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SIMDPS: Smart Industrial Monitoring and Disaster Prevention System

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AI Models for Blockchain-Based Intelligent Networks in IoT Systems

Part of the book series: Engineering Cyber-Physical Systems and Critical Infrastructures ((ECPSCI,volume 6))

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Abstract

Safety regulations have been unable to keep up with the needs of rapid industrialization over the last few decades. We propose SIMDPS, a novel Arduino-based smart industry monitoring system with various gas, ambient, and disaster detection sensors, GPS (Global Positioning System) and Wi-Fi modules, an alerting mechanism, and a prediction model. This system can be installed in industries to maintain workplace safety and anticipate disasters beforehand. SIMDPS was deployed in our university’s automotive testing workshops, and the readings gathered were used to evaluate its performance. The mean absolute error and root mean square error values were found to be 0.79 and 1.02 for the temperature sensor, 0.94 and 1.12 for the humidity sensor, and 1.07 and 1.23 for the carbon monoxide sensor, respectively, compared to field-proven devices. These low error values indicate the high accuracy of our proposed system. We also trained a multiple linear regression model on our dataset to achieve an accuracy of 87%. This system will help prevent tragedies and monitor the working conditions of industries to maintain safety and the peak efficiency of machinery.

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References

  1. Cushman & Wakefield (2021) Global manufacturing risk index 2022. https://www.cushmanwakefield.com/en/insights/global-manufacturing-risk-index

  2. FICCI quarterly survey on Indian manufacturing sector. https://ficci.in/SEDocument/20612/manufacturing-survey.pdf

  3. Ga E, Gb N, Rc R (2021) Smart industry monitoring and controlling system using IoT. Smart Intell Comput Commun Technol 38:449

    Google Scholar 

  4. Venkata Subbaiah B, Venkata Sreekanth Reddy E, Abhishek K, Pavan Kumar Reddy, An IoT based smart industry monitoring system by using raspberry PI 3

    Google Scholar 

  5. Ganeshan C, Kumar Singh S (2018) Smart industrial system for monitoring, control and security using internet of things. In: 2018 2nd international conference on trends in electronics and informatics (ICOEI). IEEE

    Google Scholar 

  6. Kumar NS, Chandrasekaran G, Rajamanickam KP (2021) An integrated system for smart industrial monitoring system in the context of hazards based on the internet of things. Int J Safety Secur Eng 11(1):123–127

    Google Scholar 

  7. Gupta PK, Sai Koushik B, Deeban Chakravarthy V (2020) IoT based smart industry monitoring system by using Arduino with GSM. Int J Adv Sci Technol 29(05):9082–9088. http://sersc.org/journals/index.php/IJAST/article/view/18978

  8. Peng Y, Wu IC (2021) A cloud-based monitoring system for performance analysis in IoT industry. J Supercomput 77:9266–9289. https://doi.org/10.1007/s11227-021-03640-8

    Article  Google Scholar 

  9. Deekshath R et al (2018) IoT based environmental monitoring system using arduino UNO and thingspeak. Int J Sci Technol Eng 4(9):68–75

    Google Scholar 

  10. Kishore Kumar R, Nishanth N, Suriya Prakash SK, Dhanush Anand SB, IoT based industrial monitoring system using Arduino

    Google Scholar 

  11. Solanki S, Gaur D, Rasiq G (2021) IoT based industrial monitoring system. https://doi.org/10.1007/978-981-15-9873-9_28

  12. Aravind R, Yadikiumarani, Meghna K, Divyashree R, Naregowda H (2021) IOT based real time data monitoring for industry. Int J Eng Res Technol (IJERT) NCCDS—2021 9(12)

    Google Scholar 

  13. Rajalakshmi R, Vidhya J (2019) Toxic environment monitoring using sensors based on IoT. Int J Recent Technol Eng

    Google Scholar 

  14. Lohith D, Kumar KV, Reddy IBK, Jeyaramya V, IoT Industry Protection using Arduino

    Google Scholar 

  15. Rupali SG, Mahajan P (2018) Home and industrial safety system for fire and gas leakage detection. Int Res J Eng Technol

