Smart Home Safety Using Fire and Gas Detection System

Year : 2024 | Volume :11 | Issue : 01 | Page : 35-43
By

Sonali K. Godase

Aasavari Badave

Akanksha Pawale

Tejaswini Andhale

  1. Assistant Professor, SKN Sinhgad College of Engineering, Korti, Pandharpur, Maharashtra, India
  2. Students, SKN Sinhgad College of Engineering, Korti, Pandharpur, Maharashtra, India
  3. Students, SKN Sinhgad College of Engineering, Korti, Pandharpur, Maharashtra, India
  4. Students, SKN Sinhgad College of Engineering, Korti, Pandharpur, Maharashtra, India

Abstract

In a world where technology is advancing at a rapid pace, one goal of smart homes is to protect the safety of the house and, most importantly, the people who live there. An essential part of our suggested systems’ functionality is the inclusion of fire and gas detection sensors. The increasing use of smart homes has shown new ways of increasing security and safety through including intelligent techniques in proper ways in our proposed systems. This research paper proposes a “Smart Home Safety System” specially designed to reduce the risks comes with gas and fire uses. The system proves how we can utilize automation techniques and sensors to respond and detect threats in our day-to-day life of real- time. The basic apparatus/components of our proposed system include upgraded fire & gas detecting sensors systematically placed in the smart home architects. Those sensors continuously checks/monitors the quality of air which consists harmful gases and detects the presence of temperatures which are different from normal room temperature & smoke indicates fire. The observations which are detected from the sensors are processed to system’s CCU (Central Control Unit).

Keywords: CCU, CUBE IDE, C language , PCB Board , 5v Buzzer

[This article belongs to Recent Trends in Fluid Mechanics(rtfm)]

How to cite this article: Sonali K. Godase, Aasavari Badave, Akanksha Pawale, Tejaswini Andhale. Smart Home Safety Using Fire and Gas Detection System. Recent Trends in Fluid Mechanics. 2024; 11(01):35-43.
How to cite this URL: Sonali K. Godase, Aasavari Badave, Akanksha Pawale, Tejaswini Andhale. Smart Home Safety Using Fire and Gas Detection System. Recent Trends in Fluid Mechanics. 2024; 11(01):35-43. Available from: https://journals.stmjournals.com/rtfm/article=2024/view=152265

