Lightweight Digit Recognition in Smart Metering System Using Narrowband Internet of Things and Federated Learning DOI Creative Commons
Vladimir Nikić, Dušan Bortnik, Milan Lukić

et al.

Future Internet, Journal Year: 2024, Volume and Issue: 16(11), P. 402 - 402

Published: Oct. 31, 2024

Replacing mechanical utility meters with digital ones is crucial due to the numerous benefits they offer, including increased time resolution in measuring consumption, remote monitoring capabilities for operational efficiency, real-time data informed decision-making, support time-of-use billing, and integration smart grids, leading enhanced customer service, reduced energy waste, progress towards environmental sustainability goals. However, cost associated replacing their counterparts a key factor contributing relatively slow roll-out of such devices. In this paper, we present low-cost power-efficient solution retrofitting existing metering infrastructure, based on state-of-the-art communication artificial intelligence technologies. The edge device developed contains camera capturing images dial meter, 32-bit microcontroller capable running digit recognition algorithm, an NB-IoT module (E)GPRS fallback, which enables nearly ubiquitous connectivity even difficult radio conditions. Our methodology, on-device training inference, augmented federated learning, achieves high level accuracy (97.01%) while minimizing consumption overhead (87 μWh per day average).

Language: Английский

Optimized LSTM-Based Electric Power Consumption Forecasting for Dynamic Electricity Pricing in Demand Response Scheme of Smart Grid DOI Creative Commons

P. Balakumar,

Senthil Kumar R

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104356 - 104356

Published: Feb. 1, 2025

Language: Английский

Citations

2
Revolutionizing smart grid-ready management systems: A holistic framework for optimal grid reliability DOI Creative Commons
Adila El Maghraoui, Hicham El Hadraoui, Younes Ledmaoui

et al.

Sustainable Energy Grids and Networks, Journal Year: 2024, Volume and Issue: 39, P. 101452 - 101452

Published: June 18, 2024

Existing energy management systems are becoming increasingly insecure and inefficient due to the rapid adoption of smart grid technology. Current research indicates that effectively managing dynamic flows, adjusting changing needs, protecting against new cyber threats remain significant challenges for system. An advanced comprehensive plan grids is therefore required, capable addressing these delicate multifaceted problems. The proposed framework addresses through unifying several key aspects, it includes an data acquisition system captures real-time from various sources, enabling monitoring flow analysis. By integrating predictive algorithms, provides precise demand forecasting, which essential adaptive management. A contribution incorporation AI-based module diagnostics prognostics, leverages machine learning techniques shift reactive proactive maintenance strategies. optimal power (OPF) optimization represents a central component framework. It employs computational methods ensure efficient cost-effective distribution, particularly in incorporating renewable sources. Additionally, architectural strengthened by robust cybersecurity designed safeguard wide range threats, maintaining integrity both operational consumer data. This paper also practical implementation such as compatibility with existing infrastructure, investment costs, need specialized training. solution benchmark operations, ensuring more sustainable systems.

Language: Английский

Citations

7
Toward secure industrial internet of behaviours: a federated learning-based lightweight human behaviour recognition method with selective state space models DOI
Bingtao Hu, Ruirui Zhong, Yixiong Feng

et al.

International Journal of Production Research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 17

Published: Jan. 9, 2025

Human behaviour recognition is one of the most fundamental tasks in Industrial Internet Behaviour (IIoB) and crucial for safe reliable IIoB. Existing methods lacks adaptability transferability. In addition, there a data isolation problem among different users. Therefore, an urgent requirement to construct secure adaptive human model IIoB without violating privacy Mamba, structured state space that integrates selection mechanism scan module, used time series modelling tasks. To tackle aforementioned problems, Federated Learning-based lightweight with selective models proposed. First, we design integrating Mamba residual structure achieve modelling. considering training efficiency, decentralised dynamic FL framework designed collaborative training, including: initial source users, aggregation strategy based on weighting, fine-tuning module small-sample data, improve efficiency accuracy recognition. Extensive experiments are conducted prove superior performance proposed method.

Language: Английский

Citations

1
Innovative approaches to green digital twin technologies of sustainable smart cities using a novel hybrid decision-making system DOI Creative Commons

Jifeng Cao,

Cristi Spulbăr, Serkan Eti

et al.

Journal of Innovation & Knowledge, Journal Year: 2025, Volume and Issue: 10(1), P. 100651 - 100651

Published: Jan. 1, 2025

Language: Английский

Citations

1
Advances in Federated Learning: Applications and Challenges in Smart Building Environments and Beyond DOI Creative Commons
Mohamed Rafik Aymene Berkani, Ammar Chouchane, Yassine Himeur

et al.

