Fuzzy allocation model for health care data management on IoT assisted wearable sensor platform

Abstract In recent times, networking, information, and communication technologies are widely employed and getting spread in the area of medical applications. In a short period, real-time applications for healthcare monitoring produce a huge amount of data. Data is to be bursting in the case of critical applications; hence there is a greater need to enable reliable communication methods that ensure energy efficiency. A large amount of data results in high data transmission, network congestion, and latency, which in turn cause a hop increment between the IoT and cloud servers, and thus data might be unprocessed and insufficient for end-users. Since Fog computing is a distributed intermediate layer between the edge network and the cloud environment, latency can be reduced to a remarkable level, and the reliable communication can be achieved with the help of fog computing on IoT assisted wearable sensor platform. This research proposes a dynamic model for analysis and the Heuristic Hybrid Time Slot Fuzzy-Allocation Algorithm (HHTSF-AA), which improves health monitoring by indulging IoT assisted wearable sensor platform. Fog computing assisted wearable sensor platform is the most suitable and reliable platform for robust life-critical applications that are likely not to be delayed in communication. Besides, routing data packets are equipped with a low-cost energy minimum selection algorithm incorporated to improve the overall network performance. Dynamic slot assignment reduces time in a network and allows high levels of network capacity channel utilization.

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