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Location-based beamforming architecture for efficient farming applications with drones

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Standard

Location-based beamforming architecture for efficient farming applications with drones. / Wang, Wenbo; Okati, Niloofar; Tanash, Islam; Riihonen, Taneli; Lohan, Elena-Simona.

2019 International Conference on Localization and GNSS, ICL-GNSS 2019. ed. / Elena-Simona Lohan; Alexander Rugamer; Jari Nurmi; Wolfgang Koch; Albert Heuberger. IEEE, 2019.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Wang, W, Okati, N, Tanash, I, Riihonen, T & Lohan, E-S 2019, Location-based beamforming architecture for efficient farming applications with drones. in E-S Lohan, A Rugamer, J Nurmi, W Koch & A Heuberger (eds), 2019 International Conference on Localization and GNSS, ICL-GNSS 2019. IEEE, International Conference on Localization and GNSS, Nuremberg, Germany, 4/06/19. https://doi.org/10.1109/ICL-GNSS.2019.8752698

APA

Wang, W., Okati, N., Tanash, I., Riihonen, T., & Lohan, E-S. (2019). Location-based beamforming architecture for efficient farming applications with drones. In E-S. Lohan, A. Rugamer, J. Nurmi, W. Koch, & A. Heuberger (Eds.), 2019 International Conference on Localization and GNSS, ICL-GNSS 2019 IEEE. https://doi.org/10.1109/ICL-GNSS.2019.8752698

Vancouver

Wang W, Okati N, Tanash I, Riihonen T, Lohan E-S. Location-based beamforming architecture for efficient farming applications with drones. In Lohan E-S, Rugamer A, Nurmi J, Koch W, Heuberger A, editors, 2019 International Conference on Localization and GNSS, ICL-GNSS 2019. IEEE. 2019 https://doi.org/10.1109/ICL-GNSS.2019.8752698

Author

Wang, Wenbo ; Okati, Niloofar ; Tanash, Islam ; Riihonen, Taneli ; Lohan, Elena-Simona. / Location-based beamforming architecture for efficient farming applications with drones. 2019 International Conference on Localization and GNSS, ICL-GNSS 2019. editor / Elena-Simona Lohan ; Alexander Rugamer ; Jari Nurmi ; Wolfgang Koch ; Albert Heuberger. IEEE, 2019.

Bibtex - Download

@inproceedings{31ab7c37205b4421950ea5a2ee887c71,
title = "Location-based beamforming architecture for efficient farming applications with drones",
abstract = "This paper proposes a drone-based architecture with location-based beamforming (LBBF)and edge computing support for efficient crop harvesting and management in order to reduce the food waste in the food chain in farming applications. Monitoring the crop is a crucial part in the food chain. In this work, for monitoring purpose we consider synthetic aperture radar (SAR)mounted on the unmanned aerial vehicles (UAVs). In order to provide the edge computing information with good reliability, small latency and good throughput, we introduce a LBBF technique for the uplink connectivity. Firstly, the LBBF algorithm is proposed for the scenario where a single user is connected to the base station under analog beamforming scheme. Secondly, in the context of LBBF, we apply an optimization of the antenna size under the uniform rectangular array (URA)assumption. Thirdly, we implement a numerical analysis to compare LBBF with the traditional channel state information (CSI)-based beamforming. We show that the LBBF outperforms the CSI-based beamforming in the noisy environments according to the investigated performance metrics, namely the reliability of the connectivity and the capacity. In addition, the LBBF also has smaller latency than CSI-based beamforming.",
keywords = "Farming, Location-based beamforming (LBBF), Unmanned aerial vehicles (UAVs)",
author = "Wenbo Wang and Niloofar Okati and Islam Tanash and Taneli Riihonen and Elena-Simona Lohan",
year = "2019",
month = "6",
day = "1",
doi = "10.1109/ICL-GNSS.2019.8752698",
language = "English",
editor = "Elena-Simona Lohan and Alexander Rugamer and Jari Nurmi and Wolfgang Koch and Albert Heuberger",
booktitle = "2019 International Conference on Localization and GNSS, ICL-GNSS 2019",
publisher = "IEEE",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Location-based beamforming architecture for efficient farming applications with drones

AU - Wang, Wenbo

AU - Okati, Niloofar

AU - Tanash, Islam

AU - Riihonen, Taneli

AU - Lohan, Elena-Simona

PY - 2019/6/1

Y1 - 2019/6/1

N2 - This paper proposes a drone-based architecture with location-based beamforming (LBBF)and edge computing support for efficient crop harvesting and management in order to reduce the food waste in the food chain in farming applications. Monitoring the crop is a crucial part in the food chain. In this work, for monitoring purpose we consider synthetic aperture radar (SAR)mounted on the unmanned aerial vehicles (UAVs). In order to provide the edge computing information with good reliability, small latency and good throughput, we introduce a LBBF technique for the uplink connectivity. Firstly, the LBBF algorithm is proposed for the scenario where a single user is connected to the base station under analog beamforming scheme. Secondly, in the context of LBBF, we apply an optimization of the antenna size under the uniform rectangular array (URA)assumption. Thirdly, we implement a numerical analysis to compare LBBF with the traditional channel state information (CSI)-based beamforming. We show that the LBBF outperforms the CSI-based beamforming in the noisy environments according to the investigated performance metrics, namely the reliability of the connectivity and the capacity. In addition, the LBBF also has smaller latency than CSI-based beamforming.

AB - This paper proposes a drone-based architecture with location-based beamforming (LBBF)and edge computing support for efficient crop harvesting and management in order to reduce the food waste in the food chain in farming applications. Monitoring the crop is a crucial part in the food chain. In this work, for monitoring purpose we consider synthetic aperture radar (SAR)mounted on the unmanned aerial vehicles (UAVs). In order to provide the edge computing information with good reliability, small latency and good throughput, we introduce a LBBF technique for the uplink connectivity. Firstly, the LBBF algorithm is proposed for the scenario where a single user is connected to the base station under analog beamforming scheme. Secondly, in the context of LBBF, we apply an optimization of the antenna size under the uniform rectangular array (URA)assumption. Thirdly, we implement a numerical analysis to compare LBBF with the traditional channel state information (CSI)-based beamforming. We show that the LBBF outperforms the CSI-based beamforming in the noisy environments according to the investigated performance metrics, namely the reliability of the connectivity and the capacity. In addition, the LBBF also has smaller latency than CSI-based beamforming.

KW - Farming

KW - Location-based beamforming (LBBF)

KW - Unmanned aerial vehicles (UAVs)

U2 - 10.1109/ICL-GNSS.2019.8752698

DO - 10.1109/ICL-GNSS.2019.8752698

M3 - Conference contribution

BT - 2019 International Conference on Localization and GNSS, ICL-GNSS 2019

A2 - Lohan, Elena-Simona

A2 - Rugamer, Alexander

A2 - Nurmi, Jari

A2 - Koch, Wolfgang

A2 - Heuberger, Albert

PB - IEEE

ER -