Fast Trajectory Planning for UAV-Enabled Maritime IoT Systems: A Fermat-Point Based Approach

Lyu, Ling; Chu, Zhenhang; Lin, Bin; Dai, Yanpeng; Cheng, Nan

doi:10.1109/lwc.2021.3127205

Cited by 20 publications

(9 citation statements)

References 12 publications

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“…(i) UAV trajectory based on Fermat point (FTP) [35]: this scheme first regards each user as the vertex of a triangle to form multiple triangles. Then, the Fermat points of each triangle are taken as the hovering points of the UAV.…”

Section: Simulation Resultsmentioning

confidence: 99%

Deep Reinforcement Learning-Based UAV Data Collection and Offloading in NOMA-Enabled Marine IoT Systems

Dai

Liang

Lyu

et al. 2022

Wireless Communications and Mobile Computing

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The rapid growth of maritime wireless communication demand and the complex offshore wireless communication environment have brought challenges to ensure the real-time and reliability of data transmission in the marine Internet of Things (MIoT). Unmanned aerial vehicles (UAVs) have great advantages in enhancing coverage and channel quality. Hence, we investigate a UAV-assisted data collection and data offloading system based on nonorthogonal multiple access (NOMA) technology in this paper. We jointly optimize the buoy-UAV association relationship, transmit powers, and the UAV trajectory to minimize the total mission completion time while ensuring data transmission requirements. We first propose a UAV trajectory optimization algorithm based on deep reinforcement learning (DRL). Then, we design a heuristic algorithm to effectively solve the subproblem of power control and the association relationship. Finally, we propose a joint optimization scheme to solve the minimization problem. Simulation results show the effectiveness of the proposed scheme.

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Section: Simulation Resultsmentioning

confidence: 99%

Deep Reinforcement Learning-Based UAV Data Collection and Offloading in NOMA-Enabled Marine IoT Systems

Dai

Liang

Lyu

et al. 2022

Wireless Communications and Mobile Computing

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“…According to the presented results, the adopted method has improved convergence time when compared with other methods. In [117], the authors evaluate the performance of fast UAV trajectory planning algorithm based on the Fermat-point theory for a maritime IoT with USVs. Results indicate that the proposed approach can significantly improve data collection rate of USVs and provide fast convergence at the same time.…”

Section: A Enhancing Communication Performancementioning

confidence: 99%

A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

Νομικός

Gkonis

Bithas

et al. 2023

IEEE Open J. Commun. Soc.

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Maritime activities represent a major domain of economic growth with several emerging maritime Internet of Things use cases, such as smart ports, autonomous navigation, and ocean monitoring systems. The major enabler for this exciting ecosystem is the provision of broadband, low-delay, and reliable wireless coverage to the ever-increasing number of vessels, buoys, platforms, sensors, and actuators. Towards this end, the integration of unmanned aerial vehicles (UAVs) in maritime communications introduces an aerial dimension to wireless connectivity going above and beyond current deployments, which are mainly relying on shore-based base stations with limited coverage and satellite links with high latency. Considering the potential of UAV-aided wireless communications, this survey presents the state-of-the-art in UAV-aided maritime communications, which, in general, are based on both conventional optimization and machine-learning-aided approaches. More specifically, relevant UAV-based network architectures are discussed together with the role of their building blocks. Then, physical-layer, resource management, and cloud/edge computing and caching UAV-aided solutions in maritime environments are discussed and grouped based on their performance targets. Moreover, as UAVs are characterized by flexible deployment with high re-positioning capabilities, studies on UAV trajectory optimization for maritime applications are thoroughly discussed. In addition, aiming at shedding light on the current status of real-world deployments, experimental studies on UAV-aided maritime communications are presented and implementation details are given. Finally, several important open issues in the area of UAV-aided maritime communications are given, related to the integration of sixth generation (6G) advancements. These future challenges include physical-layer aspects, non-orthogonal multiple access schemes, radical learning paradigms for swarms of UAVs and unmanned surface and underwater vehicles, as well as UAV-aided edge computing and caching.

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“…Furthermore, the deployment of UAV-RIS is expected to reduce the total path loss of this system, which is positively correlated with the total Euclidean distance between the UAV-RIS and all UTs. Therefore, this study discusses two state-of-the-art UAV trajectory designs, the density-aware deployment method and the Fermat point-based approach, to evaluate the proposed dual-domain EH model [43], [44].…”

Section: Uav Trajectory Designmentioning

confidence: 99%

“…Following [44], the trajectory of the UAV-RIS can be obtained by finding the horizontal Fermat point between all UTs in the Cartesian coordinate system. Unlike the K-means algorithm that minimizes the squared Euclidean distances, the Fermat point aims to minimize the sum of the Euclidean distances from the point to each vertex.…”

Section: B Fermat Point-based Approachmentioning

confidence: 99%

Energy Harvesting Reconfigurable Intelligent Surface for UAV Based on Robust Deep Reinforcement Learning

Peng

Wang

2023

IEEE Trans. Wireless Commun.

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Integrating unmanned aerial vehicles with RIS (UAV-RIS) can offer ubiquitous deployment services in communication-disabled areas, but is limited by the on-board energy of the UAVs. In this paper, a novel energy harvesting (EH) scheme on top of the UAV-RIS system, called EH-RIS scheme, is developed for the next generation high performance wireless system. The proposed EH-RIS scheme extends the simultaneous wireless information and power transfer (SWIPT) system by splitting the passive reflected arrays on the geometric space for transporting information and harvesting energy simultaneously. However, pedestrian mobility, and rapid channel changes post challenges to efficient resource allocation in wireless systems. Thus, a robust deep reinforcement learning (DRL)-based algorithm is developed to improve the proposed EH-RIS scheme for guaranteeing the quality of service (QoS) under dynamic wireless environments. The simulation results demonstrate the effectiveness and efficiency of the proposed robust DRL-based EH-RIS system, which not only outperform the existing stateof-the-art solutions but also approach to the performance of the exhaustive search method.

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Fast Trajectory Planning for UAV-Enabled Maritime IoT Systems: A Fermat-Point Based Approach

Cited by 20 publications

References 12 publications

Deep Reinforcement Learning-Based UAV Data Collection and Offloading in NOMA-Enabled Marine IoT Systems

Deep Reinforcement Learning-Based UAV Data Collection and Offloading in NOMA-Enabled Marine IoT Systems

A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

Energy Harvesting Reconfigurable Intelligent Surface for UAV Based on Robust Deep Reinforcement Learning

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