Look One Step Ahead: Forward-Looking Incentive Design with Strategic Privacy for Proactive Service Provisioning over Air-Ground Integrated Edge Networks

2026-04-15

AI Summary

This study addresses three key challenges in on-demand UAV-assisted services for ground vehicles within integrated aerial-terrestrial edge networks: dynamic matching uncertainty, trajectory privacy leakage, and real-time decision latency. To tackle these issues, the authors propose LOSA, a novel framework that synergistically combines trajectory similarity clustering with a double auction mechanism in a two-stage service provisioning architecture. In the lookahead phase, preference lists are generated using an adjustable differential privacy budget, while the real-time phase employs a lightweight one-step-ahead protocol to efficiently execute matches. Experimental evaluations on real-world datasets—DAIR-V2X, HighD, and RCooper—demonstrate that LOSA simultaneously achieves high fidelity, individual rationality, and budget balance, significantly enhancing privacy preservation and reducing transaction latency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

Abstract

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.

Problem

• Air-Ground Integrated Networks
• Privacy-Preserving Service Provisioning
• Incentive Design
• Trajectory Privacy
• Proactive Resource Allocation

Innovation

• forward-looking incentive design
• strategic privacy
• air-ground integrated networks
• double auction mechanism
• one-step-ahead agreement