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

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 [Formula: see text], exceeding STRO by 22.2% and SHJ by 116.1%. With eavesdropper uncertainty [Formula: see text] 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.