Sherif Abdelwahed, Ph.D.

Professor, Department of Electrical and Computer Engineering VCU College of Engineering

  • Richmond VA

Dr. Abdelwahed is a professor in the Department of Electrical and Computer Engineering

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Spotlight

5 min

Small buildings, big impact: OpenCyberCity Director Sherif Abdelwahed, Ph.D., talks about smart city research and the new capabilities of VCU Engineering’s miniature city

Municipalities around the world have invested significant resources to develop connected smart cities that use the Internet of Things (IoT) to improve sustainability, safety and efficiency. With this increased demand for IoT experience, the VCU College of Engineering formed the OpenCyberCity testbed in 2022. The 1:12 scale model city provides a realistic, small-scale cityscape where students and researchers can experiment with new and existing smart city technology. Sherif Abdelwahed, Ph.D., electrical and computer engineering professor, is director of OpenCyberCity. He recently answered some questions about new developments within the testbed. The OpenCyberCity is a smart city testbed, but are there any real-life cities that one could call a smart city? Several real-life locales are considered smart cities due to their extensive use of technology and data-driven initiatives to optimize infrastructure and services. Dubai is one of the most notable. They have implemented smart transportation systems, buildings and artificial intelligence to transform the city’s operations and make them more efficient. Other reputable smart cities include Singapore and Seoul, which utilize smart energy management, smart transportation and comprehensive data analytics for improved urban planning and services. Seoul, in particular, has an initiative with smart grids and connected street lights, which VCU Engineering’s own OpenCyberCity test bed is working to implement. How does the OpenCyberCity address privacy? With so much technology related to monitoring, how are individual citizens protected from these technologies? Privacy is a major concern for smart cities and it is one of the main research directions for VCU Engineering’s OpenCyberCity. We are developing several techniques to prevent unwanted surveillance of personal information. Sensitive data is protected by solid protocols and access restrictions that only allow authorized users to view the data. Our aim is to find a reasonable middle ground between technological progress and privacy rights, staying within legal and ethical bounds. Some techniques to address privacy concerns include: Data Anonymization: This makes it difficult to trace back information to individual identities. Within the testbed, we will evaluate how to protect individual privacy while maintaining data utility and assess the impact on data quality. Secure Data Storage and Transmission: Encrypt data to protect it from unauthorized access. In the smart city testbed, these access control mechanisms will be implemented within the testbed’s infrastructure. We will also test different data handling processes and access control models to determine their ability to safeguard sensitive data. Privacy Impact Assessments: Regularly evaluate potential privacy risks of new smart city projects in order to mitigate them and ensure the ethical handling of data by those with access. Policy and Regulation Development: Data and insights generated from OpenCyberCity experiments can inform the development of cybersecurity policies and regulations for smart cities. How is the College of Engineering’s OpenCyberCity test bed different from similar programs at other institutions? While other universities have similar smart-city-style programs, each has their own specialty. The VCU College of Engineering’s OpenCyberCity test bed focuses on real-world contexts, creating a physical space where new technologies, infrastructure, energy-efficient transportation and other smart city services can be tested in a controlled environment. Our lab monitors real-time data and develops smart buildings, smart hospitals and smart manufacturing buildings to enhance the city’s technologies. Recent additions to the OpenCyberCity allow for expanded research opportunities like: Advanced Manufacturing: Students can apply advanced manufacturing techniques in a controlled environment. They can also test new materials, processes and automation technologies to improve efficiency and product quality. Energy Efficiency Testing: Environmental engineers and sustainability experts can evaluate energy consumption patterns within the smart manufacturing unit to implement energy-saving measures and assess their impact on sustainability. Production Optimization: Manufacturers can use real-time data from the smart manufacturing unit to optimize production schedules, minimize downtime and reduce waste. Predictive maintenance algorithms also help prevent equipment breakdowns. Education and Training: Hands-on experience with state-of-the-art manufacturing technologies helps train the workforce of the future. Integration with Smart City Services: Data generated by the manufacturing unit can be integrated with smart city services. For example, production data can inform supply chain management and energy consumption data can contribute to overall city energy efficiency initiatives. How has the OpenCyberCity changed in the last year? Is the main focus still data security? What started with research examining, analyzing and evaluating the security of next-generation (NextG) applications, smart city operations and medical devices has expanded. Data security is now only one aspect of OpenCyberCity. Its scope has grown to encompass more expansive facets of cybersecurity like automation and data analytics in the domain of smart manufacturing systems. The implementation of a smart manufacturing system in 2023 is something students really enjoy. Thanks to the vendor we used, undergraduate students had the option to develop functionality for various features of the manufacturing plant. Graduate students were also able to research communications protocols and cybersecurity within the smart manufacturing system. What does the smart manufacturing system entail and what kind of work is occurring within that system? An automated system is there for students to work with. Robot arms, microcontrollers, conveyor belts, ramps, cameras and blocks to represent cargo form an environment that emulates a real manufacturing setting. We’re currently brainstorming an expansion of the smart manufacturing system in collaboration with the Commonwealth Cyber Initiative (CCI). We plan to set up two building models, one for manufacturing and one for distribution, linked by a sky bridge conveyor system that moves items between the locations. Students work to leverage convolutional neural networks that use images to facilitate machine learning. When paired with the advanced cameras, it forms a computer vision system that can accurately place blocks in a variety of lighting conditions, which can be a challenge for other systems. By having to optimize the communication protocols that command the smart manufacturing system’s robotic arms, students also get a sense for real-world constraints . The Raspberry Pi that functions as the controller for the system is limited in power, so finding efficiencies that also enable stability and precision with the arms is key. Is there an aspect of cybersecurity for these automated systems? Yes. Devices, sensors and communication networks integral to the IoT found in smart manufacturing systems and smart cities generate and share vast amounts of data. This makes them vulnerable to cybersecurity threats. Some of the issues we look to address include: Data Privacy: Smart systems collect and process vast amounts of data, including personal and sensitive information. Protecting this data from unauthorized access and breaches is a top priority. Device Vulnerabilities: Many IoT devices used in smart systems have limited computational resources and may not receive regular security updates, making them vulnerable to exploitation. Interconnectedness: The interconnected nature of smart city components increases the attack surface. A breach in one system can potentially compromise the entire network. Malware and Ransomware: Smart systems are susceptible to malware and ransomware attacks, which can disrupt services and extort organizations for financial gain. Insider Threats: Employees with malicious intent or negligence can pose significant risks to cybersecurity. Potential solutions to these problems include data encryption, frequent software updates, network segmentation with strict access controls, real-time intrusion detection (with automated responses to detected threats), strong user authentication methods, security training for users and the development of well-designed incident response plans.

