Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System

Dong, Li; Liu, Zhibin; Jiang, Feibo; Wang, Kezhi

doi:10.1109/jiot.2022.3185012

Cited by 35 publications

(10 citation statements)

References 38 publications

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“…Reference [25] employs a variable length trajectory planning algorithm, which includes a genetic algorithm to update the stopping point deployment and deal with the problem of associating UAVs with stopping points and user devices. Reference [26] proposed a joint deployment and trajectory optimization framework for UAV applications in IoT systems. UAV deployment optimization is performed by introducing an adaptive whale optimization algorithm and UAV flight trajectory optimization by introducing elastic ring self-organizing mapping.…”

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

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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.

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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

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“…✓ ✓ [21] ✓ ✓ a three-layer structure. EDDE is first used to search the optimal FP that all UAVs fly from the DC to their destination charging stations (DCSs).…”

Section: Referencementioning

confidence: 99%

Joint Optimization of Deployment and Flight Planning of Multi-UAVs for Long-Distance Data Collection From Large-Scale IoT Devices

Zhang

Huang

et al. 2024

IEEE Internet Things J.

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Internet of Things (IoT) devices have been widely deployed to build smart cities. How to efficiently collect data from large-scale IoT devices is a valuable and challenging research topic. Benefiting from agility, flexibility, and deployability, an unmanned aerial vehicle (UAV) has great potential to be an aerial base station. However, given the limited battery capacity, the flight time of a UAV is limited. This paper focuses on using multi-UAVs to execute long-distance data collection from largescale IoT devices. We design a multi-UAVs-assisted large-scale IoT data collection system. The core facilities of this system are the data center and charging stations, which are equipped with a limited number of charging piles to provide charging services for UAVs. To ensure the efficient operation of the system, the problem of deployment and flight planning of UAVs is formulated as a joint optimization problem. To solve the problem, a population-based optimization algorithm with a three-layer structure, namely EDDE-DPDE, is proposed. It includes two core components: elitedriven differential evolution (EDDE) and differential evolution with a dynamic population (DPDE), which are two variants of differential evolution. Thanks to ideas of reusing elite individuals and historical information, the proposed EDDE-DPDE shows an improvement of at least 11.11% compared with four powerful algorithms in terms of average travel time.

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“…Similarly, a SWIPT system was proposed in [83] to maximize the harvested energy while constrained by the QoS requirements. Moreover, an IRS-Assisted UAV IoT data collection platform was studied in [88]. The authors jointly optimize the UAV's deployment and trajectory and the IRS's phase shift to minimize the energy consumption of the UAV and all IoT devices.…”

Section: A Classical Optimization Techniques For Irs Deployment In Ua...mentioning

confidence: 99%

IRS-Empowered 6G Networks: Deployment Strategies, Performance Optimization, and Future Research Directions

et al. 2022

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The performance of the envisioned 6G network is fundamentally constrained by the uncontrollable and random wireless communication channel. Intelligent reflecting surfaces (IRSs) have emerged as one of the potential solutions to overcome this challenge by smartly controlling the incident signal to enhance the energy efficiency and spectrum efficiency of the 6G network. In addition, the future 6G network will incorporate several enabling technologies, including artificial intelligence and machine learning (AI/ML), integrated terrestrial and non-terrestrial (TNT) networks, multi-access edge computing (MEC), non-orthogonal multiple access (NOMA), and terahertz/millimeter wave (THz/mmWave) communication techniques. Therefore, this paper provides a contemporary and comprehensive overview of the envisioned IRS-empowered 6G networks from the perspective of its architecture, deployment strategy, integration of IRS technology with other 6G-enabling technologies, and physical layer security (PLS). Finally, we highlight design challenges and future research directions aimed at improving 6G network performance.INDEX TERMS Intelligent reflecting surfaces, Terahertz communication, Terrestrial and non-terrestrial (TNT) networks, Unmanned Aerial Vehicles, Reinforcement learning, Ultra-reliable and Low-latency communications.

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Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System

Cited by 35 publications

References 38 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

Joint Optimization of Deployment and Flight Planning of Multi-UAVs for Long-Distance Data Collection From Large-Scale IoT Devices

IRS-Empowered 6G Networks: Deployment Strategies, Performance Optimization, and Future Research Directions

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