College - Author 1
College of Engineering
Department - Author 1
Electrical Engineering Department
Degree Name - Author 1
BS in Electrical Engineering
Date
6-2025
Primary Advisor
Steve Dunton, College of Engineering, Electrical Engineering Department
Additional Advisors
Dennis Derrickson, College of Engineering, Electrical Engineering Department
Abstract/Summary
Constant data processing is essential for effective data collection, particularly in remote and hard-to-reach areas. The need for efficient, autonomous data acquisition and transmission is increasingly addressed by integrating machine learning within larger communication networks. This paper explores the application of machine learning techniques with Internet of Things (IoT) mesh networks. Utilizing the LoRa communication protocol, data is captured and analyzed on an edge device and transmitted to a larger communication network. The ClusterDuck Protocol (CDP), developed by OWL Integrations, serves as an IoT mesh network designed to allow for communication in areas lacking traditional static infrastructure such as WiFi or power lines. This project investigates the implementation of supervised machine learning algorithms including support vector machines (SVM), logistic regression, and convolutional neural networks (CNN) to enhance remote data processing capabilities and transmit results within the CDP network at low power. To study this, this project utilizes computer vision machine learning techniques to identify the presence of animals in proximity. The findings of this research highlight the potential for machine learning to enhance LoRa-enabled IoT networks. This paper analyzes multiple techniques to determine trade-offs between accuracy and power consumption, providing groundwork for designing robust IoT networks capable of supporting autonomous, low-maintenance environmental monitoring systems in resource-constrained, remote areas.
URL: https://digitalcommons.calpoly.edu/eesp/681