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<p>Intelligent mining demands real-time processing of UAV sensor streams under latency, safety, and energy constraints. We study a dual-edge architecture in which a ground base station (BS) and an aerial edge server (AES) collaboratively serve aerial users (FDMA), while a protective jammer UAV adapts its trajectory subject to speed/acceleration limits and rotary-wing propulsion. The system models 3GPP A2G channels (probabilistic LoS/NLoS and state-conditioned fading) and passive ground-eavesdroppers with bounded location uncertainty. We formulate a robust energy-efficiency maximization that epigraphs worst-case eavesdropper rates, introduces secrecy-QoS slack variables for feasibility, and enforces slot-level task causality. The fractional objective is handled via Dinkelbach, and three-block BCD solves the problem with conservative SCA surrogates; a micro-AO resolves the bi-convex throughput epigraph in the radio block, and the trajectory block uses affine secrecy bounds plus an SOC treatment of induced power in the rotary-wing model. Under 3GPP Urban Macro calibration, the proposed scheme attains 60.5 kbits/J at <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(U=10\)</EquationSource></InlineEquation>, exceeding STRO by 22.2% and SHJ by 116.1%. With eavesdropper uncertainty <InlineEquation ID="IEq2"><EquationSource Format="TEX">\(\rho _\ell =15\)</EquationSource></InlineEquation>&#xa0;m, energy efficiency degrades only 13.1%, whereas non-robust CENR collapses to 12.0 kbits/J (76.9% drop). The optimized jammer path is 1250&#xa0;m (vs. 1131&#xa0;m straight line; +10.5%) to secure stronger jamming geometry; propulsion dominates the energy budget at 4800&#xa0;J (86.8% of total), while SLT despite saving 6.3% propulsion energy, incurs a 730.9% increase in RF jamming to 2742&#xa0;J. The algorithm runs in 1.95&#xa0;s/iteration on average (trajectory-frozen: 0.98&#xa0;s/iteration), converges within 5–7 iterations, and captures 85% of its total gain in the first 3 iterations–validating near-real-time feasibility for secure, energy-efficient offloading in mining operations.</p>

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Joint UAV trajectory and offloading optimization with robust secrecy for intelligent mining

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月30日

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  • Abdulbasit A. Darem,
  • Khalid Almalki,
  • Abdulrahman Alzahrani,
  • Khalid N. R. Alharbi,
  • Asma A. Alhashmi,
  • Laith A. Darem

摘要

Intelligent mining demands real-time processing of UAV sensor streams under latency, safety, and energy constraints. We study a dual-edge architecture in which a ground base station (BS) and an aerial edge server (AES) collaboratively serve aerial users (FDMA), while a protective jammer UAV adapts its trajectory subject to speed/acceleration limits and rotary-wing propulsion. The system models 3GPP A2G channels (probabilistic LoS/NLoS and state-conditioned fading) and passive ground-eavesdroppers with bounded location uncertainty. We formulate a robust energy-efficiency maximization that epigraphs worst-case eavesdropper rates, introduces secrecy-QoS slack variables for feasibility, and enforces slot-level task causality. The fractional objective is handled via Dinkelbach, and three-block BCD solves the problem with conservative SCA surrogates; a micro-AO resolves the bi-convex throughput epigraph in the radio block, and the trajectory block uses affine secrecy bounds plus an SOC treatment of induced power in the rotary-wing model. Under 3GPP Urban Macro calibration, the proposed scheme attains 60.5 kbits/J at \(U=10\), exceeding STRO by 22.2% and SHJ by 116.1%. With eavesdropper uncertainty \(\rho _\ell =15\) m, energy efficiency degrades only 13.1%, whereas non-robust CENR collapses to 12.0 kbits/J (76.9% drop). The optimized jammer path is 1250 m (vs. 1131 m straight line; +10.5%) to secure stronger jamming geometry; propulsion dominates the energy budget at 4800 J (86.8% of total), while SLT despite saving 6.3% propulsion energy, incurs a 730.9% increase in RF jamming to 2742 J. The algorithm runs in 1.95 s/iteration on average (trajectory-frozen: 0.98 s/iteration), converges within 5–7 iterations, and captures 85% of its total gain in the first 3 iterations–validating near-real-time feasibility for secure, energy-efficient offloading in mining operations.