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<p>Unmanned Aerial Vehicles (UAVs)-assisted edge computing enables resource-limited mobile devices (MDs) to handle computation and resource-intensive applications. In mobile edge computing networks (MECNs), UAVs extend coverage to remote MDs that are beyond the range of terrestrial edge servers (TESs). However, the key challenge in UAV-assisted MEC systems lies in ensuring reliable task execution within strict deadlines while simultaneously minimizing delay and energy consumption without overloading limited edge resources. For mission-critical applications, it is crucial to ensure that all tasks are executed by TESs within their deadlines. Additionally, minimizing delay and energy consumption and achieving load balancing are crucial while maximizing the task completion rate (TCR). This paper introduces an MECN that integrates UAVs as the aerial layer and TESs as the terrestrial layer. A quantum-inspired particle swarm optimization-based offloading strategy (QIPSO-TOS) is proposed to facilitate coverage-aware task offloading. Quantum particles (QPs) provide a complete and valid offloading solution, with a hashing-based decoding of QPs to map tasks either directly to TESs or through a relay UAV. The fitness function incorporates TCR, energy consumption, delay, and load balancing. The design of experiment is conducted using the Taguchi method. Extensive simulations across various scenarios, followed by statistical analysis, demonstrate that QIPSO-TOS outperforms existing strategies, achieving an average of 74.06% TCR and 1.61 times better load balancing.</p>

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Coverage-aware offloading in multi-UAV-aided terrestrial MEC networks using quantum-inspired particle swarm optimization

  • 期刊论文

  • 发表日期：

    2026年7月7日

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  • Marlom Bey,
  • Pratyay Kuila,
  • Biswadip Bandyopadhyay,
  • Banavath Balaji Naik

摘要

Unmanned Aerial Vehicles (UAVs)-assisted edge computing enables resource-limited mobile devices (MDs) to handle computation and resource-intensive applications. In mobile edge computing networks (MECNs), UAVs extend coverage to remote MDs that are beyond the range of terrestrial edge servers (TESs). However, the key challenge in UAV-assisted MEC systems lies in ensuring reliable task execution within strict deadlines while simultaneously minimizing delay and energy consumption without overloading limited edge resources. For mission-critical applications, it is crucial to ensure that all tasks are executed by TESs within their deadlines. Additionally, minimizing delay and energy consumption and achieving load balancing are crucial while maximizing the task completion rate (TCR). This paper introduces an MECN that integrates UAVs as the aerial layer and TESs as the terrestrial layer. A quantum-inspired particle swarm optimization-based offloading strategy (QIPSO-TOS) is proposed to facilitate coverage-aware task offloading. Quantum particles (QPs) provide a complete and valid offloading solution, with a hashing-based decoding of QPs to map tasks either directly to TESs or through a relay UAV. The fitness function incorporates TCR, energy consumption, delay, and load balancing. The design of experiment is conducted using the Taguchi method. Extensive simulations across various scenarios, followed by statistical analysis, demonstrate that QIPSO-TOS outperforms existing strategies, achieving an average of 74.06% TCR and 1.61 times better load balancing.