Airborne Cognitive Networking: Design, Development, and Deployment

Sklivanitis, George; Gannon, Adam; Tountas, Konstantinos; Pados, Dimitris A.; Batalama, Stella N.; Reichhart, Stephen; Medley, Michael J.; Thawdar, Ngwe; Lee, Ulysses; Matyjas, John D.; Pudlewski, Scott; Drozd, Andrew L.; Amanna, Ashwin; Latus, Fred; Goldsmith, Zachary K.; Díaz, David Marín

doi:10.1109/access.2018.2857843

Cited by 26 publications

(19 citation statements)

References 53 publications

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“…. , λ 5 ) = (3,5,10,8,7). Moreover, the service time for each user is calculated based on a random Exponential distribution with a fixed rate and we assumed that all the users have a fixed rate, µ = (µ s , µ s , .…”

Section: Numerical Resultsmentioning

confidence: 99%

UAV-Assisted Communication in Remote Disaster Areas Using Imitation Learning

Shamsoshoara

Afghah

Blasch

et al. 2021

IEEE Open J. Commun. Soc.

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The damage to cellular towers during natural and man-made disasters can disturb the communication services for cellular users. One solution to the problem is using unmanned aerial vehicles to augment the desired communication network. The paper demonstrates the design of a UAV-Assisted Imitation Learning (UnVAIL) communication system that relays the cellular users' information to a neighbor base station. Since the user equipment (UEs) are equipped with buffers with limited capacity to hold packets, UnVAIL alternates between different UEs to reduce the chance of buffer overflow, positions itself optimally close to the selected UE to reduce service time, and uncovers a network pathway by acting as a relay node. UnVAIL utilizes Imitation Learning (IL) as a data-driven behavioral cloning approach to accomplish an optimal scheduling solution. Results demonstrate that UnVAIL performs similar to a human expert knowledge-based planning in communication timeliness, position accuracy, and energy consumption with an accuracy of 97.52% when evaluated on a developed simulator to train the UAV.

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Section: Numerical Resultsmentioning

confidence: 99%

UAV-Assisted Communication in Remote Disaster Areas Using Imitation Learning

Shamsoshoara

Afghah

Blasch

et al. 2021

IEEE Open J. Commun. Soc.

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“…A notable exception to this is Sklivanitis et al [ 119 ]. The authors designed, developed, and validated through real indoor and outdoor experiments a CR platform for UAVs.…”

Section: State-of-the-art Of Cr-based Uavsmentioning

confidence: 99%

“…Chen et al [95] Young and Bostian [27] Miko and Nemeth [96] Harounabadi et al [97] Brown et al [98] Reyes et al [10] Guevara et al [99] Anderson et al [100] Mikó and Németh [101] Andryeyev et al [102] Saleem et al [9] VonEhr et al [103] Jacob et al [15] Tato et al [104] Murphy et al [105] Sboui et al [106] Gutierrez et al [107] Gonzalez and Fung [108] Horapong et al [109] Zhang et al [110] Ghazzai et al [111] Cai et al [112] Noble et al [113] Petrolo et al [114] Shi et al [115] Huang et al [116] Shi et al [117] Liu et al [118] Sklivanitis et al [119] Pärlin et al [120] Dunne and Keenlance [121] Shamaei et al [122] Santana et al [51] Jadon et al [123] Torabi et al [124] Xu et al [125] Pan et al [126] Shen et al [127] Aftab et al [128] Adane [129] Almasoud and Kamal [130] Matheou et al [131] Wang et al [132] Murphy et al…”

Section: Q1 Q2 Q3 Q4 Q5mentioning

confidence: 99%

Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview

Santana

Cristo

Branco

2021

Sensors

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Unmanned Aerial Vehicles (UAVs) demand technologies so they can not only fly autonomously, but also communicate with base stations, flight controllers, computers, devices, or even other UAVs. Still, UAVs usually operate within unlicensed spectrum bands, competing against the increasing number of mobile devices and other wireless networks. Combining UAVs with Cognitive Radio (CR) may increase their general communication performance, thus allowing them to execute missions where the conventional UAVs face limitations. CR provides a smart wireless communication which, instead of using a transmission frequency defined in the hardware, uses software transmission. CR smartly uses free transmission channels and/or chooses them according to application’s requirements. Moreover, CR is considered a key enabler for deploying technologies that require high connectivity, such as Smart Cities, 5G, Internet of Things (IoT), and the Internet of Flying Things (IoFT). This paper presents an overview on the field of CR for UAV communications and its state-of-the-art, testbed alternatives for real data experiments, as well as specifications to build a simple and low-cost testbed, and indicates key opportunities and future challenges in the field.

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“…Mode [1] Mode [2] Mode [3] Mode [4] we developed a spectrum sharing model where one UAV acts as a relay and forwards data for a terrestrial network in exchange for the required spectrum and the rest of the UAVs utilize this spectrum to transmit the sensed information. The emergency center determines the regions for the primary and secondary networks to maximize the throughput and it allocates the UAVs into the chosen regions based on a predefined grid plane to maximize the network lifetime and reduce the energy consumption.…”

Section: Mode[0]mentioning

confidence: 99%

An Autonomous Spectrum Management Scheme for Unmanned Aerial Vehicle Networks in Disaster Relief Operations

et al. 2020

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This paper studies the problem of spectrum shortage in an unmanned aerial vehicle (UAV) network during critical missions such as wildfire monitoring, search and rescue, and disaster monitoring. Such applications involve a high demand for high-throughput data transmissions such as real-time video-, image-, and voice-streaming where the assigned spectrum to the UAV network may not be adequate to provide the desired Quality of Service (QoS). In these scenarios, the aerial network can borrow additional spectrum from the available terrestrial networks in trade of a relaying service for them. We propose a spectrum sharing model in which the UAVs are grouped into two classes of relaying UAVs that service the spectrum owner and the sensing UAVs that perform the disaster relief mission using the obtained spectrum. The operation of the UAV network is managed by a hierarchical mechanism in which a central controller assign the tasks of the UAVs based on their resources and determine their operation region based on the level of priority of impacted areas and then the UAVs autonomously fine-tune their position using a modelfree reinforcement learning algorithm to maximize the individual throughput and prolong their lifetime. We analyze the performance and the convergence for the proposed method analytically and with extensive simulations in different scenarios.INDEX TERMS Autonomous UAV networks, multi-agent systems, Q-learning, reinforcement learning, spectrum sharing.

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Airborne Cognitive Networking: Design, Development, and Deployment

Cited by 26 publications

References 53 publications

UAV-Assisted Communication in Remote Disaster Areas Using Imitation Learning

UAV-Assisted Communication in Remote Disaster Areas Using Imitation Learning

Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview

An Autonomous Spectrum Management Scheme for Unmanned Aerial Vehicle Networks in Disaster Relief Operations

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