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Blockchain for Intelligent Gas Monitoring in Smart City Scenario

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Blockchain Technology for Smart Cities

Part of the book series: Blockchain Technologies ((BT))

Abstract

The increasing urbanization demands development of smart cities. Smart cities can be considered as to serve the requirement of its citizen in better way. The smart cities have many applications involving intelligent gas monitoring. In this chapter we will find about the intelligent gas monitoring in smart city scenario. We will see the aspects of smart gas monitoring, understand the concept of gas sensing, requirement of gas monitoring viz., classification and quantification of gases/odors and the brief introduction about the gas sensing. Further, we will understand the application of blockchain in the intelligent gas monitoring.

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References

  1. Harrison C, Donnelly IA (2011) A theory of smart cities. In: Proceedings of the 55th annual meeting of the ISSS-2011, Hull, UK, vol 55, No 1

    Google Scholar 

  2. Ismagilova E, Hughes L, Dwivedi YK, Raman KR (2019) Smart cities: advances in research—an information systems perspective. Int J Inf Manage 47:88–100

    Article  Google Scholar 

  3. Deakin M, Waer H (2011) From intelligent to smart cities. Intell Build Int 3(3):140–152

    Article  Google Scholar 

  4. Liu X, Cheng S, Liu H, Hu S, Zhang D, Ning H (2012) A survey on gas sensing technology. Sensors 12(7):9635–9665

    Article  Google Scholar 

  5. Ponzoni A, Baratto C, Cattabiani N, Falasconi M, Galstyan V, Nunez-Carmona E, Rigoni F, Sberveglieri V, Zambotti G, Zappa D (2017) Metal oxide gas sensors, a survey of selectivity issues addressed at the SENSOR Lab, Brescia (Italy). Sensors 17(4):714

    Article  Google Scholar 

  6. Gardner JW, Bartlett PN (1994) A brief history of electronic noses. Sens Actuat B: Chem 18(1–3):210–211

    Article  Google Scholar 

  7. Persaud K, Dodd G (1982) Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose. Nature 299(5881):352

    Article  Google Scholar 

  8. Zarcomb S (1984) Theoretical basis for identification and measurement of air contaminants using an array of sensors having partially overlapping sensitivities. Sens Actuators 6:225–243

    Article  Google Scholar 

  9. Gutierrez-Osuna R, Nagle HT (1999) A method for evaluating data-preprocessing techniques for odour classification with an array of gas sensors. IEEE Trans Syst Man Cybern Part B (Cybern) 29(5):626–632

    Article  Google Scholar 

  10. Feng S, Farha F, Li Q, Wan Y, Xu Y, Zhang T, Ning H (2019) Review on smart gas sensing technology. Sensors 19(17):3760

    Article  Google Scholar 

  11. Brattain WH, Bardeen J (1953) Surface properties of germanium. Bell Syst Tech J 32(1):1–41

    Article  Google Scholar 

  12. Arshak K, Moore E, Lyons GM, Harris J, Clifford S (2004) A review of gas sensors employed in electronic nose applications. Sensor Rev 24(2):181–198

    Article  Google Scholar 

  13. Mishra A, Rajput NS (2018) A novel modular ANN architecture for efficient monitoring of gases/odours in real-time. Mater Res Exp 5(4):045904

    Article  Google Scholar 

  14. Mishra A, Rajput NS, Singh D (2018) Performance evaluation of normalized difference based classifier for efficient discrimination of volatile organic compounds. Mater Res Exp 5(9):095901

    Article  Google Scholar 

  15. Di Natale C, Davide F, D’Amico A (1995) Pattern recognition in gas sensing: well-stated techniques and advances. Sens Actuat B: Chem 23(2–3):111–118

    Article  Google Scholar 

  16. Korotcenkov G, Cho BK (2017) Metal oxide composites in conductometric gas sensors: achievements and challenges. Sens Actuat B: Chem 244:182–210

    Article  Google Scholar 

  17. Rajput NS, Das RR, Mishra VN, Singh KP, Dwivedi R (2010) A neural net implementation of SPCA pre-processor for gas/odor classification using the responses of thick film gas sensor array. Sens Actuat B: Chem 148(2):550–558

    Article  Google Scholar 

  18. Mishra A, Rajput NS, Han G (2017) NDSRT: an efficient virtual multi-sensor response transformation for classification of gases/odors. IEEE Sens J 17(11):3416–3421

    Article  Google Scholar 

  19. Zhang GP (2000) Neural networks for classification: a survey. IEEE Trans Syst Man Cybern Part C (Appl Rev) 30(4):451–462

    Article  MathSciNet  Google Scholar 

  20. Kermani BG, Schiffman SS, Nagle HT (2005) Performance of the Levenberg–Marquardt neural network training method in electronic nose applications. Sens Actuat B: Chem 110(1):13–22

    Article  Google Scholar 

  21. Kumar R, Das RR, Mishra VN, Dwivedi R (2010) A neuro-fuzzy classifier-cum-quantifier for analysis of alcohols and alcoholic beverages using responses of thick-film tin oxide gas sensor array. IEEE Sens J 10(9):1461–1468

    Article  Google Scholar 

  22. Sharma S, Mishra VN, Dwivedi R, Das RR (2013) Quantification of individual gases/odors using dynamic responses of gas sensor array with ASM feature technique. IEEE Sens J 14(4):1006–1011

    Google Scholar 

  23. Kim S, Deka GC (2019) Advanced applications of blockchain technology

    Google Scholar 

  24. Kim S, Deka GC, Peng Z (2019) Role of blockchain technology in IoT applications. In: Advances in computers

    Google Scholar 

  25. Nakamoto S (2016) Bitcoin: a peer-to-peer electronic cash system, December 2008

    Google Scholar 

  26. Shrestha R, Bajracharya R, Shrestha AP, Nam SY (2019) A new-type of blockchain for secure message exchange in VANET. Digit Commun Netw

    Google Scholar 

  27. Shrestha R, Nam SY (2019) Regional blockchain for vehicular networks to prevent 51% attacks. IEEE Access 7:95021–95033

    Article  Google Scholar 

  28. Aitzhan NZ, Svetinovic D (2016) Security and privacy in decentralized energy trading through multi-signatures, blockchain and anonymous messaging streams. IEEE Trans Dependable Secure Comput 15(5):840–852

    Article  Google Scholar 

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Acknowledgements

This research was supported by Korea Research Fellowship program funded by the Ministry of Science and ICT through the National Research Foundation of Korea(NRF-2019H1D3A1A01071115).

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Correspondence to Ashutosh Mishra .

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Mishra, A., Shrestha, R., Kim, S., Rajput, N.S. (2020). Blockchain for Intelligent Gas Monitoring in Smart City Scenario. In: Singh, D., Rajput, N. (eds) Blockchain Technology for Smart Cities. Blockchain Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-15-2205-5_3

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  • DOI: https://doi.org/10.1007/978-981-15-2205-5_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2204-8

  • Online ISBN: 978-981-15-2205-5

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