publications

2025

1. BuildSys ’25

   PhD Forum Abstract: Towards Secure Edge AI for Structural Health Monitoring

   Sheikh Muhammad Farjad

   in Proceedings of the 12th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys ’25), Golden, CO, USA, Nov 2025

   Abs DOI PDF

   On-device deep learning (DL) is reshaping structural health monitoring (SHM), but deploying models at the edge makes them increasingly vulnerable to adversarial attacks, particularly model extraction. This threat is severe for critical infrastructure, given the limited security focus of Edge AI systems. This dissertation develops a secure Edge AI system for SHM through four key contributions: (a) an adversarial risk taxonomy for assessing SHM system susceptibility to adversarial attacks, (b) a secure edge architecture and testbed for deployment, (c) an investigation of model extraction attacks using eBPF-collected metrics, and (d) a novel kernel-level defense to secure deployment.

2. BuildSys ’25

   Zeki: A Containerized Pipeline for Deep Learning Deployment for Edge-based Structural Health Monitoring

   Sheikh Muhammad Farjad and Robin Gandhi

   in Proceedings of the 12th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys ’25), Golden, CO, USA, Nov 2025

   Abs DOI PDF Code

   The deployment of deep learning (DL) models on edge devices offers significant opportunities for structural health monitoring (SHM), particularly by enabling localized, low-latency inference in built environments. However, practical deployment on resource-constrained platforms faces barriers in scalability, reliability, and security. In response, we introduce Zeki, a unified, security-aware, containerized pipeline for deploying DL models for edge-based SHM. Zeki unifies model optimization (via LiteRT and quantization), container-level hardening, and benchmarking-driven model-device co-selection into a reproducible workflow. We evaluate Zeki by deploying convolutional neural networks (CNNs) for crack detection on Raspberry Pi 4 and BeagleBone AI-64 along with a server. Results show significant improvements in inference latency and memory efficiency compared to unoptimized baselines. Beyond performance, Zeki establishes a systematic methodology for safe, resilient, and evidence-based edge deployment of DL in safety-critical SHM settings, enabling long-term infrastructure monitoring.

3. 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.

2023

1. Investigating the Characteristics and Performance of Augmented Reality Applications on Head-Mounted Displays: A Study of the Hololens Application Store

   Pubudu Wijesooriya, Sheikh Muhammad Farjad, Nikolaos Stergiou, and 1 more author

   In 2023 IEEE International Conference on Communications Workshops (ICC Workshops), Rome, Italy, Apr 2023

   Abs DOI PDF

   Augmented Reality (AR) based on Head-Mounted Displays (HMDs) has gained significant traction over the recent years. Nevertheless, it remains unclear what AR HMD-based applications have been developed over the years and what their system performance is when they are run on HMDs. In this paper, we aim to shed light into this direction. Our study focuses on the applications available on the Microsoft Hololens application store given the wide use of the Hololens headset. Our study has two major parts: (i) we collect metadata about the applications available on the Microsoft Hololens application store to understand their characteristics (e.g., categories, pricing, permissions requested, hardware and software compatibility); and (ii) we interact with these applications while running on a Hololens 2 headset and collect data about systems-related metrics (e.g., memory and storage usage, time spent on CPU and GPU related operations) to investigate the systems performance of applications. Our study has resulted in several interesting findings, which we share with the research community.

2020

1. Cluster Analysis and Statistical Modeling: A Unified Approach for Packet Inspection

   Sheikh Muhammad Farjad and Asad Arfeen

   In 2020 International Conference on Cyber Warfare and Security (ICCWS), Islamabad, Pakistan, Apr 2020

   Abs DOI PDF

   A secure network layer capable of distinguishing between malicious and genuine traffic flows is the need of every transit service provider, edge network, corporate customer, and a common Internet user. With the emergence of advanced technologies, the demand for security has been drastically increased over the past decade. The analysis of network traffic is essential for various tasks like security, capacity planning, and visibility at various levels. In this paper, a novel architecture is proposed which exploits two powerful techniques for network traffic inspection, that is, cluster analysis and statistical modeling, and unifies them in a single framework. The proposed architecture leverages the clustering technique and statistical modeling for analyzing and inspecting the network traffic. Instead of selecting NetFlow records as the primary format, this research paper presents an approach that employs Packet Capture (PCAP) data format for network analysis. The clustering technique can be used for classifying benign and malicious traffic but there may arise many uncertainties caused by various dynamic factors due to emerging application mixture. Our proposed model uses statistical modeling for supplementing the results obtained from clustering. This unified approach for traffic analysis reduces the chances of the false alert generation that substantially deteriorates the security ecosystem. The proposed architecture inspects different parameters of network traffic to uncover any strong correlation for identifying malicious network traffic flows.

2019

1. EAI

   A Novel IoT Switching Model Based on Cloud-Centric RTDBS

   Sheikh Muhammad Farjad

   EAI Endorsed Transactions on Internet of Things, Jul 2019

   DOI PDF
2. Exploiting Real-time Database for Efficient Electrical Switching 🏆

   Sheikh Muhammad Farjad

   In Proceedings of the 34th Multi-topic International Symposium 2019, Karachi, Sindh, Pakistan, Feb 2019

   Awarded PDF

   Silver Medal

3. Exploiting Real-time Database for Efficient Electrical Switching 🏆

   Sheikh Muhammad Farjad

   In Proceedings of the 34th IEEEP All Pakistan Students’ Seminar, Karachi, Sindh, Pakistan, Jan 2019

   Awarded PDF

   Silver Paper Award