I'm currently a third-year PhD student in computer science at University of Houston. My PhD focus is in wireless networking and embedded systems, and my adviser is Dr. Omprakash Gnawali.
The goal of NSL is to investigate issues in wireless and sensor networks, network protocols and architectures, mobile sensing, social networks, and distributed systems, and to find solutions to the technical problems in these areas.
Most of my recent research has been on design, implementation and deployment of sensor networks and analyzing data collected from them to find interesting patterns and also solutions to systems area problems. Internet of Things will shape human future and I am happy to be part of IoT research community.
Participant's engagement into energy saving programs is main contributing factor in the program's final savings. We designed several highly interactive energy saving activities and asked people in Hawaii to follow our guidelines. Our results show 32% engagement in our program which far beyond the industry standard which is 10% to 15%.
New generation of USB malwares (such as BadUSB) are not detectable by conventional anti-malware solutions. We collected data for USB devices used in our campus for period of 3 months. We applied machine learning techniques to uniquely identify each USB stick via extracted features from our data. Finally, we were able to build a effective white listing approach with accuracy of 98.5%. Our solution is simple but practical.
We planned several activities aiming to help people to save energy. We designed a program and asked volunteers to perform those activities at their homes for 3 months. We have access to monthly electricity bills for all the program’s participants. Our investigation revealed that energy savings among participants has close relationship to level of their engagement into the program. People who were highly involved into program saved much more energy (5%) compare to participants who did not engage enough.
In this work, we investigated several approaches to accurately initialize real time clocks with sub-second accuracy. Our experiments show by accurately initializing real time clocks, a time synchronized sensor network can be built without any energy or complexity overhead of running sophisticated time synchronization protocol.
Joint work with Alex Szalay
Developed shell script to download home page of 500 governmental websites (once per minute) Applied text processing and data mining techniques to extract back-to-normal operation timestamp for each website measured performance of IT infrastructure in US government departments. As an example, we found out that 4% of US websites took longer than one day to update after shutdown ended
This research studies non-isothermal displacement flow of two miscible fluids in an inclined (tilt angle ranging from 0° to 90°) duct. We used IR camera temperature imaging to track the fluid finger as it develops in the duct.
In this work, we deployed different types of sensors (Moisture, Temperature and ...) in soil and collect data for long periods. This work is in collaboration with Department of Earth and Planetary Sciences to help scientists study soil features during different natural events.
Human brain has a high potential process capacity and we want to utilize it to build an anti-phishing solution. Our way to reach to interact with brain is through its outputs. In this work, we attach several sensors (Gaze Tracker, SPO2, Airflow, Heart Rate and ...) to human body and asked our participants to distinguish Phishing emails from original ones. We are interested in common patterns among our participants.
In this project, we tried to quantify and compare network performance under different cellphone carriers. We developed an Android application to collected network performance information. We noticed significant difference between carriers at same time and location. We proposed an efficient solution based on end user service sharing which enhances network performance.