Controlling Packet Drops to Improve Freshness of Information

Kavitha, Veeraruna; Altman, Eitan

doi:10.1007/978-3-030-87473-5_7

Cited by 10 publications

(18 citation statements)

References 17 publications

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“…In addition, since the system becomes empty after the occurrence of this transition, the second component of the age vector x(t) becomes irrelevant, and thus its corresponding value in the updated age vector xA l is 0. Now, recall that in order to use (8) to derive the MGF of AoI at the destination, one needs to find a non-negative limit vq , ∀q ∈ Q, satisfying (6). It can be shown that this condition holds for all of the considered queueing disciplines studied in this paper by solving the set of equations in (6).…”

Section: A Eh Is Only Allowed When the System Is Emptymentioning

confidence: 92%

“…In [6], the average peak AoI was also analyzed for several queueing disciplines when a status update delivery error may occur probabilistically. The authors of [7]- [9] demonstrated that the achievable average values of AoI and peak AoI could be improved by: i) introducing deadlines for status updates waiting in the queue for service [7], ii) controlling status update drops (when arriving status updates find the server busy) [8], and iii) introducing a waiting duration before service (for the M/G/1 queue under LCFS-WP and LCFS-PW) [9]. The average AoI and average peak AoI were also analyzed for various discrete time queueing systems in [10] under FCFS and LCFS disciplines.…”

Section: A Related Workmentioning

confidence: 99%

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Closed-form Characterization of the MGF of AoI in Energy Harvesting Status Update Systems

Abd-Elmagid¹,

Dhillon²

2021

Preprint

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This paper considers a real-time status update system in which an energy harvesting (EH)-powered transmitter node observes some physical process, and sends its sensed measurements in the form of status updates to a destination node. The status update and harvested energy packets are assumed to arrive at the transmitter according to independent Poisson processes, and the service time of each status update is assumed to be exponentially distributed. We quantify the freshness of status updates when they reach the destination using the concept of Age of Information (AoI). Unlike most of the existing analyses of AoI focusing on the evaluation of its average value when the transmitter is not subject to energy constraints, our analysis is focused on understanding the distributional properties of AoI through the characterization of its moment generating function (MGF). In particular, we use the stochastic hybrid systems (SHS) framework to derive closed-form expressions of the MGF of AoI under several queueing disciplines at the transmitter, including non-preemptive and preemptive in service/waiting strategies. Using these MGF results, we further obtain closed-form expressions for the first and second moments of AoI in each queueing discipline. We demonstrate the generality of this analysis by recovering several existing results for the corresponding system with no energy constraints as special cases of the new results.Our numerical results verify the analytical findings, and demonstrate the necessity of incorporating the higher moments of AoI in the implementation/optimization of real-time status update systems rather than just relying on its average value.

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Section: A Eh Is Only Allowed When the System Is Emptymentioning

confidence: 92%

Section: A Related Workmentioning

confidence: 99%

Closed-form Characterization of the MGF of AoI in Energy Harvesting Status Update Systems

Abd-Elmagid¹,

Dhillon²

2021

Preprint

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“…From ( 7) and (10), when the first element of the continuous state x(t) represents the AoI at the destination node, the expectation and the MGF of AoI at the destination node respectively converge to:…”

Section: Problem Statement and Solution Approachmentioning

confidence: 99%

Age of Information in Multi-source Updating Systems Powered by Energy Harvesting

Abd-Elmagid¹,

Dhillon²

2021

Preprint

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This paper considers a multi-source real-time updating system in which an energy harvesting (EH)powered transmitter node has multiple sources generating status updates about several physical processes.The status updates are then sent to a destination node where the freshness of each status update is measured in terms of Age of Information (AoI). The status updates of each source and harvested energy packets are assumed to arrive at the transmitter according to independent Poisson processes, and the service time of each status update is assumed to be exponentially distributed. Unlike most of the existing queueing-theoretic analyses of AoI that focus on characterizing its average when the transmitter has a reliable energy source and is hence not powered by EH (referred henceforth as a non-EH transmitter), our analysis is focused on understanding the distributional properties of AoI in multi-source systems through the characterization of its moment generating function (MGF). In particular, we use the stochastic hybrid systems (SHS) framework to derive closed-form expressions of the average/MGF of AoI under several queueing disciplines at the transmitter, including non-preemptive and source-agnostic/sourceaware preemptive in service strategies. The generality of our results is demonstrated by recovering several existing results as special cases.

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“…Age-dependent preemptive policies have been studied in [22]- [24] for various system settings. It is shown in [22] that within a class of stationary Markov policies where the decision only depends on the instantaneous AoI at the destination, persistent policies such as to always drop the new update or the old update is optimal for certain service time distributions. Our setup is different from [22] in the sense that: 1) the service time (i.e., transmission time of each update) has a specific distribution that is not captured in [22].…”

Section: Introductionmentioning

confidence: 99%

“…It is shown in [22] that within a class of stationary Markov policies where the decision only depends on the instantaneous AoI at the destination, persistent policies such as to always drop the new update or the old update is optimal for certain service time distributions. Our setup is different from [22] in the sense that: 1) the service time (i.e., transmission time of each update) has a specific distribution that is not captured in [22]. 2) Our policy is not only dependent on the AoI at the destination, but also on the age of the unfinished update and the number of successfully delivered symbols of it.…”

Section: Introductionmentioning

confidence: 99%

Age-Optimal Transmission of Rateless Codes in an Erasure Channel

Feng

Yang

2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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In this paper, we examine a status updating system where updates generated by the source are sent to the monitor through an erasure channel. We assume each update consists of k symbols and the symbol erasure in each time slot follows an independent and identically distributed (i.i.d.) Bernoulli process. We assume rateless coding scheme is adopted at the transmitter and an update can be successfully decoded if k coded symbols are received successfully. We assume perfect feedback available at the source, so that it knows whether a transmitted symbol has been erased instantly. Then, at the beginning of each time slot, the source has the choice to start transmitting a new update, or continue with the transmission of the previous update if it is not delivered yet. We adopt the metric "Age of Information" (AoI) to measure the freshness of information at the destination, where the AoI is defined as the age of the latest decoded update at the destination. Our objective is to design an optimal online transmission scheme to minimize the time-average AoI. The transmission decision is based on the instantaneous AoI, the age of the update being transmitted, as well as the number of successfully delivered symbols of the update. We formulate the problem as a Markov Decision Process (MDP) and identify the monotonic threshold structure of the optimal policy. Numerical results corroborate the structural properties of the optimal solution.Index Terms-Age of information, rateless codes, Markov Decision Process (MDP).

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Controlling Packet Drops to Improve Freshness of Information

Cited by 10 publications

References 17 publications

Closed-form Characterization of the MGF of AoI in Energy Harvesting Status Update Systems

Closed-form Characterization of the MGF of AoI in Energy Harvesting Status Update Systems

Age of Information in Multi-source Updating Systems Powered by Energy Harvesting

Age-Optimal Transmission of Rateless Codes in an Erasure Channel

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