The Internet of Things (IoT) has fundamentally changed how we engage with the environment that surrounds us by facilitating the connection of various devices and the flow of data between them. However, as the complexi...
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In light of social networks' expansion and the ever-increasing number of subscribers who daily sign up to them. Detecting and counting triangles of subscribers in these networks are the basis of community detectio...
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Some applications, like round-based consensus algorithms, require all the nodes from a system to send a message to the same node (the leader) at the same time. In a Mobile Ad-Hoc Network (MANET), this situation is lik...
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Privacy, scalability, and reliability are significant challenges in unmanned aerial vehicle (UAV) networks as distributedsystems, especially when employing machine learning (ML) technologies with substantial data exc...
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ISBN:
(纸本)9798350303490
Privacy, scalability, and reliability are significant challenges in unmanned aerial vehicle (UAV) networks as distributedsystems, especially when employing machine learning (ML) technologies with substantial data exchange. Recently, the application of federated learning (FL) to UAV networks has improved collaboration, privacy, resilience, and adaptability, making it a promising framework for UAV applications. However, implementing FL for UAV networks introduces drawbacks such as communication overhead, synchronization issues, scalability limitations, and resource constraints. To address these challenges, this paper presents the Blockchain-enabled Clustered and Scalable Federated Learning (BCS-FL) framework for UAV networks. This improves the decentralization, coordination, scalability, and efficiency of FL in large-scale UAV networks. The framework partitions UAV networks into separate clusters, coordinated by cluster head UAVs (CHs), to establish a connected graph. Clustering enables efficient coordination of updates to the ML model. Additionally, hybrid inter-cluster and intra-cluster model aggregation schemes generate the global model after each training round, improving collaboration and knowledge sharing among clusters. The numerical findings illustrate the achievement of convergence while also emphasizing the trade-offs between the effectiveness of training and communication efficiency.
Traditional charge consumption prediction methods are hindered by the challenges posed by the dispersed locations of distributed energy stations, the complexity of network planning, and the stringent requirements for ...
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The rapid advancement of generative artificial intelligence (GAI) has led to the creation of transformative applications such as ChatGPT, which significantly boosts text processing efficiency and diversifies audio, im...
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One of the most crucial and expensive parts of electricity transmission and distribution systems is power transformers. In power distribution and transmission networks, quickly diagnosing power transformer problems is...
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Blockchain applications require metadata to be associated with the blockchain ledger. Metadata is often stored in centralized cloud servers, which limits the decentralization of the blockchain. Alternatively, metadata...
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Rapid detection and well-timed intervention are essential to mitigate the impacts of wildfires. Leveraging remote sensed data from satellite networks and advanced AI models to automatically detect hotspots (i.e., ther...
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ISBN:
(纸本)9798350360332;9798350360325
Rapid detection and well-timed intervention are essential to mitigate the impacts of wildfires. Leveraging remote sensed data from satellite networks and advanced AI models to automatically detect hotspots (i.e., thermal anomalies caused by active fires) is an effective way to build wildfire monitoring systems. In this work, we propose a novel dataset containing time series of remotely sensed data related to European fire events and a Self-Supervised Learning (SSL)-based model able to analyse multi-temporal data and identify hotspots in potentially near real time. We train and evaluate the performance of our model using our dataset and Thraws, a dataset of thermal anomalies including several fire events, obtaining an F1 score of 63.58.
Byzantine consensus protocols aim at maintaining safety guarantees under any network synchrony model and at providing liveness in partially or fully synchronous networks. However, several Byzantine consensus protocols...
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