Modality-Tailored Age of Information for Multimodal Data in Edge Computing Systems

TL;DR

MAoI introduces modality-aware freshness by incorporating content dynamics and semantic importance across image, audio, and other signals. It derives a closed-form expression for the average MAoI and formulates an energy-constrained optimization to minimize MAoI, solved via the Joint Sampling Offloading Optimization (JSO) algorithm that alternates sampling interval updates and interference-aware offloading with Lagrange multiplier updates. The approach demonstrates that MAoI better captures multimodal freshness than AoI and that JSO achieves substantial MAoI reductions compared to baselines while respecting per-device energy budgets. The results support practical deployment of modality-tailored freshness metrics to guide resource allocation in MEC-based multimodal edge intelligence.

Abstract

As Internet of Things (IoT) systems scale and device heterogeneity grows, multimodal data have become ubiquitous. Meanwhile, evaluating the freshness of multimodal data is essential, as stale updates would delay task execution, degrade decision accuracy, and undermine safety in latency-sensitive services. However, existing freshness metrics such as Age of Information (AoI) are not suitable for multimodal data, as they do not capture modality-specific characteristics. In this paper, we propose a metric, namely, Modality-Tailored Age of Information (MAoI), to provide a unified and decision-relevant evaluation of freshness for resource management and policy optimization for multimodal data. This metric integrates modality-specific semantic and temporal characteristics, reflecting both age evolution and content importance for multimodal data in multi-access edge computing (MEC) systems. Then, the closed-form expression of the average MAoI is derived, and an MAoI minimization problem is formulated, where sampling intervals and offloading decisions are optimized with practical energy constraints. To effectively solve this problem, a Joint Sampling Offloading Optimization (JSO) algorithm is proposed to jointly optimize the sampling intervals and offloading decisions. It is a block coordinate descent-based algorithm where an optimal sampling-interval subalgorithm is used to update the sampling intervals, and an interference-aware best-response offloading subalgorithm is proposed to update the offloading decisions alternately. Finally, a comprehensive simulation is performed, confirming that the MAoI metric effectively quantifies multimodal freshness compared to traditional AoI, and the JSO algorithm significantly minimizes the average MAoI compared to state-of-the-art algorithms.

Figures (11)

• Figure 1: System Model. IoT devices with heterogeneous sensors generate multimodal updates, including image, audio, and other signal modalities. These updates are either processed locally or offloaded to a BS and executed at the MEC server. Processed results from the local processor or the MEC server are forwarded to the cloud. For local computing, the system time consists of sensing time, waiting time due to limited computing resources, and local computation time, with energy consumption mainly from computation. If the computation updates are offloaded to the MEC server, the system time consists of sensing time, transmission time, and edge computation time (in parallel across modalities), with energy consumption mainly from transmission.
• Figure 2: An example of the evolution of MAoI for the image modality.
• Figure 3: System average MAoI vs. the number of devices for JSO and JSO-A under different energy budgets
• Figure 4: System average AoI vs. the number of devices for JSO and JSO-A under different energy budgets
• Figure 5: Per-modality and system average MAoI increments vs. the audio modality weight increment ($\Delta\Psi_{d,2}$) under fixed $D$ and $E_d^{\max}$.

Theorems & Definitions (6)

• Definition 1
• Definition 2
• Lemma 1
• proof
• Corollary 1
• proof