    Google Scholar 

  16. Prasanti V, Venkataramana T, IoT Based Industrial Automation Control System Using Arduino

    Google Scholar 

  17. Merchant HK, Ahire DD, Industrial automation using IoT with Raspberry Pi

    Google Scholar 

  18. Georgewill O, Ezeofor C (2016) Design and implementation of SMS-based industrial/homes gas leakage monitoring & detection alarm system. Int J Eng Trends Technol 35:410–416. https://doi.org/10.14445/22315381/IJETT-V35P283

  19. Kavitha BC, Alagappan V (2019) IoT based intelligent industry monitoring system 63–65. https://doi.org/10.1109/SPIN.2019.8711597

  20. Liu JH, Chen YF, Lin TS, Chen CP, Chen PT, Wen TH, … Jiang JA (2012) An air quality monitoring system for urban areas based on the technology of wireless sensor networks. Int J Smart Sens Int Syst 5(1)

    Google Scholar 

  21. Banhazi TM (2009) User friendly air quality monitoring system. Appl Eng Agric 25(2):281–290

    Article  Google Scholar 

  22. Okigbo CA, Seeam A, Guness SP, Bellekens X, Bekaroo G, Ramsurrun V (2020) Low cost air quality monitoring: comparing the energy consumption of an arduino against a raspberry Pi based system. In: Madhavje K, Soyjaudah S (eds) Proceedings of the 2nd international conference on intelligent and innovative computing applications. ICONIC’20, 24–25 Sept 2020, Plaine Magnien, Mauritius. ISBN 9781450375580 [Conference or Workshop Item]. https://doi.org/10.1145/3415088.3415124

  23. Dhingra S, Madda RB, Gandomi AH, Patan R, Daneshmand M (2019) Internet of Things mobile–air pollution monitoring system (IoT-Mobair). IEEE Internet Things J 6(3):5577–5584

    Article  Google Scholar 

  24. Deshpande A, Pitale P, Sanap S (2016) Industrial automation using Internet of Things (IOT). Int J Adv Res Comput Eng Technol (IJARCET) 5(2):266–269

    Google Scholar 

  25. Okokpujie KO, Noma-Osaghae E, Odusami M, John SN, Oluga O (2018) A smart air pollution monitoring system. Int J Civ Eng Technol (IJCIET) 9(9):799–809

    Google Scholar 

  26. Das A, Sarma MP, Sarma KK, Mastorakis N (2018) Design of an IoT based real time environment monitoring system using legacy sensors. In: 22nd international conference on circuits, systems, communications and computers (CSCC 2018), vol 210

    Google Scholar 

  27. Simbeye DS (2017) Industrial air pollution monitoring system based on wireless sensor networks. J Inf Sci Comput Technol, November 21

    Google Scholar 

  28. Ziętek B, Banasiewicz A, Zimroz R, Szrek J, Gola S, A portable environmental data-monitoring system for air hazard evaluation in deep underground mines

    Google Scholar 

  29. Kanan R, Elhassan O, Bensalem R, An IoT-based autonomous system for workers’ safety in construction sites with real-time alarming, monitoring, and positioning strategies

    Google Scholar 

  30. Kaur N, Mahajan R, Bagai D (2016) Air quality monitoring system based on Arduino microcontroller

    Google Scholar 

  31. Charaim F, Erol YB, Pister K (2016) Wireless gas leakage detection and localization. Institute of Electrical and Electronics Engineers

    Google Scholar 

  32. Kanappan A, Hariprasad K (2017) Toxic gas and radiation detection monitoring using IoT. Int J Eng Res Technol

    Google Scholar 

  33. Elsisi M, Mahmoud K, Lehtonen M, Darwish MM (2021) Reliable industry 4.0 based on machine learning and IOT for analyzing, monitoring, and securing smart meters. Sensors 21(2):487

