Age of information of a server with energy requirements

Doncel, Josu

doi:10.7717/peerj-cs.354

Cited by 5 publications

(5 citation statements)

References 32 publications

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“…From these illustrations, we also conclude that the average Age of Information is not monotone with respect to α (note that in [19] it has been shown that the average Age of Information of a system with a single server with energy harvesting is monotone with respect to α). For instance, we see that, when p = 0.45 and λ = 10, the average Age of Information increases when λ is small, then decreases and finally decreases again.…”

Section: Convergence Analysis Of Propositionmentioning

confidence: 70%

“…• the work of [19] where it is analyzed a system with a single server and energy harvesting. In our work, we consider the same energy harvesting model, but with two parallel servers.…”

Section: Contributionmentioning

confidence: 99%

“…We aim to compare the average Age of Information of the model under study in this paper with that of a system with a single server with energy harvesting (the average Age of Information of the latter model has been studied in [19]). For this purpose, we consider the following systems: (i) a single server with arrival rates λ and α of data packets and energy packets, respectively, and service rate 2µ (which is represented with a dotted line); (ii) a single server with arrival rates λ/2 and α of data packets and energy packets, respectively, and service rate 2µ (which is represented with a solid line); and (iii) two parallel servers with p = 0.5, i.e., the arrival rate to each server is λ/2, the service rate is µ and the arrival rate of energy packets is α (which is represented with a dashed line).…”

Section: Parallel Servers Vs Single Servermentioning

confidence: 99%

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Age of Information of Parallel Server Systems with Energy Harvesting

Doncel

2021

Entropy

Self Cite

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Motivated by current communication networks in which users can choose different transmission channels to operate and also by the recent growth of renewable energy sources, we study the average Age of Information of a status update system that is formed by two parallel homogeneous servers and such that there is an energy source that feeds the system following a random process. An update, after getting service, is delivered to the monitor if there is energy in a battery. However, if the battery is empty, the status update is lost. We allow preemption of updates in service and we assume Poisson generation times of status updates and exponential service times. We show that the average Age of Information can be characterized by solving a system with eight linear equations. Then, we show that, when the arrival rate to both servers is large, the average Age of Information is one divided by the sum of the service rates of the servers. We also perform a numerical analysis to compare the performance of our model with that of a single server with energy harvesting and to study in detail the aforementioned convergence result.

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Section: Convergence Analysis Of Propositionmentioning

confidence: 70%

“…• the work of [19] where it is analyzed a system with a single server and energy harvesting. In our work, we consider the same energy harvesting model, but with two parallel servers.…”

Section: Contributionmentioning

confidence: 99%

Section: Parallel Servers Vs Single Servermentioning

confidence: 99%

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Age of Information of Parallel Server Systems with Energy Harvesting

Doncel

2021

Entropy

Self Cite

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“…There have been a number of works focusing on analyzing AoI when transmitters rely on energy harvesting to communicate, e.g., [3]- [40]. In summary, these works may be categorized according to the following three differentiating aspects:…”

Section: A Related Workmentioning

confidence: 99%

Timely Status Updating Over Erasure Channels Using an Energy Harvesting Sensor: Single and Multiple Sources

Arafa¹,

Yang²,

Ulukuş³

et al. 2021

Preprint

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A status updating system is considered in which data from multiple sources are sampled by an energy harvesting sensor and transmitted to a remote destination through an erasure channel. The goal is to deliver status updates of all sources in a timely manner, such that the cumulative long-term average age-of-information (AoI) is minimized. The AoI for each source is defined as the time elapsed since the generation time of the latest successful status update received at the destination from that source. Transmissions are subject to energy availability, which arrives in units according to a Poisson process, with each energy unit capable of carrying out one transmission from only one source. The sensor is equipped with a unit-sized battery to save the incoming energy. A scheduling policy is designed in order to determine which source is sampled using the available energy. The problem is studied in two main settings: no erasure status feedback, and perfect instantaneous feedback.

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“…Thus, the WSN faces numerous problems concerning energy distribution ( Razzaque & Dobson, 2014 ). The utilization of power according to the amount of consumption among several components limits the lifetime of the energy sources Doncel, 2021 . Thus, it is essential to preserve the energy to maintain a balance in terms of power distribution.…”

Section: Introductionmentioning

confidence: 99%

Hybrid state analysis with improved firefly optimized linear congestion models of WSNs for DDOS & CRA attacks

Basith

Shankar

2022

PeerJ Computer Science

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A decentralized form represents a wireless network that facilitates the computers to direct communication without any router. The mobility of individual nodes is necessary within the restricted radio spectrum where contact is often possible on an Adhoc basis. The routing protocol must face the critical situation in these networks forwarding exploration between communicating nodes may create the latency problem in the future. The assault is one of the issues has direct impact network efficiency by disseminating false messages or altering routing detail. Hence, an enhanced routing approach proposes to defend against such challenges. The efficiency of the designated model of wireless devices relies on various output parameters to ensure the requirements. The high energy efficient algorithms: LEACH with FUZZY LOGIC, GENETIC, and FIREFLY are the most effective in optimizing scenarios. The firefly algorithm applies in a model of hybrid state logic with energy parameters: data percentage, transmission rate, and real-time application where the architecture methodology needs to incorporate the design requirements for the attacks within the specified network environment, which can affect energy and packet distribution under various system parametric circumstances. These representations can determine with the statistical linear congestion model in a wireless sensor network mixed state environment.

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Age of information of a server with energy requirements

Cited by 5 publications

References 32 publications

Age of Information of Parallel Server Systems with Energy Harvesting

Age of Information of Parallel Server Systems with Energy Harvesting

Timely Status Updating Over Erasure Channels Using an Energy Harvesting Sensor: Single and Multiple Sources

Hybrid state analysis with improved firefly optimized linear congestion models of WSNs for DDOS & CRA attacks

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