Deployment Optimization in UAV Aided Vehicle Localization

Yang, Jiayan; Liang, Tianhao; Zhang, Tingting

doi:10.1109/vtc2021-spring51267.2021.9448898

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…UAV placement problem which tries to maximize the coverage region is a nonconvex problem and proved to be NP‐hard (X. Liu et al, 2019; Lyu et al, 2017) in general. In UAV placement problem, UAVs horizontal and/or vertical positioning, inter‐UAV safety distance maintenance, cost, UAV numbers, coverage rate, and users–UAV connectivity are important factors needed to be considered in the deployment problem (Gao et al, 2021; Ghazal, 2021; Kim & Lee, 2018, 2020; Lahmeri et al, 2021; Q. Liu et al, 2018; Masroor et al, 2021a; Rahimi et al, 2021; J. Yang, Liang, et al, 2021; C. Zhang et al, 2021). Trajectory/path planning : One of the essential factors to optimize the UAV network's performance is UAV trajectory design or path planning (Mozaffari et al, 2019).…”

Section: Analysis Of the Specific Questionsmentioning

confidence: 99%

“…The FANETs issues that can be addressed by supervised and unsupervised learning methods are discussed as follows according to UAVs horizontal and/or vertical placement, distance, cost, UAV numbers, coverage rate, and users-UAV connectivity are important factors needed to be considered in the deployment problem (Gao et al, 2021;Ghazal, 2021;Lahmeri et al, 2021;Q. Liu et al, 2018;Masroor et al, 2021a;Rahimi et al, 2021;J. Yang, Liang, et al, 2021;.…”

Section: Analysis Of the Specific Questionsmentioning

confidence: 99%

“…In this regard, low cost and high mobility, multiple UAV‐mounted BSs are sent to target regions to provide temporary wireless communication services. UAVs horizontal and/or vertical placement, distance, cost, UAV numbers, coverage rate, and users–UAV connectivity are important factors needed to be considered in the deployment problem (Gao et al, 2021; Ghazal, 2021; Lahmeri et al, 2021; Q. Liu et al, 2018; Masroor et al, 2021a; Rahimi et al, 2021; J. Yang, Liang, et al, 2021; C. Zhang et al, 2021). ML algorithms are used to predict the optimal position of the UAVs by identifying the overloaded traffic areas by predicting users' demands and positions (Nouri, Abouei, et al, 2021; Nouri, Fazel, et al, 2021; Oliveira et al, 2021).…”

Section: Analysis Of the Specific Questionsmentioning

confidence: 99%

See 2 more Smart Citations

A systematic literature review of flying ad hoc networks: State‐of‐the‐art, challenges, and perspectives

Pasandideh

Costa

Kunst

et al. 2023

Journal of Field Robotics

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Unmanned aerial vehicles (UAVs), also known as drones, communicate, collaborate, and form flying ad hoc networks (FANETs) to perform many different missions, ranging from delivery tasks to agriculture applications. Recently, FANETs have been integrated with different technologies, such as artificial intelligence (AI), virtual reality, and Internet of Things. Such new avenues for the use of UAVs directly impact the research on FANETs and cause some major challenges, such as security and physical layer issues, resource management, and UAV positioning issues that need to be addressed. Several researchers have been working for the last few years to propose AI and machine learning (ML)‐based solutions for different use cases in UAV‐based networks. They present the limitations of the existing research work and highlight some possible future works on FANETs. However, exhibiting the trends in the UAV research papers in a quantitative manner is still required to motivate researchers to rethink the research on FANETs. Therefore, this study covers more than 170 scientific publications extracted from five trusted academic databases published from 2013 to 2021 to provide a thorough overview of the main research and development statistics in the area of FANETs, the open challenges existing in this area and the ML‐based solutions to solve these challenges. In addition, the investigation of emerging technologies integrated with FANETs, as well as the simulation tools employed for evaluating FANETs' performance are discussed. Moreover, the future research directions in the area of FANETs are considered within a prospective vision discussion.

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Section: Analysis Of the Specific Questionsmentioning

confidence: 99%

Section: Analysis Of the Specific Questionsmentioning

confidence: 99%

Section: Analysis Of the Specific Questionsmentioning

confidence: 99%

See 1 more Smart Citation

A systematic literature review of flying ad hoc networks: State‐of‐the‐art, challenges, and perspectives

Pasandideh

Costa

Kunst

et al. 2023

Journal of Field Robotics

View full text Add to dashboard Cite

show abstract

“…Various wireless performance metrics such as throughput, latency, energy or power consumption, and localization accuracy were optimized in the UAV deployment problems of IoV networks [4]- [8]. While placing UAVs, the authors in [4] took into account the signal occlusion effect in the threedimensional (3D) highway interchanges instead of the common two-dimensional (2D) roads.…”

Section: A Related Workmentioning

confidence: 99%

“…Further, a UAV placement and transmit power allocation approach was proposed. Yang et al utilized UAVs to assist vehicle localization in which the placement of UAVs will affect the localization accuracy [8]. To minimize a lower bound on the localization error, a joint transmit power and bandwidth allocation approach along with a UAV placement method were proposed.…”

Section: A Related Workmentioning

confidence: 99%

Evenness-Aware Data Collection for Edge-Assisted Mobile Crowdsensing in Internet of Vehicles

Liu

Wang

et al. 2023

IEEE Internet Things J.

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Unmanned aerial vehicles (UAVs) are envisioned to provide diverse services from the air. The service quality may rely on the wireless performance which is affected by the UAV's position. In this paper, we focus on the UAV placement problem in the Internet of Vehicles, where the UAV is deployed to monitor the road traffic and sends the monitored videos to vehicles. The studied problem is formulated as video resolution maximization by optimizing over the UAV's position. Moreover, we take into account the maximal transmission delay and impose a probabilistic constraint. To solve the formulated problem, we first leverage the techniques in extreme value theory (EVT) and Gaussian process regression (GPR) to characterize the influence of the UAV's position on the delay performance. Based on this characterization, we subsequently propose a proactive resolution selection and UAV placement approach, which adaptively places the UAV according to the geographic distribution of vehicles. Numerical results justify the joint usage of EVT and GPR for maximal delay characterization. Through investigating the maximal transmission delay, the proposed approach nearly achieves the optimal performance when vehicles are evenly distributed, and reduces 10% and 19% of the 999-th 1000-quantile over two baselines when vehicles are biased distributed.

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Large-scale mobile users deployment optimization based on a two-stage hybrid global HS-DE algorithm in multi-UAV-enabled mobile edge computing

Ouyang

Liu

Zhang

et al. 2023

Engineering Applications of Artificial Intelligence

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Deployment Optimization in UAV Aided Vehicle Localization

Cited by 7 publications

References 11 publications

A systematic literature review of flying ad hoc networks: State‐of‐the‐art, challenges, and perspectives

A systematic literature review of flying ad hoc networks: State‐of‐the‐art, challenges, and perspectives

Evenness-Aware Data Collection for Edge-Assisted Mobile Crowdsensing in Internet of Vehicles

Large-scale mobile users deployment optimization based on a two-stage hybrid global HS-DE algorithm in multi-UAV-enabled mobile edge computing

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