Fog Computing‐Assisted Energy‐Efficient Resource Allocation for High‐Mobility MIMO‐OFDMA Networks

Lu, Lingyun; Wang, Tian; Ni, Wei; Li, Kai; Gao, Bo

doi:10.1155/2018/5296406

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…In this regard, researchers 44 suggested a resource allocation algorithm mainly focusing on optimizing and reducing the Doppler effect. More precisely, they aimed to formulate and maximize the system's energy.…”

Section: Energy Management In Fog Computingmentioning

confidence: 99%

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

Hashemi,

Sahafi,

Rahmani

et al. 2023

Softw Pract Exp

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The energy demand for Internet of Things (IoT) applications is increasing with a rise in IoT devices. Rising costs and energy demands can cause serious problems. Fog computing (FC) has recently emerged as a model for location‐aware tasks, data processing, fast computing, and energy consumption reduction. The Fog computing model assists cloud computing in fast processing at the network's edge, which also exerts a vital role in cloud computing. Due to the fast computing in fog servers, different quality of service (QoS) approaches have been proposed in various sections of the fog system, and several quality factors have been considered in this regard. Despite the significance of QoS in Fog computing, no extensive study has focused on QoS and energy consumption methods in this area. Therefore, this article investigates previous research on the use and guarantee of Fog computing. This article reviews six general approaches that discuss the published articles between 2015 and late May 2023. The focal point of this paper is evaluating Fog computing and the energy consumption strategy. This article further shows the advantages, disadvantages, tools, types of evaluation, and quality factors according to the selected approaches. Based on the reviewed studies, some open issues and challenges in Fog computing energy consumption management are suggested for further study.

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Section: Energy Management In Fog Computingmentioning

confidence: 99%

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

Hashemi,

Sahafi,

Rahmani

et al. 2023

Softw Pract Exp

View full text Add to dashboard Cite

show abstract

“…Lu et al developed a biodegradable solar cell with a lifetime of 4 months placed inside a rat, which could produce a power of 60 milliwatts, equal to powering a LED. 336 Gutruf et al developed battery-less multifunctional and permanently implantable pacemakers that are considered suitable for usage in animal models like mice. The tools allow continuous pacing of actively mobile mice in configurations suitable with CT and MRI mapping despite physical or operational deterioration (Fig.…”

Section: Medical Devices and Implantsmentioning

confidence: 99%

Novel low-carbon energy solutions for powering emerging wearables, smart textiles, and medical devices

Brindha

Sundarrajan

Rao

et al. 2022

Energy Environ. Sci.

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“…The process is iterated until the MEC achieves a desirable classification accuracy of the patient physiological analysis. Due to time-varying data quantity and channels, the energy consumption of the edge clients [6], [7] on data training and transmission can be greatly different from each other. Although scheduling the edge clients with a large dataset improves the learning accuracy of FL, analyzing large datasets rises energy consumption at the edge clients.…”

Section: Introductionmentioning

confidence: 99%

Federated Learning for Energy-balanced Client Selection in Mobile Edge Computing

Jing-jing

Tovar

et al. 2021

2021 International Wireless Communications and Mobile Computing (IWCMC)

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Mobile edge computing (MEC) has been consideredas a promising technology to provide seamless integration ofmultiple application services. Federated learning (FL) is carriedout at edge clients in MEC for privacy-preserving training ofdata processing models. Despite that the edge clients with smalldata payloads consume less energy on FL training, the small datapayload gives rise to a low learning accuracy due to insufficientinput to the FL training. Inadequate selection of the edge clientscan result in a large energy consumption at the edge clients,or a low learning accuracy of the FL training. In this paper,a new FL-based client selection optimization is proposed tobalance the trade-off between energy consumption of the edgeclients and the learning accuracy of FL. We first show thatthis optimization problem is NP-complete. Next, we proposea FL-based energy-accuracy balancing heuristic algorithm toapproximate the optimal client selection in polynomial time. Thenumerical results show the advantage of our proposed algorithm.

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Fog Computing‐Assisted Energy‐Efficient Resource Allocation for High‐Mobility MIMO‐OFDMA Networks

Cited by 4 publications

References 30 publications

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

Novel low-carbon energy solutions for powering emerging wearables, smart textiles, and medical devices

Federated Learning for Energy-balanced Client Selection in Mobile Edge Computing

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