Sherif Abdelwahed, Ph.D.

Biography

Sherif Abdelwahed is a Professor of Electrical and Computer Engineering (ECE) at Virginia Commonwealth University (VCU), where he teaches and conducts research in the area of computer engineering, with specific interests in autonomic computing, cyber-physical systems, formal verification and cyber-security. Before joining VCU in August 2017, he served as the associate director of the Distributed Analytics and Security Institute at Mississippi State University (MSU). He was also is also an Associate Professor in the ECE Department at MSU.

He received his Ph.D in 2002 from the Department of Electrical and Computer Engineering at the University of Toronto under the supervision of Professor W. M. Wonham. Prior to joining Mississippi State University, he was a research assistant professor at the Department of Electrical Engineering and Computer Science and senior research scientist at the Institute for Software Integrated Systems, Vanderbilt University, from 2001-2007. From 2000-2001 he worked as a research scientist with the system diagnosis group at the Rockwell Scientific Company.

Throughout his academic tenure Dr. Abdelwahed attracted research funding from industrial and government agencies including NSF, NASA, Boeing, ONR, PNNL, ERDC DARPA, Microsoft, and Qatar Foundation, with more than 12 million dollars awarded covering 20 major projects. He also established the first NSF I/UCRC center at Mississippi State University, the Center for Autonomic Computing (CAC).

