Efficient 3D Placement of UAVs with QoS Assurance in Ad Hoc Wireless Networks

Perabathini, Bhanukiran; Tummuri, Kiranteja; Agrawal, Akshay; Varma, Vineeth S.

doi:10.1109/icccn.2019.8846947

Cited by 21 publications

(14 citation statements)

References 9 publications

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“…This approach is claimed to improve end-user quality of experience. An optimization algorithm based on particle swarm optimization has been explored to reduce total power consumed by base stations and optimizing quality of service in a UAV-based wireless communication system [35]. This algorithm is claimed to reduce deployment and operational costs, but does not consider quality of service in an aerial environment.…”

Section: Related Workmentioning

confidence: 99%

Internet of Unmanned Aerial Vehicles: QoS Provisioning in Aerial Ad-Hoc Networks

Kumar

Kaiwartya

et al. 2020

Sensors

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Aerial ad-hoc networks have the potential to enable smart services while maintaining communication between the ground system and unmanned aerial vehicles (UAV). Previous research has focused on enabling aerial data-centric smart services while integrating the benefits of aerial objects such as UAVs in hostile and non-hostile environments. Quality of service (QoS) provisioning in UAV-assisted communication is a challenging research theme in aerial ad-hoc networks environments. Literature on aerial ad hoc networks lacks cooperative service-oriented modeling for distributed network environments, relying on costly static base station-oriented centralized network environments. Towards this end, this paper proposes a quality of service provisioning framework for a UAV-assisted aerial ad hoc network environment (QSPU) focusing on reliable aerial communication. The UAV’s aerial mobility and service parameters are modelled considering highly dynamic aerial ad-hoc environments. UAV-centric mobility models are utilized to develop a complete aerial routing framework. A comparative performance evaluation demonstrates the benefits of the proposed aerial communication framework. It is evident that QSPU outperforms the state-of-the-art techniques in terms of a number of service-oriented performance metrics in a UAV-assisted aerial ad-hoc network environment.

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Section: Related Workmentioning

confidence: 99%

Internet of Unmanned Aerial Vehicles: QoS Provisioning in Aerial Ad-Hoc Networks

Kumar

Kaiwartya

et al. 2020

Sensors

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“…To specify the optimal 3D UAV placement, both Air-to-Ground and Outdoor-to-Indoor path loss models are employed to optimize the data rates of indoor and outdoor users under a UAV transmit power budget. Meanwhile, the research in [182] focuses on the 3D UAV placement problem with QoS awareness in ad hoc wireless networks. The number of aerial base stations and transmit power of each station are jointly formulated and then solved by a PSO optimization algorithm in an iterative manner.…”

Section: B Uav Placement and Path Planningmentioning

confidence: 99%

Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

Pham¹,

Nguyen²,

Mirjalili³

et al. 2020

Preprint

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Due to the proliferation of smart devices and emerging applications, many next-generation technologies have been paid for the development of wireless networks. Even though commercial 5G has just been widely deployed in some countries, there have been initial efforts from academia and industrial communities for 6G systems. In such a network, a very large number of devices and applications are emerged, along with heterogeneity of technologies, architectures, mobile data, etc., and optimizing such a network is of utmost importance. Besides convex optimization and game theory, swarm intelligence (SI) has recently appeared as a promising optimization tool for wireless networks. As a new subdivision of artificial intelligence, SI is inspired by the collective behaviors of societies of biological species. In SI, simple agents with limited capabilities would achieve intelligent strategies for high-dimensional and challenging problems, so it has recently found many applications in nextgeneration wireless networks (NGN). However, researchers may not be completely aware of the full potential of SI techniques. In this work, our primary focus will be the integration of these two domains: NGN and SI. Firstly, we provide an overview of SI techniques from fundamental concepts to well-known optimizers. Secondly, we review the applications of SI to settle emerging issues in NGN, including spectrum management and resource allocation, wireless caching and edge computing, network security, and several other miscellaneous issues. Finally, we highlight open challenges and issues in the literature, and introduce some interesting directions for future research.

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“…Besides approaches that assume free-space propagation [8], a large number of works rely on the empirical model from [9]; see e.g. [10][11][12][13][14].The main limitation is that such models provide shadowing values in average scenarios, e.g. in a generic urban environment, but are likely to yield highly suboptimal placements in a specific environment.…”

Section: Introductionmentioning

confidence: 99%

Aerial Base Station Placement Leveraging Radio Tomographic Maps

Romero

Viet

Leus

2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Mobile base stations on board unmanned aerial vehicles (UAVs) promise to deliver connectivity to those areas where the terrestrial infrastructure is overloaded, damaged, or absent. A fundamental problem in this context involves determining a minimal set of locations in 3D space where such aerial base stations (ABSs) must be deployed to provide coverage to a set of users. While nearly all existing approaches rely on average characterizations of the propagation medium, this work develops a scheme where the actual channel information is exploited by means of a radio tomographic map. A convex optimization approach is presented to minimize the number of required ABSs while ensuring that the UAVs do not enter nofly regions. A simulation study reveals that the proposed algorithm markedly outperforms its competitors.

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Efficient 3D Placement of UAVs with QoS Assurance in Ad Hoc Wireless Networks

Cited by 21 publications

References 9 publications

Internet of Unmanned Aerial Vehicles: QoS Provisioning in Aerial Ad-Hoc Networks

Internet of Unmanned Aerial Vehicles: QoS Provisioning in Aerial Ad-Hoc Networks

Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

Aerial Base Station Placement Leveraging Radio Tomographic Maps

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