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Experiences Deploying an Always-on Farm Network

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Mark KimballAuthors Info & Claims

GetMobile: Mobile Computing and Communications, Volume 21, Issue 2

Pages 16 - 21

https://doi.org/10.1145/3131214.3131220

Published: 04 August 2017 Publication History

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Abstract

Data-driven techniques for agriculture can help farmers reduce waste, increase farm output and ensure sustainability for the environment. The key enabler for such techniques is an always-on connected IoT system that can sense the different characteristics of the farm and generate short-term and long-term actionable insights for the farmer. Yet building such a system is very challenging due to sparse Internet connectivity and lack of reliable power sources. This is further exacerbated by weather variability that stresses the system in numerous ways. We discuss how we built and deployed Farmbeats [6] in the face of these challenges. We hope our experiences will aid researchers who are beginning to explore deployments in farming or other weakly connected, power-starved scenarios, such as construction, oil fields, mining, and others.

References

[1]

Wibotic: http://www.wibotic.com/.

Google Scholar

[2]

M. H. Almarshadi and S. M. Ismail. Effects of precision irrigation on productivity and water use efficiency of alfalfa under different irrigation methods in arid climates. Journal of Applied Sciences Research, 2011.

Google Scholar

[3]

P. Bahl, R. Chandra, P. P. C. Lee, V. Misra, J. Padhye, D. Rubenstein, and Y. Yu. Opportunistic use of client repeaters to improve performance of wlans. In Proceedings of the 2008 ACM CoNEXT Conference, CoNEXT '08, pages 29:1--29:12, New York, NY, USA, 2008. ACM.

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Google Scholar

[4]

J. Lowenberg-DeBoer. The precision agriculture revolution: Making the modern farmer. https://www.foreignaffairs.com/articles/unitedstates/2015-04-20/precision-agriculture-revolution.

Google Scholar

[5]

United Nations General Assembly. Food production must double by 2050 to meet demand from worlds growing population, innovative strategies needed to combat hunger, experts tell second committee, 2009.

Google Scholar

[6]

D. Vasisht, Z. Kapetanovic, J. Won, X. Jin, R. Chandra, A. Kapoor, S. Sinha, M. Sudarshan, and S. Stratman. Farmbeats: An IoT platform for data-driven agriculture. In NSDI, 2017.

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Greenlee ERothrock BKim HZegura EHester J(2024)“The Devil You Know”: Barriers and Opportunities for Co-Designing Microclimate Sensors, A Case Study of ManoomiACM Journal on Computing and Sustainable Societies10.1145/36856952:3(1-30)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1145/3685695
Yen BKapetanovic Z(2024)Towards Designing Self-Powered Biodegradable Sensors For Agricultural ApplicationsProceedings of the 2nd Workshop on Advances in Environmental Sensing Systems for Smart Cities10.1145/3661813.3661818(19-24)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3661813.3661818
Ojha TMisra SRaghuwanshi N(2021)Internet of Things for Agricultural Applications: The State of the ArtIEEE Internet of Things Journal10.1109/JIOT.2021.30514188:14(10973-10997)Online publication date: 15-Jul-2021
https://doi.org/10.1109/JIOT.2021.3051418
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Experiences Deploying an Always-on Farm Network
1. Applied computing
  1. Physical sciences and engineering

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cover image GetMobile: Mobile Computing and Communications

GetMobile: Mobile Computing and Communications Volume 21, Issue 2

June 2017

34 pages

ISSN:2375-0529

EISSN:2375-0537

DOI:10.1145/3131214

Editor:
Eyal de Lara
University of Toronto

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Association for Computing Machinery

New York, NY, United States

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Published: 04 August 2017

Published in SIGMOBILE-GETMOBILE Volume 21, Issue 2

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Greenlee ERothrock BKim HZegura EHester J(2024)“The Devil You Know”: Barriers and Opportunities for Co-Designing Microclimate Sensors, A Case Study of ManoomiACM Journal on Computing and Sustainable Societies10.1145/36856952:3(1-30)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1145/3685695
Yen BKapetanovic Z(2024)Towards Designing Self-Powered Biodegradable Sensors For Agricultural ApplicationsProceedings of the 2nd Workshop on Advances in Environmental Sensing Systems for Smart Cities10.1145/3661813.3661818(19-24)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3661813.3661818
Ojha TMisra SRaghuwanshi N(2021)Internet of Things for Agricultural Applications: The State of the ArtIEEE Internet of Things Journal10.1109/JIOT.2021.30514188:14(10973-10997)Online publication date: 15-Jul-2021
https://doi.org/10.1109/JIOT.2021.3051418
Maghami ISobral VMorsy MLach JGoodall J(2021)Exploring the complementary relationship between solar and hydro energy harvesting for self-powered water monitoring in low-light conditionsEnvironmental Modelling & Software10.1016/j.envsoft.2021.105032140(105032)Online publication date: Jun-2021
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Oteyo IMarra MKimani SMeuter WBoix E(2021)A Survey on Mobile Applications for Smart AgricultureSN Computer Science10.1007/s42979-021-00700-x2:4Online publication date: 24-May-2021
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Kulkarni DNair S(2020)Agrilogistics - A Genetic Programming Based ApproachSociety with Future: Smart and Liveable Cities10.1007/978-3-030-45293-3_7(83-96)Online publication date: 4-Apr-2020
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Lee KKuhl SBockholt HRogers BReed DSanielevici S(2018)A Cloud-Based Scientific Gateway for Internet of Things Data AnalyticsProceedings of the Practice and Experience on Advanced Research Computing: Seamless Creativity10.1145/3219104.3219123(1-8)Online publication date: 22-Jul-2018
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Yang WWang XCao SWang HMao S(2018)Multi-Class Wheat Moisture Detection with 5GHz Wi-Fi: A Deep LSTM Approach2018 27th International Conference on Computer Communication and Networks (ICCCN)10.1109/ICCCN.2018.8487423(1-9)Online publication date: Jul-2018
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The dynamic North Florida dairy farm model: A user-friendly computerized tool for increasing profits while minimizing N leaching under varying climatic conditions

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