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Preventing overloaded dissemination in healthcare applications using NonDelay tolerant dissemination technique
Wearable Sensors (WSs) are widely used in healthcare applications to monitor patient health. During the data transmission, dissemination requires additional time to transmit the details with minimum computation difficulties. The existing techniques consume high overloaded while transmitting data in...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10412841/ https://www.ncbi.nlm.nih.gov/pubmed/37576207 http://dx.doi.org/10.1016/j.heliyon.2023.e18783 |
Sumario: | Wearable Sensors (WSs) are widely used in healthcare applications to monitor patient health. During the data transmission, dissemination requires additional time to transmit the details with minimum computation difficulties. The existing techniques consume high overloaded while transmitting data in healthcare applications. The research problem is overcome by applying the non-delay-tolerant dissemination technique (NDTDT) to prevent overloaded dissemination and augment immediate, swift message delivery. The dissemination techniques utilize the intelligent decision-making process to provide the accumulated details to the healthcare center. The proposed approach is reliable in mitigating the errors due to inconsistent and discrete sensing intervals between the WSs. The constraints due to delay and interrupted transmission losses are reduced by selecting appropriate slots for WS information handling. This technique aims at maximizing the delivery of accumulated WS information through non-submissive or underlay dissemination. The method is designed to reduce dissemination delay and maximize successful message delivery. Two variations, sensors and data flows, validate the proposed NDTDT system's performance. The model increases the delivery rate by 0.91% and 0.932%, the dissemination probability by 0.964% and 0.98%, and the final metrics involved are an average delay of 12.78 ms and 11.67 ms. |
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