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Prioritization-Driven Congestion Control in Networks for the Internet of Medical Things: A Cross-Layer Proposal.

Raymundo Buenrostro-MariscalPedro C Santana-MancillaOsval Antonio Montesinos-LópezMabel Vazquez-BrisenoJuan Ivan Nieto-Hipolito
Published in: Sensors (Basel, Switzerland) (2023)
Real-life implementation of the Internet of Things (IoT) in healthcare requires sufficient quality of service (QoS) to transmit the collected data successfully. However, unsolved challenges in prioritization and congestion issues limit the functionality of IoT networks by increasing the likelihood of packet loss, latency, and high-power consumption in healthcare systems. This study proposes a priority-based cross-layer congestion control protocol called QCCP, which is managed by communication devices' transport and medium access control (MAC) layers. Unlike existing methods, the novelty of QCCP is how it estimates and resolves wireless channel congestion because it does not generate control packets, operates in a distributed manner, and only has a one-bit overhead. Furthermore, at the same time, QCCP offers packet scheduling considering each packet's network load and QoS. The results of the experiments demonstrated that with a 95% confidence level, QCCP achieves sufficient performance to support the QoS requirements for the transmission of health signals. Finally, the comparison study shows that QCCP outperforms other TCP protocols, with 64.31% higher throughput, 18.66% less packet loss, and 47.87% less latency.
Keyphrases
  • healthcare
  • health information
  • mental health
  • randomized controlled trial
  • primary care
  • public health
  • machine learning
  • deep learning
  • social media
  • affordable care act