Reliable Epidemic Outbreak Prevention in Opportunistic IoT Based On Optimized Block Chain



Epidemic detection, Interplay detection, Connector terminus, User terminus, Object terminus, Block chain optimization


With the growth of IoT and realization of its advantages, it has been broadly seen as a potential solution for reducing the demands on healthcare systems. As a result, significant effort has been expended in establishing novel IoT-enabled systems aimed at addressing issues in the healthcare industry and detecting pandemic diseases. Existing detection techniques, on the other hand, have been shredded due to restricted manipulation of perceiving technologies, resulting in controlling interplay between people and their movement in geographical locations. This can be addressed by employing Interplay detected data perception, which exploits interplay detection to detect the spread of infectious disease. In addition to deploying perceiving technologies and taking into account the context of system cadre, Terminus Embraced Cadre (TEC) is proposed. Then, to solve the epidemic detection, epidemic circumstances entail exhaustive data assortment and control from various entities, the Commute Index scheme is utilized which includes a commute index to monitoring the patient's route. Finally, a Preferment committed block chain optimization is used for data exchange reliability, in which the endorser's preferment manages the data. As a consequence, the unique Trustworthy Epidemic Interplay Contemplated Detection Optimizing Preference in Opportunistic IoT diagnoses epidemic diseases with logical chassis and delivers extensive data assortment while committing to reliable data interchange. According to the results, the proposed strategy outperforms the other current model in terms of reliability.


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Archibong, Belinda, Francis Annan, and Uche Eseosa Ekhator-Mobayode, “The Epidemic Effect: On the Politics and Economic Burden of Infectious Disease,” Available at SSRN 3571766, 2020.

T. Gottwald, G. Poole, T. McCollum, D. Hall, J. Hartung, J. Bai, & W. Schneider, “Canine olfactory detection of a vectored phytobacterial pathogen, Liberibacter asiaticus, and integration with disease control,” Proceedings of the National Academy of Sciences, vol. 117, no. 7, pp. 3492-3501, 2020.

Chaudhary, D. S. . (2022). Analysis of Concept of Big Data Process, Strategies, Adoption and Implementation. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(1), 05–08.

Ai, Tao, Zhenlu Yang, Hongyan Hou, Chenao Zhan, Chong Chen, Wenzhi Lv, Qian Tao, Ziyong Sun, and Liming Xia, “Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases,” Radiology, pp. 200642, 2020.

Lu, Shubiao, et al., “Alert for non‐respiratory symptoms of Coronavirus Disease 2019 (COVID‐19) patients in epidemic period: a case report of familial cluster with three asymptomatic COVID‐19 patients,” Journal of medical virology, 2020.

Zeng, Daniel, Zhidong Cao, and Daniel B. Neill, “Artificial intelligence–enabled public health surveillance—from local detection to global epidemic monitoring and control,” Artificial Intelligence in Medicine, Academic Press, pp. 437-453, 2020.

Kabir, K. M. Ariful, and Jun Tanimoto, “Analysis of epidemic outbreaks in two-layer networks with different structures for information spreading and disease diffusion, “Communications in Nonlinear Science and Numerical Simulation, vol. 72, pp. 565-574, 2019.

Dursun, M., & Goker, N. (2022). Evaluation of Project Management Methodologies Success Factors Using Fuzzy Cognitive Map Method: Waterfall, Agile, And Lean Six Sigma Cases. International Journal of Intelligent Systems and Applications in Engineering, 10(1), 35–43.

F. Xiang, X. Wang, X. He, Z. Peng, B. Yang, Zhang, Jianchu, & W.L. Ma, “Antibody detection and dynamic characteristics in patients with coronavirus disease,” Clinical Infectious Diseases, vol. 71, no. 8, pp. 1930-1934, 2020.

X. Chen, & B. Yu, “First two months of the 2019 Coronavirus Disease (COVID-19) epidemic in China: real-time surveillance and evaluation with a second derivative model,” Global health research and policy, vol. 5, no. 1, pp. 1-9, 2020.

Nguyen, Trieu, Dang Duong Bang, and Anders Wolff, “Novel coronavirus disease (COVID-19): paving the road for rapid detection and point-of-care diagnostics,” Micro machines, vol. 11, no. 3, pp. 306, 2019.

M. Meraj, S. P. Singh, P. Johri, & M. T. Quasim, “An investigation on infectious disease patterns using Internet of Things (IoT),” In 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), IEEE, pp. 599-604, 2020, October.

M. U. Ashraf, A. Hannan, S. M. Cheema, Z. Ali, & A. Alofi, “A Detection and Tracking Contagion using IoT-Edge Technologies: Confronting COVID-19 Pandemic,” In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), IEEE, pp. 1-6, 2020, June.

V. Singh, H. Chandna, A. Kumar, S. Kumar, N. Upadhyay, & K. Utkarsh, “IoT-Q-Band: A low cost internet of things based wearable band to detect and track absconding COVID-19 quarantine subjects,” EAI Endorsed Transactions on Internet of Things, vol. 6, no. 21, 2020.

M. S. Rahman, N. C. Peeri, N. Shrestha, R. Zaki, U. Haque, & S. H. Ab Hamid, “Defending against the Novel Coronavirus (COVID-19) Outbreak: How Can the Internet of Things (IoT) help to save the World?,” Health Policy and Technology, 2020.

