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Entropy-Based Age-Aware Scheduling Strategy for UAV-Assisted IoT Data Transmission.

Lulu Jing1, Hai Wang1, Zhen Qin2,3

  • 1College of Communication Engineering, Army Engineering University of PLA, Nanjing 210000, China.

Entropy (Basel, Switzerland)
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Summary

This study optimizes data transmission for Internet of Things (IoT) networks using unmanned aerial vehicles (UAVs). A new scheduling strategy significantly reduces data staleness penalties in environmental monitoring systems.

Keywords:
age of informationgain-index-based policyl-conditional cross-entropyrestless multi-armed banditunmanned aerial vehicle relay

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Internet of Things (IoT) networks require efficient data transmission, especially for real-time monitoring.
  • Unmanned Aerial Vehicles (UAVs) offer a flexible solution for relaying data from distributed devices.
  • Data staleness, a measure of information timeliness, significantly impacts system performance and decision-making.

Purpose of the Study:

  • To develop an efficient scheduling strategy for UAV-relayed IoT data transmission.
  • To minimize system loss considering both data staleness (Age of Information) and entropy.
  • To address the challenge of optimizing data collection from multiple IoT devices via a UAV.

Main Methods:

  • A novel system loss metric, L-conditional cross-entropy, was defined to capture Age of Information (AoI) and entropy effects.
  • The scheduling problem was formulated as a Restless Multi-Armed Bandit (RMAB) problem.
  • Lagrange relaxation was applied to decompose the problem and derive a low-complexity, gain-index-based scheduling algorithm.

Main Results:

  • The proposed gain-index-based scheduling strategy effectively reduces long-term average system loss.
  • Numerical simulations demonstrated significant performance improvements compared to baseline strategies (random, round-robin, periodic, MAX-AoI).
  • The algorithm successfully balances data timeliness and information entropy for optimized data transmission.

Conclusions:

  • The developed gain-index scheduling policy is a highly effective method for UAV-assisted IoT data transmission.
  • This approach significantly mitigates the negative impacts of data staleness in real-time monitoring applications.
  • The study provides a valuable framework for optimizing data collection in dynamic IoT environments.