    Google Scholar 

  34. Ramamurthy H, Prabhu BS, Gadh R, Madni AM (2007) Wireless industrial monitoring and control using a smart sensor platform. IEEE Sens J 7(5):611–618

    Article  Google Scholar 

  35. Ray PP, Mukherjee M, Shu L (2017) Internet of things for disaster management: state of-the-art and prospects. IEEE Access 5:18818–18835. https://doi.org/10.1109/ACCESS.2017.2752174

    Article  Google Scholar 

  36. Kök İ, Şimşek MU, Özdemir S (2017) A deep learning model for air quality prediction in smart cities. In: 2017 IEEE international conference on big data (big data), pp 1983–1990. https://doi.org/10.1109/BigData.2017.8258144

  37. Mehta Y, Manohara Pai MM, Mallissery S, Singh S (2016) Cloud enabled air quality detection, analysis and prediction—a smart city application for smart health. In: 2016 3rd MEC international conference on big data and smart city (ICBDSC), pp 1–7. https://doi.org/10.1109/ICBDSC.2016.7460380

  38. Haque AKMB, Bhushan B, Dhiman G (2022) Conceptualizing smart city applications: requirements, architecture, security issues, and emerging trends. Expert Syst 39(5):e12753. https://doi.org/10.1111/exsy.12753

    Article  Google Scholar 

  39. Haque AKMB, Bhushan B, Hasan M, Zihad MM (2022) Revolutionizing the industrial internet of things using blockchain: an unified approach. In: Balas VE, Solanki VK, Kumar R (eds) Recent advances in internet of things and machine learning. Intelligent systems reference library, vol 215. Springer, Cham. https://doi.org/10.1007/978-3-030-90119-6_5

  40. Malik A, Bhushan B, Kumar A, Chaganti R (2022) Opportunistic internet of things (OIoT): elucidating the active opportunities of opportunistic networks on the way to IoT. In: Sharma R, Sharma D (eds) New trends and applications in internet of things (IoT) and big data analytics. Intelligent systems reference library, vol 221. Springer, Cham. https://doi.org/10.1007/978-3-030-99329-0_14

  41. Bhushan B (2022) Middleware and security requirements for internet of things. In: Sharma DK, Peng SL, Sharma R, Zaitsev DA (eds) Micro-electronics and telecommunication engineering. ICMETE 2021. Lecture notes in networks and systems, vol 373. Springer, Singapore. https://doi.org/10.1007/978-981-16-8721-1_30

  42. Mehta S, Bhushan B, Kumar R (2022) Machine learning approaches for smart city applications: emergence, challenges and opportunities. In: Balas VE, Solanki VK, Kumar R (eds) Recent advances in internet of things and machine learning. Intelligent systems reference library, vol 215. Springer, Cham. https://doi.org/10.1007/978-3-030-90119-6_12

  43. Uyanık GK, Güler N (2013) A study on multiple linear regression analysis. Procedia Soc Behav Sci 106:234–240

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Vellore Institute of Technology, Vellore, India

    Arushi Jain, David Velho & K. S. Sendhil Kumar

  2. Meenakshi Sundararajan Engineering College, Chennai, India

    U. Sai Sakthi

Authors
  1. Arushi Jain
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  2. David Velho
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  3. K. S. Sendhil Kumar
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  4. U. Sai Sakthi
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Corresponding author

Correspondence to K. S. Sendhil Kumar .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, India

    Bharat Bhushan

  2. School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Arun Kumar Sangaiah

  3. Intelligent Systems Laboratory (ISL), Department of Computer Science, Kennesaw State University, Marietta, GA, USA

    Tu N. Nguyen

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Jain, A., Velho, D., Sendhil Kumar, K.S., Sai Sakthi, U. (2023). SIMDPS: Smart Industrial Monitoring and Disaster Prevention System. In: Bhushan, B., Sangaiah, A.K., Nguyen, T.N. (eds) AI Models for Blockchain-Based Intelligent Networks in IoT Systems. Engineering Cyber-Physical Systems and Critical Infrastructures, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-031-31952-5_4

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