30 research outputs found
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Deployment of an aerial platform system for rapid restoration of communications links after a disaster: A machine learning approach
Having reliable telecommunication systems in the immediate aftermath of a catastrophic event makes a huge difference in the combined effort by local authorities, local fire and police departments, and rescue teams to save lives. This paper proposes a physical model that links base stations that are still operational with aerial platforms and then uses a machine learning framework to evolve ground-to-air propagation model for such an ad hoc network. Such a physical model is quick and easy to deploy and the underlying air-to-ground (ATG) propagation models are both resilient and scalable and may use a wide range of link budget, grade of service (GoS), and quality of service (QoS) parameters to optimise their performance and in turn the effectiveness of the physical model. The prediction results of a simulated deployment of such a physical model and the evolved propagation model in an ad hoc network offers much promise in restoring communication links during emergency relief operations
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Autonomous flying IoT: A synergy of machine learning, digital elevation, and 3D structure change detection
Copyright © 2022 The Author(s). The research work presented in this paper has been funded by a national research project whose aims are to enable an Unmanned Aerial Vehicle (UAV) to fly autonomously with the use of a Digital Elevation Model (DEM) of the target area and to detect terrain changes with the use of a 3D Structure Change Detection Model (3D SCDM). A Convolutional Neural Network (CNN) works with both models in training the UAV in autonomous flying and in detecting terrain changes. The usability of such an autonomous flying IoT is demonstrated through its deployment in the search for water resources in areas where a satellite would not normally be able to retrieve images, e.g., inside gorges, ravines, or caves. Our experiment results show that it can detect water flows by considering different surface shapes such as standing water polygons, watersheds, water channel incisions, and watershed delineations with a 99.6% level of accuracy.This work was supported by the Food; Taif University through the research project TURSP-2020/265
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Correction to: Performance optimization of tethered balloon technology for public safety and emergency communications
This is a correction to the article available at: http://bura.brunel.ac.uk/handle/2438/18557. The original article has been corrected: https://doi.org/10.1007/s11235-019-00580-
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Performance optimization of tethered balloon technology for public safety and emergency communications
The original version of this article was revised: The co-author name “M. C. Angelides” and email address has been updated. A correction to this article is available online at https://doi.org/10.1007/s11235-019-00589-1 and at: https://bura.brunel.ac.uk/handle/2438/19704
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A Machine Learning Approach to Evolving an Optimal Propagation Model for Last Mile Connectivity using Low Altitude Platforms
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An enhanced design of a 5G MIMO antenna for fixed wireless aerial access
© The Author(s) 2021. A recent market prediction is that 5G Fixed Wireless Access (FWA) will more than double over the next five years and trials at the same period in London suggest promising results. However, the shift to 5G FWA has raised a new set of research challenges in relation to speed of deployment and re-deployment, coverage, power consumption, end user mobility and last mile connectivity, to name just a few, because of the much higher expectations. A recent review reveals that key 5G Physical Layer technologies that will enable wide mobile and FWA have not kept up pace. In response to some of those research challenges, this paper presents the design of a 5G Multiple Input Multiple Output (MIMO) Antenna that is mounted on a tethered aerostat, and the combination of which serves as a 5G FWA aerial station. The antenna design features several novelties and the aerial station can provide last mile connectivity to a wide coverage footprint, with moderate power consumption and operating at high speeds. Both the evaluation of the antenna performance using several key performance indicators and the validation of the aerial station as a 5G FWA in a wireless sensor network (WSN) proof-of-concept application reveal efficiency gains.Taif University research project TURSP-2020/265
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A serious gaming approach for optimization of energy allocation in CubeSats
Data availability: All data generated or analysed during this study are included in this article.Copyright © The Author(s) 2023. Energy consumption remains an open challenge in aerial systems such as CubeSats and therefore optimization of its allocation is a top priority for maximizing operational capacity. Our research review reveals a plethora of approaches for optimization of energy allocation and all achieving varying degrees of success and not without any compromises. In this paper, we exploit the use of serious gaming in a novel energy allocation algorithm that aims at minimizing energy consumption to maximize the utilities of both CubeSats and terrestrial sensors. To demonstrate this, we use Stackelberg for serious gaming and standalone topology for CubeSat configuration. The experimental results show that the use of a Stackelberg game approach for optimization has led to reduction in the required transmission energy in sensors, an improved link performance between the CubeSat and ground sensors, and an increase in network lifetime and performance without resorting into sensor power enhancements or other external power sources. The overall average operational capacity improvement predictions range between 22 to 27% across all performance indicators of energy efficiency across RF chains of link budgets
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