I'm currently a fifth-year PhD student in computer science at University of Houston. My research interests are in the area of IoT applications in smart buildings with the focus on wireless communication and indoor localization. I am member of networked system laboratory (NSL) working under supervision of Prof. 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 in my PhD thesis, I am trying to build a robust indoor localization system by using wireless communication and sensing. Internet of Things will shape human future and I am happy to be part of IoT research community. Download my Resume
In the presence of Line of Sight (LoS) signals, UWB based indoor localization system can locate target with small errors (< 10cm) but in situations with non line of sight (NLoS) signals, UWB systems work at a much reduced accuracy. Our proposed system, utilizes the statistical characteristics of reflected multipath components of UWB signals in different locations as fingerprints and locates the target node based on previously seen patterns. Our evaluation shows that our solution can locate objects within square of 20cm × 20cm with accuracy of 97% using only one anchor which outperforms existing solutions in both robustness and accuracy.
Joint work with Kyungki Kim.
It is known that NLOS propagation of electromagnetic waves can severely affect the localization accuracy. We provide a systematic study to investigate effects of signal refraction and attenuation on UWB signals in different construction materials by examining the Channel Impulse Response (CIR) and ranging accuracy. Further, we present failure scenarios for common NLOS identification and mitigation techniques.
Joint work with Milad Heydariaan .
Different technologies and sensing platforms have proposed for accurate and efficient people counting. However, these solutions are expensive, hard to deploy, or privacy invasive. We investigate the possibility of placing an 8*8 IR array sensor at the doorways and counting the number of people inside rooms. We evaluated our solution via several controlled and uncontrolled real-world environments. The results show an average of 93% accuracy in estimating the number of occupants in rooms.
Joint work with Sirajum Munir.
Recent studies have shown that adjusting HVAC systems based on the number of people inside the room can save at least 38% of energy consumed for reheating. We investigate the possibility of utilizing wireless and light sensing technologies in the doorways to track the entrances/exits to/from the room. Our solution is inexpensive, unobtrusive with much fewer deployment constraints compared to existing people counting solutions. We place low cost photodiode on door frames to detect changes in light illuminance level when there are people walking through the door. We evaluated our solution via several controlled and uncontrolled real-world environments. The results show an average of 96% accuracy in estimating the number of occupants in rooms.
Joint work with Shengrong Yin .
Many energy-saving programs are designed to help people to improve their behavior by providing feedback. The critical factor in the effectiveness of energy saving programs is user engagement. In this project, we design several energy saving activities considering simplicity as the primary goal. To evaluate the effectiveness of proposed energy saving guidelines, volunteers at Oahu, Hawaii are provided smart meters and are asked to follow proposed activities in their daily life. Our results show that the simplicity of our proposed energy saving activities boosted up participation rate to 35%.
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
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.
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.