Age of information under random updates

Kam, Clement; Kompella, Sastry; Ephremides, Anthony

doi:10.1109/isit.2013.6620189

Cited by 238 publications

(141 citation statements)

References 5 publications

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“…In this plot, packet 2 is shown to be served before packet 1, so the monitor recognizes packet 2 as its most recently generated packet. We refer to packet 2 as an informative packet and packet 1 as an obsolete packet, as in [2]. Note that for a two-server system, two consecutive packets cannot be made obsolete, since both packets will occupy both servers, and one of them must complete service prior to any future packets entering service.…”

Section: M/m/2 System Modelmentioning

confidence: 99%

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Effect of message transmission diversity on status age

Kam

Kompella

Ephremides

2014

2014 IEEE International Symposium on Information Theory

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We investigate the performance of a status monitoring system, in which a sensor sends random status updates over a network to a remote monitor. Specifically, we analyze the status age metric, which characterizes how old the information at the monitor is from the last received status update. The system on which we focus is a single queue with 2 servers (specifically, an M/M/2). In a dynamic network, different status packets may take different routes to the monitor, which allows for the possibility of packets arriving out of order. In the case of the status monitoring system, only the latest status is useful. Studying a system with 2 servers allows for the possibility of packets to arrive out-of-order while still having to queue. We present the exact approach to computing the analytical status age, and we provide an approximation that matches very closely with the simulated age. We also compare with the M/M/∞ and M/M/1, and we demonstrate the tradeoff between status age and network resource consumption.

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Section: M/m/2 System Modelmentioning

confidence: 99%

“…The status age has also been characterized for a system in which the source is separated from the monitor by a network [2], in which packets generated at the source immediately enter the network and reach the monitor after a random exponential time. This model can be viewed as a system with infinite memoryless servers.…”

Section: Introductionmentioning

confidence: 99%

Effect of message transmission diversity on status age

Kam

Kompella

Ephremides

2014

2014 IEEE International Symposium on Information Theory

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“…The transmission of status updates through a network cloud was investigated in [5], where the authors calculate the average status age, considering that all packets are transmitted immediately after generation, and some packets are rendered obsolete, due to the random service times in the network. This model corresponds to the extreme case of an infinite number of servers, and it presents a case in which resources are wasted transmitting all the available samples.…”

Section: Introductionmentioning

confidence: 99%

Age of information with packet management

Costa

Codreanu

Ephremides

2014

2014 IEEE International Symposium on Information Theory

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338

241

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We consider a system in which random status updates arrive at a source node, and should be transmitted through a wireless network to the intended destination node. The status updates are samples of a random process, transmitted as packets, containing the time stamp to identify the moment the sample was generated. The time it takes to successfully transmit a packet to the destination is modeled as an exponentially distributed service time. The status update age at the receiver is the time elapsed since the last received update was generated. In this paper, we analyze the age in the case that the source node has the capability to manage the arriving samples and decide which packets will be transmitted to the destination. In addition to the average age, we investigate the average of a new metric, called peak age, which provides information about the maximum value of the Age, achieved immediately before receiving an update.

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“…Along the same generalization direction as in [2], one may ask: what would happen if we increase the number of queues available, i.e., if the source is able to serve multiple updates at the same time? This question is tackled in [3], where a single Poisson process is sending updates over an infinite number of queues with exponential service time.…”

Section: Introductionmentioning

confidence: 99%

Age of information: The gamma awakening

Najm

Nasser

2016

2016 IEEE International Symposium on Information Theory (ISIT)

180

110

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Abstract-Status update systems is an emerging field of study that is gaining interest in the information theory community. We consider a scenario where a monitor is interested in being up to date with respect to the status of some system which is not directly accessible to this monitor. However, we assume a source node has access to the status and can send status updates as packets to the monitor through a communication system. We also assume that the status updates are generated randomly as a Poisson process. The source node can manage the packet transmission to minimize the age of information at the destination node, which is defined as the time elapsed since the last successfully transmitted update was generated at the source. We use queuing theory to model the source-destination link and we assume that the time to successfully transmit a packet is a gamma distributed service time. We consider two packet management schemes: LCFS with preemption and LCFS without preemption. We compute and analyze the average age and the average peak age of information under these assumptions. Moreover, we extend these results to the case where the service time is deterministic.

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Age of information under random updates

Cited by 238 publications

References 5 publications

Effect of message transmission diversity on status age

Effect of message transmission diversity on status age

Age of information with packet management

Age of information: The gamma awakening

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