This study proposes enhanced oversight of smart homes by leveraging the social networks of homeowners to co-monitor for emergencies, while being mindful of privacy preserving features necessary for adoption. A pilot co-design workshop was conducted to determine features for co-monitoring. A group of four participants provided early findings and informed modifications to study design, and new insights for user behavior were emphasized. By refining the study design, we hope to better target users’ tacit knowledge in future workshops. Early findings include the users’ need for more than simply sharing access to a camera during an emergency in the home; users desired control over the microphone, the camera video stream, and the length of time. We believe this work will contribute to a broader understanding of features that better meet the needs and goals of smart device owners to enable co-monitoring.
This is an ongoing research project focused on creating a framework for capturing various artifacts concerning Internet of Things devices. Research has shown a severe lack of frameworks focusing on collecting data from and about IoT devices. Mozilla’s WebThings Gateway focuses on collecting this information from the devices. This project expects to find methods of IoT data collection through a proposed test-bed utilizing the WebThings Gateway.
Open Government Data (OGD) promotes transparency, innovation, and value creation that makes information gathered by the government about the city and community open to all. The City of Cincinnati Open Government Data Portal allows citizens to access local data as part of a local OGD initiative. Although these datasets are available and are used by a broad audience, little is known about how users engage with this data and the general usability of the platforms. To learn more about this audience, this study is conducted in two steps 1)- a think-aloud activity and 2)- an online survey. Through these activities, we aim to gather information about how the users are interacting with the available data and for what purpose are they interacting with different sections of the portal. After gathering relevant data from the think-aloud activity, we aim to generate a questionnaire by analyzing all the information collected in the previous event at a larger scale. Using a web-based survey shared with individuals via Qualtrics, we will explore the use of the portal to gain more insight and knowledge on user requirements and their suggestions. The endpoint of this study is to develop insights that will help us understand user expectations and how changes could benefit the portal.
Internet of Things (IoT) enabled smart homes to have made our daily lives easier, but these conveniences have also introduced security concerns. IoT devices hold security risks as well as smart home hubs and gateways. Gateways present a centralized point of communication among devices that can create a backdoor into network data for hackers but also present a detection opportunity. Intrusion detection is a common way to detect anomalies in network traffic. This paper introduces early work on an intrusion detection system (IDS) by detecting anomalies in the smart home network using Extreme Learning Machine and Artificial Immune System (AIS ELM). AIS uses the clonal Algorithm for the optimization of the input parameters, and ELM analyzes the input parameter for better convergence in detecting anomalous activity. The larger goal of this work is to apply this approach to a smart home network gateway and combined it with a push notification system that will allow the homeowner to identify any abnormalities in the smart home network and take appropriate action.
