Energy-Efficient UAV Routing for Wireless Sensor Networks

Baek, Jaeuk; Han, Sang Ik; Han, Youngnam

doi:10.1109/tvt.2019.2959808

Cited by 150 publications

(64 citation statements)

References 28 publications

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“…In UAV group networks [260,261], the mobility and altitude of UAVs, transmitted power, inter-UAV distances, extraneous noise, uneven UAV access users' traffic distribution in both spatial and temporal domains, will largely affect the routing protocol [262] and conventional routing protocols fail to work. Several novel routing algorithms have been proposed recently for UAV group networks to achieve better performance [263][264][265][266][267][268]. Nevertheless, routing strategies which can adapt to high mobility, dynamic topology, and different routing capabilities in LEO satellite networks and UAV group networks are still in their early stage and need further studies.…”

Section: Space-air-ground-sea Integrated Networkmentioning

confidence: 99%

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

You

Wang

Huang

et al. 2020

Sci. China Inf. Sci.

1,210

495

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The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

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Section: Space-air-ground-sea Integrated Networkmentioning

confidence: 99%

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

You

Wang

Huang

et al. 2020

Sci. China Inf. Sci.

1,210

495

View full text Add to dashboard Cite

show abstract

“…Recently, unscrewed aerial vehicles (UAVs) have been widely adopted to utilize network resources such as the Internet (population), sensor networks and three-dimensional (3D) wireless networks [14]. The entire network of wireless sensor networks (WSNs) sends messages about the node, an important and simple operation.…”

Section: Related Workmentioning

confidence: 99%

Avant-Grade framework Routing Optimization Based on Established Network Lifetime and Energy Management in WSN

Kumar¹,

Jayapandian²,

Balasubramanie³

2021

Preprint

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Wireless Sensor Network (WSN) is known to be a highly resource constrained class of network where energy consumption is one of the prime concerns. The existing system of LTDC has more drawbacks in network routing optimization lifetime and energy level improvement needed. In this research, a cross layer design methodology was adopted to design an energy efficient routing protocol entitled Avant-Grade framework Routing Optimization [AGFRO]. AGFRO is designed to minimize energy consumed in each node by reducing the amount of time in which a sensor node is in an idle listening state and reducing the average communication distance over the network. The performance of the proposed system has been critically evaluated in the context of network lifetime, throughput, and energy consumption of the network per individual basis and per data packet basis. The research results were analyzed and benchmarked against the well-known AGFRO protocols. The outcomes show a significant improvement in the WSN in terms of energy efficiency and the overall performance of WSN.

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“…They can also be used as on-demand airborne relays connecting a remote user and a cellular BS separated by significant obstacles [ 4 ]. For wireless sensor networks (WSNs), UAVs can be utilized to disseminate/collect control and data information from ground-deployed wireless sensors [ 5 , 6 ]. For mobile ad-hoc networks (MANETs), such as vehicular ad-hoc networks (VANETs), UAVs can assist the management and control of VANETs and extend their scalability and coverage [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Gateway Selection in Millimeter Wave UAV Wireless Networks Using Multi-Player Multi-Armed Bandit

Mohamed

Hashima

Aldosary

et al. 2020

Sensors

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Recently, unmanned aerial vehicle (UAV)-based communications gained a lot of attention due to their numerous applications, especially in rescue services in post-disaster areas where the terrestrial network is wholly malfunctioned. Multiple access/gateway UAVs are distributed to fully cover the post-disaster area as flying base stations to provide communication coverage, collect valuable information, disseminate essential instructions, etc. The access UAVs after gathering/broadcasting the necessary information should select and fly towards one of the surrounding gateways for relaying their information. In this paper, the gateway UAV selection problem is addressed. The main aim is to maximize the long-term average data rates of the UAVs relays while minimizing the flights’ battery cost, where millimeter wave links, i.e., using 30~300 GHz band, employing antenna beamforming, are used for backhauling. A tool of machine learning (ML) is exploited to address the problem as a budget-constrained multi-player multi-armed bandit (MAB) problem. In this setup, access UAVs act as the players, and the arms are the gateway UAVs, while the rewards are the average data rates of the constructed relays constrained by the battery cost of the access UAV flights. In this decentralized setting, where information is neither prior available nor exchanged among UAVs, a selfish and concurrent multi-player MAB strategy is suggested. Towards this end, three battery-aware MAB (BA-MAB) algorithms, namely upper confidence bound (UCB), Thompson sampling (TS), and the exponential weight algorithm for exploration and exploitation (EXP3), are proposed to realize gateways selection efficiently. The proposed BA-MAB-based gateway UAV selection algorithms show superior performance over approaches based on near and random selections in terms of total system rate and energy efficiency.

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Energy-Efficient UAV Routing for Wireless Sensor Networks

Cited by 150 publications

References 28 publications

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

Avant-Grade framework Routing Optimization Based on Established Network Lifetime and Energy Management in WSN

Gateway Selection in Millimeter Wave UAV Wireless Networks Using Multi-Player Multi-Armed Bandit

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