skip to main content
10.1145/3469264.3469803acmconferencesArticle/Chapter ViewAbstractPublication PagesmobisysConference Proceedingsconference-collections
research-article

Position: drone camera communication meets robotic soil sensing

Published:24 June 2021Publication History

ABSTRACT

This paper positions the idea of enhancing the sensing capabilitiesof unmanned aerial vehicles (UAV or Drones) using LED-Cameracommunication, camera perception, and adaptive sampling algo-rithms. In this paper, considering soil moisture measurement overa specific geographic area as the application, we propose a systemthat consists of drone assisted mobile ground robots(MGRs) thatperform collaborative sensing and communicate with the droneusing visible light. The key idea is to combine aerial images andMGR sensed moisture values to generate a dynamically adaptablemoisture map. Camera communication eliminates the need to estab-lish continuous radio communication in remote locations and offersvisual association that enables the drone to localize the MGRs. Thissystem pushes the state-of-the-art in sensing by accurately sensinga region with high spatio-temporal resolution while optimizing thetime and energy required to create a moisture map of a geographi-cal area. As the sensing resolution is dynamic, we hypothesize thatthe proposed system can efficiently predict, detect, and map soilmoisture hotspots.

References

  1. Carlos Guestrin, Andreas Krause, and Ajit Paul Singh. Near-optimal sensor placements in gaussian processes. In Proceedings of the 22nd International Conference on Machine Learning, ICML '05, page 265--272, New York, NY, USA, 2005. Association for Computing Machinery. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. P. Senthil and I.S. Akila. Automated robotic moisture monitoring in agricultural fields. In 2018 International Seminar on Intelligent Technology and Its Applications (ISITIA), pages 375--380, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  3. C. Y. Yeh, H. R. Lin, Y. L. Chen, S. Y. Huang, and J. C. Wen. Remote Sensing Soil Moisture Analysis by Unmanned Aerial Vehicles Digital Imaging. In AGU Fall Meeting Abstracts, volume 2017, pages H41D--1464, December 2017.Google ScholarGoogle Scholar
  4. Deepak Vasisht, Zerina Kapetanovic, Jongho Won, Xinxin Jin, Ranveer Chandra, Sudipta Sinha, Ashish Kapoor, Madhusudhan Sudarshan, and Sean Stratman. Farmbeats: An iot platform for data-driven agriculture. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17), pages 515--529, Boston, MA, March 2017. USENIX Association. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Chiu-Kuo Liang and Yu-Hsiung Lin. A coverage optimization strategy for mobile wireless sensor networks based on genetic algorithm. In 2018 IEEE International Conference on Applied System Invention (ICASI), pages 1272--1275, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  6. Yew Teck Tan, Abhinav Kunapareddy, and Marin Kobilarov. Gaussian process adaptive sampling using the cross-entropy method for environmental sensing and monitoring. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 6220--6227, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  7. Janna Huuskonen and Timo Oksanen. Soil sampling with drones and augmented reality in precision agriculture. Computers and Electronics in Agriculture, 154:25--35, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  8. Bhawana Chhaglani, Abhay Sheel Anand, Nakul Garg, and Ashwin Ashok. Evaluating led-camera communication for drones. In Proceedings of the Workshop on Light Up the IoT, LIOT '20, page 18--23, New York, NY, USA, 2020. Association for Computing Machinery.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Yukito Onodera, Hiroki Takano, Daisuke Hisano, and Yu Nakayama. Drone positioning for visible light communication with drone-mounted led and camera, 2020.Google ScholarGoogle Scholar
  10. Nan Cen. Flight: Toward programmable visible-light-band wireless uav networking. In Proceedings of the Workshop on Light Up the IoT, LIOT '20, page 24--29, New York, NY, USA, 2020. Association for Computing Machinery.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Position: drone camera communication meets robotic soil sensing

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        IoL '21: Proceedings of the Workshop on Internet of Lights
        June 2021
        36 pages
        ISBN:9781450386043
        DOI:10.1145/3469264

        Copyright © 2021 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 24 June 2021

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        IoL '21 Paper Acceptance Rate6of6submissions,100%Overall Acceptance Rate6of6submissions,100%

        Upcoming Conference

        MOBISYS '24

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader