Опубликована: Апрель 19, 2024
Язык: Английский
Energies, Год журнала: 2024, Номер 17(17), С. 4501 - 4501
Опубликована: Сен. 8, 2024
This review paper thoroughly explores the impact of artificial intelligence on planning and operation distributed energy systems in smart grids. With rapid advancement techniques such as machine learning, optimization, cognitive computing, new opportunities are emerging to enhance efficiency reliability electrical From demand generation prediction flow optimization load management, is playing a pivotal role transformation infrastructure. delves deeply into latest advancements specific applications within context systems, including coordination resources, integration intermittent renewable energies, enhancement response. Furthermore, it discusses technical, economic, regulatory challenges associated with implementation intelligence-based solutions, well ethical considerations related automation autonomous decision-making sector. comprehensive analysis provides detailed insight how reshaping grids highlights future research development areas that crucial for achieving more efficient, sustainable, resilient system.
Язык: Английский
Процитировано
10
Computer Networks, Год журнала: 2024, Номер 245, С. 110358 - 110358
Опубликована: Март 30, 2024
Язык: Английский
Процитировано
8
Journal of Network and Computer Applications, Год журнала: 2025, Номер 237, С. 104130 - 104130
Опубликована: Фев. 7, 2025
Язык: Английский
Процитировано
1
Internet of Things, Год журнала: 2025, Номер unknown, С. 101599 - 101599
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0
Applied Sciences, Год журнала: 2024, Номер 14(18), С. 8558 - 8558
Опубликована: Сен. 23, 2024
The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet Things (IIoT) in terms distributed artificial intelligence, pervasive edge computing communication and big-data-based intelligent decision algorithms. We show that sensor data fusion be carried out IoT smart industrial urban environments by cooperative perception inference tasks. Our analyses debate on wireless communication, vehicular autonomous networks, algorithm technologies equipment manufacturing environments. Mobile cloud task processing capabilities decentralized network control power grid system monitoring were thereby analyzed. results contributions clarify sustainable energy efficiency generation together with support environmental systems operate efficiently intelligence networks. PRISMA used, its web-based Shiny app flow design, the search outcomes screening procedures integrated. A quantitative literature performed July 2024 original published between 2019 2024. Study screening, evidence map visualization, extraction reporting tools, machine learning classifiers, reference management software harnessed for qualitative data, collection, management, analysis synthesis. Dimensions VOSviewer deployed visualization analysis.
Язык: Английский
Процитировано
4
Cyber-Physical Systems, Год журнала: 2025, Номер unknown, С. 1 - 28
Опубликована: Янв. 7, 2025
This paper proposes a credit-driven practical byzantine fault tolerance based on consensus algorithm for sustainable 6G communication to address the potential threat of malicious activity in green and 6G-based Industrial Internet Things (IIoT) applications. The dual-layer architecture allows simultaneous verification transactions execution read-write operations. According simulation studies, CD-PBFT enhances performance stability while efficiently reducing validation time. Empirical findings demonstrate that achieves both efficiency security by network transaction latency an average 34.8% increasing throughput 25.2% compared PBFT.
Язык: Английский
Процитировано
0
Studies in computational intelligence, Год журнала: 2025, Номер unknown, С. 127 - 147
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Internet of Things, Год журнала: 2025, Номер unknown, С. 101568 - 101568
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0
Deleted Journal, Год журнала: 2025, Номер 28(1)
Опубликована: Апрель 15, 2025
Abstract The Industrial Internet of Things (IIoT) brings together industrial devices in a network that gathers and analyzes data real-time for making data-driven decisions. Federated learning is popular approach collaboratively training multiple edge using an intermediate server rounds. This can be applied various fields, including anomaly detection, asset management, energy efficiency, quality control, predictive maintenance. However, performance affected by limited non-independent, identically distributed (non-IID) data. Additionally, also face resource constraints large datasets. paper proposes cluster-assisted custom federated improving the prediction resources required training. initializes model broadcasting initial parameters, then start After on current round’s data, transmit updated performance, distribution back to server. Then, clusters based their minimize non-IID. Parameter aggregation undertaken within cluster improve aggregated parameter sent respective members. Assuming secure internal network, work share samples round increase dataset size diversity. Earlier portion datasets are excluded from reduce drift. Comprehensive experimental evaluation with testbed proves effectiveness proposed over state-of-the-art.
Язык: Английский
Процитировано
0