IoT-inspired Framework for Real-time Prediction of Forest Fire
DOI:
https://doi.org/10.15837/ijccc.2022.3.4371Keywords:
Forest Fire, Internet of Things (IoT), Risk Assessment, ForecastingAbstract
Wildfires are one of the most devastating catastrophes and can inflict tremendous losses to life and nature. Moreover, the loss of civilization is incomprehensible, potentially extending suddenly over vast land sectors. Global warming has contributed to increased forest fires, but it needs immediate attention from the organizations involved. This analysis aims to forecast forest fires to reduce losses and take decisive measures in the direction of protection. Specifically, this study suggests an energy-efficient IoT architecture for the early detection of wildfires backed by fog-cloud computing technologies. To evaluate the repeatable information obtained from IoT sensors in a time-sensitive manner, Jaccard similarity analysis is used. This data is assessed in the fog processing layer and reduces the single value of multidimensional data called the Forest Fire Index. Finally, based on Wildfire Triggering Criteria, the Artificial Neural Network (ANN) is used to simulate the susceptibility of the forest area. ANN are intelligent techniques for inferring future outputs as these can be made hybrid with fuzzy methods for decision-modeling. For productive visualization of the geographical location of wildfire vulnerability, the Self-Organized Mapping Technique is used. Simulation of the implementation is done over multiple datasets. For total efficiency assessment, outcomes are contrasted in comparison to other techniqueS.
References
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[34] Mian Ahmad Jan, Priyadarsi Nanda, Xiangjian He, and Ren Ping Liu. A sybil attack detection scheme for a forest wildfire monitoring application. Future Generation Computer Systems, 80:613- 626, 2018. https://doi.org/10.1016/j.future.2016.05.034
[35] Josué Toledo-Castro, Iván Santos-González, Pino Caballero-Gil, Candelaria Hernández-Goya, Nayra Rodríguez-Pérez, and Ricardo Aguasca-Colomo. Fuzzy-based forest fire prevention and detection by wireless sensor networks. In The 13th International Conference on Soft Computing Models in Industrial and Environmental Applications, pages 478-488. Springer, 2018. https://doi.org/10.1007/978-3-319-94120-2_46
[2] Michael Goss, Daniel L Swain, John T Abatzoglou, Ali Sarhadi, Crystal A Kolden, A Park Williams, and Noah S Diffenbaugh. Climate change is increasing the likelihood of extreme autumn wildfire conditions across california. Environmental Research Letters, 15(9):094016, 2020. https://doi.org/10.1088/1748-9326/ab83a7
[3] Tania Schoennagel, Jennifer K Balch, Hannah Brenkert-Smith, Philip E Dennison, Brian J Harvey, Meg A Krawchuk, Nathan Mietkiewicz, Penelope Morgan, Max A Moritz, Ray Rasker, et al. Adapt to more wildfire in western north american forests as climate changes. Proceedings of the National Academy of Sciences, 114(18):4582-4590, 2017. https://doi.org/10.1073/pnas.1617464114
[4] Carolyn Black, Yohannes Tesfaigzi, Jed A Bassein, and Lisa A Miller. Wildfire smoke exposure and human health: Significant gaps in research for a growing public health issue. Environmental toxicology and pharmacology, 55:186-195, 2017. https://doi.org/10.1016/j.etap.2017.08.022
[5] L Collins, P Griffioen, G Newell, and A Mellor. The utility of random forests for wildfire severity mapping. Remote Sensing of Environment, 216:374-384, 2018. https://doi.org/10.1016/j.rse.2018.07.005
[6] Piyush Jain, Sean CP Coogan, Sriram Ganapathi Subramanian, Mark Crowley, Steve Taylor, and Mike D Flannigan. A review of machine learning applications in wildfire science and management. Environmental Reviews, 28(4):478-505, 2020. https://doi.org/10.1139/er-2020-0019
[7] Munish Bhatia, Sandeep K Sood, and Simranpreet Kaur. Quantum-based predictive fog scheduler for iot applications. Computers in Industry, 111:51-67, 2019. https://doi.org/10.1016/j.compind.2019.06.002
