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This paper introduces Look One-Step Ahead (LOSA), a framework for privacy-aware service provisioning in air-ground integrated networks that decomposes the process into a privacy-aware look-ahead phase and a lightweight real-time execution phase. LOSA uses a double auction mechanism with trajectory similarity clustering to establish binding one-step-ahead agreements, balancing trajectory exposure and matching accuracy. Experiments on real-world datasets demonstrate that LOSA achieves superior privacy protection and lower transaction latency compared to baselines.
Coordinating UAVs and ground vehicles for on-demand edge services doesn't have to sacrifice privacy or efficiency: LOSA's look-ahead mechanism achieves both.
In air-ground integrated networks (AGINs), unmanned aerial vehicles (UAVs) provide on-demand edge services to ground vehicles. Realizing this vision requires carefully designed incentives to coordinate interactions among self-interested participants. This is exacerbated by the dynamic nature of AGINs, where spatio-temporal variations introduce significant uncertainty in matching UAVs and vehicles. Existing real-time service provisioning typically relies on precise trajectory information, raising privacy concerns and incurring decision latency. To address these challenges, we propose look one-step ahead (LOSA), a novel framework for efficient and privacy-aware service provisioning. By exploiting predictable vehicle travel times between intersections, LOSA decomposes the process into two coupled phases: (i) a privacy-aware look-ahead phase and (ii) a lightweight real-time execution phase. The look-ahead phase allows vehicles to adaptively adjust privacy budgets based on historical utility, balancing trajectory exposure and matching accuracy. Leveraging this, a double auction mechanism establishes binding one-step-ahead agreements (OSAAs) through trajectory similarity clustering, while constructing preference lists to hedge against mobility uncertainty. The execution phase then enforces pre-established OSAAs and preference lists, resolving real-time resource conflicts without costly re-negotiations. This design reduces computational overhead and preserves robustness. We analytically corroborate that LOSA guarantees truthfulness, individual rationality, and budget balance. Experiments on real-world datasets (DAIR-V2X, HighD, and RCooper) demonstrate that LOSA achieves superior privacy protection while lowering transaction latency compared to baseline approaches.
