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Dynamic Path Planning Using Software-Defined Access in Time-Sensitive Healthcare Communication Network

Dynamic Path Planning Using Software-Defined Access in Time-Sensitive Healthcare Communication Network

Kannamma R., Umadevi K. S.
Copyright: © 2022 |Volume: 3 |Issue: 1 |Pages: 11
ISSN: 2644-1675|EISSN: 2644-1683|EISBN13: 9781683183938|DOI: 10.4018/IJBDIA.312851
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MLA

R., Kannamma, and Umadevi K. S. "Dynamic Path Planning Using Software-Defined Access in Time-Sensitive Healthcare Communication Network." IJBDIA vol.3, no.1 2022: pp.1-11. http://doi.org/10.4018/IJBDIA.312851

APA

R., K. & K. S., U. (2022). Dynamic Path Planning Using Software-Defined Access in Time-Sensitive Healthcare Communication Network. International Journal of Big Data Intelligence and Applications (IJBDIA), 3(1), 1-11. http://doi.org/10.4018/IJBDIA.312851

Chicago

R., Kannamma, and Umadevi K. S. "Dynamic Path Planning Using Software-Defined Access in Time-Sensitive Healthcare Communication Network," International Journal of Big Data Intelligence and Applications (IJBDIA) 3, no.1: 1-11. http://doi.org/10.4018/IJBDIA.312851

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Abstract

IEEE 802.1 Time-Sensitive Networking (TSN) assures a guaranteed data delivery with limited latency, low jitter, and amazingly low loss of data in handling time-critical traffic. TSN handles different quality of service (QoS) requirements and frame preemption is one of the key features of TSN. In the healthcare sector networking technology preferred by large organizations uses an enormous number of nodes, and thereby, the complexity of the network increases. Since the priority of the medical data varies at times based on the patient's health, dynamic traffic scheduling mechanisms are preferred. To improve the efficiency of the network, the software-defined access mechanism is used to control the network switches and bridges in the time-sensitive network. This work uses reinforcement learning to identify and eliminate the bridges dropping packets, and the alternative path is used to schedule the real-time data traffic. It is perceived that it performs well for the time-critical data in congestion network, increases the throughput, and reduces latency.

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