I have been working on various research projects since 2016. Through my research, I have gained valuable experience in networking, wireless sensor networks, virtual machines, and IoT. My fields of interest are IoT, embedded systems, and wireless sensor networks. Below you can explore my past and ongoing research projects.
Flood Detection Using IoT Based Wireless Sensor Network, funded by NASA West Virginia Space Grant Consortium Undergraduate Fellowship
Abstract: Flash flooding is a very sudden and destructive natural disaster. Per the National Oceanic and Atmospheric Administration (NOAA), in the United States the national 30-year average for flood deaths is 127 a year. In this paper, I will propose improvements that build upon a system that enabled the creation of a cheap and effective wireless sensor platforms that provide real time data on the current state of a body of a rising body of water and serves as an early warning system for flooding. Through these improvements, the longevity of the system and the effective range of the network will be drastically improved, resulting in sensor network that is completely autonomous and sustainable.
REU INCLUSION at University of Illinois at Urbana-Champaign’s National Center for Supercomputing Applications (NCSA), funded by the Nation Science Foundation Grant 1659702
Data Storage and Analysis Framework for Semiconductor Nanocrystals Used in Bioimaging
Abstract: Semiconductor nanocrystals are nanoscopic light-emitting particles that have many industrial applications. In this paper, a methodology to automatically analyze nanocrystal sample data for correlations between structural properties and optical properties will be proposed. Using Scikit-learn, a machine learning library for Python, the sample data will be categorized and labeled. Once this is done, the data will then be used to predict how slight changes in nanocrystal structural properties will affect the crystal’s optical properties and, in particular, positions and shape of peaks in optical spectra. This data can then be used to optimize crystal properties for individual products and applications, in our case, Biological Imaging.
Abstract: Load testing is one of the means for evaluating the performance of Ultra Large Scale Systems (ULSS). At the end of a load test, performance analysts must analyze thousands of performance counters from hundreds of machines under test. These performance counters are measures of run-time system properties such as CPU utilization, Disk I/O, memory consumption, and network traffic. Analysts observe counters to find out if the system is meeting its Service Level Agreements (SLAs). Typically, to save time and money, load tests are conducted in a virtualized environment prior to release of the enterprise software. Nevertheless, to date there do not exist any research to study if there exists any overhead in the use of virtual machines for load testing in comparison to physical machines. In our work, we perform a study on two open-source benchmarks systems DS2 and Rubis, where load test are conducted on both virtual and physical environments. We compare the results of load tests conducted using both the environment, identify any discrepancy (if exists). Further, we explore the rational of the observed discrepancies.