How to Choose the Best Embedded Processing Platform for on-Board UAV Image Processing ?

Hulens, Dries; Verbeke, Jon; Goedemé, Toon

doi:10.5220/0005359403770386

Cited by 41 publications

(23 citation statements)

References 16 publications

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“…Without loss of generality, we assume that all drones move with a fixed speed denoted by v d . The hover and hardware power levels, denoted by P hov and P har , can be expressed, respectively, as [43]:…”

Section: Drone Power Modelmentioning

confidence: 99%

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Spatial and Temporal Management of Cellular HetNets with Multiple Solar Powered Drones

Alsharoa

Ghazzai

Kadri

et al. 2020

IEEE Trans. on Mobile Comput.

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This paper proposes an energy management framework for cellular heterogeneous networks (HetNets) supported by dynamic solar powered drones. A HetNet composed of a macrocell base station (BS), micro cell BSs, and drone small cell BSs are deployed to serve the networks' subscribers. The drones can land at pre-planned locations defined by the mobile operator and at the macrocell BS site where they can charge their batteries. The objective of the framework is to jointly determine the optimal trips of the drones and the MBSs that can be safely turned off in order to minimize the total energy consumption of the network. This is done while considering the cells' capacities and the minimum receiving power guaranteeing successful communications. To do so, an integer linear programming problem is formulated and optimally solved for three cases based on the knowledge level about future renewable energy statistics of the drones. A low complex relaxed solution is also developed. Its performances are shown to be close to those of the optimal solutions. However, the gap increases as the network becomes more congested. Numerical results investigate the performance of the proposed drone-based approach and show notable improvements in terms of energy saving and network capacity.Index Terms-Drone-based communications, dynamic drone small cells, energy harvesting, energy management, heterogeneous networks.

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Section: Drone Power Modelmentioning

confidence: 99%

“…In case of cell overlap between the MBS and an active DBS, we assume that the drone has the priority in serving the users in the intersection region once deployed. In Table IV, we present the values of the remaining parameters used in the simulations [37], [43]. In Fig.…”

Section: A Simulation Parametersmentioning

confidence: 99%

Spatial and Temporal Management of Cellular HetNets with Multiple Solar Powered Drones

Alsharoa

Ghazzai

Kadri

et al. 2020

IEEE Trans. on Mobile Comput.

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“…The total energy consumption E tot d of a communicating drone d is composed of two components, namely the hover and transition energies needed for its movement and the communication energy needed to relay the transceivers' data. The models of the hover power and the transition power levels of a drone d, denoted by P hov d and P tr d , are given as [16]:…”

Section: B Drone Power Modelmentioning

confidence: 99%

Trajectory Optimization for Cooperative Dual-Band UAV Swarms

Ghazzai

Ghorbel

Kassler

et al. 2018

2018 IEEE Global Communications Conference (GLOBECOM)

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Unmanned aerial vehicles (UAVs) have gained a lot of popularity in diverse wireless communication fields. They can act as high-altitude flying relays to support communications between ground nodes due to their ability to provide line-of-sight links. With the flourishing Internet of Things, several types of new applications are emerging. In this paper, we focus on bandwidth hungry and delay-tolerant applications where multiple pairs of transceivers require the support of UAVs to complete their transmissions. To do so, the UAVs have the possibility to employ two different bands namely the typical microwave and the highrate millimeter wave bands. In this paper, we develop a generic framework to assign UAVs to supported transceivers and optimize their trajectories such that a weighted function of the total service time is minimized. Taking into account both the communication time needed to relay the message and the flying time of the UAVs, a mixed non-linear programming problem aiming at finding the stops at which the UAVs hover to forward the data to the receivers is formulated. An iterative approach is then developed to solve the problem. First, a mixed linear programming problem is optimally solved to determine the path of each available UAV. Then, a hierarchical iterative search is executed to enhance the UAV stops' locations and reduce the service time. The behavior of the UAVs and the benefits of the proposed framework are showcased for selected scenarios.Index Terms-3D positioning and path planning, cooperative networks, mmWave communications, unmanned aerial vehicles.

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Section: Drone Power Modelmentioning

confidence: 99%

“…Assume that the drone has the priority in serving the users in the intersection region once deployed. In Table 6.2, we present the values of the remaining parameters used in the simulations [130], [133].…”

Section: Simulation Parametersmentioning

confidence: 99%

Energy efficient planning and operation models for wireless cellular networks

Alsharoa

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Specially to my father who inspired me with his thoughts and wisdom, who gave me a good example on how to live with respect in this life and to my mother who brought me virtue, and from her I learned the principles of modesty, tolerance, and kindness to all people. I would also like to dedicate this thesis to my small family; to my wife for her constant love, support, and patience and to my children, Mustafa and Ghina, you have made me stronger and more fulfilled than I could have ever imagined. I would also like to thank my friends for their constant support during writing this thesis.

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How to Choose the Best Embedded Processing Platform for on-Board UAV Image Processing ?

Cited by 41 publications

References 16 publications

Spatial and Temporal Management of Cellular HetNets with Multiple Solar Powered Drones

Spatial and Temporal Management of Cellular HetNets with Multiple Solar Powered Drones

Trajectory Optimization for Cooperative Dual-Band UAV Swarms

Energy efficient planning and operation models for wireless cellular networks

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