Main Article Content
This system intends to show an automatic design that can detect, alert, and regulate gas leakage by employing an exhaust fan to remove gas from the area when there is a leak and a sprinkler to put out a fire in the event of a fire break-out. An alarm is triggered, an exhaust fan moves the gas outside, and a liquid crystal display (LCD) indicates how the system is functioning despite any distortions in the event of a gas leak. The Arduino UNO serves as the system's primary controller, and the buzzer serves as a notification device. In the case of a gas leak, notifications are sent via the GSM module to the registered cellphone number. The installation of a monitoring system for gas leaks in vulnerable areas is one of the steps done to stop accidents caused by this gas leak. The system uses a gas sensor (MQ) to find liquefied petroleum gas (LPG) leaks and a buzzer to notify nearby businesses, organizations, or individuals of the problem. The appliance is designed to be used in homes where running heaters and other natural gas or LPG-powered equipment can be challenging. For businesses or sectors that rely on natural gas or LPG for their operations, the system can also be used for other things.
Y. Zheng, Z. Li, X. Xu, and Q. Zhao, "Dynamic defenses in cyber security: Techniques, methods and challenges," Digital Communications and Networks, 2021.Available at: https://doi.org/10.1016/j.dcan.2021.07.006.
W. Guangzhi, "Application of adaptive resource allocation algorithm and communication network security in improving educational video transmission quality," Alexandria Engineering Journal, vol. 60, pp. 4231-4241, 2021.Available at: https://doi.org/10.1016/j.aej.2021.02.026.
H. D. J. Borolla, A. Razak, and A. Mallongi, "The difference in the number of complaints from patient health services using national health insurance at regional general hospitals," Sanitary Gazette, vol. 35, pp. S12-S14, 2021.Available at: https://doi.org/10.1016/j.gaceta.2020.12.004.
P. Prievozník, S. Strelcová, and E. Sventeková, "Economic security of public transport provider in a three-dimensional model," Transportation Research Procedia, vol. 55, pp. 1570-1577, 2021.Available at: https://doi.org/10.1016/j.trpro.2021.07.146.
M. Lozano and A. Solé-Auró, "Happiness and life expectancy by main occupational position among older workers: Who will live longer and happy?," SSM-Population Health, vol. 13, p. 100735, 2021.Available at: https://doi.org/10.1016/j.ssmph.2021.100735.
D. Rashkovetsky, F. Mauracher, M. Langer, and M. Schmitt, "Wildfire detection from multisensor satellite imagery using deep semantic segmentation," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 7001-7016, 2021.Available at: https://doi.org/10.1109/jstars.2021.3093625.
Y. Xie, J. Zhu, Y. Cao, Y. Zhang, D. Feng, Y. Zhang, and M. Chen, "Efficient video fire detection exploiting motion flicker-based dynamic features and deep static features," IEEE Access, vol. 8, pp. 81904-81917, 2020.Available at: https://doi.org/10.1109/access.2020.2991338.
M. Ajith and M. Martínez-Ramón, "Unsupervised segmentation of fire and smoke from infra-red videos," IEEE Access, vol. 7, pp. 182381-182394, 2019.Available at: https://doi.org/10.1109/access.2019.2960209.
H. Zhang, L. Dou, B. Xin, J. Chen, M. Gan, and Y. Ding, "Data collection task planning of a fixed-wing unmanned aerial vehicle in forest fire monitoring," IEEE Access, vol. 9, pp. 109847-109864, 2021.Available at: https://doi.org/10.1109/access.2021.3102317.
C. A. Graff, S. R. Coffield, Y. Chen, E. Foufoula-Georgiou, J. T. Randerson, and P. Smyth, "Forecasting daily wildfire activity using poisson regression," IEEE Transactions on Geoscience and Remote Sensing, vol. 58, pp. 4837-4851, 2020.Available at: https://doi.org/10.1109/tgrs.2020.2968029.
G. Xu, Q. Zhang, D. Liu, G. Lin, J. Wang, and Y. Zhang, "Adversarial adaptation from synthesis to reality in fast detector for smoke detection," IEEE Access, vol. 7, pp. 29471-29483, 2019.Available at: https://doi.org/10.1109/access.2019.2902606.
M. D. Nguyen, H. N. Vu, D. C. Pham, B. Choi, and S. Ro, "Multistage real-time fire detection using convolutional neural networks and long short-term memory networks," IEEE Access, vol. 9, pp. 146667-146679, 2021.Available at: https://doi.org/10.1109/access.2021.3122346.
