On Sampling Time Maximization in Wireless Powered Internet of Things

Yang, Chang‐Lin; Chin, Kwan-Wu; He, Tengjiao; Liu, Ying

doi:10.1109/tgcn.2019.2907913

Cited by 12 publications

(6 citation statements)

References 23 publications

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“…Determining optimal sampling time for IoT devices for energy harvesting by saving batteries. Hence state space contains continuous quantities, a linear function approximation was used and a set of novel features were introduced to represent the large state space [349].…”

Section: Classification Of Publications By Mathematical Approach Of Application Methodologymentioning

confidence: 99%

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

2021

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Reinforcement learning (RL) methods can successfully solve complex optimization problems. Our article gives a systematic overview of major types of RL methods, their applications at the field of Industry 4.0 solutions, and it provides methodological guidelines to determine the right approach that can be fitted better to the different problems, and moreover, it can be a point of reference for R&D projects and further researches.

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Section: Classification Of Publications By Mathematical Approach Of Application Methodologymentioning

confidence: 99%

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

2021

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“…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%

AI based Service Management for 6G Green Communications

Mao,

Tang,

Yuichi

et al. 2021

Preprint

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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.

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“…Compared to the traditional energy harvesting techniques which utilize the ambient sources such as the solar and wind, the RF energy harvesting is more reliable due to independence on environmental factors. The hybrid access point (HAP) is a kind of RF energy source, which can be configured to support energy transfer and communication demands [204], [205], [206]. However, it consumes extra energy when the HAP sends dedicated signals for energy transfer to associated devices.…”

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

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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.

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On Sampling Time Maximization in Wireless Powered Internet of Things

Cited by 12 publications

References 23 publications

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

AI based Service Management for 6G Green Communications

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

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