Social-Aware Peer Discovery for Energy Harvesting-Based Device-To-Device Communications

Hailemariam, Zelalem Legese; Lai, Yuan‐Cheng; Chen, Yen-Hung; Wu, Yu-Hsueh; Chang, Arthur

doi:10.3390/s19102304

Cited by 5 publications

(6 citation statements)

References 31 publications

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“…The study presented in [141] extends preceding works that use the social behaviour of the mobile users to adapt the transmission speeds of messages used for the peer discovery in D2D networks under the user equipment′s (UEs) power consumption constraint. The authors introduce a three-phase energy-ratio rate decision (ERRD) algorithm, which in the first phase schedules the power budget of the network among the UEs based on their social ratios and in the second phase, based on harvested energy, allocates power quantum of each UE.…”

Section: A Review Of Articles For Special Issue On Green Energy-ementioning

confidence: 90%

Greener, Energy-Efficient and Sustainable Networks: State-Of-The-Art and New Trends

Lörincz

Capone

2019

Sensors

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Although information and communications technologies (ICTs) have the potential of enabling powerful social, economic and environmental benefits, ICT systems give a non-negligible contribution to world electricity consumption and carbon dioxide (CO2) footprint. This contribution will sustain since the increased demand for user′s connectivity and an explosion of traffic volumes necessitate continuous expansion of current ICTs services and deployment of new infrastructures and technologies which must ensure the expected user experiences and performance. In this paper, analyses of costs for the global annual energy consumption of telecommunication networks, estimation of ICT sector CO2 footprint contribution and predictions of energy consumption of all connected user-related devices and equipment in the period 2011–2030 are presented. Since presented estimations of network energy consumption trends for main communication sectors by 2030 shows that highest contribution to global energy consumption will come from wireless access networks and data centres (DCs), the rest of the paper analyses technologies and concepts which can contribute to the energy-efficiency improvements of these two sectors. More specifically, different paradigms for wireless access networks such as millimetre-wave communications, Long-Term Evolution in unlicensed spectrum, ultra-dense heterogeneous networks, device-to-device communications and massive multiple-input multiple-output communications have been analysed as possible technologies for improvement of wireless networks energy efficiency. Additionally, approaches related to the DC resource management, DCs power management, green DC monitoring and thermal management in DCs have been discussed as promising approaches to improvement of DC power usage efficiency. For each of analysed technologies, future research challenges and open issues have been summarised and discussed. Lastly, an overview of the accepted papers in the Special Issue dedicated to the green, energy-efficient and sustainable networks is presented.

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Section: A Review Of Articles For Special Issue On Green Energy-ementioning

confidence: 90%

Greener, Energy-Efficient and Sustainable Networks: State-Of-The-Art and New Trends

Lörincz

Capone

2019

Sensors

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“…Using and from ( 14) and ( 15), respectively, we normalize the ε problem in (10) to scale its values between 0 and 1:…”

Section: ) Formulation Of Constraint-free Objective Functionmentioning

confidence: 99%

“…A major concern that arises in social-aware D2D communications is the energy consumption of devices [10]. As the cellular devices used for D2D communications have limited battery power, D2D communications may drain the battery rather quickly [11].…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm-Based Energy Efficiency Maximization for Social-Aware Device-to-Device Communications

Nadeem

Cho

2021

IEEE Access

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In this paper, we propose a novel energy efficiency maximization scheme for social-aware device-to-device (D2D) communications based on a genetic algorithm (GA). The proposed scheme incorporates both social and physical parameters of users to model the energy efficiency maximization problem. The formulated problem considers the spectral reuse, spectral efficiency, and the transmit power constraints of both cellular and D2D users to satisfy their quality of service requirements. Moreover, an algorithm based on the self-adaptive penalty function is applied to convert the constrained problem into an unconstrained problem. Next, GA is utilized to maximize the unconstrained problem. The feasibility of the proposed scheme is shown by computing its time complexity in terms of big-O notation. Moreover, the convergence of the proposed scheme is analyzed by comparing the maximum and average values of the overall energy efficiencies for different iterations. Likewise, the performance is evaluated in terms of overall energy efficiency and system throughput for various D2D scenarios. To demonstrate the efficiency of the proposed scheme, the results are compared with those for a static penalty-based GA algorithm. Furthermore, to demonstrate the significance of combining the two types of parameters (i.e., social and physical), the performance of the proposed scheme is compared with schemes based on only social or physical parameters.

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“…Regular batteries recharge in the growing number of wearable devices is an inconvenient and time‐consuming task. Recently, a newly energy harvesting technique provides a promising solution to break the battery limitation of the wearable devices 2 . Compared with the common ambient energy sources such as solar, 3 light, 4 wind 5 that are highly influenced by environment and weather, human kinetic energy coming from our daily movement is an emerging renewable energy source for low‐power wearable devices 6 .…”

Section: Introductionmentioning

confidence: 99%

Energy allocation for activity recognition in wearable devices with kinetic energy harvesting

Ling

Meng²,

Tian

et al. 2021

Softw Pract Exp

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Harvesting kinetic energy from body movement is regarded as a promising rechargeable energy source for wearable devices with low‐power. Energy allocation is essential for motion‐based rechargeable devices since the great variability of energy gained from movement. Based on the realistic characteristics of an ultra‐low‐power wearable devices and our measurement observations, we propose the optimization framework allocating energy to maximize the average accuracy of human activity recognition and provide an offline and online algorithm, respectively. We evaluate the proposed energy allocation approach with real‐world human activity and kinetic energy harvesting datasets. Experimental results validate that our proposed energy allocation approach can maximize the energy allocation utility and improve energy efficiency of wearable devices.

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Social-Aware Peer Discovery for Energy Harvesting-Based Device-To-Device Communications

Cited by 5 publications

References 31 publications

Greener, Energy-Efficient and Sustainable Networks: State-Of-The-Art and New Trends

Greener, Energy-Efficient and Sustainable Networks: State-Of-The-Art and New Trends

Genetic Algorithm-Based Energy Efficiency Maximization for Social-Aware Device-to-Device Communications

Energy allocation for activity recognition in wearable devices with kinetic energy harvesting

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