Closed-Form Expression for the Average Age of Information in a Multi-Source M/G/1 Queueing Model

Moltafet, Mohammad; Leinonen, Markus; Codreanu, Marian

doi:10.1109/itw44776.2019.8989346

Cited by 25 publications

(18 citation statements)

References 26 publications

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“…In this section, we evaluate the average AoI in a multisource M/M/1 queueing model and compare our exact expression in (22) with the results in existing works [8] and [10]. Fig.…”

Section: Validation and Simulation Resultsmentioning

confidence: 99%

“…As it can be seen, the simulation result and our proposed solution overlap perfectly. Due to the calculation errors in [8] and [10], both curves have a gap to the correct average AoI value. In addition, this figure illustrates that generating the status update packets too frequently or too rarely does not minimize the average AoI.…”

Section: Validation and Simulation Resultsmentioning

confidence: 99%

“…(25)]. The dissimilarity comes from the fact that in [10], we wrongly used steady-state properties of a queueing system in calculating…”

Section: The Third Conditional Expectation In (8)mentioning

confidence: 99%

“…The setup was earlier addressed in [3], [8], where the authors derived an approximate expression for the average AoI by neglecting the statistical dependency between certain random variables (see Section III-C). In [10], we made a first attempt to correct the error in [3], [8]. Unfortunately, the average AoI expression derived there is still inaccurate (see Remark 2 in Section III-C).…”

Section: Introductionmentioning

confidence: 99%

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An Exact Expression for the Average AoI in a Multi-Source M/M/1 Queueing Model

Moltafet

Leinonen

Codreanu

2020

2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications

Self Cite

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Information freshness is crucial in a wide range of wireless applications where a destination needs the most recent measurements of a remotely observed random process. In this paper, we study the information freshness of a singleserver multi-source M/M/1 queueing model under a firstcome first-served (FCFS) serving policy. The information freshness of the status updates of each source is evaluated by the average age of information (AoI). We derive an exact expression for the average AoI for the multi-source M/M/1 queueing model. Simulation results are provided to validate the derived exact expression for the average AoI. Index Terms-Information freshness, age of information (AoI), multi-source queueing model.

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“…In this section, we evaluate the average AoI in a multisource M/M/1 queueing model and compare our exact expression in (22) with the results in existing works [8] and [10]. Fig.…”

Section: Validation and Simulation Resultsmentioning

confidence: 99%

Section: Validation and Simulation Resultsmentioning

confidence: 99%

“…(25)]. The dissimilarity comes from the fact that in [10], we wrongly used steady-state properties of a queueing system in calculating…”

Section: The Third Conditional Expectation In (8)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Exact Expression for the Average AoI in a Multi-Source M/M/1 Queueing Model

Moltafet

Leinonen

Codreanu

2020

2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications

Self Cite

View full text Add to dashboard Cite

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“…In other words, the AoI of each sensor is the time elapsed since the last received status update packet was generated at the sensor. The average AoI is the most commonly used metric to evaluate the AoI [2]- [12].…”

Section: Introductionmentioning

confidence: 99%

Power Minimization in Wireless Sensor Networks With Constrained AoI Using Stochastic Optimization

Moltafet

Leinonen

Codreanu

et al. 2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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In this paper, we consider a system where multiple low-power sensors communicate timely information about a random process to a sink. The sensors share orthogonal subchannels to transmit such information in the form of status update packets. Freshness of the sensors' information at the sink is characterized by the Age of Information (AoI), and the sensors can control the sampling policy by deciding whether to take a sample or not. We formulate an optimization problem to minimize the time average total transmit power of sensors by jointly optimizing the sampling action of each sensor, the transmit power allocation, and the subchannel assignment under the constraints on the maximum time average AoI and maximum power of each sensor. To solve the optimization problem, we use the Lyapunov drift-plus-penalty method. Numerical results show the performance of the proposed algorithm versus the different parameters of the system.

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Age of information analysis for internet of things information status update system with cellular backhaul and dedicated energy harvesting beacons

Hao

Jia

2022

Int J Communication

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SummaryThe Internet of Things (IoT) that is usually deployed with the assistance of cellular backhaul and energy harvesting (EH) is characterized by the status update freshness. However, the traditional nature EH results in the loss of information freshness due to the randomness of nature energy process. Although using wireless energy transferring (WET) to charge sensors can overcome this problem, this method has low‐energy efficiency. With these considerations, this paper proposes a novel IoT system under cellular communication scenario by simultaneously exploiting the nature EH and WET. The proposed IoT system is consisted of a cellular backhaul subsystem, a wireless EH and transferring (WEHT) subsystem, and a wireless sensor status update subsystem. The WEHT subsystem employs the dedicated power beacons harvesting energy from natural energy source and transferring the harvested energy to sensor. For the proposed IoT system, this paper first investigates the WEHT subsystem and constructs the Markov Chain (MC) of discrete energy states as well as the MC state transition matrix. Second, the average AoI and peak AoI (PAoI) are formulated by separately considering cellular backhaul and EHTBs. The AoI (PAoI) comparison shows that the proposed EHTB‐based IoT system can outperform the conventional non‐EHTB one where the sensor directly harvests energy from natural source. At the same time, the numerical results exploit the impact of system parameters on AoI and PAoI, respectively. It is found that there exists a trade‐off between the nature energy arrival and EHTB energy transfer so that the average minimum AoI and PAoI are achieved.

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Closed-Form Expression for the Average Age of Information in a Multi-Source M/G/1 Queueing Model

Cited by 25 publications

References 26 publications

An Exact Expression for the Average AoI in a Multi-Source M/M/1 Queueing Model

An Exact Expression for the Average AoI in a Multi-Source M/M/1 Queueing Model

Power Minimization in Wireless Sensor Networks With Constrained AoI Using Stochastic Optimization

Age of information analysis for internet of things information status update system with cellular backhaul and dedicated energy harvesting beacons

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