Average age of information for status update systems with an energy harvesting server

Farazi, Shahab; Klein, Andrew G.; Brown, Donald R.

doi:10.1109/infcomw.2018.8406846

Cited by 114 publications

(62 citation statements)

References 33 publications

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“…Additionally, the average AoI under the FCFS discipline is derived in [29] [30] by SHS for a system where only one status packet is allowed in the system if the battery is not empty (identical to the case K = 0 if service rate µ → ∞). The average AoI (see [29, Eq.…”

Section: A Fcfs Disciplinementioning

confidence: 99%

“…The problem considered is similar to the one in [29] and [30], and they focus solely on average AoI in a system with unit buffer size, and investigate the average AoI by SHS. Different from [29] and [30], this paper applies the conventional stochastic analysis with queuing model, and analyzes the non-linear AoI-based performance arbitrary buffer and battery capacity. The contribution of this paper is summarized as follows: 1) A method to obtain the closed-form AoI-based penalty is established for both FCFS and LCFS disciplines when the service time is negligible, since the service time is usually much smaller than packet generation intervals and energy arrival intervals in real-world applications.…”

Section: Introductionmentioning

confidence: 99%

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Closed-Form Analysis of Non-Linear Age of Information in Status Updates With an Energy Harvesting Transmitter

Zheng

Zhou

Jiang

et al. 2019

IEEE Trans. Wireless Commun.

106

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Timely status updates are crucial to enabling applications in massive Internet of Things (IoT). This paper measures the data-freshness performance of a status update system with an energy harvesting transmitter, considering the randomness in information generation, transmission and energy harvesting.The performance is evaluated by a non-linear function of age of information (AoI) that is defined as the time elapsed since the generation of the most up-to-date status information at the receiver. The system is formulated as two queues with status packet generation and energy arrivals both assumed to be Poisson processes. With negligible service time, both First-Come-First-Served (FCFS) and Last-Come-First-Served (LCFS) disciplines for arbitrary buffer and battery capacities are considered, and a method for calculating the average penalty with non-linear penalty functions is proposed. The average AoI, the average penalty under exponential penalty function, and AoI's threshold violation probability are obtained in closed form. When the service time is assumed to follow exponential distribution, matrix geometric method is used to obtain the average peak AoI. The results illustrate that under the FCFS discipline, the status update frequency needs to be carefully chosen according to the service rate and energy arrival rate in order to minimize the average penalty. Index TermsAge of information, energy harvesting, status update, queuing theory, internet of things.

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Section: A Fcfs Disciplinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Closed-Form Analysis of Non-Linear Age of Information in Status Updates With an Energy Harvesting Transmitter

Zheng

Zhou

Jiang

et al. 2019

IEEE Trans. Wireless Commun.

106

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“…Transmissions with modes 1 and 2 are denoted by solid and dashed arrows, respectively. Note that the raises in the energy in intervals t ∈ [0, 2], [6,9] are due to the harvested energy, whereas between t ∈ [3,4], energy is recovered. On the other hand, between t ∈ [8,9], both the harvested and recovered units of energy are stored in the battery.…”

Section: B Energy Harvesting Processmentioning

confidence: 99%

“…Energy harvesting information update systems has been studied in the AoI literature for various scenarios [4]- [10]. References [7]- [9] investigate the optimal scheduling policies to minimize average AoI under transmission errors whereas the same metric is used in [4], [6], [10] for error-free channel. The study in [5] aims to identify the achievable average message rate and AoI for an energy harvesting AoI system.…”

Section: Introductionmentioning

confidence: 99%

“…The study in [5] aims to identify the achievable average message rate and AoI for an energy harvesting AoI system. In these studies, energy units are assumed to be arriving randomly at each time slot, either according to a simple Bernouili [5] or Poisson distribution [4], [6]- [10]. On the other hand, existing literature on the design of wireless sensor networks emphasizes the impact of effectively modeling the energy harvesting process on the analysis of the functionality and lifetime of the network [11], [12].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Transmission Policies for Energy Harvesting Age of Information Systems with Battery Recovery

Tunc¹,

Panwar²

2019

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

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We consider an energy harvesting information update system where a sensor is allowed to choose a transmission mode for each transmission, where each mode consists of a transmission power-error pair. We also incorporate the battery phenomenon called battery recovery effect where a battery replenishes the deliverable energy if kept idle after discharge. For an energy-limited age of information (AoI) system, this phenomenon gives rise to the interesting trade-off of recovering energy after transmissions, at the cost of increased AoI. Considering two metrics, namely peak-age hitting probability and average age as the worst-case and average performance indicators, respectively, we propose a framework that formulates the optimal transmission scheme selection problem as a Markov Decision Process (MDP). We show that the gains obtained by considering both battery dynamics and adjustable transmission power together are much higher than the sum gain achieved if they are considered separately. We also propose a simple methodology to optimize the system performance taking into account worst-case and average performances jointly.

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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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Average age of information for status update systems with an energy harvesting server

Cited by 114 publications

References 33 publications

Closed-Form Analysis of Non-Linear Age of Information in Status Updates With an Energy Harvesting Transmitter

Closed-Form Analysis of Non-Linear Age of Information in Status Updates With an Energy Harvesting Transmitter

Optimal Transmission Policies for Energy Harvesting Age of Information Systems with Battery Recovery

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

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