References

  1. Park G, Lyu G, Jo Y, Gu J, Eun J, Kim H. Development of Gas Safety Management System for Smart- Home Services. International Journal of Distributed Sensor Networks. 2013;9(10). doi:10.1155/2013/591027
  2. Salhi, T. Silverston, T. Yamazaki and T. Miyoshi, “Early Detection System for Gas Leakage and Fire in Smart Home Using Machine Learning,” 2019 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 2019, pp. 1-6, doi: 10.1109/ICCE.2019.8661990.
  3. Kunal Kumar, Navneet Sen, Sheikh Azid, Utkal Mehta,A Fuzzy Decision in Smart Fire and Home Security System,Procedia Computer Science,Volume 105,A Kalaiarasi., D. Deepak Kumar, B. Bharathraj, v.Naveen Vishak, S. Jasith, L. Raja, “Industry – specific Intelligent Fire Management System: A survey”, 20232017
  4. Ralevski and B. R. Stojkoska, “IoT based system for detection of gas leakage and house fire in smart kitchen environments,” 2019 27th Telecommunications Forum (TELFOR),Belgrade,Serbia,2019,pp.1-4, doi: 10.1109/TELFOR48224.2019.8971021.
  5. Kweon, S.-J.; Park, J.-H.; Park, C.-O.; Yoo, H.-J.; Ha, S. Wireless Kitchen Fire Prevention System Using Electrochemical Carbon Dioxide Gas Sensor for Smart Home. Sensors 2022
  6. Marhoon, H.M., Mahdi, M.I., Hussein, E.D., & Ibrahim, A.R. (2018). Designing and Implementing Applications of Smart Home Appliances. Modern Applied Science.
  7. C. Jose and R. Malekian, “Improving Smart Home Security: Integrating Logical Sensing Into Smart Home,” in IEEE Sensors Journal, vol. 17, no. 13, pp. 4269-4286, 1 July1, 2017, doi: 10.1109/JSEN.2017.2705045.
  8. Deshpande, H. S. and Karande, K. J. (2014, April). Efficient implementation of AES algorithm on FPGA. In 2014 International Conference on Communication and Signal Processing (pp. 1895-1899). IEEE.
  9. Swami, S. S. (2017, August). An efficient FPGA implementation of discrete wavelet transform for image compression. In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) (pp. 3385-3389). IEEE.
  10. Mane, P. B. (2018). High speed area efficient FPGA implementation of AES algorithm. International Journal of Reconfigurable and Embedded Systems, 7(3), 157-165.
  11. Kulkarni, P. R. and; Mane, P. B. (2017). Robust invisible watermarking for image authentication. In Emerging Trends in Electrical, Communications and Information Technologies: Proceedings of ICECIT-2015(pp. 193-200). Springer Singapore.
  12. Mane, P. B. (2016). Area efficient high speed FPGA based invisible watermarking for image authentication. Indian journal of Science and Technology.
  13. Kashid, M. M., Karande, K. J. (2022, November). IoT-based environmental parameter monitoring using machine learning approach. In Proceedings of the International Conference on Cognitive and Intelligent Computing: ICCIC 2021, Volume 1 (pp. 43-51). Singapore: Springer Nature Singapore.
  14. Mane, D. P. (2017). An Efficient implementation of DWT for image compression on reconfigurable platform. International Journal of Control Theory and Applications, 10(15), 1-7.
  15. Mandwale, A. J. (2015, January). Different Approaches for Implementation of Viterbi decoder on reconfigurable platform. In 2015 International Conference on Pervasive Computing (ICPC) (pp. 1-4). IEEE.
  16. Nagane, U. P. (2021). Moving object detection and tracking using Matlab. Journal of Science and Technology, 6, 86-89.
  17. Jadhav, M. M. et al (2021). Machine learning based autonomous fire combat turret. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(2), 2372-2381.
  18. Mane, D. P. (2019). High throughput and area efficient FPGA implementation of AES algorithm. International Journal of Engineering and Advanced Technology, 8(4).
  19. Shinde, G. N. (2021). An approach for robust digital image watermarking using DWT‐PCA. Journal of Science and Technology, 6(1).
  20. Shinde G. (2019). A robust digital image watermarking using DWT-PCA. International Journal of Innovations in Engineering Research and Technology, 6(4), 1-7.
  21. Kalyankar, P. A., Thigale, S. P., Chavhan, P. G., and; Jadhav, M. M. (2022). Scalable face image retrieval using AESC technique. Journal of Algebraic Statistics, 13(3), 173-176.
  22. Kulkarni, P. (2015). Robust invisible digital image watermarking using discrete wavelet transform. International Journal of Engineering Research and; Technology (IJERT), 4(01), 139-141.
  23. Mane, D. P. (2018). Secure and area efficient implementation of digital image watermarking on reconfigurable platform. International Journal of Innovative Technology and Exploring Engineering, 8(2), 56-61.
