Joint Deployment Optimization and Flight Trajectory Planning for UAV Assisted IoT Data Collection: A Bilevel Optimization Approach

Han, Shoufei; Zhu, Kun; Zhou, MengChu; Liu, Xiaojing

doi:10.1109/tits.2022.3180288

Cited by 34 publications

(25 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite the great advantages of UAVs in assisting data collection for the IoT network, how to improve their efficiency remains a key issue to be addressed. In recent years, scholars have turned their attention to UAV deployment optimization and flight trajectory planning PLOS ONE [15]. Recently, reference [16] gave a more comprehensive review of UAV-based sensing networks.…”

Section: Related Studiesmentioning

confidence: 99%

A PSO-based energy-efficient data collection optimization algorithm for UAV mission planning

Lin,

Wang,

Tian

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

With the development of the Internet of Things (IoT), the use of UAV-based data collection systems has become a very popular research topic. This paper focuses on the energy consumption problem of this system. Genetic algorithms and swarm algorithms are effective approaches for solving this problem. However, optimizing UAV energy consumption remains a challenging task due to the inherent characteristics of these algorithms, which make it difficult to achieve the optimum solution. In this paper, a novel particle swarm optimization (PSO) algorithm called Double Self-Limiting PSO (DSLPSO) is proposed to minimize the energy consumption of the unmanned aerial vehicle (UAV). DSLPSO refers to the operational principle of PSO and incorporates two new mechanisms. The first mechanism is to restrict the particle movement, improving the local search capability of the algorithm. The second mechanism dynamically adjusts the search range, which improves the algorithm’s global search capability. DSLPSO employs a variable population strategy that treats the entire population as a single mission plan for the UAV and dynamically adjusts the number of stopping points. In addition, the proposed algorithm was also simulated using public and random datasets. The effectiveness of the proposed DSLPSO and the two new mechanisms has been verified through experiments. The DSLPSO algorithm can effectively improve the lifetime of the UAV, and the two newly proposed mechanisms have potential for optimization work.

show abstract

Section: Related Studiesmentioning

confidence: 99%

A PSO-based energy-efficient data collection optimization algorithm for UAV mission planning

Lin,

Wang,

Tian

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…However, they still only considered one UAV in the scenario. Moreover, the Capacities of UAV/UAVs-aided D2D networks [12] [21] [22] [23] This work authors in [15] aimed to reduce the energy consumption of a UAV under UAV-assist Internet-of-Things system. Specifically, they adopted a bi-level optimization approach to optimize the UAV position deployment and trajectory, where an upper-level method was to optimize the UAV position deployment and a lower-level one was to design UAV trajectory, respectively.…”

Section: A Flight Energy Consumption Of Uavsmentioning

confidence: 99%

Resource Scheduling for UAVs-Aided D2D Networks: A Multi-Objective Optimization Approach

Pan,

Liu,

Sun

et al. 2024

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs)-aided device-todevice (D2D) networks have attracted great interests with the development of 5G/6G communications, while there are several challenges about resource scheduling in UAVs-aided D2D networks. In this work, we formulate a UAVs-aided D2D network resource scheduling optimization problem (NetResSOP) to comprehensively consider the number of deployed UAVs, UAV positions, UAV transmission powers, UAV flight velocities, communication channels, and UAV-device pair assignment so as to maximize the D2D network capacity, minimize the number of deployed UAVs, and minimize the average energy consumption over all UAVs simultaneously. The formulated NetResSOP is a mixed-integer programming problem (MIPP) and an NPhard problem, which means that it is difficult to be solved in polynomial time. Moreover, there are trade-offs between the optimization objectives, and hence it is also difficult to find an optimal solution that can simultaneously make all objectives be optimal. Thus, we propose a non-dominated sorting genetic algorithm-III with a Flexible solution dimension mechanism, a Discrete part generation mechanism, and a UAV number adjustment mechanism (NSGA-III-FDU) for solving the problem comprehensively. Simulation results demonstrate the effectiveness and the stability of the proposed NSGA-III-FDU under different scales and settings of the D2D networks.

show abstract

“…Reference [20] firstly solves the initial path of traversing the sensing node through the traveling salesman problem and then uses the communication range of the sensing node, so that the UAV does not have to fly directly above the node but only needs to be within its communication range, predetermining the task point, shortening the length of the whole data collection path, and reducing the energy consumption of the UAV. Reference [21] proposes a fast cruise path algorithm for UAVs. The algorithm starts from the specified starting point, traverses each task point once and only once, and finally reaches a flight path at the specified end point.…”

Section: Uav Path Planning Algorithmmentioning

confidence: 99%

An Enhanced Energy-Efficient Data Collection Optimization Algorithm for UAV Swarm in the Intelligent Internet of Things

Sun

Cai

Wang

et al. 2023

Drones

View full text Add to dashboard Cite

In the case of limited endurance of unmanned aerial vehicles (UAVs), in order to further improve UAV data collection efficiency, this paper puts forward EDC-UAVIIoT: an enhanced energy-efficient data collection optimization algorithm for UAV swarm in the intelligent Internet of Things. First of all, the algorithm optimizes the UAV cruise path through the intelligent Internet of Things routing mechanism, avoids the occurrence of data errors in the packet transmission process, and uses the end-to-end transmission error probability model. The error probability of data packets in the transmission process is calculated to improve the efficiency of data collection tasks and data throughput. Secondly, considering the relationship between energy harvesting and energy consumption balance, this paper uses semi-definite programming and a convex approximation algorithm to transform the non-convex optimization problem into a convex optimization problem and realize the mapping relationship between the UAV cluster node and the target node coordinates, which reduces the computational complexity. Finally, the simulation results show that the EDC-UAVIIoT algorithm is compared with other algorithms in network energy consumption, running time, network delay, and network throughput. The numerical values are increased by 7.03%, 10.16%, 12.39%, and 8.82%, respectively, thus verifying the effectiveness and stability of the proposed EDC-UAVIIoT algorithm.

show abstract

Joint Deployment Optimization and Flight Trajectory Planning for UAV Assisted IoT Data Collection: A Bilevel Optimization Approach

Cited by 34 publications

References 58 publications

A PSO-based energy-efficient data collection optimization algorithm for UAV mission planning

A PSO-based energy-efficient data collection optimization algorithm for UAV mission planning

Resource Scheduling for UAVs-Aided D2D Networks: A Multi-Objective Optimization Approach

An Enhanced Energy-Efficient Data Collection Optimization Algorithm for UAV Swarm in the Intelligent Internet of Things

Contact Info

Product

Resources

About