[8] David MJS Bowman, Grant J Williamson, John T Abatzoglou, Crystal A Kolden, Mark A Cochrane, and Alistair MS Smith. Human exposure and sensitivity to globally extreme wildfire events. Nature ecology & evolution, 1(3):1-6, 2017. https://doi.org/10.1038/s41559-016-0058
[9] Munish Bhatia and Sandeep K Sood. A comprehensive health assessment framework to facilitate iot-assisted smart workouts: A predictive healthcare perspective. Computers in Industry, 92:50- 66, 2017. https://doi.org/10.1016/j.compind.2017.06.009
[10] Francisco Moreira, Davide Ascoli, Hugh Safford, Mark A Adams, José M Moreno, José MC Pereira, Filipe X Catry, Juan Armesto, William Bond, Mauro E González, et al. Wildfire management in mediterranean-type regions: paradigm change needed. Environmental Research Letters, 15(1):011001, 2020. https://doi.org/10.1088/1748-9326/ab541e
[11] Yifang Ban, Puzhao Zhang, Andrea Nascetti, Alexandre R Bevington, and Michael A Wulder. Near real-time wildfire progression monitoring with sentinel-1 sar time series and deep learning. Scientific Reports, 10(1):1-15, 2020. https://doi.org/10.1038/s41598-019-56967-x
[12] Crystal D McClure and Daniel A Jaffe. Us particulate matter air quality improves except in wildfire-prone areas. Proceedings of the National Academy of Sciences, 115(31):7901-7906, 2018. https://doi.org/10.1073/pnas.1804353115
[13] Munish Bhatia, Simranpreet Kaur, Sandeep K Sood, and Veerawali Behal. Internet of thingsinspired healthcare system for urine-based diabetes prediction. Artificial Intelligence in Medicine, 107:101913, 2020. https://doi.org/10.1016/j.artmed.2020.101913
[14] JT Fasullo, BL Otto-Bliesner, and S Stevenson. Enso's changing influence on temperature, precipitation, and wildfire in a warming climate. Geophysical Research Letters, 45(17):9216-9225, 2018. https://doi.org/10.1029/2018GL079022
[15] Zachary S Wettstein, Sumi Hoshiko, Jahan Fahimi, Robert J Harrison, Wayne E Cascio, and Ana G Rappold. Cardiovascular and cerebrovascular emergency department visits associated with wildfire smoke exposure in california in 2015. Journal of the American Heart Association, 7(8):e007492, 2018. https://doi.org/10.1161/JAHA.117.007492
[16] Erin L Landguth, Zachary A Holden, Jonathan Graham, Benjamin Stark, Elham Bayat Mokhtari, Emily Kaleczyc, Stacey Anderson, Shawn Urbanski, Matt Jolly, Erin O Semmens, et al. The delayed effect of wildfire season particulate matter on subsequent influenza season in a mountain west region of the usa. Environment international, 139:105668, 2020. https://doi.org/10.1016/j.envint.2020.105668
[17] Heather Anu Kramer, Miranda H Mockrin, Patricia M Alexandre, and Volker C Radeloff. High wildfire damage in interface communities in california. International journal of wildland fire, 28(9):641-650, 2019. https://doi.org/10.1071/WF18108
[18] Ashkan Yousefpour, Caleb Fung, Tam Nguyen, Krishna Kadiyala, Fatemeh Jalali, Amirreza Niakanlahiji, Jian Kong, and Jason P Jue. All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture, 98:289- 330, 2019. https://doi.org/10.1016/j.sysarc.2019.02.009
[19] Munish Bhatia. Fog computing-inspired smart home framework for predictive veterinary healthcare. Microprocessors and Microsystems, 78:103227, 2020. https://doi.org/10.1016/j.micpro.2020.103227
[20] Ivan Novkovic, Goran B Markovic, Djordje Lukic, Slavoljub Dragicevic, Marko Milosevic, Snezana Djurdjic, Ivan Samardzic, Tijana Lezaic, and Marija Tadic. Gis-based forest fire susceptibility zonation with iot sensor network support, case study-nature park golija, serbia. Sensors, 21(19):6520, 2021. https://doi.org/10.3390/s21196520
[21] Raj Vikram and Ditipriya Sinha. Fogfire: fog assisted iot enabled forest fire management. Evolutionary Intelligence, pages 1-22, 2021. https://doi.org/10.1007/s12065-021-00666-y
[22] Ikjune Yoon, Dong Kun Noh, Dongeun Lee, Rony Teguh, Toshihisa Honma, and Heonshik Shin. Reliable wildfire monitoring with sparsely deployed wireless sensor networks. In 2012 IEEE 26th International Conference on Advanced Information Networking and Applications, pages 460-466. IEEE, 2012. https://doi.org/10.1109/AINA.2012.107
