Update or Wait: How to Keep Your Data Fresh

Abstract

Abstract-In this work, we study how to optimally manage the freshness of information updates sent from a source node to a destination via a channel. A proper metric for data freshness at the destination is the age-of-information, or simply age, which is defined as how old the freshest received update is since the moment that this update was generated at the source node (e.g., a sensor). A reasonable update policy is the zero-wait policy, i.e., the source node submits a fresh update once the previous update is delivered and the channel becomes free, which achieves the maximum throughput and the minimum delay. Surprisingly, this zero-wait policy does not always minimize the age. This counter-intuitive phenomenon motivates us to study how to optimally control information updates to keep the data fresh and to understand when the zero-wait policy is optimal. We introduce a general age penalty function to characterize the level of dissatisfaction on data staleness and formulate the average age penalty minimization problem as a constrained semiMarkov decision problem (SMDP) with an uncountable state space. We develop efficient algorithms to find the optimal update policy among all causal policies, and establish sufficient and necessary conditions for the optimality of the zero-wait policy. Our investigation shows that the zero-wait policy is far from the optimum if (i) the age penalty function grows quickly with respect to the age, (ii) the packet transmission times over the channel are positively correlated over time, or (iii) the packet transmission times are highly random (e.g., following a heavy-tail distribution).

Optimizing Information Freshness in Wireless Networks: A Stochastic Geometry Approach

“…The notion was originally conceived to maintain timely status update in a standard first-come-first-served (FCFS) queue [1]. Soon after that, a host of research has been conducted to investigate different schemes aimed at minimizing the AoI, whereas the results range from controlling the update generating policy [2]- [8], deploying last-come-first-served (LCFS) queue [9], to proactively discarding stale packets at the source node H. H. Yang and T. Q. S. Quek are with the Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore (e-mail: howard yang@sutd.edu.sg, tonyquek@sutd.edu.sg).…”

Context-Aware Information Lapse for Timely Status Updates in Remote Control Systems

“…2) If the estimation error Q(t) increases linearly when status information is not updated (i.e., A(t) = 1), and the weight ω(t) is time-invariant, the UoI is equivalent to the non-linear AoI defined in [6].…”

Analysis on Computation-Intensive Status Update in Mobile Edge Computing

“…A just-in-time policy was proposed in [7] to solve the problem according to the knowledge of the system state, for example, to generate messages only when the server is idle. The policy is also called zero-wait policy [8] or the work-conserving policy [9]. The authors in [8] also noticed that there can be better polices other than the zerowait policy in many scenarios.…”

“…The policy is also called zero-wait policy [8] or the work-conserving policy [9]. The authors in [8] also noticed that there can be better polices other than the zerowait policy in many scenarios. In [10], peak age was taken as a new measurement of the freshness of information because of its analytically convenience.…”