GuardSight

GuardSight:

This innovative product saves people, time, money and lives using intelligent data analytics through IoT devices in residential and commercial properties.

Why:

We believe that prevention is key. The the small leakage in pipelines can result in a minimum of thousands of dollars in water damage, architectural fractures, burned structures through fires, and compromise the structural safety of buildings. We want to prevent these incidents. Additionally, we made this project to really make cities a safer place by letting users know about potential or early stage incidents so that they make safely escape and quickly respond to prevent and if necessary mitigate harm.

Who is Our Target Market:

Both private and public Real Estate and property owners who seek to have an efficient method of maintaining properties at low overheads as well as preventing human injury. This is especially crucial for industries that place high demands on industrial locations and processing.

What/How:

• We used a Arduino 101 attached to a Baseshield and Intel IoT sensory collectors to collect light, sound, and sensor vibration data. A Honeywell Thermostat device was used to collect temperature readings. A Honeywell Water Leak Detector detected water leaks and measured humidity .

• All sensor data was uploaded to Firebase and synced in real-time. This data was analyzed for abnormalities on a custom server by comparing new readings to past sensor readings as well as Las Vegas city datasets. Upon the detection of abnormalities, users are alerted via a SMS text to take action and informed of current sensor readings. Upon detection of an abnormality, users can view a live stream of the situation in any browser, mobile or desktop. Additionally, users conveniently have the ability to see sensor data graphs in both a mobile app as well as a web console at any time.

• Custom server is written in Node.js with calls to the Firebase and Honeywell Lyric API. Web console is written in Javascript with calls to Firebase with graphs made in Chart.js. Mobile app is written in Java for the Android platform with calls to Firebase.

Future:

We see this project as having major potential for large-scale growth and impact. For example, we think that through machine learning our current algorithms can become even faster and smarter at detecting problems in buildings. We also believe that one venue for expansion is using drones in a incentivized consumer opt-in program through utility companies where drones could fly over buildings and homes to collect sensor data that would be relayed to local, state, and federal organizations, allowing them to get a better understanding of the safety levels around the country.