  24. Deshpande, H. S. and Karande, K. J. (2015, April). Area optimized implementation of AES algorithm on FPGA. In 2015 International Conference on Communications and Signal Processing (ICCSP) (pp. 0010-0014). IEEE.
  25. Ghodake, R. G. (2016). Sensor based automatic drip irrigation system. Journal for Research, 2(02).
  26. Mane, P. B. (2019). High-Speed area-efficient implementation of AES algorithm on reconfigurable platform. Computer and Network Security, 119.
  27. Mane, P. B. (2014, October). Area optimization of cryptographic algorithm on less dense reconfigurable platform. In 2014 International Conference on Smart Structures and Systems (ICSSS) (pp. 86-89). IEEE.
  28. Takale, S. (2022). DWT-PCA Based Video Watermarking. Journal of Electronics, Computer Networking and Applied Mathematics (JECNAM) ISSN, 2799-1156.
  29. Patale, J. P., Jagadale, A. B., and; Pise, A. (2023). A Systematic survey on Estimation of Electrical Vehicle. Journal of Electronics, Computer Networking and Applied Mathematics (JECNAM) ISSN, 2799-1156.
  30. Jadhav, M. M., and; Seth, M. (2022). Painless machine learning approach to estimate blood glucose level with non-invasive devices. In Artificial Intelligence, Internet of Things (IoT) and Smart Materials for Energy Applications (pp. 83-100). CRC Press.
  31. Kondekar, R. P. (2017). Raspberry Pi based voice operated Robot. International Journal of Recent Engineering Research and Development, 2(12), 69-76.
  32. Maske, Y., Jagadale, A. B., and; Pise, A. C. (2023). Development of BIOBOT System to Assist COVID Patient and Caretakers. European Journal of Molecular and Clinical Medicine, 3472-3480.
  33. Maske, Y., Jagadale, M. A., and; Pise, M. A. (2021). Implementation of BIOBOT System for COVID Patient and Caretakers Assistant Using IOT. International Journal of Information Technology and;Amp, 30-43.
  34. Jadhav, H. M., Mulani, A., and; Jadhav, M. M. (2022). Design and development of chatbot based on reinforcement learning. Machine Learning Algorithms for Signal and Image Processing, 219-229.
  35. Gadade, B. (2022). Automatic System for Car Health Monitoring. International Journal of Innovations in Engineering Research and Technology, 57-62.
  36. Kamble, A., (2022). Google assistant based device control. Int. J. of Aquatic Science, 13(1), 550-555.
  37. Mandwale, A., and; Mulani, A. O. (2015, January). Different Approaches for Implementation of Viterbi decoder. In IEEE International Conference on Pervasive Computing (ICPC).
  38. Mulani, A. O., Jadhav, M. M., and; Seth, M. (2022). Painless Non‐invasive blood glucose concentration level estimation using PCA and machine learning. The CRC Book entitled Artificial Intelligence, Internet of Things (IoT) and Smart Materials for Energy Applications. Internet of Things (IoT) and Smart Materials for Energy Applications.
  39. Boxey, A., Jadhav, A., Gade, P., Ghanti, P., and; Mulani, A. O. (2022). Face Recognition using Raspberry Pi. Journal of Image Processing and Intelligent Remote Sensing (JIPIRS) ISSN 2815-0953.
  40. Takale, S., and; Mulani, D. A. Video Watermarking System. International Journal for Research in Applied Science and; Engineering Technology (IJRASET), 10.
  41. Shinde, M. R. S., and; Mulani, A. O. (2015). Analysisof Biomedical Image Using Wavelet Transform. International Journal of Innovations in Engineering Research and Technology, 2(7), 1-7.
  42. Mandwale, A., and; Mulani, A. O. (2014, December). Implementation of Convolutional Encoder and; Different Approaches for Viterbi Decoder. In IEEE International Conference on Communications, Signal Processing Computing and Information technologies.
  43. Ghodake, R. G., and; Mulani, A. O. (2018). Microcontroller Based Automatic Drip Irrigation System. In Techno-Societal 2016: Proceedings of the International Conference on Advanced Technologies for Societal Applications (pp. 109-115). Springer International Publishing.
  44. Mulani, A. O., and; Mane, P. B. (2016), “Fast and Efficient VLSI Implementation of DWT for Image Compression”, International Journal of Control Theory and Applications, 9(41), pp.1006-1011.
  45. Shinde, R., and; Mulani, A. O. (2015). Analysis of Biomedical Image‖. International Journal on Recent and; Innovative trend in technology (IJRITT).
  46. Patale, J. P., Jagadale, A. B., Mulani, A. O., and; Pise, A. (2022). Python Algorithm to Estimate Range of Electrical Vehicle. Telematique, 7046-7059.
  47. Utpat, V. B., Karande, D. K., and; Mulani, D. A. Grading of Pomegranate Using Quality Analysis‖. International Journal for Research in Applied Science and; Engineering Technology (IJRASET), 10.
