Truthful and Trustworthy IoT AI Agents via Immediate-Penalty Enforcement under Approximate VCG Mechanisms

TL;DR

This paper proposes a trust-enforcement mechanism for IoT energy trading that ensures truthful reporting using an approximate VCG auction combined with immediate penalties, even under noisy monitoring and strategic behavior.

Contribution

It introduces a novel immediate-penalty enforcement framework integrated with approximate VCG mechanisms to promote truthful bidding in IoT energy systems under imperfect information.

Findings

The mechanism guarantees truthful bidding within a single round despite approximation errors.

Optimal penalty thresholds are derived to counteract sensing noise and strategic manipulation.

Experimental results show stable, interpretable bidding behaviors with improved allocation accuracy.

Abstract

The deployment of autonomous AI agents in Internet of Things (IoT) energy systems requires decision-making mechanisms that remain robust, efficient, and trustworthy under real-time constraints and imperfect monitoring. While reinforcement learning enables adaptive prosumer behaviors, ensuring economic consistency and preventing strategic manipulation remain open challenges, particularly when sensing noise or partial observability reduces the operator's ability to verify actions. This paper introduces a trust-enforcement framework for IoT energy trading that combines an approximate Vickrey-Clarke-Groves (VCG) double auction with an immediate one-shot penalty. Unlike reputation- or history-based approaches, the proposed mechanism restores truthful reporting within a single round, even when allocation accuracy is approximate and monitoring is noisy. We theoretically characterize the incentive gap induced by approximation and derive a penalty threshold that guarantees truthful bidding under bounded sensing errors. To evaluate learning-enabled prosumers, we embed the mechanism into a multi-agent reinforcement learning environment reflecting stochastic generation, dynamic loads, and heterogeneous trading opportunities. Experiments show that improved allocation accuracy reduces deviation incentives, the required penalty matches analytical predictions, and learned bidding behaviors remain stable and interpretable despite imperfect monitoring. These results demonstrate that lightweight penalty designs can reliably align strategic IoT agents with socially efficient energy-trading outcomes.