arXiv subjects

Ratun Rahman

Publications and source records attributed to Ratun Rahman.

23 records · Page 2Linked to original sources

arXiv2024

Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach

Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) are used to record household energy consumption. Traditional machine learning (ML) methods are often employed for load forecasting but require data sharing which raises data privacy concerns. Federated learning (FL) can address this issue by running distributed ML models at local SMs without data exchange. However, current FL-based approaches struggle to achieve efficient load forecasting due to imbalanced data distribution across heterogeneous SMs. This paper presents a novel personalized federated learning (PFL) method to load prediction under non-independent and identically distributed (non-IID) metering data settings. Specifically, we introduce meta-learning, where the learning rates are manipulated using the meta-learning idea to maximize the gradient for each client in each global round. Clients with varying processing capacities, data sizes, and batch sizes can participate in global model aggregation and improve their local load forecasting via personalized learning. Simulation results show that our approach outperforms state-of-the-art ML and FL methods in terms of better load forecasting accuracy.

cs.LG↗

arXiv2024

False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learning

Smart metering networks are increasingly susceptible to cyber threats, where false data injection (FDI) appears as a critical attack. Data-driven-based machine learning (ML) methods have shown immense benefits in detecting FDI attacks via data learning and prediction abilities. Literature works have mostly focused on centralized learning and deploying FDI attack detection models at the control center, which requires data collection from local utilities like meters and transformers. However, this data sharing may raise privacy concerns due to the potential disclosure of household information like energy usage patterns. This paper proposes a new privacy-preserved FDI attack detection by developing an efficient federated learning (FL) framework in the smart meter network with edge computing. Distributed edge servers located at the network edge run an ML-based FDI attack detection model and share the trained model with the grid operator, aiming to build a strong FDI attack detection model without data sharing. Simulation results demonstrate the efficiency of our proposed FL method over the conventional method without collaboration.

cs.LG↗

arXiv2024

Best Practices for Facing the Security Challenges of Internet of Things Devices Focusing on Software Development Life Cycle

In the past few years, the number of IoT devices has grown substantially, and this trend is likely to continue. An increasing amount of effort is being put into developing software for the ever-increasing IoT devices. Every IoT system at its core has software that enables the devices to function efficiently. But security has always been a concern in this age of information and technology. Security for IoT devices is now a top priority due to the growing number of threats. This study introduces best practices for ensuring security in the IoT, with an emphasis on guidelines to be utilized in software development for IoT devices. The objective of the study is to raise awareness of potential threats, emphasizing the secure software development lifecycle. The study will also serve as a point of reference for future developments and provide a solid foundation for securing IoT software and dealing with vulnerabilities.

cs.SE↗

arXiv2023

VREd: A Virtual Reality-Based Classroom for Online Education Using Unity3D WebGL

Virtual reality is the way of the future. The use of virtual reality is expanding over time across all sectors, from the entertainment industry to the military and space. VREd is a similar concept where a virtual reality-based classroom is used for online education where the user will have better interaction and more control. Unity3D and WebGL software have been used for implementation. Students or learners accustomed to contemporary technologies may find the traditional educational system unappealing because of its flaws. Incorporating the latest technologies can increase the curiosity and learning abilities of students. The system architecture of VREd is similar to that of an actual classroom, allowing both students and teachers to access all of the course materials and interact with one another using only an internet connection. The environment and the background are also customizable. Therefore, all the users can comfortably use the system and feel at home. We can create an effective educational system that raises educational quality by utilizing virtual reality.

cs.CY↗

arXiv2022

NFS: A Hand Gesture Recognition Based Game Using MediaPipe and PyGame

This paper represents a game which interacts with humans via hand gesture movement. Nowadays, apps like this seem rare, and there seems to be a window opening for this kind of application to be more prevalent and useful in the near future. This application is based on hand gesture movement instead of being dependent on a keyboard and mouse. The main issue was to figure out how to utilize machine learning to make this application work as it should be. First, two games were selected one with a traditional controller and another with hand gesture method. Then these two games based on the difficulty to use, fun elements, gameplay, and replayability were compared. Though the difficulty increases but the other three aspects improve significantly. After going through all of that a conclusion can be drawn that people are more likely to play a simple hand gesture-based game.

cs.HC↗