Sum energy maximization for UAV-enabled wireless power transfer networks with nonlinear energy harvesting model

Zhang, Qianyuan; Wang, Zhengqiang; Zhang, Pengtao; Zhang, Hao; Wang, Xiaoyu; Fan, Zifu

doi:10.1109/itnec48623.2020.9085191

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…On one hand, the locations of EV charging stations is of great importance to balance the charging demand and power network stability, as well as improve the charging efficiency and A. Koufakis, 2016 [180] EV-to-EV Optimal matching between EVs Q. Zhang, 2020 [181] EV-to-EV Optimizing the deployment of flying UAVs power network capacity [170], [175]. On the other head, the economic cost in deploying the EV charging stations should be assessed for realistic application scenarios [172], [174].…”

Section: ) Deployment Of Charging Stationsmentioning

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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Section: ) Deployment Of Charging Stationsmentioning

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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“…Unmanned aerial vehicles (UAVs) are especially suitable for unexpected or temporary events for the features of costeffective and swiftly deployed [7]. As a result, UAVs have attracted significant interest in assisted wireless network for various application such as data collection, network topology building, energy harvesting, etc [8]- [10]. In addition, UAVs can act as intermediate relays for air-ground integrated mobile edge networks [11] (AGMEN), due to their high maneuverability and flexibility for on-demand placement.…”

Section: Introductionmentioning

confidence: 99%

Joint Power and QoE Optimization Scheme for Multi-UAV Assisted Offloading in Mobile Computing

Wang

Gao

2021

IEEE Access

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Recent years, unmanned aerial vehicles (UAVs) have attracted much attention for providing intermediate relay to ground mobile user equipments (UEs) for their flexible mobility. UEs can offload computing-intensive task to mobile cloud computing (MCC) or mobile edge computing (MEC) for fast processing. However, with multi-UAV and ground mobile UEs in the system, heterogeneous performance requirement as well as fast-changing communication condition make the system more complicated. Meanwhile, both UEs and UAVs are battery-driven. How to optimize the energy efficiency for UEs' transmission and UAVs' position should be carefully considered. Since this is a non-convex and mixedinteger optimization problem, a heuristic joint power and quality of experience (HJPQ) algorithm is proposed in this paper, where the UEs' offloading delay, MIMO channel, transmission power, as well as UAVs' placement are jointly optimized. The numeral simulations not only reveal the effectiveness of HJPQ, but also guarantee the great quality of experience (QoE) performance for UEs with different priorities. Furthermore, the comparison experiments with random assignment and deep deterministic policy gradient (DDPG) show the superiority of HJPQ in lower complexity, faster convergence, shorter offloading delay as well as higher energy efficiency.

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Advanced Wireless Technologies for Industrial Automation

Lyu

Guan²,

Cheng³

et al. 2023

Wireless Networks

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Sum energy maximization for UAV-enabled wireless power transfer networks with nonlinear energy harvesting model

Cited by 8 publications

References 11 publications

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

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

Joint Power and QoE Optimization Scheme for Multi-UAV Assisted Offloading in Mobile Computing

Advanced Wireless Technologies for Industrial Automation

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