2022 Research in the Capitol

Location

Iowa State House, Rotunda

Presentation Type

Open Access Poster Presentation

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

File Format

application/pdf

Available for download on Wednesday, March 01, 2023

Share

COinS
 
Feb 21st, 11:30 AM Feb 21st, 1:30 PM

Leveraging Machine Learning for Detecting IoT-based Interference in Operational WiFi Networks

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.