C. Chaoxia, W. Shang, and F. Zhang, "Information-guided flame detection based on faster R-CNN," IEEE Access, vol. 8, pp. 58923-58932, 2020.Available at: https://doi.org/10.1109/access.2020.2982994.
Z. Liu, X. Yang, Y. Liu, and Z. Qian, "Smoke-detection framework for high-definition video using fused spatial-and frequency-domain features," IEEE Access, vol. 7, pp. 89687-89701, 2019.Available at: https://doi.org/10.1109/access.2019.2926571.
J. Zhang, H. Zhu, P. Wang, and X. Ling, "ATT squeeze U-Net: A lightweight network for forest fire detection and recognition," IEEE Access, vol. 9, pp. 10858-10870, 2021.Available at: https://doi.org/10.1109/access.2021.3050628.
J. Shi, W. Wang, Y. Gao, and N. Yu, "Optimal placement and intelligent smoke detection algorithm for wildfire monitoring cameras," IEEE Access, vol. 8, pp. 72326-72339, 2020.Available at: https://doi.org/10.1109/access.2020.2987991.
Z. Lin, F. Chen, B. Li, B. Yu, H. Jia, M. Zhang, and D. Liang, "A contextual and multitemporal active-fire detection algorithm based on FengYun-2G S-VISSR data," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, pp. 8840- 8852, 2019.Available at: https://doi.org/10.1109/tgrs.2019.2923248.
M. Nakip, C. Güzelíş, and O. Yildiz, "Recurrent trend predictive neural network for multi-sensor fire detection," IEEE Access, vol. 9, pp. 84204-84216, 2021.Available at: https://doi.org/10.1109/access.2021.3087736.
X. Huang and L. Du, "Fire detection and recognition optimization based on virtual reality video image," IEEE Access, vol. 8, pp. 77951-77961, 2020.Available at: https://doi.org/10.1109/access.2020.2990224.
Y. Cao, F. Yang, Q. Tang, and X. Lu, "An attention enhanced bidirectional LSTM for early forest fire smoke recognition," IEEE Access, vol. 7, pp. 154732-154742, 2019.Available at: https://doi.org/10.1109/access.2019.2946712.
Y. Li, L. Yu, C. Zheng, Z. Ma, S. Yang, F. Song, K. Zheng, W. Ye, Y. Zhang, and Y. Wang, "Development and field deployment of a mid-infrared CO and CO2 dual-gas sensor system for early fire detection and location," Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 270, p. 120834, 2022.Available at: https://doi.org/10.1016/j.saa.2021.120834.
H. S. Sucuoglu, I. Bogrekci, and P. Demircioglu, "Development of mobile robot with sensor fusion fire detection unit," IFAC-Papers Online, vol. 51, pp. 430-435, 2018.Available at: https://doi.org/10.1016/j.ifacol.2018.11.324.
S. Majid, F. Alenezi, S. Masood, M. Ahmad, E. S. Gündüz, and K. Polat, "Attention based CNN model for fire detection and localization in real-world images," Expert Systems with Applications, vol. 189, p. 116114, 2022.Available at: https://doi.org/10.1016/j.eswa.2021.116114.
K. Muhammad, J. Ahmad, and S. W. Baik, "Early fire detection using convolutional neural networks during surveillance for effective disaster management," Neurocomputing, vol. 288, pp. 30-42, 2018.Available at: https://doi.org/10.1016/j.neucom.2017.04.083.
A. Solórzano, J. Eichmann, L. Fernandez, B. Ziems, J. M. Jiménez-Soto, S. Marco, and J. Fonollosa, "Early fire detection based on gas sensor arrays: Multivariate calibration and validation," Sensors and Actuators B: Chemical, vol. 352, p. 130961, 2022.Available at: https://doi.org/10.1016/j.snb.2021.130961.
Z. Lin, F. Chen, Z. Niu, B. Li, B. Yu, H. Jia, and M. Zhang, "An active fire detection algorithm based on multi-temporal FengYun-3C VIRR data," Remote Sensing of Environment, vol. 211, pp. 376-387, 2018.Available at: https://doi.org/10.1016/j.rse.2018.04.027.
Z. Li, J. Han, W. Chen, and J. Yi, "Detection of vapors from overheated PVC cables with modified sea urchin-like ZnO for fire warning," Sensors and Actuators B: Chemical, vol. 350, p. 130841, 2022.Available at: https://doi.org/10.1016/j.snb.2021.130841.
