Beyond Age: Urgency of Information for Timeliness Guarantee in Status Update Systems

Abstract

Timely status updating is crucial for future applications that involve remote monitoring and control, such as autonomous driving and Industrial Internet of Things (IIoT). Age of Information (AoI) has been proposed to measure the freshness of status updates. However, it is incapable of capturing critical systematic context information that indicates the timevarying importance of status information, and the dynamic evolution of status. In this paper, we propose a context-based metric, namely the Urgency of Information (UoI), to evaluate the timeliness of status updates. Compared to AoI, the new metric incorporates both time-varying context information and dynamic status evolution, which enables the analysis on context-based adaptive status update schemes, as well as more effective remote monitoring and control. The minimization of average UoI for a status update terminal with an updating frequency constraint is investigated, and an update-index-based adaptive scheme is proposed. Simulation results show that the proposed scheme achieves a near-optimal performance with a low computational complexity.

A UoI-Optimal Policy for Timely Status Updates with Resource Constraint

“…However, the increment in estimation error during a single slot cannot be infinite in practical systems. Therefore, in contrast to [27,30], we assumed that increment A(t) obeys a truncated Gaussian distribution, i.e., the probability density function (PDF) of A(t) is:…”

“…However, the timeliness metrics mentioned above pay no attention to the significance of context information. To solve this problem, Urgency of Information (UoI) has been proposed in [26][27][28] to measure the influence of inaccurate information on performance under different contexts. To be specific, UoI uses a time-variant context-aware weight ω(t) to distinguish different contexts.…”

“…To solve this problem, Refs. [27,30] proposed update-indexbased adaptive schemes with Lyapunov optimization but did not conduct a theoretical analysis of their optimality. In addition, the constrained Markov decision process (CMDP) formulation was only used in the simulation for a numerically solved benchmark.…”

“…• In contrast to [27,30], we assumed that the context-aware weight is a first-order irreducible positive recurrent Markov process or independent and identically distributed (i.i.d.) over time.…”

Space-air-ground integrated vehicular network for connected and automated vehicles: Challenges and solutions

“…To this end, to measure the timeliness of status updates in remote control systems, we propose a new metric called the Urgency of Information (UoI) (previously the contextaware information lapse in Refs. [60,61]), which is defined as the product of weight !.t / and cost ı.Q.t //:…”