Energy-Efficient UAV Flight Planning for a General PoI-Visiting Problem with a Practical Energy Model

Huang, Jianping; Shan, Feng; Xiong, Runqun; Shao, Yuchao; Luo, Junzhou

doi:10.1109/icccn52240.2021.9522183

Cited by 4 publications

(13 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this regard, a heuristic algorithm based on SCA and Dinkelbach's model was proposed to determine the optimal trajectory. A flight planning mechanism for solving point of interest visiting problems involving data collection, edge computing, and surveillance operations was proposed [207] to minimize the flight energy consumption of the UAV. The flight turning and switching cost were modeled as a graph and then transferred to a travelling salesman problem after which a heuristic algorithm was proposed to determine the optimal flight path.…”

Section: Conventional Approachesmentioning

confidence: 99%

“…Year Category Specific Algorithm CA ML [201] 2020 Heuristic [113] 2017 Heuristic, MILP [25] 2019 Heuristic [145] 2020 Heuristic [26] 2020 Heuristic [202] 2020 Heuristic [203] 2021 Heuristic [204] 2018 Heuristic [70] 2019 GA [205] 2018 Heuristic [206] 2019 Heuristic [129] 2020 GA [115] 2021 Heuristic [207] 2021 Heuristic [208] 2019 Heuristic [209] 2018 Heuristic [210] 2018 Heuristic [211] 2019 Heuristic [212] 2020 Heuristic [213] 2020 Heuristic [214] 2020 Heuristic, MILP [216] 2019 Heuristic [215] 2021 Heuristic [217] 2020 Heuristic [218] 2020 Heuristic [219] 2021 Heuristic [220] 2021 Heuristic [142] 2016 Heuristic, MILP [221] 2018 GA, MILP [222] 2018 LP, Convex Optimization [223] 2020 RL [224] 2021 DQN [225] 2020 DDPG [226] 2021 Q-learning In [249], the authors addressed the energy-efficient resource allocation problem in a two-hop uplink communication for Space-Air-Ground Internet of remote things (SAG-IoRT) networks assisted with unmanned aerial vehicle (UAV) relays. The goal is to maximize system energy efficiency by jointly optimizing sub-channel selection, uplink transmission power control, and UAV relays deployment.…”

Section: Papermentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Abubakar

Ahmad

Omeke

et al. 2023

Drones

View full text Add to dashboard Cite

Wireless communication networks have been witnessing unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Although there are many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance capacity due to their easy implementation, pop-up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity where it is needed. However, UAVs mostly have limited energy storage, hence, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed—conventional and machine learning (ML). Such classification helps understand the state-of-the-art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above-mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trends in the literature.

show abstract

Section: Conventional Approachesmentioning

confidence: 99%

Section: Papermentioning

confidence: 99%

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Abubakar

Ahmad

Omeke

et al. 2023

Drones

View full text Add to dashboard Cite

show abstract

“…When these studies in the UAV literature were examined, it was observed that they studies were gathered under three main topics; battery life prediction, 1,2 anomaly detection 3-14 and instantaneous power consumption prediction. [15][16][17][18][19][20][21] The battery life prediction using machine learning is of great importance for the UAVs to fulfill their duties as determined by the moderator and to avoid any problems during the flight. In 2017, Mansouri et al examined flights under different conditions and performed the prediction of the remaining useful life (RUL) of Li-on batteries.…”

mentioning

confidence: 99%

“…In recent years, researchers have conducted numerous studies on this topic for this reason. [15][16][17][18][19][20][21] In 2018, the effects of payload, communication, environmental parameters (wind, air density, etc. ), and movement parameters (direction, speed, angle, etc.)…”

mentioning

confidence: 99%

“…20 After the 2020s, researchers focused on developing algorithms and smart power supply systems to ensure energy consumption efficiency. 18,19 Recently, the use of TCN for UAV power consumption prediction has increased rapidly due to its high prediction performance, as seen in the above studies. As a consequence, the researchers developed the TCN model in an effort to construct more accurate power consumption prediction models.…”

mentioning

confidence: 99%

See 1 more Smart Citation

UAV instantaneous power consumption prediction using LR‐TCN with simple moving average

Dudukcu,

Taskiran,

Kahraman

2023

Concurrency and Computation

View full text Add to dashboard Cite

SummaryThe usage of multi‐rotor unmanned aerial vehicles (UAVs) has rapidly increased in a variety of industries recently, which has caused a quick rise in the quantity of research works on the topic. Remaining battery life prediction, anomaly detection and instantaneous power consumption prediction are among the topics that attract the most attention of researchers. This article presents the development and utilization of a modified temporal convolutional network (TCN) model, a commonly employed approach for anomaly detection and instantaneous power consumption prediction in unmanned aerial vehicles (UAVs). The first modification to the TCN model was to use the Leaky ReLu activation function in place of the rectified linear unit (ReLu) activation function from the original TCN model. In the next step, instantaneous power consumption prediction for UAVs was performed by using the data obtained from both sensors and simple moving average (SMA) algorithm. As a result of the tests performed with the created simulation setup, it has been clearly shown that the proposed method gives better results compared to other deep learning models used for comparison with the lowest RMSE of 0.0496.

show abstract

Energy-Efficient General PoI-Visiting by UAV with a Practical Flight Energy Model

Shan

Huang

Dong

et al. 2022

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

Energy-Efficient UAV Flight Planning for a General PoI-Visiting Problem with a Practical Energy Model

Cited by 4 publications

References 25 publications

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

UAV instantaneous power consumption prediction using LR‐TCN with simple moving average

Energy-Efficient General PoI-Visiting by UAV with a Practical Flight Energy Model

Contact Info

Product

Resources

About