Eyhab Al-Masri

Self-Adaptive Apporach for Improving Active Surgical Robots in Hospitals

Multi-Criteria Decision Methods (MCDM) are commonly used in Operations Research. In this research, we utilize these methods for improving the decision making of sharing resources across distributed systems. For example, we employ MCDM TOPSIS for enhancing the decision making of operations involving surgical robots. In specific, we utilize TOPSIS for simulating a hepatectomy surgical use case. We are currently building an IoT-based Surgical Robot (IoTSR) framework that employs edge computing capabilities for assisting surgical teams during minimally invasive procedures involving surgical robots.

Seamless Migration for Offloading Tasks across MEC Environments

Mobile Edge Computing (MEC) has emerged as a paradigm for providing an environment that supports cloud-computing capabilities at the edge of a mobile network. For instance, mobile edge servers attached to cellular base stations can significantly help reduce network overhead and latency across the edge clouds. However, as mobile users constantly change their geographical location, it becomes tremendously difficult to identify an optimal service migration plan. Hence, the service migration process across cellular edge environments remains a critical challenge. In this project, we research innovative ways for creating seamless migration plans to offload tasks across MEC environments.

Using N-Version Programming for Resilient Distributed Edge-based Systems

Best Paper Award 3rd @ IEEE ICSITech 2020. Distributed Edge-based systems need to become much more fault-tolerant in order to ensure the delivery of secure, reliable, robust, and dynamic services while addressing unexpected failures that may occur in terms of both hardware and software. In this research, we introduce a novel design diversity technique for making edge-systems more resilient by employing a N-version anomaly-based Fault Detection (NvABFD) technique. The main goal of this research is to enhance the reliability and fault tolerance of distributed edge-based systems.

Computation Offloading across Hybrid Fog Environments

As the complexity and requirements of IoT systems evolve, there comes a need for adaptable optimization algorithms that improve the process of task allocation, particularly across fog infrastructures. In this research, we are investigating the development of optimization strategies for the allocation of tasks across hybrid fog-based environments. We examine a number of optimization techniques such as the particle swarm optimization. The overall goal of this research project is to optimize the resources used by IoT tasks within fog-environments.

Resource Provisioning across Distributed Edge Environments

Best Poster Award @ ACM WWW 2020. As more IoT devices become increasingly ubiquitous, dynamic resource allocation for edge computing environments becomes extremely time consuming and challenging task. In this research, we employ our Edgify framework to dynamically provision edge- and cloud-based resources while utilizing separation of concerns concepts.

Observability of Microservices Across IoT Environments

Modern-day Internet of Things (IoT) systems are composed of a number of microservices for completing a business process. Quality of Service (QoS) is a vital measure for any microservice which assures the overall performance and potential downtime of the system. However, it is difficult to guarantee the observability of an IoT system without thorough analysis and prediction. In this research we are investigating the use of distributed tracing for building a prediction model based on observability attributes measured using telemetry for microservices. Observability will be valuable from a business point of view since it helps in understanding the resilience and robustness of IoT systems.

Recycle.io: An IoT-Enabled Framework for Urban Waste Management

An article featuring this research work at UW Tacoma.

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.

Integrating Hardware Prototyping Platforms into the Classroom

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.

Wirelessly Detecting ECG Heartbeat Abnormalities

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.