Design and implementation of a low-power low-cost smart embedded system for remote animal monitoring

Abstract

This Master’s thesis serves as the foundation for an innovative wildlife monitoring system, encompassing hardware design, firmware and software development, and offering insights into future directions. Leveraging the research group’s extensive experience in research, development, and field deployment of wildlife technology solutions, the thesis has culminated in a device with versatile capabilities suitable for a wide range of applications. A central focus of this work is on energy efficiency, prioritizing low-power operation to facilitate extended field deployments and reduce maintenance requirements. The integration of AI capabilities is a core component, enabling real-time data analysis within the embedded system. The system’s architecture is thoughtfully designed to seamlessly integrate data from diverse sources, including visual, acoustic, and environmental inputs, providing comprehensive insights into the natural world. Modularity in communication networks empowers the system to adapt to varying project requirements and network environments. The successful integration of hardware and software components enhances system performance, ensuring seamless data flow and efficient communication between different modules. The thesis underscores the importance of comprehensive testing, performance characterization, and real-world field testing for future research. In summary, this work represents a crucial step in the development of a versatile, energy-efficient, and AI-enhanced wildlife monitoring system with the potential to make substantial contributions to the field of conservation technology.