Advancing the Development and Utilization of Data Infrastructure for Smart Homes

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.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Computer Science & Applications

Department dc:contributor.department

Computer Science and#38; Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Anik, Sheik Murad Hassan

Chairs dc:contributor.committeechair

• Meng, Na
• Gao, Xinghua

Committee members dc:contributor.committeemember

• Chang, Soowon
• Zeng, Haibo
• Tilevich, Eli
• Ji, Bo

• Data Infrastructure
• Machine Learning Application
• Building Engineering
• IoT
• Smart Home System
• Automation Configuration

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:41427

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/121124