Building UAV-Based Testbeds for Autonomous Mobility and Beamforming Experimentation

Shi, Yan; Wensowitch, John; Ward, Amanda J.; Badi, Mahmoud; Camp, Joseph

doi:10.1109/seconw.2018.8396345

Cited by 13 publications

(9 citation statements)

References 7 publications

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“…They also surveyed the main contributions presented in the literature around Mavlink, including enhancement and extension, security, applications, integration with IoT, and swarm. An implementation of Mavlink protocol was given in [42], where a robust control framework on a customized UAV platform was built using Mavlink. A USRP B200 mini, a Pixhawk2 flight controller, a embedded Linux system (Raspberry Pi3) to communicate with USRP B200 mini, and a 3DR Solo Quadcopter were used to realize the control information exchanging for UAV.…”

Section: Communication Protocolmentioning

confidence: 99%

A survey of prototype and experiment for UAV communications

Song

Zeng

et al. 2021

Sci. China Inf. Sci.

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Unmanned aerial vehicle (UAV) communications have attracted significant attention from both academia and industry. To facilitate the large-scale usage of UAVs for various applications in practice, we provide a comprehensive survey on the prototype and experiment for UAV communications. To this end, we first provide an overview on the general architecture of the prototype and experiment for UAV communications, and then present experimental verification for air-to-ground channel models and UAV energy consumption models. Next, we discuss measurement experiments on two promising paradigms of UAV communications, namely cellular-connected UAVs and UAV-enabled aerial communication platforms. For the former, we focus on the feasibility study and address the interference mitigation issue. For UAV-enabled aerial communication platforms, we present three scenarios, namely UAV-enabled aerial base stations, UAVenabled aerial relays and UAV-enabled aerial data collection/dissemination. Finally, we point out some promising future directions for prototype and experimental measurements of UAV communications.

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Section: Communication Protocolmentioning

confidence: 99%

A survey of prototype and experiment for UAV communications

Song

Zeng

et al. 2021

Sci. China Inf. Sci.

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“…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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“…Energy consumption has shown to reduce at the expense of a higher delay [14,39], which is up to twice the amount of energy consumption in a PC [Unclear] [29]. • send location information from an unmanned aerial vehicle (UAV), which is a dynamic node, to a ground BS, which is a static PC, so that the ground station can monitor the location of the UAV [40]. Energy consumption has shown to reduce, while the communication and information processing capabilities of RP3 are sufficient [40].…”

Section: Usrp/gnu Radio With Rp3 Testbedsmentioning

confidence: 99%

An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios

et al. 2021

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This paper demonstrates a route selection mechanism on a testbed with heterogeneous device-to-device (D2D) wireless communication for a 5G network scenario. The source node receives information about the primary users’ (PUs’) (or licensed users’) activities and available routes from the macrocell base station (or a central controller) and makes a decision to select a multihop route to the destination node. The source node from small cells can either choose: (a) a route with direct communication with the macrocell base station to improve the route performance; or (b) a route with D2D communication among nodes in the small cells to offload traffic from the macrocell to improve spectrum efficiency. The selected D2D route has the least PUs’ activities. The route selection mechanism is investigated on our testbed that helps to improve the accuracy of network performance measurement. In traditional testbeds, each node (e.g., Universal Software Radio Peripheral (USRP) that serves as the front-end communication block) is connected to a single processing unit (e.g., a personal computer) via a switch using cables. In our testbed, each USRP node is connected to a separate processing unit, i.e., raspberry Pi3 B+ (or RP3), which offers three main advantages: (a) control messages and data packets are exchanged via the wireless medium; (b) separate processing units make decisions in a distributed and heterogeneous manner; and (c) the nodes are placed further apart from one another. Therefore, in the investigation of our route selection scheme, the response delay of control message exchange and the packet loss caused by the operating environment (e.g., ambient noise) are implied in our end-to-end delay and packet delivery ratio measurement. Our results show an increase of end-to-end delay and a decrease of packet delivery ratio due to the transmission of control messages and data packets in the wireless medium in the presence of the dynamic PUs’ activities. Furthermore, D2D communication can offload 25% to 75% traffic from macrocell base station to small cells.

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Building UAV-Based Testbeds for Autonomous Mobility and Beamforming Experimentation

Cited by 13 publications

References 7 publications

A survey of prototype and experiment for UAV communications

A survey of prototype and experiment for UAV communications

Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview

An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios

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