Abstract
Our planet’s surface is one-third covered in forests. Despite being a great source of natural resources, the world has experienced numerous dangerous calamities, both natural and man-made, which have done a lot of damage to humanity. The forest fire is one such catastrophe. This paper shows a workaround system to detect forest fires as well as a monitoring station to simultaneously monitor the forest area in order to prevent the forest from suffering severe losses from fire. IoT is the technology in use. Here the sensors detect any changes in the environment, and with the integration of the Node MCU Microcontroller, the recorded fluctuation is sent to Cloud Storage which is “Google Firebase.” The concerned officials can monitor the relevant system based on real-time updates in the monitoring station. This early detection of a forest fire would help to take necessary measures to control and prevent the fire which might spread throughout the forest.
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References
Divya, A., Kavithanjali, T., Dharshini, P.: IoT enabled forest fire detection and early warning system. In: 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1–5. IEEE (2019)
Pareek, S., Shrivastava, S., Jhala, S., Siddiqui, J.A., Patidar, S.: IoT and image processing based forest monitoring and counteracting system. In: 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI) (48184), pp. 1024–1027. IEEE (2020)
Dubey, V., Kumar, P., Chauhan, N.: Forest fire detection system using IoT and artificial neural network. In: Bhattacharyya, S., Hassanien, A., Gupta, D., Khanna, A., Pan, I. (eds.) International Conference on Innovative Computing and Communications. LNNS, vol. 55, pp. 323–337. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-2324-9_33
Vega-Rodríguez, R., Sendra, S., Lloret, J., RomeroDíaz, P., Garcia-Navas, J.L.: Low cost LoRa based network for forest fire detection. In: 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS), pp. 177–184. IEEE (2019)
Kalatzis, N., Avgeris, M., Dechouniotis, D., Papadakis-Vlachopapadopoulos, K., Roussaki, I., Papavassiliou, S.: Edge computing in IoT ecosystems for UAV-enabled early fire detection. In: 2018 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 106–114. IEEE (2018)
Chitra, C., Maryam, S., Samreen, S., Shruthipriya, N., Subhash: Forest fire detection using IoT devices. Int. J. Res. Eng. Sci. Manag. 3(7) (2020). ISSN (Online) 2581-5792
Jayaram, K., Janani, K., Jeyaguru, R., Kumaresh, R., Muralidharan, N.: Forest fire alerting system with GPS Co-ordinates using IoT. In: 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), pp. 488–491. IEEE (2019)
Mehta, K., Sharma, S., Mishra, D.: Internet-of Things enabled forest fire detection system. In: 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 20–23. IEEE (2021)
Majumder, K., Chakrabarti, K., Shaw, R.N., Ghosh, A.: Genetic algorithm-based two-tiered load balancing scheme for cloud data centers. In: Bansal, J.C., Fung, L.C.C., Simic, M., Ghosh, A. (eds.) Advances in Applications of Data-Driven Computing. AISC, vol. 1319, pp. 1–19. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-6919-1_1
Rahman, Md.A., Hasan, S.T., Kader, M.A.: Computer vision based industrial and forest fire detection using support vector machine (SVM). In: 2022 International Conference on Innovations in Science, Engineering and Technology (ICISET), pp. 233–238. IEEE (2022)
Tajammul, M., Shaw, R.N., Ghosh, A., Parveen, R.: Error detection algorithm for cloud outsourced big data. In: Bansal, J.C., Fung, L.C.C., Simic, M., Ghosh, A. (eds.) Advances in Applications of Data-Driven Computing. AISC, vol. 1319, pp. 105–116. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-6919-1_8
Yatbaz, Y., Yazici, A.: An effective forest fire detection framework using heterogeneous wireless multimedia sensor networks. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 18(2), 1–21 (2022)
Garg, N., Obaidat, M.S., Wazid, M., Das, A.K., Singh, D.P.: SPCS-IoTEH: secure privacy-preserving communication scheme for IoT-enabled e-health applications. In: ICC 2021-IEEE International Conference on Communications, pp. 1–6. IEEE (2021)
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Mehta, K., Sharma, S. (2023). Cloud Computing Enabled Autonomous Forest Fire Surveillance System Using Internet-of-Things. In: Shaw, R.N., Paprzycki, M., Ghosh, A. (eds) Advanced Communication and Intelligent Systems. ICACIS 2023. Communications in Computer and Information Science, vol 1920. Springer, Cham. https://doi.org/10.1007/978-3-031-45121-8_10
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DOI: https://doi.org/10.1007/978-3-031-45121-8_10
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