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Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6TiSCH Environments.

Fransiskus Xaverius Kevin Koesnadi1, Sang-Hwa Chung1

  • 1Department of Information Convergence Engineering, Pusan National University, Busan 46241, Republic of Korea.

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Summary

This study introduces a K-means clustering strategy for 6TiSCH networks to optimize Industrial Internet of Things (IIoT) performance. The method enhances cell allocation, reducing latency and improving data delivery in congested environments.

Keywords:
6TiSCHK-meanscell allocationnode densitywireless sensor network

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Area of Science:

  • Networking and Communications
  • Wireless Sensor Networks
  • Industrial Internet of Things (IIoT)

Background:

  • The 6TiSCH protocol (IEEE 802.15.4e) is vital for IIoT, employing time-slotted channel hopping (TSCH) for reliable communication.
  • Congestion and inefficient resource distribution are significant challenges in 6TiSCH networks, impacting performance.

Purpose of the Study:

  • To propose and evaluate an innovative cell allocation strategy for 6TiSCH networks using K-means clustering.
  • To optimize resource distribution, reduce network congestion, and enhance overall performance in IIoT applications.

Main Methods:

  • Implemented a node position clustering strategy using the K-means algorithm to group nodes.
  • Dynamically adjusted cell capacities based on traffic patterns and spatial node distribution within clusters.
  • Evaluated the strategy using the 6TiSCH simulator and analyzed performance with Routing Protocol for Low-Power and Lossy Networks (RPL) objective functions (OF0, MRHOF).

Main Results:

  • The K-means clustering approach significantly improved slot frame utilization and reduced communication latency.
  • Under OF0, latency decreased by 30.01%, joining time improved by 15.95%, packet delivery ratio increased by 8%, and throughput rose by 13.82%.
  • With MRHOF, packet delivery ratio improved by 12.34%, latency reduced by 21.06%, joining time was 12.68% faster, and throughput increased by 25.97%.

Conclusions:

  • The proposed K-means based cell allocation strategy effectively addresses congestion in 6TiSCH networks.
  • This method substantially enhances network performance metrics, offering a robust solution for IIoT applications.