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Green and friendly media transmission algorithms for wireless body sensor networks

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A Correction to this article was published on 25 April 2023

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Abstract

Video transmission is considered as a quite significant step towards health monitoring of the emergency patients during any critical incident. However, the energy hungry video transmission and slow progress in battery technologies have become a major and serious problem for the evolution of video technology in WBSNs. Therefore, the need arose to conduct research on sustainable, “Green”, i.e., energy-efficient and “Friendly”, i.e., battery-friendly technologies to cater the need of upcoming mobile and portable devices. The main challenge addressed in this research is how to increase the battery lifetime during on-demand Variable Bit Rate (VBR) video transmission from medical video server to base station in WBSNs. In order to overcome this problem, sustainable, Green and Friendly frame transmission algorithms are enunciated i.e. Lazy Algorithm (LA) and Battery-friendly Smoothing Algorithm (BSA) with analytical battery model. The proposed algorithms minimize transmission energy consumption, battery charge consumption and high current profile. These algorithms also prolongs the battery lifetime of those sensor nodes during video transmission. Experimental results demonstrates that BSA outperforms LA to minimize battery drain by improving its lifetime up to 19.8 %. However, the LA performs better than BSA in context of transmission energy saving up to 49.49 %. Furthermore, a video transmission framework of Remote Medical Education System (RMES) for elderly persons and infants is proposed to provide viable and sustainable battery solutions to serve the community.

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Correspondence to Ali Hassan Sodhro.

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The original online version of this article was revised: The label "Load Currents (in thousands)" in the x-axis of figure 7 in the original publication of this article were incorrect.

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Sodhro, A.H., Li, Y. & Shah, M.A. Green and friendly media transmission algorithms for wireless body sensor networks. Multimed Tools Appl 76, 20001–20025 (2017). https://doi.org/10.1007/s11042-016-4084-9

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  • DOI: https://doi.org/10.1007/s11042-016-4084-9

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