Abstract
Forest fires, by disrupting the ecological equilibrium and exacerbating global warming, pose a threat to both wildlife and the overall environmental stability. To safeguard our ecosystems, it is imperative to predict and detect forest fires at an early stage. Wireless Sensor Networks (WSNs) have gained popularity due to their cost-effectiveness, low power consumption, and portability in achieving this goal. This research introduces an innovative method based on mid-point K-means clustering to forecast three forest activity zones: high-active (fire-prone), medium-active, and low-active zones. This system excels in identifying high-active zones with remarkable accuracy (98%). The sensor node at the high-active zone’s center continuously transmits data to the Base Station (BS), promptly notifying the relevant authorities of potential forest fires. In contrast, the medium-active zone’s sensor node periodically shares environmental data, while the low-active zone’s node conserves energy by not transmitting data to the BS, thereby enhancing network longevity and energy efficiency.
Similar content being viewed by others
Data availability
Forest fire data set are collected from Forest fires- UCI machine learning data repository.
References
Arioua M, El Assari Y, Ez-Zazi I, El Oualkadi A (2016) “Multi-hop cluster based routing approach for wireless sensor networks. Procedia Comput Sci 83:584–591
Bhanja S, Metia S, Das A (2022) A hybrid neuro-fuzzy prediction system with butterfly optimization algorithm for PM2.5 forecasting. Microsyst Technol. https://doi.org/10.1007/s00542-022-05252-5
Bharany S, Sharma S, Frnda J, Shuaib M, Khalid MI, Hussain S, Ullah SS (2022) Wildfire monitoring based on energy efficient clustering approach for FANETS. Drones 6(8):193
Breiman L (2017) Classification and regression trees. Routledge, New York
Cao F, Liang J, Jiang G (2009) An initialization method for the K -means algorithm using neighborhood model. Comput Math Appl 58(3):474–483. https://doi.org/10.1016/j.camwa.2009.04.017
Cortez P, Morais ADJR (2007) A data mining approach to predict forest fires using meteorological data. In: Proceedings of the 13th Portuguese Conference on Artificial Intelligence
Forest Fires - UCI Machine Learning Repository” https://archive.ics.uci.edu/dataset/162/forest+fires. Accessed 28 June 2023
Gaur A, Singh A, Kumar A, Kumar A, Kapoor K (2020) Video flame and smoke based fire detection algorithms: a literature review. Fire Technol 56:1943–1980
Jan MA, Nanda P, He X, Liu RP (2018) A Sybil attack detection scheme for a forest wildfire monitoring application. Futur Gener Comput Syst 80:613–626
Jeong YS, Chung YJ, Park JH (2011) Visualisation of efficiency coverage and energy consumption of sensors in wireless sensor networks using heat map. IET Commun 5(8):1129–1137
Jiao Z, Zhang Y, Mu L, Xin J, Jiao S, Liu H, Liu D (2020) A yolov3-based learning strategy for real-time uav-based forest fire detection”. In: 2020 Chinese Control and decision conference (CCDC) (pp 4963–4967). IEEE
Jilbab A, Bourouhou A (2020) Efficient forest fire detection system based on data fusion applied in wireless sensor networks. Int J Electr Eng Inf 12(1):1–18
Kadir EA, Irie H, Rosa SL (2019) Modeling of wireless sensor networks for detection land and forest fire hotspot. In: 2019 International Conference on Electronics, Information, and Communication (ICEIC) (pp 1–5). IEEE
Kansal A, Singh Y, Kumar N, Mohindru V (2015) Detection of forest fires using machine learning technique: a perspective. In: 2015 Third International Conference on Image Information Processing (ICIIP), pp 241–245
Kass GV (1980) An exploratory technique for investigating large quantities of categorical data. J R Stat Soc Ser C Appl Stat 29(2):119–127. https://doi.org/10.2307/2986296