Dr. Abdelwahed has chaired several international conferences and conference tracks, and has served as technical committee member at various national and international conferences. He received the StatePride Faculty award for 2010 and 2011, the Bagley College of Engineering Hearin Faculty Excellence award in 2010, and recently the 2016 Faculty Research Award from the Bagley College of Engineering at MSU. He gas more than 140 publications and is a senior member of the IEEE.

Industry Expertise

Computer Software
Electrical Engineering
Information Technology and Services

Areas of Expertise

Smart City Infrastructure
Model-integrated Computing
System Diagnosis and Fault Analysis
Formal Verification
Model based Design and Analysis of Cyber-phyiscal Systems
Autonomic Computing

Accomplishments

R&D Lead

Established and led the VCU Center for Analytics and Smart Technologies and built VCU’s smart city testbed. Served as associate director of the Distributed Analytics and Security Institute at MSU. Co-founded the first NSF-funded I/UCRC for Autonomic Computing. Developed a diagnosis/prognosis system for a major avionics firm and led creation of a fault management tool licensed to NASA.

Research Funding

More than $16.5 million awarded in research grants covering 24 major projects ($4.9 million at Virginia Commonwealth University, $9.6 million at Mississippi State University, and $1.9 million at Vanderbilt University). These projects were funded by industrial research institutes, foundations, and government agencies, including NSF, NASA, Boeing, ONR, DARPA, ERDC, Northrop-Grumman, Microsoft, Micron, DoD, DoE VRIC, and Qatar Foundation.

Publications

Peer-reviewed journal papers: 42 published/accepted. Other publications include 8 book chapters, more than 140 peer-reviewed conference papers. papers, 10 tech. reports, 8 posters, and 14 presentations.

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Education

University of Toronto

PhD

Electrical and Computer Engineering

2002

Affiliations

  • Virginia Commonwealth University

Media Appearances

Interview with Dr. Sherif Abdelwahed

EEweb  online

Check the following link for details.

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Shooting for the moon, VCU joins state higher education push in cybersecurity

Richmond Times-Dispatch  print

2018-09-07

Leaders of the Department of Electrical and Computer Engineering and Department of Computer Science were featured in a front-page story about cybersecurity in the Richmond Times-Dispatch.

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Research Focus

Model-based Design and Analysis of Cyber-Physical Systems

This project aims to develop and validate a model-based control framework for enabling self-managing and self-healing capabilities in cyber-physical systems. The central idea is to integrate the monitoring and control processes within a common model-based framework that will continually optimize system behavior in response to both changes within the system as well as to external changes in the operating environment. The developed control structure have been applied to two major systems, namely, the advanced life support system developed for future space mission by NASA and the new generation of the Navy electric ship designs. This research is funded by ONR, NASA and NSF.

Autonomic (Self-managing) Computing Systems

This project aims to develop the theoretical foundation and demonstrate technologies for model-driven engineering of self-managing distributed computing systems. A lookahead control policy is developed for managing the performance and resource requirement of a wide class of computation systems operating in uncertain environment. This research is currently supported by NSF, ERDC, and Microsoft.

Cyber security

In this work, a security management technology is being developed by integrating system control, optimization, and security analysis tasks into a common model-based framework that can apply early-prevention mechanisms and react to cyber-attacks by employing optimal responses that are evaluated by risk assessment techniques. In this approach relevant security features are observed by online monitors and analyzed for early detection of threats. The corresponding cyber-security alerts are then sent to a controller whenever the system monitored features are deviated from the normal regions. An intrusion detection system that employs both signature and anomaly-based techniques to detect both known and unknown attacks will be utilized in our approach for early detection of cyber threats. The system will also employs real-time learning to capture and analyze signatures of unknown attacks. This research is funded by ERDC and QF.

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Patents

Enhanced systems, apparatus, and methods for improved automated and autonomous operation of logistics ground support equipment

US 2020/0033872 A1

2020-01-15

An improved retrofit assembly for ground support equipment provides autonomous operation of the logistics ground support equipment (GSE). The assembly has a refit control system attached to the GSE (with a vehicle dynamics control processor and a data preprocessing control processor), retrofitted proprioceptive sensors coupled to the vehicle dynamics control processor monitoring operating parameters and characteristics of the GSE, retrofitted exteroceptive
sensors coupled to the data preprocessing control processor that monitor the exterior environment of the GSE, and refit actuators for different control elements on the GSE, and to autonomously alter the motion of the GSE.

