SCBC: Smart city monitoring with blockchain using Internet of Things for and neuro fuzzy procedures

Shitharth Selvarajan; Hariprasath Manoharan; Celestine Iwendi; Taher Al-Shehari; Muna Al-Razgan; Taha Alfakih; Shitharth Selvarajan; Hariprasath Manoharan; Celestine Iwendi; Taher Al-Shehari; Muna Al-Razgan; Taha Alfakih

doi:10.3934/mbe.2023922

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 12: 20828-20851. doi: 10.3934/mbe.2023922

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Research article

SCBC: Smart city monitoring with blockchain using Internet of Things for and neuro fuzzy procedures

1.
School of Built Environment, Engineering and Computing, Leeds Beckett University, LS1 3HE Leeds, United Kingdom
2.
Department of Electronics and Communication Engineering, Panimalar Engineering College, Poonamallee, Chennai-600123, Tamil Nadu, India
3.
University of Bolton, United Kingdom
4.
Department of Self-Development Skills-Computer Skills, Common First Year Deanship, King Saud University, 11362, Riyadh, Saudi Arabia
5.
Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11345, Saudi Arabia
6.
Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia

Received: 16 August 2023 Revised: 09 October 2023 Accepted: 08 November 2023 Published: 20 November 2023

The security of the Internet of Things (IoT) is crucial in various application platforms, such as the smart city monitoring system, which encompasses comprehensive monitoring of various conditions. Therefore, this study conducts an analysis on the utilization of blockchain technology for the purpose of monitoring Internet of Things (IoT) systems. The analysis is carried out by employing parametric objective functions. In the context of the Internet of Things (IoT), it is imperative to establish well-defined intervals for job execution, ensuring that the completion status of each action is promptly monitored and assessed. The major significance of proposed method is to integrate a blockchain technique with neuro-fuzzy algorithm thereby improving the security of data processing units in all smart city applications. As the entire process is carried out with IoT the security of data in both processing and storage units are not secured therefore confidence level of monitoring units are maximized at each state. Due to the integration process the proposed system model is implemented with minimum energy conservation where 93% of tasks are completed with improved security for about 90%.
- Internet of Things (IoT),
- blockchain,
- neuro fuzzy,
- smart cities
Citation: Shitharth Selvarajan, Hariprasath Manoharan, Celestine Iwendi, Taher Al-Shehari, Muna Al-Razgan, Taha Alfakih. SCBC: Smart city monitoring with blockchain using Internet of Things for and neuro fuzzy procedures[J]. Mathematical Biosciences and Engineering, 2023, 20(12): 20828-20851. doi: 10.3934/mbe.2023922

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Abstract

The security of the Internet of Things (IoT) is crucial in various application platforms, such as the smart city monitoring system, which encompasses comprehensive monitoring of various conditions. Therefore, this study conducts an analysis on the utilization of blockchain technology for the purpose of monitoring Internet of Things (IoT) systems. The analysis is carried out by employing parametric objective functions. In the context of the Internet of Things (IoT), it is imperative to establish well-defined intervals for job execution, ensuring that the completion status of each action is promptly monitored and assessed. The major significance of proposed method is to integrate a blockchain technique with neuro-fuzzy algorithm thereby improving the security of data processing units in all smart city applications. As the entire process is carried out with IoT the security of data in both processing and storage units are not secured therefore confidence level of monitoring units are maximized at each state. Due to the integration process the proposed system model is implemented with minimum energy conservation where 93% of tasks are completed with improved security for about 90%.

