2022 Research in the Capitol
Location
Iowa State House, Rotunda
Presentation Type
Open Access Poster Presentation
Keywords
Wireless LANs--Protection; Internet of things--Protection; Electromagnetic interference--Detection; Machine learning:
Abstract
IoT (Internet of Things) devices have become increasingly popular in recent years. IoT includes many smart home devices such as an Amazon Echo, smart lightbulbs, and smart sensors. These devices often include different networking protocols than are used in most WiFi devices but are in the same wireless band, leading to the possibility of interference. With the rise in the number of IoT devices, it is important to understand how they impact the existing WiFi networks that many people deploy in their home or business. In this research project, wireless traffic data will be collected in an environment containing both WiFi devices and devices using protocols commonly found in IoT devices, such as Zigbee. We aim to answer if the co-existence of WiFi and IoT networks have a negative impact on the performance on WiFi networks and if so, can machine learning techniques be applied to detect this interference.
Start Date
21-2-2022 11:30 AM
End Date
21-2-2022 1:30 PM
Event Host
University Honors Programs, Iowa Regent Universities
Faculty Advisor
Dheryta Jaisinghani
Department
Department of Computer Science
Copyright
©2022 Josh Pulse
File Format
application/pdf
Recommended Citation
Pulse, Josh, "Leveraging Machine Learning for Detecting IoT-based Interference in Operational WiFi Networks [Poster]" (2022). Research in the Capitol. 7.
https://scholarworks.uni.edu/rcapitol/2022/all/7
Leveraging Machine Learning for Detecting IoT-based Interference in Operational WiFi Networks [Poster]
Iowa State House, Rotunda
IoT (Internet of Things) devices have become increasingly popular in recent years. IoT includes many smart home devices such as an Amazon Echo, smart lightbulbs, and smart sensors. These devices often include different networking protocols than are used in most WiFi devices but are in the same wireless band, leading to the possibility of interference. With the rise in the number of IoT devices, it is important to understand how they impact the existing WiFi networks that many people deploy in their home or business. In this research project, wireless traffic data will be collected in an environment containing both WiFi devices and devices using protocols commonly found in IoT devices, such as Zigbee. We aim to answer if the co-existence of WiFi and IoT networks have a negative impact on the performance on WiFi networks and if so, can machine learning techniques be applied to detect this interference.