L. Yuan, R. A. Thomas, J. H. Rowland, and L. Zhou, "Early fire detection for underground diesel fuel storage areas," Process Safety and Environmental Protection, vol. 119, pp. 69-74, 2018.Available at: https://doi.org/10.1016/j.psep.2018.07.022.
R. Krebs, J. Owens, and H. Luckarift, "Formation and detection of hydrogen fluoride gas during firefighting scenarios," Fire Safety Journal, vol. 127, p. 103489, 2022.Available at: https://doi.org/10.1016/j.firesaf.2021.103489.
S. M. Nemalidinne and D. Gupta, "Nonsubsampled contourlet domain visible and infrared image fusion framework for fire detection using pulse coupled neural network and spatial fuzzy clustering," Fire Safety Journal, vol. 101, pp. 84-101, 2018.Available at: https://doi.org/10.1016/j.firesaf.2018.08.012.
S. Shen, W. Li, M. Wang, D. Wang, Y. Li, and D. Li, "Methane near-infrared laser remote detection under non cooperative target condition based on harmonic waveform recognition," Infrared Physics & Technology, vol. 120, p. 103977, 2022.Available at: https://doi.org/10.1016/j.infrared.2021.103977.
J. Wang, R. Xu, Y. Xia, and S. Komarneni, "Ti2CTx MXene: A novel p-type sensing material for visible light enhanced room temperature methane detection," Ceramics International, vol. 47, pp. 34437-34442, 2021.Available at: https://doi.org/10.1016/j.ceramint.2021.08.357.
T. Keyes, G. Ridge, M. Klein, N. Phillips, R. Ackley, and Y. Yang, "An enhanced procedure for urban mobile methane leak detection," Heliyon, vol. 6, p. e04876, 2020.Available at: https://doi.org/10.1016/j.heliyon.2020.e04876.
Z. Wang, T. Chang, X. Zeng, H. Wang, L. Cheng, C. Wu, J. Chen, Z. Luo, and H.-L. Cui, "Fiber optic multipoint remote methane sensing system based on pseudo differential detection," Optics and Lasers in Engineering, vol. 114, pp. 50-59, 2019.Available at: https://doi.org/10.1016/j.optlaseng.2018.10.013.
C. Li, M. Guo, B. Zhang, C. Li, B. Yang, and K. Chen, "Miniature single-fiber photoacoustic sensor for methane gas leakage detection," Optics and Lasers in Engineering, vol. 149, p. 106792, 2022.Available at: https://doi.org/10.1016/j.optlaseng.2021.106792.
J. Xia, C. Feng, F. Zhu, S. Ye, S. Zhang, A. Kolomenskii, Q. Wang, J. Dong, Z. Wang, and W. Jin, "A sensitive methane sensor of a ppt detection level using a mid-infrared interband cascade laser and a long-path multipass cell," Sensors and Actuators B: Chemical, vol. 334, p. 129641, 2021.Available at: https://doi.org/10.1016/j.snb.2021.129641.
J. Wang, L. P. Tchapmi, A. P. Ravikumar, M. McGuire, C. S. Bell, D. Zimmerle, S. Savarese, and A. R. Brandt, "Machine vision for natural gas methane emissions detection using an infrared camera," Applied Energy, vol. 257, p. 113998, 2020.Available at: https://doi.org/10.1016/j.apenergy.2019.113998.
A. Sampaolo, S. Csutak, P. Patimisco, M. Giglio, G. Menduni, V. Passaro, F. K. Tittel, M. Deffenbaugh, and V. Spagnolo, "Methane, ethane and propane detection using a compact quartz enhanced photoacoustic sensor and a single interband cascade laser," Sensors and Actuators B: Chemical, vol. 282, pp. 952-960, 2019.Available at: https://doi.org/10.1016/j.snb.2018.11.132.
J. Pangerl, M. Müller, T. Rück, S. Weigl, and R. Bierl, "Characterizing a sensitive compact mid-infrared photoacoustic sensor for methane, ethane and acetylene detection considering changing ambient parameters and bulk composition (N2, O2 and H2O)," Sensors and Actuators B: Chemical, vol. 352, p. 130962, 2022.Available at: https://doi.org/10.1016/j.snb.2021.130962.
M. R. Johnson, D. R. Tyner, and A. J. Szekeres, "Blinded evaluation of airborne methane source detection using Bridger Photonics LiDAR," Remote Sensing of Environment, vol. 259, p. 112418, 2021.Available at: https://doi.org/10.1016/j.rse.2021.112418.
