Prof. Arif I. Sarwat

The fifth generation (5G) wireless network technology is to be standardized by 2020, where main goals are to improve capacity, reliability, and energy efficiency, while reducing latency and massively increasing connection density. An integral part of 5G is the capability to transmit touch perception type real-time communication empowered by applicable robotics and haptics equipment at the network edge. In this regard, we need drastic changes in network architecture including core and radio access network (RAN) for achieving end-to-end latency on the order of 1 ms. In this paper, we present a detailed survey on the emerging technologies to achieve low latency communications considering three different solution domains: RAN, core network, and caching. We also present a general overview of 5G cellular networks composed of software defined network (SDN), network function virtualization (NFV), caching, and mobile edge computing (MEC) capable of meeting latency and other 5G requirements.

10 April 2023 in Electronics

Journal Article

Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries, Applications, and Algorithms

In the current world of the Internet of Things, cyberspace, mobile devices, businesses, social media platforms, healthcare systems, etc., there is a lot of data online today. Machine learning (ML) is something we need to understand to do smart analyses of these data and make smart, automated applications that use them. There are many different kinds of machine learning algorithms. The most well-known ones are supervised, unsupervised, semi-supervised, and reinforcement learning. This article goes over all the different kinds of machine-learning problems and the machine-learning algorithms that are used to solve them. The main thing this study adds is a better understanding of the theory behind many machine learning methods and how they can be used in the real world, such as in energy, healthcare, finance, autonomous driving, e-commerce, and many more fields. This article is meant to be a go-to resource for academic researchers, data scientists, and machine learning engineers when it comes to making decisions about a wide range of data and methods to start extracting information from the data and figuring out what kind of machine learning algorithm will work best for their problem and what results they can expect. Additionally, this article presents the major challenges in building machine learning models and explores the research gaps in this area. In this article, we also provided a brief overview of data protection laws and their provisions in different countries.

19 December 2019 in Journal of the Electrochemical Society

Journal Article

Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture

The use of sensors and the Internet of Things (IoT) is key to moving the world’s agriculture to a more productive and sustainable path. Recent advancements in IoT, Wireless Sensor Networks (WSN), and Information and Communication Technology (ICT) have the potential to address some of the environmental, economic, and technical challenges as well as opportunities in this sector. As the number of interconnected devices continues to grow, this generates more big data with multiple modalities and spatial and temporal variations. Intelligent processing and analysis of this big data are necessary to developing a higher level of knowledge base and insights that results in better decision making, forecasting, and reliable management of sensors. This paper is a comprehensive review of the application of different machine learning algorithms in sensor data analytics within the agricultural ecosystem. It further discusses a case study on an IoT based data-driven smart farm prototype as an integrated food, energy, and water (FEW) system.

1 January 2017 in Electric Power Systems Research

Journal Article

A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon

Kianoosh G. Boroojeni

M. Hadi Amini

Shahab Bahrami

S.S. Iyengar

Arif I. Sarwat

Orkun Karabasoglu

2 October 2019 in IEEE Transactions on Industrial Electronics

Journal Article

Resilient Control Design for Load Frequency Control System Under False Data Injection Attacks

Alireza Abaspour

Arman Sargolzaei

Parisa Forouzannezhad

Kang K. Yen

Arif I. Sarwat

Smart power grids are being enhanced by adding communication infrastructure to improve their reliability, sustainability, and efficiency. Despite all of these significant advantages, their open communication architecture and connectivity renders the power systems' vulnerability to a range of cyber-attacks. This paper proposes a novel resilient control system for Load Frequency Control (LFC) system under false data injection (FDI) attacks. It is common to use encryption in data transfer links as the first layer of defending mechanism; here, we proposed a second defense layer that can jointly detect and mitigate FDI attacks on power systems. Here, we propose a new anomaly detection (AD) technique which consists of a Luenberger observer and an artificial neural network (ANN). Since FDI attacks can happen rapidly, the observer structure is enhanced by the Extended Kalman filter (EKF) to improve the ANN ability for online detection and estimation. The resilient controller is designed based on the attack estimation, which can eliminate the need for control reconfiguration. The resiliency of the proposed design against FDI attacks is tested on the LFC system. The simulation results clearly show that the proposed control system can successfully detect anomalies and compensate for their adverse effects.

6 July 2018 in Energies

Journal Article

Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey

Integration of high volume (high penetration) of photovoltaic (PV) generation with power grids consequently leads to some technical challenges that are mainly due to the intermittent nature of solar energy, the volume of data involved in the smart grid architecture, and the impact power electronic-based smart inverters. These challenges include reverse power flow, voltage fluctuations, power quality issues, dynamic stability, big data challenges and others. This paper investigates the existing challenges with the current level of PV penetration and looks into the challenges with high PV penetration in future scenarios such as smart cities, transactive energy, proliferation of plug-in hybrid electric vehicles (PHEVs), possible eclipse events, big data issues and environmental impacts. Within the context of these future scenarios, this paper reviewed the existing solutions and provides insights to new and future solutions that could be explored to ultimately address these issues and improve the smart grid’s security, reliability and resiliency.

About Me

Dr. Arif I. Sarwat is an Eminent Scholar Chaired Professor at the Department of Electrical and Computer Engineering and Director of the FPL-FIU Solar Research Facility, Florida International University. He has authored/co-authored more than 250 peer-reviewed articles and multiple patents. He is the Principal Investigator of the state-of-the-art grid-connected 3 MW/9 MWh AI-based renewable microgrid project funded by FPL. He has multiple funded projects funded by the National Science Foundation, Industry and the Department of Energy and Industry. His research interests include smart grids, electric vehicles, high penetration renewable systems, storage, and battery management systems, grid resiliency, large-scale data analysis, artificial intelligence, advanced metering infrastructure, smart city infrastructure, cybersecurity. He is the author/co-author of a publication that won the Best Paper Award at the Resilience Week in 2017, a technical article that won both the Best Paper Award in 2016 and the Most Cited Paper Award in 2018 from Springer’s Journal of Modern Power Systems and Clean Energy. He was a recipient of the Faculty Award for Excellence in Research, Creative Activities in 2016, the College of Engineering and Computing Worlds Ahead Performance in 2016, and the FIU TOP Scholar Award in 2015, 2019, and 2023. Since 2012, he has been the Chair of the IEEE Miami Section VT and Communication. He is an Associate Editor of the ACM Computing Surveys and IEEE TRANSACTIONS.

Prof. Arif I. Sarwat

Prof. Arif I. Sarwat

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