DOI: https://doi.org/10.15368/theses.2021.65
Available at: https://digitalcommons.calpoly.edu/theses/2603
Date of Award
6-2021
Degree Name
MS in Electrical Engineering
Department/Program
Electrical Engineering
College
College of Engineering
Advisor
Joseph Callenes-Sloan
Advisor Department
Electrical Engineering
Advisor College
College of Engineering
Abstract
Cities around the world are facing increasingly significant challenges, including rapid urbanization, resource management, and environmental threats. In California for example, wildfires present an ever-growing threat that gravely harms people, destroys communities, and causes billions of dollars in damages. The task of addressing these environmental threats and many other challenges is greatly aided with widespread data collection and real-time inference. However, as IoT networks scale and require more energy for near-data analytics, the IoT endpoints require more power and complexity, limiting their deployment. Additionally, deploying endpoints in remote locations creates further challenges with higher reliability and communication constraints. In this thesis, we propose an approach for building scalable and reliable near-data analytics systems by leveraging existing power systems. The insight for this approach is that power transmission and distribution systems provide 1) an elevated vantage ideal for sensing, 2) wide coverage of remote and urban areas, 3) cost effective power supply via energy harvesting, and 4) the ability to use existing power infrastructures to further improve application accuracy. We describe an implementation of our approach using power system-based sensor and gateway nodes, and their integration with cloud processing resources. We evaluate the cost, power, and communication of this approach in the context of a remote wildfire sensing application, and demonstrate that this approach provides improved accuracy and scalability with significantly lower costs as compared to conventional approaches.