Edge computing security: State of the art and challenges

Y **ao, Y Jia, C Liu, X Cheng, J Yu… - Proceedings of the …, 2019 - ieeexplore.ieee.org
The rapid developments of the Internet of Things (IoT) and smart mobile devices in recent
years have been dramatically incentivizing the advancement of edge computing. On the one …

Artificial intelligence evolution in smart buildings for energy efficiency

H Farzaneh, L Malehmirchegini, A Bejan, T Afolabi… - Applied Sciences, 2021 - mdpi.com
The emerging concept of smart buildings, which requires the incorporation of sensors and
big data (BD) and utilizes artificial intelligence (AI), promises to usher in a new age of urban …

[Књига][B] Privacy is power

C Véliz - 2021 - igi-global.com
Privacy is Power Page 1 International Journal of Technoethics Volume 12 • Issue 2 • July-December
2021  Copyright©2021,IGIGlobal.CopyingordistributinginprintorelectronicformswithoutwrittenpermissionofIGIGlobalisprohibited …

An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study

D Murray, L Stankovic, V Stankovic - Scientific data, 2017 - nature.com
Smart meter roll-outs provide easy access to granular meter measurements, enabling
advanced energy services, ranging from demand response measures, tailored energy …

Fedgan: Federated generative adversarial networks for distributed data

M Rasouli, T Sun, R Rajagopal - arxiv preprint arxiv:2006.07228, 2020 - arxiv.org
We propose Federated Generative Adversarial Network (FedGAN) for training a GAN across
distributed sources of non-independent-and-identically-distributed data sources subject to …

[HTML][HTML] Energy sustainability in smart cities: Artificial intelligence, smart monitoring, and optimization of energy consumption

KT Chui, MD Lytras, A Visvizi - Energies, 2018 - mdpi.com
Energy sustainability is one of the key questions that drive the debate on cities' and urban
areas development. In parallel, artificial intelligence and cognitive computing have emerged …

Residential loads flexibility potential for demand response using energy consumption patterns and user segments

M Afzalan, F Jazizadeh - Applied Energy, 2019 - Elsevier
Demand response (DR) is considered an effective approach in mitigating the ever-growing
concerns for supplying the electricity peak demand. Recent attempts have shown that the …

[HTML][HTML] An active learning framework for the low-frequency non-intrusive load monitoring problem

T Todic, V Stankovic, L Stankovic - Applied Energy, 2023 - Elsevier
With the widespread deployment of smart meters worldwide, quantification of energy used
by individual appliances via Non-Intrusive Load Monitoring (NILM), ie, virtual submetering, is …

Transfer learning for multi-objective non-intrusive load monitoring in smart building

D Li, J Li, X Zeng, V Stankovic, L Stankovic, C **ao… - Applied Energy, 2023 - Elsevier
Buildings represent 39% of global greenhouse gas emissions, thus reducing carbon
emissions in buildings is of importance to greenhouse gas emissions reductions. This …

the Plegma dataset: Domestic appliance-level and aggregate electricity demand with metadata from Greece

S Athanasoulias, F Guasselli, N Doulamis, A Doulamis… - Scientific Data, 2024 - nature.com
The growing availability of smart meter data has facilitated the development of energy-
saving services like demand response, personalized energy feedback, and non-intrusive …