The smart home era is inevitably arising towards our everyday life. However, the scarcity of publicly available data remains a major hurdle in the domain, limiting people's capability of performing data analysis and their effectiveness in creating smart home automations. To mitigate this hurdle and its influence, our research explored three research directions to (1) create a better infrastructure that effectively collects and visualizes indoor-environment sensing data, (2) create a machine learning-based approach to demonstrate a novel way of analyzing indoor-environment data to facilitate human-centered building design, and (3) conduct an empirical study to explore the challenges and opportunities in existing smart home development. Specifically, we conducted three research projects. First, we created an open-source IoT-based cost-effective, distributed, scalable, and portable indoor environmental data collection system, Building Data Lite (BDL). We deployed this research prototype in 12 households, which deployment so far has collected more than 2 million records that are available to public in general. Second, building occupant persona is a very important component in human-centered smart home design, so we investigated an approach of applying state-of-the-art machine-learning models to data collected by an existing infrastructure, to enable the automatic creation of building occupant persona while minimizing human effort. Third, Home Assistant (HA) is an open-source off-the-shelf smart home platform that users frequently use to transform their residences into smart homes. However, many users seem to be stuck with the configuration scripts of home automations. We conducted an empirical study by (1) crawling posts on HA forum, (2) manually analyzing those posts to understand users' common technical concerns as well as frequently recommended resolutions, and (3) applying existing tools to assess the tool usefulness in alleviating users' pain. All our research projects will shed light on future directions in smart home design and development. / Doctor of Philosophy / My research aims to address the gaps in the smart home systems domain in terms of data availability, utilization, and, development issues. In this dissertation, I developed an IoT-based wireless sensor network to mitigate the lack of publicly available actual building data. I used machine learning tools for developing building occupant persona with real-world data which is a necessary element in human-centered smart home design. I conducted an empirical study to understand the automation configuration issues in smart home systems and presented a root-cause taxonomy of the issues investigated. The combined findings of this research can help the smart home development community and open new doors in research directions.