College of Computing & Informatics

Darian Yulin Shi

College of Engineering Computer Engineering

Faculty Mentor: Dr. Guarav Naik Computer Science

Tracking devices in the IoT using Bluetooth Low Energy

Defined as a system or network of mechanical and digital devices that are embedded with technology and connect to each other, the concept of IoT focuses on bridging the physical and digital worlds to increase the efficiency of standing protocols. Bluetooth Low Energy (BLE), a Bluetooth technology that significantly reduces power consumption but maintains similar communications ranges, has been implemented in devices called Beacons, which transmit information to local devices. They are often used in places such as retail to track and gather information on consumers, bringing up many ethical and privacy issues. Since new technology standards like this are still growing, they are susceptible to unethical hackers, placing our personal digital data at risk. To demonstrate this risk, we used an open source Bluetooth sniffing program, Blue Hydra, on a Raspberry Pi and were able to track ~40 local Bluetooth devices, while also gathering each device’s name, manufacturer, MAC address, etc. Using a simple Python script in conjunction with Blue Hydra, we are able to keep a log of when a specific Bluetooth device enters or leaves the Raspberry Pi’s range. We conclude that the privacy implications of BLE are significant and need further study.

College of Computing & Informatics

Gulam Simnani Contractor

College of Computing & Informatics

Computer Science

Faculty Mentor: Dr. Guarav Naik

Computer Science

Scott Haag Co-Mentor

Automation of Watershed Retrieval

Watersheds are defined as upslope regions that contribute to the flow of water to a common outpoint. Mapping watershed boundaries is critical for managing natural resources and is used to estimate the conditions of rivers, streams, and lakes. These models help scientists connect impacts (land cover, point sources, atmospheric conditions) to potable water, agriculture, and manufacturing. Modern computational methods to retrieve a watershed start by the creation of Digital Elevation Models (DEM). The DEM is most often created using remotely sensed technologies to map and measure the 3-D shape of the earth’s surface. The DEM is then converted into a Flow Direction Grid (FDG) that indicates how water flows over the land surface from one location to another. The most common FDG is the D8 model which stores the identity of the neighboring cell (1 to 8) where water is predicted to flow to. Raw FDGs created from DEM contain internal sinks that do not flow out. To create accurate FDG, sinks must be filled in the original DEM.

The goal of this research is to automate the process of identifying sinks in a DEM, filling them, and making a flow corrected D8 FDG. The corrected grid will then be used to retrieve watersheds boundaries.

College of Computing & Informatics

Blake Parker

College of Computing & Informatics

Computer Science

Faculty Mentor: Dr. Guarav Naik

Computer Science

Exploring WiFi Localization to create Internet of Things (IoT) empowered spaces

The IoT is a popular trend in technology being researched around the globe. Everyone and everything from individual people to big companies use the IoT to create more efficient products, services, and spaces. This project aims to investigate the IoT with the objective to design smarter buildings and safer spaces.

Data will be drawn from how people interact with different parts of a building such as lounges, workspaces, bathrooms, etc. This is made possible by analyzing how a person’s WiFi-enabled device (i.e. smart phone) interacts with different access points, or WiFi routers, set up throughout a building.

This study employs WiFi localization in order to pinpoint someone’s location. Essentially, several different Raspberry Pi 3B+ computers running Kali Linux will be set up around the office space and act as access points that communicate with nearby WiFi-enabled devices. These Raspberry Pi’s implement an open source software called FIND3 that is able locate a person’s phone, computer, tablet, etc. by tracking a device’s media access control (MAC) address. After completing machine learning, FIND3 is able to track a WiFi-enabled device using its MAC address and determine what part of a building, house, etc. the device is located.