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

TL;DR

This work tackles truthful reporting in IoT energy markets where exact VCG allocation is impractical due to edge computation limits and noisy monitoring. It introduces an immediate one-shot penalty mechanism for an $\alpha$-approximate VCG double auction, proving that a penalty exceeding $\frac{1-\alpha}{\rho}C$ guarantees truthful equilibrium under imperfect detection. The framework is integrated with a multi-agent reinforcement learning environment to validate theoretical predictions, showing that higher allocation accuracy ($\alpha$) and better monitoring ($\rho$) reduce incentives to misreport, while learned bidding aligns with the theory. The results offer a scalable, interpretable approach to trustworthy IoT AI agents in peer-to-peer energy trading, compatible with edge computing constraints and real-time operation.

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.

Figures (5)

• Figure 1: Overall architecture of the proposed $\alpha$-approximate VCG double-auction mechanism with immediate penalty in a P2P IoT energy-trading environment. Autonomous prosumer agents map local observations to bids via MARL policies. The mechanism computes the approximate VCG allocation, detects significant misreports under noisy IoT sensing, and applies penalties. Resulting allocations and payments update the physical grid state and battery SoCs.
• Figure 2: Truthfulness region in the $(\alpha,\varepsilon)$ plane (Plan A). Warm colors indicate high $\varepsilon$-truthful fraction. The sharp phase transition near $\alpha\approx 0.7$ matches the theoretical threshold at which $(1-\alpha)C$ becomes small enough for the fixed penalty to dominate.
• Figure 3: Impact of penalty $\Pi$ and discount factor $\gamma$ (Plan B). Higher penalties eliminate deviating equilibria, while higher $\gamma$ accelerates convergence by amplifying the immediate penalty signal in PPO updates.
• Figure 4: Minimal penalty threshold $\Pi^\star(\alpha,\varepsilon)$ (Plan C). Darker colors denote higher penalties. The monotonic decline with $\alpha$ and $\varepsilon$ matches the theoretical scaling $\Pi^\star\propto(1-\alpha)/\rho$.
• Figure 5: Robustness of truthful convergence under different RL hyper-parameters (Plan D). Moderate entropy values avoid premature collapse while maintaining convergence speed. Architectural variations have minimal effect.

Theorems & Definitions (4)

• Lemma 1: Bounded incentive gap
• proof
• Theorem 1: Truthful equilibrium under immediate penalty
• proof