Computers, Journal Year: 2025, Volume and Issue: 14(4), P. 124 - 124

Published: March 27, 2025

Federated Learning (FL) is a transformative decentralized approach in machine learning and deep learning, offering enhanced privacy, scalability, data security. This review paper explores the foundational concepts, architectural variations of FL, prominent aggregation algorithms like FedAvg, FedProx, FedMA, diverse innovative applications thermal comfort optimization, energy prediction, healthcare, anomaly detection within smart buildings. By enabling collaborative model training without centralizing sensitive data, FL ensures privacy robust performance across heterogeneous environments. We further discuss integration with advanced technologies, including digital twins 5G/6G networks, demonstrate its potential to revolutionize real-time monitoring, optimize resources. Despite these advances, still faces challenges, such as communication overhead, security issues, non-IID handling. Future research directions highlight development adaptive methods, measures, hybrid architectures fully leverage FL’s driving innovative, secure, efficient intelligence for next generation

Language: Английский

Citations

1
Rigorous Security Analysis of RabbitMQ Broker with Concurrent Stochastic Games DOI
Abdelhakim Baouya, Brahim Hamid, Levent Gürgen

et al.

Internet of Things, Journal Year: 2024, Volume and Issue: 26, P. 101161 - 101161

Published: March 15, 2024

Language: Английский

Citations

4
Integrating machine learning for the sustainable development of smart cities DOI Creative Commons
Manel Mrabet, Maha Sliti

Frontiers in Sustainable Cities, Journal Year: 2024, Volume and Issue: 6

Published: Dec. 19, 2024

The purpose of this study is to assess the potential machine learning in advancing Sustainable Development Goals, particularly Goal 11, which focuses on sustainable urban and community development. To reduce impacts increasing urbanization environment, it necessary prioritize development smart cities. Smart cities use information communication technology techniques enhance sustainability by improving resource management reducing environmental impact. In context, artificial intelligence enhances overall quality life, a critical component Machine learning, subset intelligence, crucial promoting This application cities, ranging from energy management, transportation efficiency, waste public safety. It highlights role algorithms improve operational minimize expenses, practical ML across several countries demonstrates its ability handle challenges increase sustainability. paper discusses variety real-world initiatives that have successfully employed develop as well in-depth studies used obtained results. also covers implementing into city projects, such data quality, model interpretability, scalability, ethical considerations. emphasizes importance high-quality data, clear models, right tools.

Language: Английский

Citations

4
An advanced hybrid deep learning model for accurate energy load prediction in smart building DOI Creative Commons

R. Sunder,

R Sreeraj,

Vince Paul

et al.

Energy Exploration & Exploitation, Journal Year: 2024, Volume and Issue: 42(6), P. 2241 - 2269

Published: Aug. 27, 2024

In smart cities, sustainable development depends on energy load prediction since it directs utilities in effectively planning, distributing and generating energy. This work presents a novel hybrid deep learning model including components of the Improved-convolutional neural network (CNN), bidirectional long short-term memory (Bi-LSTM), Graph (GNN), Transformer Fusion Layer architectures for precise forecasting. Better feature extraction results from Improved-CNN's dilated convolution residual block accommodation wide receptive fields reduced vanishing gradient problem. By capturing temporal links both directions, Bi-LSTM networks help to better grasp complicated use patterns. improve predictive capacities across linked systems by characterizing spatial relationships between energy-consuming units cities. Emphasizing critical trends guarantee reliable forecasts, transformer models attention methods manage long-term dependencies consumption data. Combining CNN, Bi-LSTM, GNN component predictions synthesizes numerous data representations increase accuracy. With Root Mean Square Error 5.7532 Wh, Absolute Percentage 3.5001%, 6.7532 Wh R 2 0.9701, fared than other ‘Electric Power Consumption’ Kaggle dataset. develops realistic that helps informed decision-making enhances efficiency techniques, promoting forecasting

Language: Английский

Citations

3
Private and heterogeneous personalized hierarchical federated learning using Conditional Generative Adversarial networks DOI

Afsaneh Afzali,

Pirooz Shamsinejadbabaki

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127003 - 127003

Published: Feb. 1, 2025

Language: Английский

Citations

0
Federated Learning in Secure Smart City Sensing DOI

Monika Gandhi,

Sushil Kumar Singh, Ravikumar Rajarathinam

et al.

Published: March 10, 2025

Language: Английский

Citations

0