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<p>This paper investigates the offloading performance of an Internet of Things (IoT) network using unmanned aerial vehicle (UAV)-enabled mobile-edge computing (MEC). It focuses on two clusters of IoT devices (IDs) that use energy harvesting (EH) from a power beacon to transfer tasks to a UAV-based MEC server via non orthogonal multiple access. A four-phase protocol is proposed for efficient EH and offloading, along with two ID and antenna selection schemes: Best ID-selection combining and Best ID-maximum ratio combining. Closed-form expressions for successful computation probability under Nakagami-<i>m</i> fading are derived, and an optimization problem is solved using the particle swarm optimization algorithm. Monte Carlo simulations validate the analysis by evaluating key system parameters, including UAV’s location, height, time switching ratio, power allocation, number of IDs, and antennas.</p>

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A UAV-enabled MEC cooperates NOMA and power beacon in offloading analysis and optimization

  • 期刊论文

  • 发表日期：

    2025年9月3日

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  • Anh-Nhat Nguyen,
  • Khai Nguyen,
  • Minh-Sang Nguyen,
  • Gia-Huy Nguyen

摘要

This paper investigates the offloading performance of an Internet of Things (IoT) network using unmanned aerial vehicle (UAV)-enabled mobile-edge computing (MEC). It focuses on two clusters of IoT devices (IDs) that use energy harvesting (EH) from a power beacon to transfer tasks to a UAV-based MEC server via non orthogonal multiple access. A four-phase protocol is proposed for efficient EH and offloading, along with two ID and antenna selection schemes: Best ID-selection combining and Best ID-maximum ratio combining. Closed-form expressions for successful computation probability under Nakagami-m fading are derived, and an optimization problem is solved using the particle swarm optimization algorithm. Monte Carlo simulations validate the analysis by evaluating key system parameters, including UAV’s location, height, time switching ratio, power allocation, number of IDs, and antennas.