How Far Back in Time a Digital Twin Reflects the State of the Physical Object: Age of Staleness

TL;DR

This paper introduces the age of staleness (AoS) metric to quantify how current a digital twin’s state is relative to its physical object, addressing limitations of the age of information (AoI).

Contribution

It proposes the AoS metric, analyzes its properties for Markov sources, and formulates an optimization framework for sampling rate allocation in digital twin networks.

Findings

AoS decreases monotonically with sampling rate.

Closed-form AoS expression for symmetric Markov sources.

Near-optimal sampling allocation using the polyblock algorithm.

Abstract

The groundbreaking metric age of information (AoI) has been introduced to measure information freshness in communication networks. As transformational as it is, AoI metric falls short in some applications, such as remote monitoring, since it is a semantic-agnostic metric which does not consider the dynamics of the random process. There is a need to quantify the performance of a remote estimator via a metric that combines freshness and semantic aspects. To this end, in this paper, we introduce a novel metric coined age of staleness (AoS) that measures when the last time that the current estimation was correct. First, we analyze a simple scenario where an $n$-ary symmetric Markov source is observed by a monitor via a constant sampling rate, obtain a closed-form expression for the AoS, and show that it is a monotonically decreasing function of the sampling rate. Next, we consider multiple distinct Markov sources, and formulate an optimization problem, where the remote monitor allocates the total sampling rate to tracking the sources. Although the optimization problem is non-convex, its structure is suitable for obtaining a near-optimal solution using the polyblock algorithm, which leverages the monotonicity of the objective function. While the new AoS metric could be applicable in many scenarios, we believe it is particularly well-suited for a digital twin network (DTN) where multiple physical objects (POs) are monitored with a total sampling rate constraint to maintain a digital representation of them, namely, their digital twin (DT).