This paper has proposed a Fog architecture-based framework, which classifies dengue patients into uninfected, infected and severely infected using a data set built in 2010. The aim of this proposed framework is to developed a latency-aware system for classifying users into different categories based on their respective symptoms using Internet of Things (IoT) sensors and audio and video files.
To achieve the aforesaid aim, a smart framework is proposed, which consist of three components, namely, IoT layer, Fog infrastructure and cloud computing. The latency of the system is reduced by using network devices located in the Fog infrastructure. Data generated by IoT layer will first be processed by Fog layer devices which are in closer proximity of the user. Raw data and data generated will later be stored on cloud infrastructure, from where it will be sent to different entities such as user, hospital, doctor and government healthcare agencies.
Experimental evaluation proved the hypothesis that using the Fog infrastructure can achieve better response time for latency sensitive applications with the least effect on accuracy of the system.
The proposed Fog-based architecture can be used with IoT to directly link it with the Fog layer.
Singh, S., Bansal, A., Sandhu, R. and Sidhu, J. (2018), "Fog computing and IoT based healthcare support service for dengue fever", International Journal of Pervasive Computing and Communications, Vol. 14 No. 2, pp. 197-207. https://doi.org/10.1108/IJPCC-D-18-00012Download as .RIS
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