A Coordinated Ambient/Dedicated Radio Frequency Energy Harvesting Scheme Using Machine Learning

Kwan, Jonathan C.; Chaulk, Jesse M.; Fapojuwo, Abraham O.

doi:10.1109/jsen.2020.3003931

Cited by 15 publications

(10 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kwan et al study the RF harvesting from intended and unintended sources and propose machine learning-based wakeup scheduling policy for on-body sensors [237]. To address the unpredictable nature and low amount of energy harvesting from the RF signals of unindented sources make it difficult to decide the wake-up time, the authors consider two machine learning techniques including linear regression and ANN to predict the wake-up time.…”

Section: Energy Harvesting and Sharingmentioning

confidence: 99%

“…Similar to [237], the authors of [238] also focus on the optimization of active time of IoT nodes which are powered by RF harvesting energy. In this paper, besides information collection and energy provision, the HAP is also responsible for setting the sampling time of the IoT devices.…”

Section: Energy Harvesting and Sharingmentioning

confidence: 99%

“…For the UAVenabled BSs, AI can be adopted to optimize the trajectory to reduce energy consumption and improve the harvesting efficiency [161], [273]. Current research mainly focuses on the maximization of minimum harvesting energy due to the disordered transmission and unplanned power control [237], [238], AI can enable the RF harvesting process to be energyaware, which can greatly reduce the wasted energy, especially for the signals from omnidirectional antennas.…”

Section: Ai-enhanced Energy Harvesting and Sharingmentioning

confidence: 99%

See 2 more Smart Citations

AI based Service Management for 6G Green Communications

Mao,

Tang,

Yuichi

et al. 2021

Preprint

View full text Add to dashboard Cite

Green communications have always been a target for the information industry to alleviate energy overhead and reduce fossil fuel usage. In current 5G and future 6G era, there is no doubt that the volume of network infrastructure and the number of connected terminals will keep exponentially increasing, which results in the surging energy cost. It becomes growing important and urgent to drive the development of green communications. However, 6G will inevitably have increasingly stringent and diversified requirements for Quality of Service (QoS), security, flexibility, and even intelligence, all of which challenge the improvement of energy efficiency. Moreover, the dynamic energy harvesting process, which will be adopted widely in 6G, further complicates the power control and network management. To address these challenges and reduce human intervene, Artificial Intelligence (AI) has been widely recognized and acknowledged as the only solution. Academia and industry have conducted extensive research to alleviate energy demand, improve energy efficiency, and manage energy harvesting in various communication scenarios. In this paper, we present the main considerations for green communications and survey the related research on AI-based green communications. We focus on how AI techniques are adopted to manage the network and improve energy harvesting toward the green era. We analyze how state-of-the-art Machine Learning (ML) and Deep Learning (DL) techniques can cooperate with conventional AI methods and mathematical models to reduce the algorithm complexity and optimize the accuracy rate to accelerate the applications in 6G. Finally, we discuss the existing problems and envision the challenges for these emerging techniques in 6G.

show abstract

Section: Energy Harvesting and Sharingmentioning

confidence: 99%

Section: Energy Harvesting and Sharingmentioning

confidence: 99%

Section: Ai-enhanced Energy Harvesting and Sharingmentioning

confidence: 99%

See 1 more Smart Citation

AI based Service Management for 6G Green Communications

Mao,

Tang,

Yuichi

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Experimental results show that the robust Bayesian learning approach can reduce the packet drop rates without jeopardizing the energy consumption of the HAP. Similarly, J. Kwan et al [210] also consider a WPCN with several HAPs to collect sensed data from the on-body sensors of a user. Then sensors can temporarily store the harvested energy using storage capacitors.…”

Section: Radio Frequency (Rf) Harvestingmentioning

confidence: 99%

Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking

Wang¹,

Zhu²,

Hossain³

2021

Preprint

View full text Add to dashboard Cite

As one of the most promising applications in future Internet of Things, Internet of Vehicles (IoV) has been acknowledged as a fundamental technology for developing the Intelligent Transportation Systems in smart cities. With the emergence of the sixth generation (6G) communications technologies, massive network infrastructures will be densely deployed and the number of network nodes will increase exponentially, leading to extremely high energy consumption. There has been an upsurge of interest to develop the green IoV towards sustainable vehicular communication and networking in the 6G era. However, as a special mobile ad-hoc network, the energy cost in an IoV system involves the communication and computation energy in addition to the fuel consumption and the electricity cost of moving vehicles. Moreover, the energy harvesting technology, which is likely to be adopted widely in 6G systems, will complicate the optimization of energy efficiency in the entire system. Current studies focus only on part of the energy issues in IoV systems without a comprehensive discussion of the state-of-the-art energy-efficient approaches and the influence of the development of 6G networks on green IoV. In this paper, we present the main considerations for green IoV from five different scenarios, including the communication, computation, traffic, Electric Vehicles (EVs), and energy harvesting management. The literatures relevant to each of the scenarios are compared from the perspective of energy optimization (e.g., with respect to resource allocation, workload scheduling, routing design, traffic control, charging management, energy harvesting and sharing, etc.) and the related factors affecting energy efficiency (e.g., resource limitation, channel state, network topology, traffic condition, etc.). In addition, we introduce the potential challenges and the emerging technologies in 6G for developing green IoV systems. Finally, we discuss the research trends in designing energy-efficient IoV systems.

show abstract

“…Apart from the above discussed frequently used ML approaches, there are various other approaches too viz. Deep reinforcement learning (Ashiquzzaman et al [59], Ke et al [60], Nguyen et al [61]), deep neural network with sparse autoencoder (Ayinde [62]), opportunistic routing (Dinh et al [63]), energy saving using simulated annealing (Kang et al [64]), and energy harvesting using artificial neural network combined with linear regression (Kwan et al [65]), Hence, it can be said that there are various dedicated research attempt towards using ML approach over WSN; however, not all the approaches are found to directly address energy problems in WSN.…”

Section: B Machine Learning Approachmentioning

confidence: 99%

Snapshot of Energy Optimization Techniques to Leverage Life of Wireless Sensor Network

Kavya¹,

Ravi²

2021

IJACSA

View full text Add to dashboard Cite

Energy Optimization in Wireless Sensor Network (WSN) deals with the techniques which targets higher degree of energy efficiency using resource-constraint sensor nodes with minimal inclusion of any different variants of resources. At present, there are various approaches and techniques towards addressing the problems of energy but not all the research implications can be considered as optimized approach. Therefore, this paper reviews all the existing energy optimization schemes, categorizes them, briefly discuss about their strength and weakness to offer a compact snapshot of existing energy optimization techniques in WSN. The paper also contributes towards exploring the updates research trends and highlights about the open-end research problems in WSN. It is anticipated that the study findings of this manuscript will offer a true picture of study effectiveness in dealing with energy challenges so that favorable direction of investigation towards evolving up optimized solution comes up with promising outcome.

show abstract

A Coordinated Ambient/Dedicated Radio Frequency Energy Harvesting Scheme Using Machine Learning

Cited by 15 publications

References 17 publications

AI based Service Management for 6G Green Communications

AI based Service Management for 6G Green Communications

Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking

Snapshot of Energy Optimization Techniques to Leverage Life of Wireless Sensor Network

Contact Info

Product

Resources

About