Kaur H, Sood SK (2019) Fog-assisted IoT-enabled scalable network infrastructure for wildfire surveillance. J Netw Comput Appl 144:171–183
Khan SS, Ahmad A (2004) Cluster center initialization algorithm for K-means clustering. Pattern Recognit Lett 25(11):1293–1302
Khan A, Tamim I, Ahmed E, Awal MA (2012) Multiple parameter based clustering (mpc): prospective analysis for effective clustering in wireless sensor network (wsn) using k-means algorithm. Wirel Sens Netw 4(1):18–24
Liu D, Xu Z, Zhou Y, Fan C (2019) Heat map visualisation of fire incidents based on transformed sigmoid risk model. Fire Saf J 109:102863
Loh W-Y, Shih Y-S (1997) Split selection methods for classification trees. Stat Sin 7(4):815–840
Mészáros L, Varga A, Kirsche M (2019) INET Framework. In: Virdis A, Kirsche M (eds) Recent advances in network simulation: the OMNeT++ environment and its ecosystem, Springer International Publishing, pp 55–106. https://doi.org/10.1007/978-3-030-12842-5_2
Mittal N, Singh U, Salgotra R, Sohi BS (2019) An energy efficient stable clustering approach using fuzzy extended grey wolf optimization algorithm for WSNs. Wirel Netw 25:5151–5172
Moussa N, El Alaoui AEB, Chaudet C (2020) A novel approach of WSN routing protocols comparison for forest fire detection. Wirel Netw 26(3):1857–1867
Moussa N, Nurellari E, El Belrhiti El A, Alaoui, (2022) A novel energy-efficient and reliable ACO-based routing protocol for WSN-enabled forest fires detection. J Ambient Intell Human Computi. https://doi.org/10.1007/s12652-022-03727-x
Nemalidinne SM, Gupta D (2018) Nonsubsampled contourlet domain visible and infrared image fusion framework for fire detection using pulse coupled neural network and spatial fuzzy clustering. Fire Saf J 101:84–101
Pandya R, Pandya J (2015) C5. 0 algorithm to improved decision tree with feature selection and reduced error pruning. Int J Comput Appl 117(16):18
Ray A, De D (2016) Energy efficient clustering protocol based on K-means (EECPK-means) -midpoint algorithm for enhanced network lifetime in wireless sensor network. IET Wirel Sen Syst 6(6):181–191
Sinha D, Kumari R, Tripathi S (2019) Semisupervised classification based clustering approach in WSN for forest fire detection. Wirel Pers Commun 109(4):2561–2605. https://doi.org/10.1007/s11277-019-06697-0
Srikanth N, Prasad MG (2018) Efficient clustering protocol using fuzzy K-means and midpoint algorithm for lifetime improvement in WSNs. Int J Intell Eng Syst 11(4):61–71
Teng Z, Kim JH, Kang DJ (2010) Fire detection based on hidden Markov models. Int J Control Autom Syst 8:822–830
Toptaş B, Hanbay D (2020) A new artificial bee colony algorithm-based color space for fire/flame detection. Soft Comput 24(14):10481–10492
Trivedi K, Srivastava AK (2014) 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 (pp 1–4). IEEE
Varga A (2010) OMNeT++. In: Wehrle K, Güneş M, Gross J (eds) Modeling and tools for network simulation, Springer Berlin Heidelberg, pp 35–59. https://doi.org/10.1007/978-3-642-12331-3_3
Vikram R, Sinha D, De D, Das AK (2021) PAFF: predictive analytics on forest fire using compressed sensing based localized Ad Hoc wireless sensor networks. J Ambient Intell Humaniz Comput 12(2):1647–1665. https://doi.org/10.1007/s12652-020-02238-x
Wang J, Gao Y, Liu W, Sangaiah AK, Kim HJ (2019) An improved routing schema with special clustering using PSO algorithm for heterogeneous wireless sensor network. Sensors 19(3):671
Žalik KR (2008) An efficient k′-means clustering algorithm. Pattern Recognit Lett 29(9):1385–1391
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Karim, R., Zahedi, M., De, D. et al. MKFF: mid-point K-means based clustering in wireless sensor network for forest fire prediction. Microsyst Technol 30, 469–480 (2024). https://doi.org/10.1007/s00542-023-05578-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00542-023-05578-8