Evaluation of the energy efficiency metrics for wireless networks

Gandhi, Asif; Newbury, Mark E.

doi:10.1002/bltj.20495

Cited by 9 publications

(3 citation statements)

References 1 publication

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“…Ghandi et al [5] measured the variation of power in relation to the corresponding variation of traffic (in Erlang) in a CDMA network. Other parameters such as the number of lost calls, the number access failures and the duration in minutes were also considered.…”

Section: Related Workmentioning

confidence: 99%

Evaluating the Impact of Congestion Reduction on Power Consumption in IoT Networks

Leite¹,

Ursini²,

Martins³

2018

Anais De XXXVI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Congestion reduction is a critical element in almost any system, and especially in those that are battery supplied and provide critical services, such as in an uninterruptible power supply of an emergency room in a hospital. In this work, we analyze the impact of congestion reduction on the network power consumption of battery-dependent devices. We built a discrete-event simulation model of an IoT network, which was analytically validated by Jackson networks, and quantitatively showed through two case studies how power consumption may be decreased in a network node by reducing congestion of the network traffic. The model and its implementation serve as the basis for the analysis of several scenarios that may support the design, planning and dimensioning of future IoT networks regarding traffic congestion. For existing networks, it may also be used for detection or even prediction of future bottlenecks. The analysis of the results indicates that a reduction in power consumption in the overall network is achievable-which may be used to extend the system's lifetime.

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Section: Related Workmentioning

confidence: 99%

Evaluating the Impact of Congestion Reduction on Power Consumption in IoT Networks

Leite¹,

Ursini²,

Martins³

2018

Anais De XXXVI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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“…The Charnes-Cooper transformation and the Dinkelbach method was used to answer the parametric approach. Gandhi et al in [9] presented a study of energy efficiency metric for wireless networks where emphasis was placed on design considerations and the direct impact of reductions of power and some performance indicators. A study of energy efficiency metrics for different components of green wireless communication systems was studied in [10].…”

Section: Introductionmentioning

confidence: 99%

Energy Efficiency Metrics in Cognitive Radio Networks: A Hollistic Overview

Orumwense

Afullo²,

Srivastava³

2022

Int. j. commun. netw. inf. secur.

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Due to the explosive progression in the number of users for new generation wireless communication networks which includes cognitive radio networks, energy efficiency has been a fundamental factor affecting its development and performance. In order to adeptly access and analyze the energy efficiency of a cognitive radio network, a standardized metric for this purpose is required. As a starting point, in this article we provided an analysis for energy efficiency metrics of a cognitive radio network in respect to its design and operation. The performance metrics and metrics developed at the different levels of a cognitive radio network are also studied. Establishing a comprehensive metric for evaluating, measuring and reporting the energy efficiency of cognitive radio networks is a crucial step in achieving an energy-efficient cognitive radio network.

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“…A multi-objective optimisation technique was proposed in [22][23][24], to lower the consumption of energy and to enhance the efficiency. The authors in [25][26][27] presented an energyefficient scheme for wireless networks to reduce the power consumption. In [28,29], an energy detection method is used for spectrum sensing, but it cannot avoid the uncertainty problem.…”

Section: Introductionmentioning

confidence: 99%

Energy‐efficient reporting scheme for cognitive radio networks

Sudhamani¹,

Saxena²,

Vunnava³

2021

IET Networks

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Cooperative spectrum sensing in cognitive radio network consumes a large amount of energy during spectrum sensing and reporting. An energy-efficient reporting scheme called the reduced energy consumption scheme for reporting has been proposed to reduce energy consumption. In this scheme, all secondary users will sense the channel and make a local decision about the spectrum. All these local decisions are forwarded to a common node known as the fusion centre. This counts the presence or absence of the primary user based on the secondary user's local decisions. Whenever the counters count is greater than or equal to the threshold then the fusion centre sends a stop reporting feedback signal to the secondary users. In this way the energy consumption is reduced by diminishing the reporting secondary users and the energy efficiency is improved. The simulation and numerical results show a notable improvement in the energy efficiency of a reduced spectrum sensing scheme compared to the conventional spectrum sensing method.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Evaluation of the energy efficiency metrics for wireless networks

Cited by 9 publications

References 1 publication

Evaluating the Impact of Congestion Reduction on Power Consumption in IoT Networks

Evaluating the Impact of Congestion Reduction on Power Consumption in IoT Networks

Energy Efficiency Metrics in Cognitive Radio Networks: A Hollistic Overview

Energy‐efficient reporting scheme for cognitive radio networks

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