Sleep Mechanism of Base Station Based on Minimum Energy Cost

Han, Dongsheng; Chen, Zhixiong

doi:10.1155/2018/4202748

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…The sources of energy consumption in MEC are the BSs and the computing platforms. Towards this end, two large trends appear in the literature to address the energy efficiency challenge: (i) search for more energy efficient transmission devices and technologies; (ii) new technology proposals aimed at improving energy consumption in BSs, such as sleep mode, as it is known that switching on/off the BS transmission power during low traffic demand can yield significant energy savings [30][31][32]. Trend (ii) is more appealing due to the fact that future BS functions will be virtualized [1], thus enabling the ease of deactivating some of the BS functions.…”

Section: Energy Saving Strategies In Mecmentioning

confidence: 99%

Softwarization in Future Mobile Networks and Energy Efficient Networks

Dlamini¹

2020

Mobile Computing

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The data growth generated by pervasive mobile devices and the Internet of Things at the network edge (i.e., closer to mobile users), couple with the demand for ultra-low latency, requires high computation resources which are not available at the end-user device. This demands a new network design paradigm in order to handle user demands. As a remedy, a new MN network design paradigm has emerged, called Mobile Edge Computing (MEC), to enable low-latency and locationaware data processing at the network edge. MEC is based on network function virtualization (NFV) technology, where mobile network functions (NFs) that formerly existed in the evolved packet core (EPC) are moved to the access network [i.e., they are deployed on local cloud platforms in proximity to the base stations (BSs)]. In order to reap the full benefits of the virtualized infrastructure, the NFV technology shall be combined with intelligent mechanisms for handling network resources. Despite the potential benefits presented by MEC, energy consumption is a challenge due to the foreseen dense deployment of BSs empowered with computation capabilities. In the effort to build greener 5G mobile network (MN), we advocate the integration of energy harvesting (EH) into future edge systems.

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Section: Energy Saving Strategies In Mecmentioning

confidence: 99%

Softwarization in Future Mobile Networks and Energy Efficient Networks

Dlamini¹

2020

Mobile Computing

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“…A comprehensive power management model employing a BS switching on/off mechanism, within a BS system powered by green energy, is presented in [21]. The model considers weather conditions, user mobility, different green energy harvesting rates, energy storage with self-discharge effect, and switching on/off frequency.…”

Section: Introductionmentioning

confidence: 99%

Online Supervisory Control and Resource Management for Energy Harvesting BS Sites Empowered with Computation Capabilities

Dlamini

Gambı́n

Munaretto³

et al. 2019

Wireless Communications and Mobile Computing

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The convergence of communication and computing has lead to the emergence of Multi-access Edge Computing (MEC), where computing resources (supported by Virtual Machines (VMs)) are distributed at the edge of the Mobile Network (MN), i.e., in Base Stations (BSs), with the aim of ensuring reliable and ultra-low latency services. Moreover, BSs equipped with Energy Harvesting (EH) systems can decrease the amount of energy drained from the power grid resulting into energetically self-sufficient MNs. The combination of these paradigms is considered here. Specifically, we propose an online optimization algorithm, called ENergy Aware and Adaptive Management (ENAAM), based on foresighted control policies exploiting (short-term) traffic load and harvested energy forecasts, where BSs and VMs are dynamically switched on/off towards energy savings and QoS provisioning. Our numerical results reveal that ENAAM achieves energy savings with respect to the case where no energy management is applied, ranging from 57% and 69%. Moreover, the extension of ENAAM within a cluster of BSs provides a further gain ranging from 9% to 16% in energy savings with respect to the optimization performed in isolation for each BS.

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“…These energy-limited devices, usually in the edge of a network [8], may cause cooperative communication to not work properly. Accordingly, some works introduce energy-harvesting (EH) technology to solve such restriction and achieve better energy efficiency [9,10]. This technology can realize the reuse of energy resources.…”

Section: Introductionmentioning

confidence: 99%

A novel secure relay selection strategy for energy-harvesting-enabled Internet of things

Huo

Fan

Jing

2018

J Wireless Com Network

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In this article, we focus on the problem of relay selection for the cooperative cognitive radio-based Internet of things. In such systems, a pair of primary user devices (PUs) can only communicate with each other through a relay. The relay is selected from a set of multi-slot energy-harvesting (EH)-enabled secondary user devices (SUs). The charging and discharging process of an SD's battery is formulated as a finite state Markov chain, and we can derive the corresponding analytical expression of steady-state distribution. Consider the non-authority of SUs, we analyze the outage performance when SUs are trusted and untrusted. When SUs are trusted, we provide the theoretical analysis expression and the lower bound expression for the outage probability. On the contrary, we propose a destination-assisted jamming strategy to secure primary communication if SUs are untrusted. In addition, we propose a Vickrey auction-based EH-enabled relay selection strategy which can be applied to the secondary system. For this auction strategy, SUs without direct links can transmit signals by selecting an EH-enabled SU as a relay. The winning SU in the process of auction can earn reward. Finally, the simulation results verify that the EH-based transmission can obtain excellent system performance without consuming excess energy, and we also study the effect of different parameters on system outage performance.

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Sleep Mechanism of Base Station Based on Minimum Energy Cost

Cited by 5 publications

References 20 publications

Softwarization in Future Mobile Networks and Energy Efficient Networks

Softwarization in Future Mobile Networks and Energy Efficient Networks

Online Supervisory Control and Resource Management for Energy Harvesting BS Sites Empowered with Computation Capabilities

A novel secure relay selection strategy for energy-harvesting-enabled Internet of things

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