Big Data on the Fly: UAV-Mounted Mobile Edge Computing for Disaster Management

Xu, Jianwen; Ota, Kaoru; Dong, Mianxiong

doi:10.1109/tnse.2020.3016569

Cited by 68 publications

(26 citation statements)

References 31 publications

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“…Proof: From (26a) (27), and ( 28), we can conclude that the objective function of problem (P1) is convex with respect to (w.r.t.) P, b k,l [n] , and b k,U [n] , since its Hessian matrix is positive semidefinite.…”

Section: Problem Formulation and Optimizationmentioning

confidence: 93%

“…The advantage of employing UAVs as MEC servers in Cyber-Physical Systems (CPSs) was outlined in [26] and the three-dimensional (3-D) UAV's trajectory was sub-optimally optimized to extend the UAV's endurance. In [27], the use of UAV-mounted edge nodes in Long Range Wide Area Networks (LoRaWANs) for disaster management was investigated. Wireless power transfer (WPT) was also introduced to prolong network's operation time and optimization problems were formulated to maximize the sum completed task-input bits [28] and the UAV's required energy [29].…”

Section: A Backgroundmentioning

confidence: 99%

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Energy Optimization in Massive MIMO UAV-Aided MEC-Enabled Vehicular Networks

et al. 2021

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This paper presents a novel unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) architecture for vehicular networks. It is considered that the vehicles should complete latencycritical computation-intensive tasks either locally with on-board computation units or by offloading part of their tasks to road side units (RSUs) with collocated MEC servers. In this direction, a hovering UAV can serve as an aerial RSU (ARSU) for task processing or act as an aerial relay and further offload the computation tasks to a ground RSU (GRSU). To significantly reduce the delay during data offloading and downloading, this architecture relies on the benefits of line-of-sight (LoS) massive multiple-input-multipleoutput (MIMO). Therefore, it is considered that the vehicles, the ARSU, and the GRSU employ large-scale antennas. A three-dimensional (3-D) geometrical representation of the MEC-enabled network is introduced and an optimization method is proposed that minimizes the computation-based and communication-based weighted total energy consumption (WTEC) of vehicles and ARSU subject to transmit power allocation, task allocation, and time slot scheduling. The results verify the theoretical derivations, emphasize on the effectiveness of the LoS massive MIMO transmission, and provide useful engineering insights.INDEX TERMS Computation offloading, energy efficiency, massive multiple-input multiple-output (MIMO), mobile edge computing (MEC), unmanned aerial vehicle (UAV), vehicular networks.

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Section: Problem Formulation and Optimizationmentioning

confidence: 93%

Section: A Backgroundmentioning

confidence: 99%

Energy Optimization in Massive MIMO UAV-Aided MEC-Enabled Vehicular Networks

et al. 2021

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“…In (6), where ρ y = cos 2 (φ) + sin 2 (φ) cos 2 (θ), ρ z = sin 2 (φ) and ρ yz = − cos (φ) sin (φ) sin (θ) . Moreover, α is the radius of the UAV antenna effective area, while w(L 2 ) is the RIS beamwidth at distance L 2 and can be expressed as…”

Section: System Modelmentioning

confidence: 99%

Outage performance analysis of RIS-assisted UAV wireless systems under disorientation and misalignment

Boulogeorgos¹,

Alexiou²,

Renzo³

2022

Preprint

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In this paper, we analyze the performance of a reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) wireless system that is affected by mixture-gamma small-scale fading, stochastic disorientation and misalignment, as well as transceivers hardware imperfections. First, we statistically characterize the end-to-end channel for both cases, i.e., in the absence as well as in the presence of disorientation and misalignment, by extracting closed-form formulas for the probability density function (PDF) and the cumulative distribution function (CDF). Building on the aforementioned expressions, we extract novel closed-form expressions for the outage probability (OP) in the absence and the presence of disorientation and misalignment as well as hardware imperfections. In addition, high signalto-noise ratio OP approximations are derived, leading to the extraction of the diversity order. Finally, an OP floor due to disorientation and misalignment is presented.

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“…But all the data produced by IoT needs low-latency processing to be useful for rescuers in real time. For this, MEC is a promising candidate due to its proximity [5] and its on-site rapidly deployable [32,33] networking structure, even in dif icult environments.…”

Section: Disaster Managementmentioning

confidence: 99%

A resource management survey for mission-critical and time-critical applications in multiaccess edge computing

Santi¹,

Mitton²

2021

ITU-J FET

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Multiaccess Edge Computing (MEC) brings additional computing power in proximity of mobile users, reducing latency, saving energy and alleviating the network's bandwidth. This proximity is beneficial, especially for mission-critical applications where each second matters, such as disaster management or military operations. Moreover, it enables MEC resources embedded on mobile units like drones or robots that are flexible to be deployed for mission-critical applications. However, the MEC servers are capacity-limited and thus need an acute management of their resources. The mobile resources also need a smart deployment scheme to deliver their services efficiently. In this survey, we review mission-critical applications, resource allocation and deployment of mobile resources techniques in the context of the MEC. First, we introduce the technical specifics and uses of MEC in mission-critical applications to highlight their needs and requirements. Then, we discuss the resource allocation schemes for MEC and assess their fit depending on the application needs. In the same fashion, we finally review the deployment of MEC mobile resources. We believe this work could serve as a helping hand to design efficient MEC resource management schemes that respond to challenging environments such as mission-critical applications.

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Big Data on the Fly: UAV-Mounted Mobile Edge Computing for Disaster Management

Cited by 68 publications

References 31 publications

Energy Optimization in Massive MIMO UAV-Aided MEC-Enabled Vehicular Networks

Energy Optimization in Massive MIMO UAV-Aided MEC-Enabled Vehicular Networks

Outage performance analysis of RIS-assisted UAV wireless systems under disorientation and misalignment

A resource management survey for mission-critical and time-critical applications in multiaccess edge computing

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