HYBRID MODEL USED FOR REDUCING LATENCY IN SMART HEALTHCARE SYSTEMS
DOI:
https://doi.org/10.36755/jac.v2i1.57Keywords:
Cloud Server, Computing, Exercise Planner, Healthcare Systems, Hybrid ModelAbstract
The Internet of Things (IoT) connects numerous devices on a worldwide scale. Emerging topics in the healthcare system include health checking, exercise planners, and remote medical aid. Fog computing always aims to implement cloud computing capability on edge devices. When utilised with Internet of Things (IoT) medical devices, the strategy is likely to exceed the minimal latencies need. Reducing network latency, processing delay, and energy consumption is crucial for IoT data transport. FC allows for the storage, processing, and and examined. To reduce high latency, cloud computing data is situated at a network edge. Here, a creative solution to the previously described issue is put forth. In an FC environment, it combines an analytical model and a hybrid fuzzy-based reinforced learning technique. The goal is to lower cloud server latency and energy consumption for IoT in healthcare. The suggested smart FC analysis strategy and algorithm uses a fuzzy inference system, optimisation techniques, and development approaches to choose and place the Internet of Things-FC context. Utilising the simulators Spyder and iFogSim, the method is assessed. The findings demonstrated that, in all comparisons, our suggested solution performed better than alternative techniques.
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