Cattle tracking with LoRa and machine learning
Date
2021-05
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Abstract
To curb the increase in livestock theft in Sub-saharan countries, this project looked
at machine learning applications like cattle tracking on a farm. Received signal strength
indicators from home available WiFis were utilized to fingerprint locations on the farm.
Where WiFis were not available, ESP866 Node MCUs were used to deploy access points.
These ESP8266 Node MCUs were also given SSIDs that could easily be used for
classification using the SSIDs as labels in the program. Two classifiers were explored, the
support vector machine (SVM) and the Decision Tree. A linear kernel with a very low
gamma value of 0.001 was used for the SVM classifier. The decision tree classifier
yielded an average accuracy of 0.89683659, while the support vector machine yielded an
average accuracy of 0.915699. The SVM classifier emerged as a suitable classifier to use
to achieve reliable accuracy in cattle tracking. Lora proved to be an essential tool in
wireless communication of the prediction results from the pendant/transmitter designed to
the receiver, hence enhancing mobility in tracking. Future works include incorporating distance calculation to make the tracking process more straightforward and efficient.
Sensor nodes that will further provide the machine learning algorithm designed with
unique data values will also be incorporated to compensate for longer farms that might
need the deployment of more Node MCU access points. This is an effort to maintain low
costs in tracking using the solution provided by this project. Therefore, the solution
presented by this project proved to be feasible.
Description
Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Computer Engineering, May 2021
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Capstone project
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Keywords
location tracking, livestock tracking