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

Autor: M. G. Sharavana Kumar, V. R. Sarma Dhulipala
Rok vydání: 2020
Předmět:
Zdroj: Measurement. 166:108249
ISSN: 0263-2241
DOI: 10.1016/j.measurement.2020.108249
Popis: 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.
Databáze: OpenAIRE
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