Y. Li, R. Wang, F. K. Tittel, and Y. Ma, "Sensitive methane detection based on quartz-enhanced photoacoustic spectroscopy with a high-power diode laser and wavelet filtering," Optics and Lasers in Engineering, vol. 132, p. 106155, 2020.Available at: https://doi.org/10.1016/j.optlaseng.2020.106155.
G. Zhang, K. Khabibullin, and A. Farooq, "An IH-QCL based gas sensor for simultaneous detection of methane and acetylene," Proceedings of the Combustion Institute, vol. 37, pp. 1445-1452, 2019.Available at: https://doi.org/10.1016/j.proci.2018.06.062.
A. Sepman, E. Thorin, Y. Ögren, C. Ma, M. Carlborg, J. Wennebro, M. Broström, H. Wiinikka, and F. M. Schmidt, "Laser-based detection of methane and soot during entrained-flow biomass gasification," Combustion and Flame, vol. 237, p. 111886, 2022.Available at: https://doi.org/10.1016/j.combustflame.2021.111886.
Y. Zhao, S. Wang, W. Yuan, S. Fan, Z. Hua, Y. Wu, and X. Tian, "Selective detection of methane by Pd-In2O3 sensors with a catalyst filter film," Sensors and Actuators B: Chemical, vol. 328, p. 129030, 2021.Available at: https://doi.org/10.1016/j.snb.2020.129030.
B. Yang, J. Xu, C. Wang, and J. Xiao, "A potentiometric sensor based on SmMn2O5 sensing electrode for methane detection," Materials Chemistry and Physics, vol. 245, p. 122679, 2020.
A. P. Sandoval-Rojas, M. T. Cortés, and J. Hurtado, "Electrochemical synthesis of poly (3, 4-ethylenedioxythiophene) doped with a new bis (pyrazolyl) methane disulfonate and its behavior towards dopamine detection," Journal of Electroanalytical Chemistry, vol. 837, pp. 200-207, 2019.Available at: https://doi.org/10.1016/j.jelechem.2019.02.041.
L. Tian, J. Sun, S. Zhang, A. A. Kolomenskii, H. A. Schuessler, Z. Wang, J. Xia, J. Chang, and Z. Liu, "Near-infrared methane sensor with neural network filtering," Sensors and Actuators B: Chemical, vol. 354, p. 131207, 2022.Available at: https://doi.org/10.1016/j.snb.2021.131207.
Z. Gong, T. Gao, L. Mei, K. Chen, Y. Chen, B. Zhang, W. Peng, and Q. Yu, "Ppb-level detection of methane based on an optimized T-type photoacoustic cell and a NIR diode laser," Photoacoustics, vol. 21, p. 100216, 2021.Available at: https://doi.org/10.1016/j.pacs.2020.100216.
M. Giglio, A. Zifarelli, A. Sampaolo, G. Menduni, A. Elefante, R. Blanchard, C. Pfluegl, M. F. Witinski, D. Vakhshoori, and H. Wu, "Broadband detection of methane and nitrous oxide using a distributed-feedback quantum cascade laser array and quartz-enhanced photoacoustic sensing," Photoacoustics, vol. 17, p. 100159, 2020.Available at: https://doi.org/10.1016/j.pacs.2019.100159.
L.-b. Ch'ien, Y.-j. Wang, A.-c. Shi, and F. Li, "Wavelet filtering algorithm for improved detection of a methane gas sensor based on non-dispersive infrared technology," Infrared Physics and Technology, vol. 99, pp. 284-291, 2019.Available at: https://doi.org/10.1016/j.infrared.2019.04.025.
M. A. Baballe, M. I. Bello, and A. S. Mahmoud, "A comparative study on gas alarm detection system," Journal of Telecommunication Control and Intelligent System, vol. 1, pp. 65-72, 2021.
M. A. Baballe, U. Y. Magashi, B. I. Garko, A. A. Umar, Y. R. Magaji, and M. Surajo, "Automatic gas leakage monitoring system using MQ-5 sensor," Review of Computer Engineering Research, vol. 8, pp. 64-75, 2021.Available at: https://doi.org/10.18488/journal.76.2021.82.64.75.
M. A. Baballe, M. I. Bello, “Gas Leakage Detection System with Alarming System”, Review of Computer Engineering, vol. 9, no. 1, pp. 30-43, DOI: 10.18488/76.v9i1.2984, 2022.
M. A. Baballe, M. I. Bello, “A Comparative Study on Gas Alarm Detection System”, Global Journal of Research in Engineering & Computer Sciences, Volume 02, Issue 01, pp. 6-12, Journal homepage: https://gjrpublication.com/journals/, Jan-Feb | 2022.