2021
DOI: 10.1109/jiot.2021.3053768
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Discrete-Time Queueing Model of Age of Information With Multiple Information Sources

Abstract: Information freshness in IoT-based status update systems has recently been studied through the Age of Information (AoI) and Peak AoI (PAoI) performance metrics. In this article, we study a discrete-time server arising in multisource IoT systems, which accepts incoming information packets from multiple information sources so as to be forwarded to a remote monitor for status update purposes. Under the assumption of Bernoulli information packet arrivals and a common general discrete phase-type service time distri… Show more

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Cited by 55 publications
(24 citation statements)
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References 32 publications
(78 reference statements)
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“…The average peak AoI was derived for the M/G/1 lastcome-first-served (LCFS) queueing model with (without) preemption in service in [23] (in [24]), and for the priority FCFS and LCFS queueing models (where the sources of information are prioritized at the transmitter) in [25]. Further, the distributions of AoI and PAoI were numerically characterized for various discrete time queues in [26], and for a probabilistically preemptive queueing model in [27] where a new arriving status update preempts the one in service with some probability. Different from [5]- [27], our focus in this paper is on the analytical characterization of distributional properties of AoI in the case where the transmitter has multiple sources of information and is powered by EH.…”
Section: A Related Workmentioning
confidence: 99%
“…The average peak AoI was derived for the M/G/1 lastcome-first-served (LCFS) queueing model with (without) preemption in service in [23] (in [24]), and for the priority FCFS and LCFS queueing models (where the sources of information are prioritized at the transmitter) in [25]. Further, the distributions of AoI and PAoI were numerically characterized for various discrete time queues in [26], and for a probabilistically preemptive queueing model in [27] where a new arriving status update preempts the one in service with some probability. Different from [5]- [27], our focus in this paper is on the analytical characterization of distributional properties of AoI in the case where the transmitter has multiple sources of information and is powered by EH.…”
Section: A Related Workmentioning
confidence: 99%
“…Recently, the mgf of AoI has been studied in [21] whereas a similar work derives the mgf of the AoI of each source in a bufferless multi-source status update system using global preemption [33]. Another recent work studies non-preemptive bufferless, globally preemptive bufferless, and single buffer with replacement policies in discrete-time for a multi-source status update system and derives the exact distributions of AoI and PAoI [34].…”
Section: Related Workmentioning
confidence: 99%
“…In the recent work [17], the authors investigate a single source discrete-time system for FCFS, preemptive LCFS, and bufferless (with packet dropping) queueing schemes and they derive closed-form expressions for the generating functions and the stationary distributions of the AoI and the PAoI. In another recent work [18], the authors study a discrete time multi-source system using the instrument of QBD Markov chains and obtain the exact distribution of per-source AoI and PAoI for systems with non-preemptive bufferless, preemptive bufferless and non-preemptive single buffer with replacement queueing disciplines.…”
Section: Related Workmentioning
confidence: 99%
“…IV. MARGINAL PMFS OF THE AOI AND PAOI SEQUENCES Inspired by [18], in order to derive the pmfs p u∆ (n) and p uΛ (n) (u ∈ {S, R}), we model the journey of each packet (originated by node u) from the instance of its generation and then its successful transmission to D (the beginning of the age cycle of the packet), till the instant at which the next packet is received successfully at D (when the age cycle ends), by a QBD-type infinite Markov chain X = (L , P ), = 1, 2, . .…”
Section: System Modelmentioning
confidence: 99%