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Research Grants

Smart Partitioning based Large-Scale Power System Analysis on High- Performance Computing Platform: Modeling, Algorithms, and Computations

NSF

2017-08-01

In order to handle the ever-growing size, complexity, and heterogeneity of the mathematical problems resulted from the power system expansion and evaluation, and conduct rapid and accurate power system analyses especially with the aid of advanced computing techniques, the project team envisions a new concept of “Smart Partitioning” as an innovative decomposition method to enhance the analysis of large-scale and complex power systems by leveraging high performance parallel computing (HPC) platform. The proposed research will systematically explore the applications of the smart partitioning concept in terms of problem modeling, solution algorithms and computational implementation to transform how steady state and dynamic large-scale power system analyses are efficiently performed on HPC platform.

Assessing and managing ICT security risks of industrial controls systems for oil and natural gas production and transmission

Qatar Foundation

2017-08-01

The pervasive use of industrial control systems with SCADA subsystems in critical energy infrastructures and the increased connectivity of these systems to corporate networks has exposed them to new security threats and made them a prime target for cyber-attacks due to the profound and catastrophic impacts they can cause to the economy and the society. Despite ongoing efforts to secure and protect them, these critical infrastructure components remain vulnerable to these attacks. Recent intensified sophisticated attacks on these systems have stressed the importance of methodologies and tools to assess and manage ICT security risks in real-time. With respect to the current state of the art, this project proposes a model-based approach to develop a quantitative and automated methodology for assessing and managing security risks of both the overall industrial control system and of its SCADA subsystems. The proposed approach is characterized by the integration of proactive and reactive security mechanisms to automate risk assessment and management.

Courses

EGRE 512: Intelligent Autonomous Systems

Intelligent Autonomous Systems represent a rapidly advancing field at the intersection of Electrical and Computer Engineering, Robotics, and Artificial Intelligence. This course offers a comprehensive exploration of the theory, technologies, and practical applications of these systems, equipping senior and graduate students with the skills and knowledge needed to design, build, and deploy intelligent autonomous systems in diverse domains. The course will provide an examination of the historical context and current trends in autonomous systems, providing insights into their evolution, societal impact, and ethical considerations. It will also introduce the multifaceted aspects of autonomous systems, encompassing perception, decision-making, and control, which are crucial for these systems to interact effectively and safely within their environments. Throughout the course, students will acquire a deep understanding of the fundamental principles and advanced techniques underlying autonomous systems. Control theory will be covered extensively, from classic PID control to modern trajectory planning and path following algorithms, empowering students to stabilize and maneuver autonomous platforms. Machine learning, a cornerstone of intelligent systems, will be a central focus. Students will gain expertise in supervised, unsupervised, and reinforcement learning, as well as delve into deep learning techniques for perception and decision-making.

ENGR 125: Practical AI

This course introduces university freshmen from all disciplines to the world of Artificial Intelligence (AI) through a non-technical lens, focusing on its applications in various sectors such as medicine, gaming, business, art, literature, and smart engineering systems. Students will learn about AI tools and techniques and engage in hands-on activities to understand how AI is integrated into real-world applications. Through discussions, assignments, and a capstone project, students will explore the potential of AI to transform industries and address societal challenges. No prior technical background is required.

EGRE 636: Introduction to Cyber Physical Systems

This course introduces students to the research, design and analysis of cyber-physical systems -- the tight integration of computing, control and communication. The main focus is on understanding existing and emerging models of CPSs, as well as physical processes in terms of differential equations and computational models for discrete time systems, such as extended finite-state machines and hybrid automata. State-charts are introduced and combined with the physical models for analysis of embedded systems. Linear temporal logic is introduced and applied to specify the desired system behavior. Tools for analytical study and verification of the satisfaction of linear temporal logic formulae are presented and discussed in numerous applications. Dependability attributes such as safety, reliability and cyber-security are discussed in the context of high integrity CPS.

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