Path Loss Using Low-Power Wireless Communication Techniques in Digital Agricultural Applications

222 Chris Rodriguez - Computer Engineering, University of California Irvine

James V. Krogmeier¹, Dennis R. Buckmaster¹, Andrew D. Balmos², Yang Wang², Fabio Rubio², Sneha Jha²

Electrical and Computer Engineering, Purdue University

Agricultural and Biological Engineering, Purdue University

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This material is based upon work supported by the IoT4Ag Engineering Research Center funded by the National Science Foundation (NSF) under NSF Cooperative Agreement Number EEC-1941529.  Any opinions, findings and conclusions, or recommendations expressed in this material are those of the author(s), and do not necessarily reflect those of the NSF.

IoT4Ag

JULY 28, 2021

REU PRESENTATION

Introduction and Problem Statement

• Efficient and reliable wireless communications is a constant problem in rural areas.

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• A proposed solution is LoRa (Short for long range).

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• Hypothesis - The wireless coverage of LoRaWAN is large enough to be practical for ISOBlue’s in-field communications and it can be integrated into the current open-source software.

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ISOBlue 2.0

Lora node

IoT4Ag

JULY 28, 2021

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Overview of the Technical Approach

• Calculate theoretical SNR (Signal to Noise Ratio).

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• Apply Friis transmission equation: power available at an antenna over the power fed.

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• Use matplotlib, to plot around ACRE (Agricultural Center for Research and Education) and calculate SNR.

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Fig 1: ACRE point

Fig 2: Random plot points around ACRE

IoT4Ag

JULY 28, 2021

REU PRESENTATION

Overview of the Technical Approach

• Several LoRa nodes (sensors) were placed around ACRE (Agricultural Center for Research and Education) to measure SNR (Signal to Noise Ratio).

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• Compare theoretical data to acquired data.

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• Due to elevation changes, we saw some performance drops in some areas around ACRE.

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LoRaWAN network as seen through the layers

LoRa node connected via UART to several microcontrollers

IoT4Ag

JULY 28, 2021

REU PRESENTATION

Results

• For our theoretical dataset we took into account thermal noise.

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• Signal is weaker the further it travels.

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• Only path loss matters, hence even values around.

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Fig 3: SNR plot calculation around ACRE

IoT4Ag

JULY 28, 2021

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Results

• Southeast region of ACRE has low performance.

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• Faulty data mostly appearing in the southern landscape shows ineffectiveness.

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• Faulty data not shown due to severe inaccuracy.

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Fig 4: SNR around ACRE using small test data set

IoT4Ag

JULY 28, 2021

REU PRESENTATION

Conclusion

• We can infer that by comparing our theoretical set and our real time in field data set for SNR around ACRE that LoRaWAN is still something that must be improved on in terms of execution and implementation.

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• We can conclude that supporting infield communications of ISOBlue with a custom built LoRaWAN network can in theory be of improvement for communication however further research must be done to study its performance in rural areas.

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IoT4Ag

JULY 28, 2021

REU PRESENTATION

Future

• Acquire more in field data.

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• Improve faultiness on LoRa data acquisition.

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• If successful, implement LoRaWAN into ISOBlue infield communications.

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IoT4Ag

JULY 28, 2021

REU PRESENTATION

Summary

Overall, the impact in which this research has on society is that we are analyzing new and superior ways in how we can acquire logistics. Being able to do this in a way where we don’t consume much power and able to do it much better will allow us to be more efficient in the way we harvest and keep track of our fields. Now more than ever with an increasing food supply demand that we need to improve the way in which we do agriculture.

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Acknowledgements

SURF, Purdue University

IoT4AG Research Center

Oats Center

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IoT4Ag

JULY 28, 2021

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References

[1] Y. Zhang, T. Arakawa, J. V. Krogmeier, C. R. Anderson, D. J. Love and D. R. Buckmaster, "Large-Scale Cellular Coverage Analyses for UAV Data Relay via Channel Modeling," ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 2020, pp. 1-6, doi: 10.1109/ICC40277.2020.9149403.

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[2] Wang Y, Liu H, Krogmeier J, Reibman A, Buckmaster D. ISOBlue HD: An Open-Source Platform for Collecting Context-Rich Agricultural Machinery Datasets. Sensors. 2020; 20(20):5768. https://doi.org/10.3390/s20205768

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[3] R. Heath, S. Peters, Y. Wang and J. Zhang, "A current perspective on distributed antenna systems for the downlink of cellular systems," in IEEE Communications Magazine, vol. 51, no. 4, pp. 161-167, April 2013, doi: 10.1109/MCOM.2013.6495775.

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222 Chris Rodriguez

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IoT4Ag

JULY 28, 2021

REU PRESENTATION