Performance Guarantees for Data Freshness in Resource-Constrained Adversarial IoT Systems

TL;DR

This paper analyzes how resource-limited adversaries impact data freshness in IoT systems by modeling queue dynamics and deriving bounds on age of information, providing insights into system resilience against attacks.

Contribution

It introduces a novel G-queue based model for adversarial IoT systems, deriving closed-form AoI expressions and bounds under resource-constrained attacks.

Findings

Adversarial interference significantly increases AoI in IoT systems.

The derived bounds help quantify worst-case data freshness degradation.

Numerical results confirm the effectiveness of the analytical models.

Abstract

Timely updates are critical for real-time monitoring and control applications powered by the Internet of Things (IoT). As these systems scale, they become increasingly vulnerable to adversarial attacks, where malicious agents interfere with legitimate transmissions to reduce data rates, thereby inflating the age of information (AoI). Existing adversarial AoI models often assume stationary channels and overlook queueing dynamics arising from compromised sensing sources operating under resource constraints. Motivated by the G-queue framework, this paper investigates a two-source M/G/1/1 system in which one source is adversarial and disrupts the update process by injecting negative arrivals according to a Poisson process and inducing i.i.d. service slowdowns, bounded in attack rate and duration. Using moment generating functions, we then derive closed-form expressions for average and peak AoI for an arbitrary number of sources. Moreover, we introduce a worst-case constrained attack model and employ stochastic dominance arguments to establish analytical AoI bounds. Numerical results validate the analysis and highlight the impact of resource-limited adversarial interference under general service time distributions.