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Wireless Sensor Network Synchronization for Precision Agriculture Applications

Author

Listed:
  • Alexandros Zervopoulos

    (Department of Informatics, Ionian University, GR-49100 Corfu, Greece)

  • Athanasios Tsipis

    (Department of Informatics, Ionian University, GR-49100 Corfu, Greece)

  • Aikaterini Georgia Alvanou

    (Department of Informatics, Ionian University, GR-49100 Corfu, Greece)

  • Konstantinos Bezas

    (Department of Informatics, Ionian University, GR-49100 Corfu, Greece)

  • Asterios Papamichail

    (Department of Informatics, Ionian University, GR-49100 Corfu, Greece)

  • Spiridon Vergis

    (Department of Informatics, Ionian University, GR-49100 Corfu, Greece)

  • Andreana Stylidou

    (Department of Informatics, Ionian University, GR-49100 Corfu, Greece)

  • Georgios Tsoumanis

    (Department of Informatics and Telecommunications, University of Ioannina, GR-47100 Arta, Greece)

  • Vasileios Komianos

    (Department of Audio and Visual Arts, Ionian University, GR-49100 Corfu, Greece)

  • George Koufoudakis

    (Department of Informatics, Ionian University, GR-49100 Corfu, Greece)

  • Konstantinos Oikonomou

    (Department of Informatics, Ionian University, GR-49100 Corfu, Greece)

Abstract

The advent of Internet of Things has propelled the agricultural domain through the integration of sensory devices, capable of monitoring and wirelessly propagating information to producers; thus, they employ Wireless Sensor Networks (WSNs). These WSNs allow real time monitoring, enabling intelligent decision-making to maximize yields and minimize cost. Designing and deploying a WSN is a challenging and multivariate task, dependent on the considered environment. For example, a need for network synchronization arises in such networks to correlate acquired measurements. This work focuses on the design and installation of a WSN that is capable of facilitating the sensing aspects of smart and precision agriculture applications. A system is designed and implemented to address specific design requirements that are brought about by the considered environment. A simple synchronization scheme is described to provide time-correlated measurements using the sink node’s clock as reference. The proposed system was installed on an olive grove to assess its effectiveness in providing a low-cost system, capable of acquiring synchronized measurements. The obtained results indicate the system’s overall effectiveness, revealing a small but expected difference in the acquired measurements’ time correlation, caused mostly by serial transmission delays, while yielding a plethora of relevant environmental conditions.

Suggested Citation

  • Alexandros Zervopoulos & Athanasios Tsipis & Aikaterini Georgia Alvanou & Konstantinos Bezas & Asterios Papamichail & Spiridon Vergis & Andreana Stylidou & Georgios Tsoumanis & Vasileios Komianos & Ge, 2020. "Wireless Sensor Network Synchronization for Precision Agriculture Applications," Agriculture, MDPI, vol. 10(3), pages 1-20, March.
  • Handle: RePEc:gam:jagris:v:10:y:2020:i:3:p:89-:d:336526
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    References listed on IDEAS

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    1. Westermann, Olaf & Förch, Wiebke & Thornton, Philip & Körner, Jana & Cramer, Laura & Campbell, Bruce, 2018. "Scaling up agricultural interventions: Case studies of climate-smart agriculture," Agricultural Systems, Elsevier, vol. 165(C), pages 283-293.
    2. Sykuta, Michael E., 2016. "Big Data in Agriculture: Property Rights, Privacy and Competition in Ag Data Services," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 19(A), pages 1-18, June.
    3. Leslie Lipper & Philip Thornton & Bruce M. Campbell & Tobias Baedeker & Ademola Braimoh & Martin Bwalya & Patrick Caron & Andrea Cattaneo & Dennis Garrity & Kevin Henry & Ryan Hottle & Louise Jackson , 2014. "Climate-smart agriculture for food security," Nature Climate Change, Nature, vol. 4(12), pages 1068-1072, December.
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    Citations

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    Cited by:

    1. F. C. S. Eiras & W. L. Zucchi, 2022. "Measuring synchronization precision in mobile sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 81(2), pages 253-267, October.
    2. Édson Luis Bolfe & Lúcio André de Castro Jorge & Ieda Del’Arco Sanches & Ariovaldo Luchiari Júnior & Cinthia Cabral da Costa & Daniel de Castro Victoria & Ricardo Yassushi Inamasu & Célia Regina Grego, 2020. "Precision and Digital Agriculture: Adoption of Technologies and Perception of Brazilian Farmers," Agriculture, MDPI, vol. 10(12), pages 1-16, December.
    3. Javier Rodríguez-Robles & Álvaro Martin & Sergio Martin & José A. Ruipérez-Valiente & Manuel Castro, 2020. "Autonomous Sensor Network for Rural Agriculture Environments, Low Cost, and Energy Self-Charge," Sustainability, MDPI, vol. 12(15), pages 1-17, July.
    4. Hamid Bagha & Ali Yavari & Dimitrios Georgakopoulos, 2022. "Hybrid Sensing Platform for IoT-Based Precision Agriculture," Future Internet, MDPI, vol. 14(8), pages 1-23, July.
    5. Ioana Marcu & Ana-Maria Drăgulinescu & Cristina Oprea & George Suciu & Cristina Bălăceanu, 2022. "Predictive Analysis and Wine-Grapes Disease Risk Assessment Based on Atmospheric Parameters and Precision Agriculture Platform," Sustainability, MDPI, vol. 14(18), pages 1-18, September.
    6. Ha Quang Thinh Ngo & Thanh Phuong Nguyen & Hung Nguyen, 2020. "Research on a Low-Cost, Open-Source, and Remote Monitoring Data Collector to Predict Livestock’s Habits Based on Location and Auditory Information: A Case Study from Vietnam," Agriculture, MDPI, vol. 10(5), pages 1-26, May.
    7. Yuhao Li & Chengguo Fu & Hui Yang & Haibo Li & Rongxian Zhang & Yaqi Zhang & Zhankui Wang, 2023. "Design of a Closed Piggery Environmental Monitoring and Control System Based on a Track Inspection Robot," Agriculture, MDPI, vol. 13(8), pages 1-25, July.

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