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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Schematic diagram for the proposed approach




How to Cite

Suggala, R. K. ., Krishna, M. V. ., & Swain, S. K. . (2022). Reliable Epidemic Outbreak Prevention in Opportunistic IoT Based On Optimized Block Chain. International Journal of Intelligent Systems and Applications in Engineering, 10(3), 305–313. Retrieved from



Research Article