References

[1]	A. W. Abbas, S. N. K. Marwat, Scalable emulated framework for IoT devices in smart logistics based cyber-physical systems: Bonded coverage and connectivity analysis, IEEE Access, 8 (2020), 138350–138372. https://doi.org/10.1109/ACCESS.2020.3012458 doi: 10.1109/ACCESS.2020.3012458
[2]	S. Pandiaraj, R. Krishnamoorthy, S. Ushasukhanya, J. V. N. Ramesh, R. A. Alsowail, S. Selvarajan, Optimization of IoT circuit for flexible optical network system with high speed utilization, Opt. Quant. Electron., 55 (2023), 1206. https://doi.org/10.1007/s11082-023-05452-x doi: 10.1007/s11082-023-05452-x
[3]	M. Padmaja, S. Shitharth, K. Prasuna, A. Chaturvedi, P. R. Kshirsagar, A. Vani, Growth of artificial intelligence to challenge security in IoT application, Wireless Personal Commun., 127 (2022), 1829–1845. https://doi.org/10.1007/s11277-021-08725-4 doi: 10.1007/s11277-021-08725-4
[4]	R. Aluvalu, V. N. S. Kumaran, M. Thirumalaisamy, S. Basheer, E. Ali aldhahri, S. Selvarajan, Efficient data transmission on wireless communication through a privacy-enhanced blockchain process, Peer J. Comput. Sci., 9 (2023), e1308. https://doi.org/10.7717/peerj-cs.1308 doi: 10.7717/peerj-cs.1308
[5]	Z. E. Ahmed, M. K. Hasan, RA Saeed, R Hassan, S Islam, R. A. Mokhtar, et al., Optimizing energy consumption for cloud Internet of Things, Front. Phys., 8 (2020), 1–10. https://doi.org/10.3389/fphy.2020.00358 doi: 10.3389/fphy.2020.00358
[6]	S. Selvarajan, G. Srivastava, A. O. Khadidos, A. O. Khadidos, M. Baza, A. Alsheri, et al., An artificial intelligence lightweight blockchain security model for security and privacy in IIoT systems, J. Cloud Comp., 12 (2023), 38. https://doi.org/10.1186/s13677-023-00412-y doi: 10.1186/s13677-023-00412-y
[7]	M. Humayun, M. S. Alsaqer, N. Jhanjhi, Energy optimization for smart cities using IoT, Appl. Artif. Intell., 36 (2022), 2037255. https://doi.org/10.1080/08839514.2022.2037255 doi: 10.1080/08839514.2022.2037255
[8]	B. Wang, F. Liu, Task arrival based energy efficient optimization in smart-IoT data center, Math. Biosci. Eng., 18 (2021), 2713–2732. http://aimspress.com/article/doi/10.3934/mbe.2021138
[9]	P. K. R. Maddikunta, G. Srivastava, T. R. Gadekallu, N. Deepa, P. Boopathy, Predictive model for battery life in IoT networks, IET Intell. Transp. Syst., 14 (2020), 1388–1395. https://doi.org/10.1049/iet-its.2020.0009 doi: 10.1049/iet-its.2020.0009
[10]	S. Rani, H. Babbar, S. H. A. Shah, A. Singh, Improvement of energy conservation using blockchain-enabled cognitive wireless networks for smart cities, Sci. Rep., 12 (2022), 13013. https://doi.org/10.1038/s41598-022-16916-7 doi: 10.1038/s41598-022-16916-7
[11]	A. Aldribi, A. Singh, Blockchain empowered smart home: A scalable architecture for sustainable smart cities, Mathematics, 10 (2022), 2378. https://doi.org/10.3390/math10142378 doi: 10.3390/math10142378
[12]	A. Ullah, S. M. Anwar, J. Li, L. Nadeem, T. Mahmood, A. Rehman, et al., Smart cities: the role of Internet of Things and machine learning in realizing a data-centric smart environment. Complex Intell. Syst., 2023 (2023), 1–23. https://doi.org/10.1007/s40747-023-01175-4 doi: 10.1007/s40747-023-01175-4
[13]	S. M. Bommu, M. Aravind, K. Babburu, L. N. Thalluri, V. Ganesh, et al., Smart city IoT system network level routing analysis and blockchain security based implementation, J. Electr. Eng. Technol., 18 (2023), 1351–1368. https://doi.org/10.1007/s42835-022-01239-4 doi: 10.1007/s42835-022-01239-4
[14]	V. Mohammadi, A. M. Rahmani, A. M. Darwesh, A. Sahafi, Trust-based recommendation systems in Internet of Things: a systematic literature review, Human Centric Comput. Inf. Sci., 9 (2019), 21. https://doi.org/10.1186/s13673-019-0183-8 doi: 10.1186/s13673-019-0183-8
[15]	S. Shitharth, A. M. Alshareef, A. O. Khadidos, K. H. Alyoubi, A. O. Khadidos, M. Uddin, A conjugate self-organizing migration (CSOM) and reconciliate multi-agent Markov learning (RMML) based cyborg intelligence mechanism for smart city security, Sci. Rep., 13 (2023), 15681. https://doi.org/10.1038/s41598-023-42257-0 doi: 10.1038/s41598-023-42257-0
[16]	D. Pamučar, D. Bozanic, A. Puška, D. Marinković, Application of neuro-fuzzy system for predicting the success of a company in public procurement, Decis. Making Appl. Manage. Eng., 5 (2022), 135–153. https://doi.org/10.31181/dmame0304042022p doi: 10.31181/dmame0304042022p
[17]	J. Simon, R. Sánta, Energy efficient smart home heating system using renewable energy source with fuzzy control design, Decis. Making Appl. Manage. Eng., 6 (2023), 948–948. https://doi.org/10.31181/dmame622023825 doi: 10.31181/dmame622023825
[18]	D. Bozanic, D. Tešić, A. Puška, A. Štilić, Y. R. Muhsen, Ranking challenges, risks and threats using fuzzy inference system, Decis. Making Appl. Manage. Eng., 6 (2023), 933–947. https://doi.org/10.31181/dmame622023926 doi: 10.31181/dmame622023926
[19]	A. K. Al-Ani, S. Ul Arfeen Laghari, H. Manoharan, S. Selvarajan, M. Uddin, Improved transportation model with Internet of Things using artificial intelligence algorithm, Comput. Mater. Contin., 76 (2023).
[20]	S. Selvarajan, H. Manoharan, C. Iwendi, R. A. Alsowail, S. Pandiaraj, A comparative recognition research on excretory organism in medical applications using artificial neural networks, Front. Bioeng. Biotechnol., 11 (2023), 1211143. https://doi.org/10.3389/fbioe.2023.1211143 doi: 10.3389/fbioe.2023.1211143
[21]	J. Chen, W. Gan, M. Hu, C. M. Chen, On the construction of a post-quantum blockchain for smart city, J. Inf. Secur. Appl., 58 (2021), 102780. https://doi.org/10.1016/j.jisa.2021.102780 doi: 10.1016/j.jisa.2021.102780

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