[23] Pouya Bolourchi and Sener Uysal. Forest fire detection in wireless sensor network using fuzzy logic. In 2013 Fifth International Conference on Computational Intelligence, Communication Systems and Networks, pages 83-87. IEEE, 2013. https://doi.org/10.1109/CICSYN.2013.32
[24] Kartik Trivedi and Ashish Kumar Srivastava. An energy efficient framework for detection and monitoring of forest fire using mobile agent in wireless sensor networks. In 2014 IEEE International Conference on Computational Intelligence and Computing Research, pages 1-4. IEEE, 2014. https://doi.org/10.1109/ICCIC.2014.7238433
[25] Juanjuan Zhao, Yongxing Liu, Yongqiang Cheng, Yan Qiang, and Xiaolong Zhang. Multisensor data fusion for wildfire warning. In 2014 10th International Conference on Mobile Ad-hoc and Sensor Networks, pages 46-53. IEEE, 2014. https://doi.org/10.1109/MSN.2014.13
[26] Alper Rifat Ulucinar, Ibrahim Korpeoglu, and A Enis Cetin. A wi-fi cluster based wireless sensor network application and deployment for wildfire detection. International Journal of Distributed Sensor Networks, 10(10):651957, 2014. https://doi.org/10.1155/2014/651957
[27] Aditi Kansal, Yashwant Singh, Nagesh Kumar, and Vandana Mohindru. Detection of forest fires using machine learning technique: A perspective. In 2015 third international conference on image information processing (ICIIP), pages 241-245. IEEE, 2015. https://doi.org/10.1109/ICIIP.2015.7414773
[28] Antonio Molina-Pico, David Cuesta-Frau, Alvaro Araujo, Javier Alejandre, and Alba Rozas. Forest monitoring and wildland early fire detection by a hierarchical wireless sensor network. Journal of Sensors, 2016, 2016. https://doi.org/10.1155/2016/8325845
[29] Rayan Mina and Youmni Ziade. Towards an optimal solution for environmental protection using wireless sensor networks. In 2016 Third International Conference on Electrical, Electronics, Computer Engineering and their Applications (EECEA), pages 120-125. IEEE, 2016. https://doi.org/10.1109/EECEA.2016.7470777
[30] Massinissa Saoudi, Ahcí¨ne Bounceur, Reinhardt Euler, and Tahar Kechadi. Data mining techniques applied to wireless sensor networks for early forest fire detection. In Proceedings of the International Conference on Internet of things and Cloud Computing, pages 1-7, 2016. https://doi.org/10.1145/2896387.2900323
[31] Sakib Abdullah, Sandor Bertalan, Stanislav Masar, Adem Coskun, and Izzet Kale. A wireless sensor network for early forest fire detection and monitoring as a decision factor in the context of a complex integrated emergency response system. In 2017 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS), pages 1-5. IEEE, 2017. https://doi.org/10.1109/EESMS.2017.8052688
[32] Felipe Taliar Giuntini, Delano Medeiros Beder, and Jo Ueyama. Exploiting self-organization and fault tolerance in wireless sensor networks: a case study on wildfire detection application. International Journal of Distributed Sensor Networks, 13(4):1550147717704120, 2017. https://doi.org/10.1177/1550147717704120
[33] Haifeng Lin, Xiaoyu Liu, Xinyue Wang, and Yunfei Liu. A fuzzy inference and big data analysis algorithm for the prediction of forest fire based on rechargeable wireless sensor networks. Sustainable Computing: Informatics and Systems, 18:101-111, 2018. https://doi.org/10.1016/j.suscom.2017.05.004
[34] Mian Ahmad Jan, Priyadarsi Nanda, Xiangjian He, and Ren Ping Liu. A sybil attack detection scheme for a forest wildfire monitoring application. Future Generation Computer Systems, 80:613- 626, 2018. https://doi.org/10.1016/j.future.2016.05.034
[35] Josué Toledo-Castro, Iván Santos-González, Pino Caballero-Gil, Candelaria Hernández-Goya, Nayra Rodríguez-Pérez, and Ricardo Aguasca-Colomo. Fuzzy-based forest fire prevention and detection by wireless sensor networks. In The 13th International Conference on Soft Computing Models in Industrial and Environmental Applications, pages 478-488. Springer, 2018. https://doi.org/10.1007/978-3-319-94120-2_46
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2022-03-14
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