On the Minimum Achievable Age of Information for General Service-Time Distributions

Champati, Jaya Prakash; Avula, Ramana R.; Oechtering, Tobias J.; Gross, James

doi:10.1109/infocom41043.2020.9155261

Cited by 16 publications

(4 citation statements)

References 21 publications

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“…Note that the service-time distribution in the above example is simple enough to compute θ * analytically and use (14) to infer whether preemptions will be beneficial or not. In general, it is not straightforward to do so for any service-time distribution.…”

Section: When Are Preemptions Beneficial?mentioning

confidence: 99%

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Minimum Achievable Peak Age of Information Under Service Preemptions and Request Delay

Champati

Avula

Oechtering

et al. 2021

IEEE J. Select. Areas Commun.

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Section: When Are Preemptions Beneficial?mentioning

confidence: 99%

“…Therefore, it is important to verify first if preemptions are beneficial for a given service-time distribution. The conditions provided in (14) and Lemma 4 are potentially useful toward this end.…”

Section: Erlang Service-time Distributionmentioning

confidence: 99%

Minimum Achievable Peak Age of Information Under Service Preemptions and Request Delay

Champati

Avula

Oechtering

et al. 2021

IEEE J. Select. Areas Commun.

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“…In M/M/1, there is infinite buffer space for the packets with first come first serve discipline. The first two systems with no and limited buffer have been extensively studied recently [17]- [21] because it is known that limited buffering helps decrease AoI. We also study the M/M/1 system here because it was originally studied in [1] and our analysis enables a comparison of these new metrics with the AoI.…”

Section: System Modelmentioning

confidence: 99%

Overage and Staleness Metrics for Status Update Systems

Zou,

Zhang,

Wei

et al. 2021

Preprint

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Status update systems consist of sensors that take measurements of a physical parameter and transmit them to a remote receiver. Age of Information (AoI) has been studied extensively as a metric for the freshness of information in such systems with and without an enforced hard or soft deadline. In this paper, we propose three metrics for status update systems to measure the ability of different queuing systems to meet a threshold requirement for the AoI. The overage probability is defined as the probability that the age of the most recent update packet held by the receiver is larger than the threshold. The stale update probability is the probability that an update is stale, i.e., its age has exceeded the deadline, when it is delivered to the receiver. Finally, the average overage is defined as the time average of the overage (i.e., age beyond the threshold), and is a measure of the average "staleness" of the update packets held by the receiver. We investigate these metrics in three typical status update queuing systems -M/G/1/1, M/G/1/2 * , and M/M/1. Numerical results show the performances for these metrics under different parameter settings and different service distributions. The differences between the average overage and average AoI are also shown. Our results demonstrate that a lower bound exists for the stale update probability when the buffer size is limited. Further, we observe that the overage probability decreases and the stale update probability increases as the update arrival rate increases.

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“…Reference [17] is on peak AoI in data preprocessing based IoT networks. [18] provides a general optimality of threshold based schemes to preempt service. On another related research front, [19] introduces packet management to improve the average AoI at the monitoring node.…”

Section: Introductionmentioning

confidence: 99%

Timely Status Updating Through Intermittent Sensing and Transmission

Özel

2020

2020 IEEE International Symposium on Information Theory (ISIT)

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Consider an energy harvesting node where generation of a status update message takes non-negligible time due to sensing, computing and analytics operations performed before making update transmissions. The node has to harmonize its (re)transmission strategy with the sensing/computing. We call this general set of problems intermittent status updating. In this paper, we consider intermittent status updating through non-preemptive sensing/computing (S/C) and transmission (Tx) operations, each costing a single energy recharge of the node, through an erasure channel with (a) perfect channel feedback and (b) no channel feedback. The S/C time for each update is independent with a general distribution. The Tx queue has a single data buffer to save the latest packet generated after the S/C operation and a single transmitter where transmission time is deterministic. Once energy is harvested, the node has to decide whether to activate S/C to generate a new update or to (re)send the existing update (if any) to the receiver. We prove that when feedback is available average peak age of information (AoI) at the receiver is minimized by a thresholdbased policy that allows only young packets to be (re)sent or else generates a new update. We additionally propose window based and probabilistic retransmission schemes for both cases (a) and (b) and obtain closed form average peak AoI expressions. Our numerical results show average peak AoI performance comparisons and improvements.

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On the Minimum Achievable Age of Information for General Service-Time Distributions

Cited by 16 publications

References 21 publications

Minimum Achievable Peak Age of Information Under Service Preemptions and Request Delay

Minimum Achievable Peak Age of Information Under Service Preemptions and Request Delay

Overage and Staleness Metrics for Status Update Systems

Timely Status Updating Through Intermittent Sensing and Transmission

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