Auctioning Future Services in Edge Networks with Moving Vehicles: N-Step Look-Ahead Contracts for Sustainable Resource Provision

2025-10-06

AI Summary

To address the spatiotemporal uncertainty of vehicular resource demands induced by high mobility and the excessive latency of real-time double auctions in edge-assisted vehicular networks, this paper proposes a prediction-driven auction framework based on *N*-step lookahead contracts. It innovatively introduces forward contract mechanisms into mobile edge network resource allocation, integrating LSTM-based multi-slot demand forecasting with role pre-assignment to enable proactive decision-making. An enhanced double auction mechanism—incorporating explicit pricing rules and违约 penalty clauses—is designed to establish a sustainable trading model that jointly optimizes energy consumption, transmission overhead, and违约 risk. The framework operates in two phases: prediction-and-contract formulation, followed by real-time execution. Extensive experiments on both real-world and synthetic vehicle trajectory datasets demonstrate that our approach significantly improves time efficiency and social welfare, reduces system energy consumption, and enhances resource allocation stability compared to state-of-the-art baselines.

Technology Category

• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Mechanism Design

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Timely resource allocation in edge-assisted vehicular networks is essential for compute-intensive services such as autonomous driving and navigation. However, vehicle mobility leads to spatio-temporal unpredictability of resource demands, while real-time double auctions incur significant latency. To address these challenges, we propose a look-ahead contract-based auction framework that shifts decision-making from runtime to planning time. Our approach establishes N-step service contracts between edge servers (ESs) using demand forecasts and modified double auctions. The system operates in two stages: first, an LSTM-based prediction module forecasts multi-slot resource needs and determines ES roles (buyer or seller), after which a pre-double auction generates contracts specifying resource quantities, prices, and penalties. Second, these contracts are enforced in real time without rerunning auctions. The framework incorporates energy costs, transmission overhead, and contract breach risks into utility models, ensuring truthful, rational, and energy-efficient trading. Experiments on real-world (UTD19) and synthetic traces demonstrate that our method improves time efficiency, energy use, and social welfare compared with existing baselines.

Problem

• Addressing unpredictable resource demands from vehicle mobility
• Reducing latency in real-time double auction mechanisms
• Ensuring sustainable resource provision through look-ahead contracts

Innovation

• N-step look-ahead contracts for resource allocation
• LSTM-based prediction for multi-slot demand forecasting
• Pre-double auction with penalty-enforced contract execution