  48. Mulani, A. O., Jadhav, M. M., and; Seth, M. (2022). Painless Non‐invasive blood glucose concentration level estimation using PCA and machine learning. The CRC Book entitled Artificial Intelligence, Internet of Things (IoT) and Smart Materials for Energy Applications.
  49. Mandwale, A., and; Mulani, A. O. (2016). Implementation of High Speed Viterbi Decoder using FPGA. International Journal of Engineering Research and; Technology﴾ IJERT.
  50. Kambale, A. (2023). HOME AUTOMATION USING GOOGLE ASSISTANT. UGC care approved journal, 32(1).
  51. Sawant, R. A., and; Mulani, A. O. Automatic PCB Track Design Machine. International Journal of Innovative Science and Research Technology, 7(9).
  52. ABHANGRAO, M. R., JADHAV, M. S., GHODKE, M. P., and; MULANI, A. Design And Implementation Of 8-bit Vedic Multiplier. JournalNX, 24-26.
  53. Seth, M. (2022). Painless Machine learning approach to estimate blood glucose level of Non-Invasive device. Artificial Intelligence, Internet of Things (IoT) and Smart Materials for Energy Applications.
  54. Korake, D. M., and; Mulani, A. O. (2016). Design of Computer/Laptop Independent Data transfer system from one USB flash drive to another using ARM11 processor. International Journal of Science, Engineering and Technology Research.
  55. Mulani, A. O., Birajadar, G., Ivković, N., Salah, B., and; Darlis, A. R. (2023). Deep learning based detection of dermatological diseases using convolutional neural networks and decision trees. Treatment du Signal, 40(6), 2819-2825.
  56. Pathan, A. N., Shejal, S. A., Salgar, S. A., Harale, A. D., and; Mulani, A. O. (2022). Hand Gesture Controlled Robotic System. Int. J. of Aquatic Science, 13(1), 487-493.
  57. Altaf O. Mulani. (2024). A Comprehensive Survey on Semi-Automatic Solar-Powered Pesticide Sprayers for Farming. Journal of Energy Engineering and Thermodynamics (JEET) ISSN 2815-0945, 4(02), 21–28. https://doi.org/10.55529/jeet.42.21.28
  58. Sandeep Kedar and A. O. Mulani (2024), IoT Based Soil, Water and Air Quality Monitoring System for Pomegranate Farming, NATURALISTA CAMPANO, Vol. 28, Issue 1.
  59. Bhanudas Gadade, A O Mulani and A.D.Harale (2024). IOT Based Smart School Bus and Student Monitoring System. NATURALISTA CAMPANO, Vol. 28, Issue 1.
  60. Anil Dhanawade, A. O Mulani and Anjali. Pise. (2024). Smart farming using IOT based Agri BOT. NATURALISTA CAMPANO, Vol. 28, Issue 1.
  61. Shweta Sadanand Salunkhe and Dr. Altaf O. Mulani. (2024). Solar Mount Design Using High-Density Polyethylene. NATURALISTA CAMPANO, Vol. 28, Issue 1.
  62. Sarda, M., Deshpande, B., Deo, S., and; Karanjkar, R. (2018). A comparative study on Maslow’s theory and Indian Ashrama system.”. International Journal of Innovative Technology and Exploring Engineering, 8(2), 48-50.
  63. Deo, S., and; Deo, S. (2019). Cybersquatting: Threat to domain name. International Journal of Innovative Technology and Exploring Engineering, 8(6), 1432-1434.
  64. Shambhavee, H. M. (2019). Cyber-Stalking: Threat to People or Bane to Technology. International Journal on Trend in Scientific Research and Development, 3(2), 350-355.
  65. Deo, S., and; Deo, D. S. (2019). Domain name and its protection in India. International Journal of Recent Technology and Engineering.
  66. Sarda, M., Deshpande, B., Deo, S., and; Pathak, M. A. (2018). Intellectual Property And Mechanical Engineering-A Study Emphasizing The Importance Of Knowledge Of Intellectual Property Rights Amongst Mechanical Engineers. International Journal of Social Science and Economic Research, 3(12), 6591-6596.
  67. Jetter J, Zhao Y, Smith KR, Khan B, Yelverton T, DeCarlo P, Hays MD. Pollutant emissions and energy efficiency under controlled conditions for household biomass cookstoves and implications for metrics useful in setting international test standards. Environmental science & technology. 2012 Oct 2;46(19):10827-34.
  68. Johnson KB, Stockwell V. Management of fire blight: a case study in microbial ecology. Annual review of phytopathology. 1998 Sep;36(1):227-48.
  69. Giri PC, Chowdhury AM, Bedoya A, Chen H, Lee HS, Lee P, Henriquez C, MacIntyre NR, Huang YC. Application of machine learning in pulmonary function assessment where are we now and where are we going?. Frontiers in physiology. 2021 Jun 24;12:678540.
  70. Patel S, Park H, Bonato P, Chan L, Rodgers M. A review of wearable sensors and systems with application in rehabilitation. Journal of neuroengineering and rehabilitation. 2012 Dec;9:1-7.
Regular Issue Subscription Original Research
Volume 11
Issue 01
Received May 6, 2024
Accepted May 29, 2024
Published June 27, 2024
Views: 24