Derive Cost-Effective Ecological Indicators for Habitat Assessment. Remote Sensing. 16. 1081. 10.3390/rs16061081.
Wang Shaofan, Li Ke, Chen Jiaao, Zhang Tao. (2023). Unmanned Aerial Vehicle Autonomous Visual Landing through Visual Attention-Based Deep Reinforcement Learning. 42nd Chinese Control Conference. PP. 4143-4148. 10.23919/CCC58697.2023.10240825.
Mu Yunjie, Ou Liyuan, Chen Wenjing, Liu Tao, Gao Demin. (2024). Superpixel-Based Graph Convolutional Network for UAV Forest Fire Image Segmentation. Drones. 8. 142. 10.3390/drones8040142.
Shrestha Kripash, La Hung Manh, Yoon Hyung-Jin. (2022). A Distributed Deep Learning Approach for A Team of Unmanned Aerial Vehicles for Wildfire Tracking and Coverage. Sixth IEEE International Conference on Robotic Computing. PP. 312-319. 10.1109/IRC55401.2022.00061.
Yang Luyinru, Zheng Jun, Zhang Baoxian. (2023). An MARL-based Task Scheduling Algorithm for Cooperative Computation in Multi-UAV-Assisted MEC Systems. International Conference on Future Communications and Networks. PP. 1-6. 10.1109/FCN60432.2023.10544292.
Akbari Mohammad, Syed Aisha, Kennedy W. Sean, Erol-Kantarci Melike. (2024). AoI-Aware Energy-Efficient SFC in UAV-Aided Smart Agriculture Using Asynchronous Federated Learning. IEEE Open Journal of the Communications Society. 5. PP. 1222-1242. 10.1109/OJCOMS.2024.3363132.
Yin Yongfeng, Guo Yang, Su Qingran, Wang Zhetao. (2022). Task Allocation of Multiple Unmanned Aerial Vehicles Based on Deep Transfer Reinforcement Learning. Drones. 6. 215. 10.3390/drones6080215.
Alsammak Ihab L. Hussein, Mahmoud Moamin A., Gunasekaran Saraswathy Shamini, Ahmed Ali Najah, AlKilabi Muhanad. (2023). Nature-Inspired Drone Swarming for Wildfires Suppression Considering Distributed Fire Spots and Energy Consumption. IEEE Access. 11. PP. 50962-50983. 10.1109/ACCESS.2023.3279416.
Chen Shihan, Ma Jialu. (2022). IEEE Conference on Telecommunications, Optics and Computer Science. PP. 1006-1011. 10.1109/TOCS56154.2022.10015980.
Liang Hao, Zhang Meng, Wang Hailan. (2019). A Neural Network Model for Wildfire Scale Prediction Using Meteorological Factors. IEEE Access. 7. PP. 176746-176755. 10.1109/ACCESS.2019.2957837.
Chhabra Rishi, Bhagat Aditya, Mishra Gaurav, Tiwari Ashish, Dhabu M.. (2024). Machine Learning and Deep LearningBased Prediction and Monitoring of Forest Fires Using Unmanned Aerial Vehicle. Applications of Machine Learning in UAV Networks. PP. 160-193. 10.4018/979-8-3693-0578-2.ch007.
Mariam Ayesha, Mushtaq Memoona, Iqbal Muhammad Munwar. (2022). Real-Time Detection, Recognition, and Surveillance using Drones. International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering. PP. 1-5. 10.1109/ETECTE55893.2022.10007285.
Kristanto Andri, Indriyanto Toto. (2024). Development of Communication System for UAV Ground Control Station with ATC Based on Controller Pilot Data Link Communication. Warta Penelitian Perhubungan. 35. PP. 112-125. 10.25104/warlit.v35i2.2308.
Andreadis Alessandro, Giambene Giovanni, Zambon Riccardo. (2023). Role of UAVs and HAPS for IoT-based Monitoring in Emergency Scenarios. International Conference on Information and Communication Technologies for Disaster Management. PP. 1-8. 10.1109/ICT-DM58371.2023.10286916.
Fesenko Herman, Illiashenko Oleg, Kharchenko Vyacheslav, Kliushnikov Ihor, Morozova Olga, Sachenko Anatoliy, Skorobohatko Stanislav. (2023). Flying Sensor and Edge Network-Based Advanced Air Mobility Systems: Reliability Analysis and Applications for Urban Monitoring. Drones. 7. 409. 10.3390/drones7070409.
Masood Arooj, Nguyen The-Vi, Truong Thanh Phung, Cho Sungrae. (2021). Content Caching in HAP-Assisted Multi-UAV Networks Using Hierarchical Federated Learning. International Conference on Information and Communication Technology Convergence. PP. 1160-1162. 10.1109/ICTC52510.2021.9621115.
Chen Wu, Zhu Jiayi, Liu Jiajia, Guo Hongzhi. (2024). A fast coordination approach for large-scale drone swarm. Journal of Network and Computer Applications. 221. 10.1016/j.jnca.2023.103769.
Ma Li, Lin Bin, Zhang Wen, Tao Jingjing, Zhu Xiaomin, Chen Hao. (2022). A Survey of Research on the Distributed Cooperation Method of the UAV Swarm based on Swarm Intelligence. IEEE 13th International Conference on Software Engineering and Service Science. PP. 305-309. 10.1109/ICSESS54813.2022.9930182.
Zaitseva Elena, Levashenko Vitaly, Mysko Vladimir, Czapp Stanislaw, Zhaxybayev, Darkhan. (2024). Availability of UAV Fleet Evaluation Based on Multi-State System. IEEE Access. 12. PP. 15290-15307. 10.1109/ACCESS.2024.3358198.
