Task Prioritization of Fog Computing Model in Healthcare Systems

Abstract

Health-related applications are one of the most sensitive area which should be delivered on time efficiently. For the storage and processing of enormous health data, Cloud Computing could not be efficient as Cloud Data Centers take large time to process and send back the results. The new paradigm, called Fog Computing is applicable in cases like these. In this research, we utilize the sample time critical healthcare system where the IoT sensors’ data is divided into critical and normal tasks where critical tasks are prioritized than normal patients’ data. To address their management, Fog Computing is used at the edge of the network. In this paper, a new fog-cloud based algorithm called Prioritized Latency Aware Energy Efficient Algorithm (PLAEE) is developed by utilizing the existing algorithms in fog system and also by process optimization of the core evaluation metrics, latency and energy usage. This algorithm shows superiority to the existing algorithms in terms of performance metrics. The experimentation is performed using Blood Pressure data collected from University of Piraeus. In terms of response time, the PLAEE is performing 45.90%, 21.95%, and 20.25% better than Cloud only, Edge-wards and Mapping Algorithm respectively. In terms of Energy Consumption, the PLAEE is performing 30.26%, 22.86%, 15.44% percentage better than Cloud only, Edge-wards and Mapping Algorithm respectively. Almost 98% of critical data are placed in the FNs according to the Tasks Managed value calculated.