L. Bai, D. Yang, X. Wang, L. Tong, X. Zhu, N. Zhong, & F. Tan, “Chinese experts’ consensus on the Internet of Things-aided diagnosis and treatment of coronavirus disease 2019 (COVID-19),” Clinical eHealth, vol. 3, pp. 7-15, 2020.

Pandya, Sharnil, Anirban Sur, and Ketan Kotecha, “Smart epidemic tunnel: IoT-based sensor-fusion assistive technology for COVID-19 disinfection,” International Journal of Pervasive Computing and Communications, 2020.

Ketu, Shwet, and Pramod Kumar Mishra, “Enhanced Gaussian process regression-based forecasting model for COVID-19 outbreak and significance of IoT for its detection,” Applied Intelligence, pp. 1-21, 2020.

B. Wang, Y. Sun, T. Q. Duong, L. D. Nguyen, & L. Hanzo, “Risk-aware identification of highly suspected covid-19 cases in social iot: A joint graph theory and reinforcement learning approach,” IEEE Access, vol. 8, 115655-115661, 2020.

Y. Sahraoui, A. Korichi, C. A. Kerrache, M. Bilal, & M. Amadeo, “Remote sensing to control respiratory viral diseases outbreaks using Internet of Vehicles,” Transactions on Emerging Telecommunications Technologies, pp. e4118, 2020.

R. N. Phursule, P. N. Mahalle, P. K. Ukhalkar, & S. R. Todmal, “Machine Learning-Based IoT-Enabled Perspective Model for Prediction of COVID-19 Test in Early Stage,” Machine Learning, vol. 29, no. 12s, pp. 2599-2604, 2020.

Maulana, Mohamad Firman, and Meriska Defrian, “Logistic Model Tree and Decision Tree J48 Algorithms for Predicting the Length of Study Period,” PIKSEL: Penelitian Ilmu Komputer Sistem Embedded and Logic, vol. 8.1, pp. 39-48, 2020.

Pei, Dongmei, Tengfei Yang, and Chengpu Zhang, “Estimation of Diabetes in a High-Risk Adult Chinese Population Using J48 Decision Tree Model,” Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, vol. 13, pp. 4621, 2020.

Lopez-Piqueres, Javier, Brayden Ware and Romain Vasseur, “Mean-field entanglement transitions in random tree tensor networks,” Physical Review B, vol. 102.6, pp. 064202, 2020.

Pérez-Hurtado, Ignacio, Miguel Á. Martínez-del-Amor, Gexiang Zhang, Ferrante Neri, Mario J. Pérez-Jiménez, “A membrane parallel rapidly-exploring random tree algorithm for robotic motion planning,” Integrated Computer-Aided Engineering, vol. 27, no. 2, pp. 121-138, 2020.

Chen, Shenglei, Geoffrey I. Webb, Linyuan Liu, and Xin Ma, “A novel selective naïve Bayes algorithm,” Knowledge-Based Systems, vol. 192, pp. 105361, 2020.

Chaudhary, D. S. . (2022). Analysis of Concept of Big Data Process, Strategies, Adoption and Implementation. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(1), 05–08.

Zhang, Huan, Liangxiao Jiang, and Liangjun Yu, “Attribute and instance weighted naive Bayes,” Pattern Recognition, vol. 111, pp. 107674, 2021.

Ahmad, Mahmood, Xiaowei Tang, and Feezan Ahmad, “Evaluation of Liquefaction-Induced Settlement Using Random Forest and REP Tree Models: Taking Pohang Earthquake as a Case of Illustration,” Natural Hazards-Impacts, Adjustments & Resilience. IntechOpen., 2020.

Saha, Sunil, et al., “Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River Basin, India,” Science of The Total Environment, vol. 730, pp. 139197, 2020.

Wang, Shuaiwei, et al., “Bco-C24: A new 3D Dirac nodal line semi-metallic carbon honeycomb for high performance metal-ion battery anodes,” Carbon, vol. 159, pp. 542-548, 2020.

Cardwell, A. David, Yunhua Shi, and Devendra K. Numburi, “Reliable single grain growth of (RE) BCO bulk superconductors with enhanced superconducting properties,” Superconductor Science and Technology, vol. 33.2, pp. 024004, 2020.

N. A. Libre. (2021). A Discussion Platform for Enhancing Students Interaction in the Online Education. Journal of Online Engineering Education, 12(2), 07–12. Retrieved from

V. Pierro, V. Fiumara, F. Chiadini, V. Granata, O. Durante, J. Neilson, C. Di Giorgio, et al., “Ternary quarter wavelength coatings for gravitational wave detector mirrors: Design optimization via exhaustive search,” Physical Review Research, vol. 3, no. 2, pp. 023172, 2021.

N. Moreno, A. Restrepo, & C. Z. Hadad, “Structure, energy, and bonding in anionic water tetramers obtained by exhaustive search,” The Journal of Chemical Physics, vol. 155, no. 4, pp. 044304, 2021.

Schematic diagram for the proposed approach




How to Cite

R. K. . Suggala, M. V. . Krishna, and S. K. . Swain, “Reliable Epidemic Outbreak Prevention in Opportunistic IoT Based On Optimized Block Chain”, Int J Intell Syst Appl Eng, vol. 10, no. 3, pp. 305–313, Oct. 2022.



Research Article