Three-Dimensional Multi-UAV Placement and Resource Allocation for Energy-Efficient IoT Communication

Nouri, Nima; Abouei, Jamshid; Sepasian, Ali Reza; Jaseemuddin, Muhammad; Anpalagan, Alagan; Plataniotis, Konstantinos N.

doi:10.1109/jiot.2021.3091166

Cited by 52 publications

(26 citation statements)

References 47 publications

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“…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). According to Oliveira et al (2021), among ML approaches tested for predicting users' positions, Gradient Boosting and Random Forest provide the best result, while Lasso, Ridge, and ElasticNet are tied at the last place. Channel estimation : Channel state information (CSI) highly impacts the performance of the UAV communication systems.…”

Section: Analysis Of the Specific Questionsmentioning

confidence: 99%

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%

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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“…NOMA allows the improvement of the uplink capacity of the system by jointly optimizing the position of the UAVs and the power control of the IoT devices. In UAV-NOMA based networks, the optimization problem focuses on power allocation and efficiency, as well as trajectory and placement, and is the focus of ongoing research [346], [347], [348], [349], [350].…”

Section: ) 5g Mmwave Uav-assisted Communication Networkmentioning

confidence: 99%

Unmanned Aerial Vehicle Communications for Civil Applications: A Review

et al. 2022

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The use of drones, formally known as unmanned aerial vehicles (UAVs), has significantly increased across a variety of applications over the past few years. This is due to the rapid advancement towards the design and production of inexpensive and dependable UAVs and the growing request for the utilization of such platforms particularly in civil applications. With their intrinsic attributes such as high mobility, rapid deployment and flexible altitude, UAVs have the potential to be utilized in many wireless system applications. On the one hand, UAVs are able to operate as flying mobile terminals within wireless/cellular networks to support a variety of missions such as goods delivery, search and rescue, precision agriculture monitoring, and remote sensing. On the other hand, UAVs can be utilized as aerial base stations to increase wireless communication coverage, reliability, and the capacity of wireless systems without additional investment in wireless systems infrastructure. The aim of this article is to review the current applications of UAVs for civil and commercial purposes. The focus of this paper is on the challenges and communication requirements associated with UAV-based communication systems. This article initially classifies UAVs in terms of various parameters, some of which can impact UAVs' communication performance. It then provides an overview of aerial networking and investigates UAVs routing protocols specifically, which are considered as one of the challenges in UAV communication. This article later investigates the use of UAV networks in a variety of civil applications and considers many challenges and communication demands of these applications. Subsequently, different types of simulation platforms are investigated from a communication and networking viewpoint. Finally, it identifies areas of future research.

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“…The authors showed considerable energy savings as well as lower energy-consumption gap across the served users compared to benchmarks. Due to its efficiency, NOMA was incorporated into the multiple-UAV-enabled MEC IoT system studied by [21] where the authors aimed to maximize the total energy efficiency while minimizing the number of UAVs and the total cost of UAV and central cloud use. This is done through jointly optimizing the transmit powers, computation and bandwidth allocation, UAVs 3D locations, and designation of the UAVs to the NOMA device clusters.…”

Section: Related Work and Novelty Of The Proposed Workmentioning

confidence: 99%

“…The UAVs are further assumed identical in terms of battery type and capacity. All the relevant parameter settings are listed in Table 1 [10], [21], [22], [23], and [27].…”

Section: B Simulation Parametersmentioning

confidence: 99%

Design and Optimization of a UAV-Enabled Non-Orthogonal Multiple Access Edge Computing IoT System

Farha

Ismail

2022

IEEE Access

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Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) in IoT systems has recently gained prominence as a solution to accommodate the low latency and high energy efficiency needs of emerging and developing 5G-and-beyond IoT applications. Given the limited spectrum available, non-orthogonal multiple access (NOMA) communication scheme provides a spectral-and power-efficient task offloading means. In this work, a novel framework is proposed for a multiple-UAV-enabled MEC IoT system incorporating uplink NOMA. The goal is to maximize the sum bits offloaded from all the IoT devices (IoTDs) in a finite service period. This is achieved through joint optimization of the associations between the IoTDs and the UAVs, the IoTD transmit powers, as well as the UAV trajectories, given UAV and IoTD energy budgets and quality-of-service (QoS) criteria. Towards that end, a specialized penalty block coordinate descent (P-BCD) algorithm is proposed to decompose the original problem into 4 subproblems that are solved alternately and iteratively. By direct comparison with several benchmark schemes including orthogonal multiple access (OMA), our proposed scheme displays performance enhancement in terms of sum bits offloaded and energy expenditure. Finally, a performance comparison is carried out for the proposed scheme with different number of employed UAVs to find the most cost-effective number of UAVs to deploy.INDEX TERMS Internet of Things (IoT), Mobile Edge Computing (MEC), Non-orthogonal multiple access (NOMA), Unmanned Aerial Vehicles (UAVs).

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Three-Dimensional Multi-UAV Placement and Resource Allocation for Energy-Efficient IoT Communication

Cited by 52 publications

References 47 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

Unmanned Aerial Vehicle Communications for Civil Applications: A Review

Design and Optimization of a UAV-Enabled Non-Orthogonal Multiple Access Edge Computing IoT System

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