Eyhab Al-Masri is an Assistant Professor in the Department of Computer Science and Systems,
School of Engineering and Technology (SET) at the University of Washington Tacoma. He earned his Ph.D. in Computer Science from the department of Computer Science at the University of Guelph
. He received his Masters degree in Electrical Engineering and B.Sc. degree in Computer Engineering both from
Florida International University.
His research focuses on the security and performance analysis of resource-constrained systems such as the embedded systems and Internet of Things (IoT).
Below are some of the ongoing research projects.
Addressing environmentally safe management of waste is becoming increasingly a challenging task. In this research, we introduce recycle.io, an Internet of Things (IoT)-enabled waste management system that is based on a serverless architecture that can identify these sources of violations. Using recycle.io, it is then possible to track the violations geographically which can help local governments, for example, to improve or enforce tighter regulations for waste disposal. Our recycle.io system uses Microsoft Azure IoT Hub for device management.
Data has become a strategic driver for the success of existing IIoT applications particularly in uncovering valuable insights and enabling smarter, faster decision making. Collecting this data via IoT devices at the right time and place in large volumes can significantly help organizations to rapidly adapt to changes in workflows, reduce downtime, grow production capacity and improve operating efficiency. This research investigates the Quality of Microservices (mQoS) provides an overall assessment of a microservice’s behavior in delivering the required functionality.
Hardware prototyping platforms such as Arduino and Raspberry Pi offer students the means for embracing the intellectual challenge by making creative ideas accessible to all learners. The aim of this research is to investigate the usefulness of integrating low cost open-source hardware platforms into engineering and computer science courses. This is research strives to facilitate the adoption, development and deployment of IoT applications by building powerful tools that will enable multiple users (e.g. software engineers, system engineers, developers and data scientists) to master all of the phases of the modern data-centric workflow.
As the number of cloud services continues to increase, selecting services of interest across one or more cloud service environments using existing service selection methods raises a number of concerns such as performance, efficiency, end-to-end reliability and most importantly quality of search results. In this research, we investigate the use of Case-Based Reasoning (CBR) approach that employs a meta-heuristic approach that is based on QoS for cloud services (QSCS) for enabling clients to effectively manage and control the quality of their applications deployed in the cloud.
To collect ECG signals, it is often necessary to place ECG electrodes on the body for critical analysis of ECG data transmitted by such electrodes. Through analyzing collected data, it is then possible to examine beat-to-beat alterations in the rate of the heartbeats. However, this process requires a multilayered setup for both hardware and software which can be costly and time consuming. This research aims to use existing IoT-based technologies for detecting the heart variability rate without the use of any heart rate sensors or wires required.
Research Opportunity (undergraduate and graduate): There are major existing challenges that are contributing to the slow adoption of
the Internet of Things (IoT). These include, but not limited to, robust connectivity (latency, availability, coverage, cost, etc.),
standardization (having standard connectivity for billions of things), interoperability and open interfaces (e.g. web services as an
enabling platform for things to talk to each other), privacy and security (e.g. how to prevent malware injection and data mishandling
in IoT devices) and domain knowledge (e.g. machine learning and vertical-specific insights). These challenges are impacting many areas (e.g.
health-care services, wearable devices, remote surgery, medical sensing equipment, medical AI, among many others).
The 5G network is the latest generation of cellular mobile communication network that has been designed to overcome many of existing IoT-related challenges particularly the robust connectivity. For example, it is anticipated that a 5G network will support faster speeds of up-to 10Gb/s and a latency of 1ms (Bluetooth needs 3ms from start to finish, 1ms matches the human body's reaction when touching an object, current 4G networks have latency of 25ms).
What is latency? Latency is the amount of time it takes for a packet to get from one end point to another. Having low latency translates to near real-time processing (e.g. the velocity in Big Data Analytics).
This project will investigate the current use of 5G network for IoT applications across different domains (e.g. shift from core-cloud to edge-cloud) and building a testbed for analyzing the performance of the 5G network for a range of IoT-applications.
If you are interested, please take a few minutes to fill out this form . The deadline for research proposals is July 15th. Major and potential companies to work for in this area: Broadcom, Qualcomm , Huawei, Ericsson, Nokia and Samsung Electronics, among many others.
I am looking for undergraduate, graduate (Master and Ph.D.) students or postdoctoral
researchers with diverse backgrounds and expertise to work on solving challenging research
problems in various areas relating to embedded systems, Cyber-Physical Systems (CPS) and Internet of Things (IoT).
My students have published first-author papers, received global awards, received campus-wide recogonition for their work, obtained internships or full-time positions at top-technology companies (e.g. Microsoft, Google, SAP, IBM) and have been admitted in top Ph.D. programs at top universities. I am always proud of my students' accomplishments and help them in developing their future career. Please visit the Publications section for reading more about some of the publications by my current and former students based on their work. If you are interested in pursuing projects or research under my supervision, please take a few minutes to fill out this form.