Sheikh Muhammad Farjad

sfarjad@unomaha.edu

Omaha, Nebraska, USA

I am a PhD student in Computing & Information Science at the University of Nebraska at Omaha, working under the supervision of Dr. Robin Gandhi. Before starting PhD, I completed my BE (Bachelor of Engineering) in Computer Systems Engineering from NED University of Engineering and Technology. My primary interest is in the security and privacy of edge computing coupled with machine learning. I am also passionate about Linux.

Besides research, I enjoy cooking, brewing coffee, exploring history and theology, strength training, running, and spending time in nature.

news

May 05, 2025	Presented our work on secure edge computing reference architecture at US Army Engineer Research and Development Center (ERDC), Vicksburg, MS, USA.
Apr 25, 2025	Received Best Full Paper Award from ACM at the 2025 ACM Southeast Conference. 🏆
Apr 25, 2025	Presented our paper at the 2025 ACM Southeast Conference, Cape Girardeau, MO, USA.

latest posts

Apr 13, 2025	edgeOps: Benchmarking ML Inference at Edge
Jun 01, 2020	Tutorial on Deploying Kali-Ubuntu MPI Cluster
Jun 01, 2020	A Brief Tutorial on SDN using Ryu Controller

selected publications

ACMSE2025

Secure Edge Computing Reference Architecture for Data-driven Structural Health Monitoring: Lessons Learned from Implementation and Benchmarking 🏆

Sheikh Muhammad Farjad, Sandeep Reddy Patllola, Yonas Kassa, and 2 more authors

in Proceedings of the 2025 ACM Southeast Conference, Cape Girardeau, MO, USA, Apr 2025

Awarded Abs DOI PDF Code

Best Full Paper Award

Structural Health Monitoring (SHM) plays a crucial role in maintaining aging and critical infrastructure, supporting applications such as smart cities and digital twinning. These applications demand machine learning models capable of processing large volumes of real-time sensor data at the network edge. However, existing approaches often neglect the challenges of deploying machine learning models at the edge or are constrained by vendor-specific platforms. This paper introduces a scalable and secure edge-computing reference architecture tailored for data-driven SHM. We share practical insights from deploying this architecture at the Memorial Bridge in New Hampshire, US, referred to as the Living Bridge project. Our solution integrates a commercial data acquisition system with off-the-shelf hardware running an open-source edge-computing platform, remotely managed and scaled through cloud services. To support the development of data-driven SHM systems, we propose a resource consumption benchmarking framework called edgeOps to evaluate the performance of machine learning models on edge devices. We study this framework by collecting resource utilization data for machine learning models typically used in SHM applications on two different edge computing hardware platforms. edgeOps was specifically studied on off-the-shelf Linux and ARM-based edge devices. Our findings demonstrate the impact of platform and model selection on system performance, providing actionable guidance for edge-based SHM system design.