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Towards 6G IoT: Tracing Mobile Sensor Nodes with Deep Learning Clustering in UAV Networks.

Yannis Spyridis1, Thomas Lagkas2, Panagiotis Sarigiannidis3

  • 1Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield S1 3JD, UK.

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Summary

Flying anchor nodes using Unmanned Aerial Vehicles (UAVs) can quickly trace mobile Internet of Things (IoT) devices. A deep learning approach optimizes UAV clustering for faster, more efficient localization in 6G networks.

Keywords:
6GIoTRSSIdeep learninggraph convolutional networksensor trackingunmanned aerial vehicles

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Area of Science:

  • Wireless communication networks
  • Internet of Things (IoT)
  • Robotics and autonomous systems

Background:

  • Upcoming 6G networks require advanced solutions for device localization and data relay.
  • Unmanned Aerial Vehicles (UAVs) are being explored as flying anchor nodes to support terrestrial IoT sensors.
  • Accurate and efficient localization of mobile IoT devices presents a significant challenge.

Purpose of the Study:

  • To develop and evaluate a novel algorithm for tracing mobile IoT devices using a swarm of UAVs.
  • To enhance the speed and efficiency of localization by optimizing UAV movement and network clustering.
  • To leverage deep learning for dynamic network partitioning and target acquisition.

Main Methods:

  • Utilized Unmanned Aerial Vehicles (UAVs) equipped with Received Signal Strength Indicator (RSSI) sensors for target localization.
  • Implemented a deep learning model based on Graph Convolutional Network (GCN) for dynamic clustering of UAVs.
  • Employed a heuristic method for dynamic cluster number determination and optimized an RSSI loss function for partitioning.
  • Developed an algorithm to retain effective clusters and return underperforming UAVs to base.

Main Results:

  • The proposed deep learning-based UAV clustering algorithm demonstrated improved performance over deterministic approaches.
  • Achieved significant reductions in the time required to reach the target mobile IoT device.
  • Reduced the total distance covered by the UAVs during the tracing process.
  • Validated through simulation experiments showcasing enhanced localization efficiency.

Conclusions:

  • The integration of UAVs as flying anchor nodes with intelligent deep learning-based localization offers a promising solution for 6G IoT networks.
  • The dynamic clustering algorithm effectively guides UAVs towards the target, optimizing resource utilization.
  • This approach enhances the capability for rapid and efficient tracing of mobile IoT devices in complex environments.