Information Aging Through Queues: A Mutual Information Perspective

Sun, Yin; Cyr, Benjamin

doi:10.1109/spawc.2018.8445873

Cited by 86 publications

(64 citation statements)

References 18 publications

(57 reference statements)

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“…This paper was presented in part at IEEE SPAWC 2018 [1]. This work was supported in part by NSF grant CCF-1813050 and ONR grant N00014-17-1-2417.…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, the penalty for data staleness is denoted by a non-decreasing function p(∆ t ) of ∆ t . The sampler design problem for optimizing these data freshness metrics, possibly with 1 A car will travel 15 meters during 0.5 seconds at the speed of 70 mph. 2 To the best of our knowledge, the issue that "the actual age ∆t is not a good representation of freshness" was firstly pointed out by Anthony Ephremides in one presentation at the Information Theory and Application (ITA) Workshop in 2015. a sampling rate constraint, is considered.…”

Section: Introductionmentioning

confidence: 99%

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Sampling for data freshness optimization: Non-linear age functions

2019

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In this paper, we study how to take sample at a data source for improving the freshness of received data samples at a remote receiver. We use non-linear functions of the age of information to measure data freshness, and provide a survey of non-linear age functions and their applications. The sampler design problem for optimizing these data freshness metrics, possibly with a sampling rate constraint, is studied. This sampling problem is formulated as a constrained Markov decision process (MDP) with a possibly uncountable state space. We present a complete characterization of the optimal solution to this MDP: The optimal sampling policy is a deterministic or randomized threshold policy, where the threshold and the randomization probabilities are characterized based on the optimal objective value of the MDP and the sampling rate constraint. The optimal sampling policy can be computed by bisection search, and the curse of dimensionality is circumvented. These age optimality results hold for (i) general data freshness metrics represented by monotonic functions of the age of information, (ii) general service time distributions of the queueing server, (iii) both continuoustime and discrete-time sampling problems, and (iv) sampling problems both with and without the sampling rate constraint. Numerical results suggest that the optimal sampling policies can be much better than zero-wait sampling and the classic uniform sampling.Index Terms-Age of information, data freshness, Markov decision process, sampling.

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“…This paper was presented in part at IEEE SPAWC 2018 [1]. This work was supported in part by NSF grant CCF-1813050 and ONR grant N00014-17-1-2417.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sampling for data freshness optimization: Non-linear age functions

2019

Self Cite

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“…In [22], the authors investigated the connection between information age and what they called effective age, which is closely related with the structure information and the pattern of sampling, and is minimized when the prediction error is minimized. Furthermore, the mutual information between received samples and source signals was used to measure the freshness of received samples in [23].…”

Section: Related Workmentioning

confidence: 99%

Age-Upon-Decisions Minimizing Scheduling in Internet of Things: To Be Random or To Be Deterministic?

Dong

Chen

Liu

et al. 2020

IEEE Internet Things J.

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We consider an Internet of Things (IoT) system in which a sensor delivers updates to a monitor with exponential service time and first-come-first-served (FCFS) discipline. We investigate the freshness of the received updates and propose a new metric termed as Age upon Decisions (AuD), which is defined as the time elapsed from the generation of each update to the epoch it is used to make decisions (e.g., estimations, inferences, controls). Within this framework, we aim at improving the freshness of updates at decision epochs by scheduling the update arrival process and the decision making process. Theoretical results show that 1) when the decisions are made according to a Poisson process, the average AuD is independent of decision rate and would be minimized if the arrival process is periodic (i.e., deterministic); 2) when both the decision process and the arrive process are periodic, the average AuD is larger than, but decreases with decision rate to, the average AuD of the corresponding system with Poisson decisions (i.e., random); 3) when both the decision process and the arrive process are periodic, the average AuD can be further decreased by optimally controlling the offset between the two processes. For practical IoT systems, therefore, it is suggested to employ periodic arrival processes and random decision processes. Nevertheless, making periodical updates and decisions with properly controlled offset also is a promising solution if the timing information of the two processes can be accessed by the monitor.Index Terms-Age of information, age upon decisions, Internet of Things, update-and-decide systems, decision scheduling.

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“…In [42], [43], a so called age penalty/utility function was employed to describe the level of dissatisfaction for having aged status updates at the destination. In [44], the authors use the mutual information between the real-time source value and the delivered samples at the receiver to quantify the freshness of the information contained in the delivered samples. In [45], an incremental update scheme for real time status updates which exploits temporal correlation between consecutive messages is considered.…”

Section: Introductionmentioning

confidence: 99%

The Cost of Delay in Status Updates and Their Value: Non-Linear Ageing

Kosta

Παππάς

Ephremides

et al. 2020

IEEE Trans. Commun.

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We consider a status update communication system consisting of a source-destination link. A stochastic process is observed at the source, where samples are extracted at random time instances, and delivered to the destination, thus, providing status updates for the source. In this paper, we expand the concept of information ageing by introducing the cost of update delay (CoUD) metric to characterize the cost of having stale information at the destination. The CoUD captures the freshness of the information at the destination and can be used to reflect the information structure of the source. Moreover, we introduce the value of information of update (VoIU) metric that captures the reduction of CoUD upon reception of an update. Using the CoUD, its by-product metric called peak cost of update delay (PCoUD), and the VoIU, we evaluate the performance of an M/M/1 system in various settings that consider exact expressions and bounds. Our results indicate that the performance of CoUD differs depending on the cost assigned per This work extends the preliminary study in [1]. A. Kosta, N. Pappas, A. Ephremides, and V. Angelakis are with the

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Information Aging Through Queues: A Mutual Information Perspective

Cited by 86 publications

References 18 publications

Sampling for data freshness optimization: Non-linear age functions

Sampling for data freshness optimization: Non-linear age functions

Age-Upon-Decisions Minimizing Scheduling in Internet of Things: To Be Random or To Be Deterministic?

The Cost of Delay in Status Updates and Their Value: Non-Linear Ageing

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